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School of Humanities, Arts, and Social Sciences welcomes six new faculty for 2026

New professors join the Comparative Media Studies/Writing, Economics, Linguistics, and Music and Theater Arts departments.

The MIT School of Humanities, Arts, and Social Sciences (SHASS) and Dean Agustín Rayo recently welcomed six new professors to the MIT community. They arrive with diverse backgrounds and vast knowledge in their areas of research.

Grisha Coleman is a full professor in the Music and Theater Arts Section. Her research explores tensions between our physiological, technological, and ecological systems; human movement, our machines, and the places we inhabit. Her practice engages an interdisciplinary approach to these explorations. Coleman received the Doris Duke Foundation’s Performing Arts Technologies Lab Award. Her work has been supported by Carnegie Mellon University’s STUDIO for Creative Inquiry, Creative Capital, the Jerome Foundation, MacDowell, the MAP Fund, the National Endowment for the Arts, the New York Foundation for the Arts, Pioneer Works, the Rockefeller Foundation Bellagio Center, Stanford University’s Mohr Visiting Artist program, and the Surdna Foundation. Coleman was previously a professor at Northeastern University and an associate professor at Arizona State University. She earned an MFA in music composition and integrated media from California Institute of the Arts.

Tung-Hui Hu is an associate professor with tenure in the Comparative Media Studies/Writing program. A poet and a scholar of digital media, he is the author of five books, most recently “Digital Lethargy: Dispatches from an Age of Disconnection” (MIT Press, 2022), “A Prehistory of the Cloud” (MIT Press, 2015), and “Greenhouses, Lighthouses” (Copper Canyon Press, 2013). Hu is interested in how concepts such as race and normal language became measurable, governable objects in the form of datasets. His research on data centers, artificial intelligence, burnout, and visual art has been featured in places such as CBS News, BBC Radio 4, WIRED, and MoMA R&D. He has been awarded fellowships from the American Academy in Rome, the National Endowment for the Arts, and the American Academy in Berlin. Prior to joining MIT, he was a faculty member at the University of Michigan.

Claire Luchette is an assistant professor in the Comparative Media Studies/Writing program. Luchette is the author of the novel “Agatha of Little Neon.” The winner of a Whiting Award and a National Book Foundation 5 Under 35 Honoree, Luchette has received fellowships from the Harvard Radcliffe Institute, the New York Public Library's Cullman Center for Scholars and Writers, MacDowell, Yaddo, and the National Endowment for the Arts. Their writing appears in Best American Short Stories, Ploughshares, and the Pushcart Prize anthology. Their second novel, “Swans,” and a story collection, “Big Whoop,” are forthcoming.

Shota Momma is an associate professor in the Department of Linguistics and Philosophy. Momma is a specialist in psycholinguistics and its interaction with linguistic theory — with a particular focus on the mechanisms of sentence production. Previously, Momma taught as an assistant professor at the University of Massachusetts Amherst. He earned a PhD in linguistics from the University of Maryland and completed a postdoctoral fellowship at the University of California San Diego. 

Lindsey Raymond PhD ’24 is an assistant professor in the Department of Economics, holding an MIT Schwarzman College of Computing shared position with the Department of Electrical Engineering and Computer Science. Her research examines how new technologies shape labor markets and market competition, and how insights from economics can inform algorithm design. She is a Schmidt Sciences AI2050 Early Career Fellow and served as a staff economist at the White House Council of Economic Advisers in 2021–22. Before joining MIT, Raymond was a postdoc at Microsoft Research. She earned her PhD from MIT and her BA from Yale University.

Makoto Harris Takao is the Class of 1957 Career Development Professor in the Music and Theater Arts Section. Working at the intersection of cultural history, religious studies, and musicology, Takao maps Japan’s entanglement with other world regions over the past 500 years. His current book project, “The Clef and the Cross: Music and Kirishitan Transculturation in Sixteenth-Century Japan,” asks what early modern Japanese Catholicism sounded like and how it was understood and expressed through Buddhist frameworks of sound, music, and movement. His work to date has appeared in such venues as Early Music, Journal of Music History Pedagogy, Journal of Religious History, Journal of Jesuit Studies, Zeithistorische Forschungen, and Oxford Bibliographies in Music. A player of the viola da gamba, Takao completed a joint PhD in history and musicology at the University of Western Australia. Before joining MIT, he was an assistant professor of musicology at the University of Illinois at Urbana-Champaign.

Following the questions where they lead

Assistant Professor Bailey Flanigan has arrived at complex computational methods for helping democracy thrive.

Ever since she was a child playing on her family’s farmland in Wisconsin, Bailey Flanigan was guided by her own selective, yet wide-ranging, curiosity. Describing her young self as spirited and a bit unruly, she directed her energies to everything from building booby traps to doing experimental construction projects to exploring an intense interest in medicine to writing fiction and music to planning nonprofit organizations to help lessen social inequality.

By high school, Flanigan was intensely drawn to particular subjects.

“I found myself unmotivated to take all the AP [advanced placement] classes for the sake of it. My interest was captured by classes where I could be creative — where I could use math to solve real-world problems, creatively write, make music, connect distant ideas, or deeply explore the humanities — and I worked on such classes obsessively, as an opportunity to explore my intuitions and interests,” she says. “Instead of joining clubs, I ended up spending a lot of time thinking and creating on my own, and trying to understand what I enjoyed.”

Today Flanigan is a shared faculty member between the MIT Schwarzman College of Computing and the MIT departments of Political Science and Electrical Engineering and Computer Science (EECS), and a principal investigator in the MIT Laboratory for Information and Decision Systems. She has been involved in research at the University of Wisconsin, the National Institutes of Health, Google, and Carnegie Mellon, Drexel, Harvard, Princeton, and Stanford universities. Her current work focuses on using computational and mathematical tools to create new avenues for meaningful democratic participation.

Perhaps not surprisingly, her path has crossed huge expanses of subject matter and specialties — from medicine and bioengineering to public health, and from economics to her joint appointment at MIT in computer science and political science, which began in fall 2025.

“My trajectory across disciplines was just a result of me chasing down the problems I felt were most pressing or inspiring at the time. Along the way, I wound up in a lot of situations where I was less well-trained or qualified in the standard ways. While this was sometimes precarious, it was also incredibly fun, and it cultivated my ability to learn the languages of new disciplines more easily — a skill pretty much essential to my current research and job.”

In college at the University of Wisconsin at Madison, Flanigan worked in a wet lab on therapeutic targets in cancer and computationally on tumor genetics. She says she found the research intellectually interesting, but eventually began to wonder about whether it would have the kind of impact she wanted.

“At the time, I started to worry that the science I was developing might only, in the best case, be used by a small, relatively wealthy fraction of the world, when there were people suffering from much more-preventable diseases in much larger numbers,” she says.

So Flanigan moved toward public health, where she researched microfluidic devices for HIV detection that could be used in low-resource settings. Still bothered by the circumstances driving these settings’ limited resources to begin with, she then started to dabble in economics.

Around the same time, Flanigan’s academic advisors were chipping away at preconceptions she held about her own abilities.

Steven Wright, a professor of law and creative writing at UW-Madison, served as Flanigan’s informal mentor throughout college, and they worked together on a case at the Wisconsin Innocence Project.

“He guided me through my evolving interests in science, social inequality, and economics,” she says. “He was one of the people most responsible for convincing me that I could aim higher in my career, and that I could actually go to places like MIT or Harvard.”

Also while she was in college, the two heads of the UW-Madison scholarship office, Debbie Berger and Julie Stubbs, sent Flanigan repeated emails, encouraging her to apply for a Goldwater Scholarship.

“I kept deleting their emails, thinking they were spam — I didn’t think I was the kind of person that would apply for something like that. Their persistence convinced me to apply, and in the process, the horizons I perceived for myself started to change,” she says.

After graduating from UW-Madison, Flanigan worked as a predoctoral research assistant in economics at Princeton. There, Professor Evita Nestoridi, now an associate professor at Stony Brook University, also provided a pivotal moment of support, letting Flanigan audit her real analysis class.

“Evita’s class was my first real exposure to formal mathematics and proofs, and I loved it so much that it completely changed my career trajectory,” Flanigan says. “Despite my initial doubts, she convinced me that I could do math at the graduate level; because of her encouragement, I applied to computer science PhD programs the subsequent fall.”

Choosing Carnegie Mellon for her PhD, Flanigan began research on social choice and democratic decision-making, serving her dual passions for technical research and the issue of “who gets what and why,” she says, quoting Nobel Prize-winning economist Al Roth. 

Flanigan has developed algorithms that randomly choose participants of citizens’ assemblies, designed for the common case where willing participants self-select in ways that do not reflect the larger population. In a policy brief, Flanigan gave a hypothetical  example of an assembly on artificial intelligence, whose willing participants might skew toward younger, more educated citizens with an interest in technology, leaving other groups underrepresented despite their stake in the issue. The tools Flanigan has developed help balance representation with such features of the selection process as equality among individuals’ chances to participate, resistance to manipulation of the process, and transparency — all of which can affect the general perception of a decision-making group’s legitimacy.

Flanigan’s work is now deployed on panelot.org, a widely used open-access website hosting algorithms for randomly selecting citizen assembly participants.

“The site basically walks practitioners through a series of otherwise very technical trade-offs, making those trade-offs legible and then optimizing according to the priorities practitioners dictate,” she says.

Flanigan says she is motivated to improve how the public makes political decisions, “because if any political solution is going to be viable, the public needs to feel that it was arrived at via a legitimate political process — at least under the forms of government I find most appealing.”

Beyond her work on citizens’ assemblies, Flanigan’s research is exploring new avenues related to how to more systematically get public input on complex decisions, and how the format of questions we ask people in preference elicitation contexts can affect the substance of what we conclude.

“I feel so lucky to be studying these questions from within both political science and EECS, because I have the freedom to explore both the political and technical substance of tools for more direct governance as deeply as I want,” she says.

Flanigan’s curiosity-driven journey through widely varying terrain feels right in the MIT environment, she says.

“From the beginning, I got this sense of belonging at MIT — like my ways of thinking and problem-solving, which had seemed peculiar in many situations, actually made me belong more,” she says. “This was a super refreshing feeling, and it has been 100 percent borne out since I arrived.”

How an influx of salt may affect microbial ecosystems

As sea levels rise and saltwater seeps into freshwater, stressed aquatic populations may retain overall growth even as diversity declines, MIT scientists find.

As sea levels rise due to climate change, encroaching sea water will likely make freshwater environments saltier. In a new study, MIT researchers have shown how that increase in salinity might affect microbial ecosystems found in environments such as rivers and estuaries.

These microbial communities play important roles in the carbon cycle, and they also help to decompose organic matter such as algae. The MIT team found that when salt levels rise, these populations lose diversity as faster-growing strains tend to take over the community, but they maintain their overall growth rate.

“At higher salinity, you lose diversity, which is ultimately not good for an ecosystem. But what we were surprised at is that in the meantime, even though diversity decreases, the growth of the community and the production of biomass is not impacted that much,” says Jana Huisman, an MIT postdoc and the lead author of the new study.

Jeff Gore, an MIT professor of physics, is the senior author of the paper, which appears today in Nature Microbiology. Martina Dal Bello, a former MIT postdoc who is now an assistant professor of ecology and evolutionary biology at Yale University, is also an author of the study.

Rising salt levels

Microbes that live in aquatic environments are typically adapted to thrive in fresh or salt water, or somewhere in between. Microbes that live in higher salt environments have cell walls that are optimized to resist osmotic pressure, and membrane transporters that can pump sodium ions out of the cell.

Freshwater lakes and rivers have salt concentrations around 1 gram of salt per liter of water (g/L), while oceans can reach 35 g/L. As the climate warms and sea levels rise, those oceanic waters may seep into estuaries and other inland bodies of water, increasing their salinity.

“When you think about climate change, you can think about rising temperatures, which is very common, but also a lot of other environmental stresses are going to increase,” Huisman says.

Huisman is from the Netherlands, a country with an extensive coastal delta, and she was interested in exploring how changes in salinity might affect microbial ecosystems in those aquatic habitats. The new study builds on previous work from Gore’s lab showing that higher seawater temperatures tend to favor slower-growing bacteria. 

For the new study, the researchers took samples from three aquatic environments with varying salinity: the Charles River near the MIT Sailing Pavilion (4 g/L), Boston Harbor (30 g/L), and a beach in Nahant, Massachusetts (35 g/L). Each community contained hundreds of species of microbes. The researchers then grew each population in three environments of varying salinity — 16, 31, or 46 g/L.

Over two weeks, the researchers measured the communities’ growth rates and found that overall, each community maintained the same growth rate at each of the three concentrations. However, in the communities exposed to higher salt environments, the overall composition became less diverse. Further studies showed that these communities tended to be dominated by faster-growing species. 

“We saw that those communities that had been propagated at higher salinity had reached a markedly different composition than the ones that lower salinity,” Huisman says.

Natural ecosystems

To explore whether their lab results might correspond to what happens in natural ecosystems, the researchers analyzed publicly available genomic data from microbes found in different aquatic ecosystems, including the Chesapeake Bay, the Gulf of Mexico, and the Baltic Sea.

For this portion of the study, the researchers focused on a genetic marker called the 16S rRNA gene copy number, which can be used as a proxy for the maximum growth rate that a species can attain. The more copies of this gene that a species has, the faster its intrinsic growth rate.

The researchers found that in these natural communities, environments with higher salinity also tended to be dominated by faster-growing species.

“When we first saw that, it was very exciting — that, indeed, what we found in the lab seems to also be represented in data from natural communities, sampled across a range of different environments,” Huisman says. “You see the same signatures in such data, and that’s highly suggestive that what we found in the lab might also be true in natural environments.”

One potential drawback to this loss of diversity is a reduction in microbial populations’ ability to withstand other types of environmental stress, the researchers say.

In this study, the researchers did not investigate the functions of the individual bacterial strains that ended up becoming more prevalent. Some of them may play beneficial roles, but it’s also possible that some of them might be pathogenic strains.

“Whether you want faster-growing species to take over or not might also be related to what the identity of those species is. That is something that I’m interested in looking at in the future,” Huisman says. 

The research was funded by a Human Frontier Science Program Fellowship and a Schmidt Science Polymath Award.

Diana Grass: Listening to the body’s language

The PhD candidate builds soft bioelectronic technologies to decode signals between the brain and the rest of the body.

Growing up in Colombia, Diana Grass had a simple response whenever someone told her something was impossible.

“I’ll figure it out.”

It’s a phrase that she still lives by today as PhD candidate in the Harvard-MIT Program in Health Sciences and Technology (HST), as she develops soft bioelectronic devices to study the physiological signals through which the brain and body communicate. 

“I’ve always been fascinated by one question: How do complex systems work?” Grass says.

An instinct to get to the bottom of things has guided Grass’ unconventional academic journey across continents and disciplines. Before becoming a neuroscientist and engineer, Grass studied philology and education to understand how language evolves, preserves knowledge, and shapes human communication. Looking back, she sees a common thread. “I wasn’t just studying language,” she says. “I was learning how complex systems communicate.”

But it wasn’t until she moved to the United States and began working as a medical interpreter that her scientific interests took a new direction.

“Every day, I translated conversations between physicians and patients with neurological disorders,” she says. “Watching those interactions sparked a fascination with the brain. I was intrigued by how a single organ could shape how we communicate, and ultimately who we are.”

Working alongside clinicians, Grass watched them rely on laboratory tests, medical imaging, and vital signs to understand what was happening inside the body. Despite remarkable advances in medical imaging and diagnostics, clinicians still rely largely on isolated snapshots of biological processes that are continuously changing inside the body.

“The body is communicating all the time,” she says. “We still lack the tools to understand its language.”

Determined to better understand the brain, Grass returned to school to study neuroscience with a minor in pre-medicine. She joined an immunology laboratory at Rutgers New Jersey Medical School, where she investigated neuroimmune communication and gained a new appreciation for the body’s interconnected physiology.

“Until then, I had been fascinated by the brain,” she says. “My work in immunology made me realize that the nervous system doesn’t function in isolation,” she says. “It continuously communicates with the immune system and peripheral organs to coordinate physiology and maintain homeostasis. To understand health and disease, we have to understand how those interactions preserve or disrupt that balance.” 

That realization transformed her scientific focus from understanding the brain to understanding how the nervous system coordinates physiology through continuous communication with the rest of the body, beginning with the immune system.

The complexity of that question ultimately brought Grass to pursue a PhD in medical engineering and medical physics with the HST program. She works in the Bioelectronics Group, led by Polina Anikeeva, the Matoula S. Salapatas Professor and head of MIT’s Department of Materials Science and Engineering. Grass also uses facilities in the T.J. Rodgers Laboratory and MIT.nano, and is part of the K. Lisa Yang Brain Body Center. 

Today, Grass develops soft bioelectronic devices that integrate seamlessly with soft peripheral tissues without damaging them, to continuously monitor multiple physiological signals while enabling electrical recording and stimulation of neural circuits. These technologies provide a new way to investigate how neural communication coordinates physiology across the entire body. This knowledge could enable earlier diagnosis, more precise therapies, and a new generation of bioelectronic medicine.

For Grass, the work has taken on an even deeper significance since becoming a mother. Grass has two school-age children and for her, the possibility of developing technologies that help detect disease earlier and personalize treatments isn’t just a scientific goal; it’s one she hopes will shape the future of medicine for the next generation. 

“I want to contribute to a future where medicine understands the body physiology well enough to predict disease instead of simply reacting to it, personalize therapies with greater precision, and ultimately give families more healthy years together,” she says. “Because once you become a parent, every scientific question becomes deeply human.”

The complexity of Grass’ research has required her to step well beyond her original training. After studying neuroscience and immunology, she immersed herself in materials science, systems physiology, device fabrication, bioelectronics, and surgery to develop the tools needed to answer fundamental biological questions.

“The scientific question was bigger than any one discipline,” she says. “HST taught me to begin with biology, not disciplines. Once you understand the biological principles, medicine, engineering, and science stop being separate fields. They become complementary ways of answering the same question.”

The constant need to learn a new discipline has been both the most rewarding and challenging part of Grass’ research so far. 

“Every time I crossed into a new discipline, I felt like an immigrant again,” she says. “I had to learn a new language, understand a new culture, and earn the trust of people who had spent their careers there.”

Grass’ passion for understanding cultures extends well beyond the lab. Soon after arriving at MIT, she co-founded the Graduate First-Generation Low-Income Student Group to create a supportive space for students and connect them with the resources they need to thrive. What began as a small initiative has grown into a community of more than 300 graduate students representing over 60 countries, connecting students with faculty, alumni, entrepreneurs, and industry leaders.

“It has been really rewarding to see new GFLI leaders emerge and continue this legacy,” Grass says.

As an avid traveler, Grass’ favorite pastime is exploring new cultures, whether that be through learning a new traditional recipe or a new language. She speaks four languages fluently and can say “thank you” in roughly 50 more.

Whether she’s cooking Thai food with her children or introducing friends to recipes from around the world, she sees food as another language capable of connecting people across cultures. That same philosophy shapes how she thinks about science.

“I’ve realized that every culture has its own language and every scientific discipline its own way of understanding the world,” she says. “Looking back, every stage of my life has been about understanding how complex systems communicate. Today, my goal is to help medicine understand the principles that govern communication across the human body in health and disease.”

For energy systems that power a reliable grid, the future is all about location

MIT researchers find that where new energy projects are built could be key to avoiding blackouts in a future with hotter weather and other challenges.

Will a warming climate and changing weather patterns lead to more grid blackouts and other energy disruptions? Answering that question requires studying both regional climate forecasts and local energy systems, including emerging renewable generation, storage, transmission lines, and demand forecasts. The lack of such studies is one reason why energy developers and grid operators rarely consider climate change when deciding where to build their next project.

Now MIT researchers have created a way to make more climate-informed energy siting choices, and shown how it can be used to make energy systems more resilient and reduce blackouts. The researchers’ framework, described today in Nature Energy, combines fine-scale meteorology with detailed simulations of energy infrastructure. It shows how the location of new energy projects will play a significant role in meeting future demand in a changing climate.

The researchers applied their framework to decarbonized energy systems in New England and Texas, finding that energy systems designed for historic climate conditions could face up to a fivefold increase in energy shortfalls, potentially leading to blackouts, by 2050. Taking climate change into account when designing the system, conversely, improved the resilience of both regions’ energy systems at no or very little additional costs.

“As we mitigate climate change with renewables, we can also adapt to climate change by using future weather projections in our power system planning, and the extra costs of that adaptation are, at least in this study, not much,” says senior author Michael Howland, MIT’s Jeffrey Cheah Career Development Professor. “It’s different from other climate adaptation studies, where building a big seawall or other mitigation efforts are really expensive. In this case, if we’re smart when we design our power system decarbonization plans, it could cost almost nothing extra to simultaneously adapt to climate change.”

Joining Howland on the paper are first author Liying Qiu, a former MIT postdoc; Rahman Khorramfar and Shen Wang, current postdocs at MIT; and Saurabh Amin, MIT’s Edmund K. Turner Professor in Civil Engineering.

A better way to think about energy projects

The world’s energy systems are in a period of change. On the demand side, that change is driven by trends like the rising demand for artificial intelligence and the electrification of industries including transportation. On the supply side, that change is driven by the plummeting costs of renewable systems like solar and wind energy.

“That drop in costs has enabled the widespread deployment of renewables, because they’re the cheapest electricity-generation solution in many locations,” Howland explains. “At the same time, for the first time in more than a decade, electricity demand is starting to increase in the U.S.”

As low-cost variable renewable energy supplies increase, matching supply and demand throughout the day can become a harder problem for energy system operators. Adding to that complexity is the fact that renewables and energy demand are both influenced by weather and climate in different ways in different regions.

In the past, researchers have generally studied the impacts of climate change on individual technologies, for instance studying how it might change global wind and solar patterns. Other studies have considered the impact of climate change on states or other large areas, overlooking the specifics of regional energy systems. More recently, region-specific studies have been done but typically relied on low-resolution, global climate models.

“That’s what climate models are good at: giving you the global picture at coarse resolution,” Howland explains. “That limits insights for regional system planning and risk assessments.”

For their paper, the MIT researchers chose to study Texas and New England because they provided two different climate types and energy systems. The team used fine-scale meteorology models and considered the influence of climate change on weather-related energy failures.

“This study explores the joint, simultaneous impacts on multiple components of the energy system, similar to compound events studied in climate science,” Howland explains. “An extreme weather event can impact wind and solar generation and electricity demand all at the same time. Our hypothesis is that’s likely to be the biggest impact we’ll see from climate change on energy systems.”

The researchers also considered the impact of using climate change models to help site energy projects, looking out to 2050 because that’s the typical lifetime of wind and solar plants being built today. They found that locations that are best suited to provide the renewable wind and solar energy that the grid needs were meaningfully different in future climate conditions than in the historic climate.

The researchers found that climate change could increase energy failures by as much as 500 percent by 2050 if the siting did not consider future climate conditions. Such failures were driven primarily by the interaction between multiday renewable shortfalls and energy system design decisions like where to build solar farms and transmission lines.

“We are telling people where you put your wind and solar matters a lot for your ability to deliver energy when you need it,” Qiu explains. “We need to think more about the when and where of adding renewables rather than only focusing on adding overall capacity.”

In New England’s power system, the researchers found that energy supply disruptions caused by climate-related weather changes necessitate investment in solar capacity and transmission lines close to energy demand centers like cities. In Texas, energy disruption risks were primarily driven by transmission constraints.

The researchers found that climate-informed designs would prioritize adding wind farms in West Texas to better align with future demand patterns. The study assumes both regions will continue adding renewable capacity, thus the researchers concluded that Texas could improve the resilience of its grid at near-zero additional cost.

“We are showing that increasing energy resilience requires more than just spending more money,” Qiu says. “It primarily requires better and smarter planning.”

A new approach to adaptation

Howland says taking a broader view of climate change’s impact on energy systems helped his team get a clearer picture of blackout risks and other potential supply problems.

“On the individual power plant level, it’s not necessarily that climate change is a dominant uncertainty, so it really comes down to how all these energy system components and energy demand relate to each other,” Howland says. “That’s where we see the biggest impact of climate change, rather than on the level of individual wind or solar plants.”

Because the researchers used expensive, high-resolution models, Howland says their new model wouldn’t be practical for grid operators to use in their daily work today, but they hope to soon develop faster models that grid operators could use more easily.

“This study shows the opportunity and the need,” Howland says. “There are risks to not adapting our system, but if we do adapt our system, there could be big opportunities that are not costly. Now the key challenge is that we have to address the massive data and translation gap we have between meteorology and energy system planning and management. Right now, there’s too big of a divide between climate and weather modelers and power system practitioners. We want to continue to break that barrier down through interdisciplinary research.”

This work was supported by the MIT Climate Grand Challenges, the MIT Climate and Sustainability Consortium, and the MIT Energy Initiative Future Energy Systems Center.

A better way to turn 2D designs into 3D models for rapid prototyping

Researchers developed an automated framework that helps AI models generate CAD programs more accurately and efficiently.

Engineers often use vision-language models to produce new designs, such as for airplane or automobile components. To simulate how those components will perform in realistic situations, they’ll use tried-and-true computer-aided design (CAD) software to generate 3D models of those designs, which they can put through virtual crash or durability tests. 

Researchers from MIT and elsewhere have now developed a system that can teach a vision-language model to automatically convert 2D designs into CAD programs that are much more accurate and functional compared to other approaches, while using only a fraction of the computation.

By improving the performance and efficiency of AI-driven CAD generation, this technique could streamline the rapid prototyping process and reduce costs. It could also help engineers identify beneficial design choices they might otherwise overlook. 

The system generates new data based on the model’s abilities as it attempts to convert a 2D image into a CAD program. The framework corrects the model’s failures and incorporates them into a dataset with its successful solutions. 

It uses these data to teach the model how to fix specific mistakes and tackle tricky problems it would struggle with on its own.

“We want engineers to be able to point our framework at an underperforming CAD model, set a compute budget, and let the system take over — turning the model’s own mistakes into better training data,” says lead author Giorgio Giannone, a research affiliate in the Design Computation and Digital Engineering (DeCoDE) Lab at MIT and a principal research scientist on the AI Innovation Team at Red Hat.

He is joined on the paper by Anna Claire Doris, a mechanical engineering graduate student at MIT; Amin Heyrani Nobari, an MIT postdoc; Kai Xu of RedHat; and co-senior authors Akash Srivastava, director of Core AI at IBM and a principal investigator at the MIT-IBM Computing Research Lab; and Faez Ahmed, associate professor of mechanical engineering at MIT, leader of the DeCoDE Lab, and a principal investigator at the MIT-IBM Computing Research Lab. The research was recently presented at the International Conference on Machine Learning.

“Nearly every physical product around us, from airplanes to appliances, begins its life as a CAD model. Industry teams are eager for AI that can help speed-up the creation of these designs, but today's models often produce simple shapes inadequate for practice. What excites me about this work is that it gives many image-to-CAD-code models a way to improve themselves, learning from their own errors rather than waiting for more human-made data — and that brings trustworthy AI design tools much closer to everyday engineering,” says Ahmed.

Model-aware data

The researchers are working toward building vision-language models (VLMs) for CAD generation. These VLMs take a 2D image and some descriptive text, and output Python code that can be executed in a CAD software program to generate a 3D model of a physical object.

They studied the challenges of deploying existing VLMs for this task and determined the main bottleneck that limits their capabilities is the lack of diverse, high-quality CAD datasets to train them. 

To remedy this, they sought to create new data to teach a model how to perform CAD generation, using a process known as data augmentation.

In data augmentation, scientists typically create new data by randomly tweaking existing data to generate more samples, often by adjusting the color, size, and shape of objects in images. 

Instead, the MIT researchers built a data augmentation system called GIFT (which stands for Geometric Inference Feedback Tuning) that generates data designed to improve the performance of one VLM for a specific task.

GIFT develops an understanding of the model’s strengths and weaknesses by testing it. Then it uses this knowledge to generate data that could improve the model’s performance on the CAD generation problems it struggles to solve.

“We want to obtain data augmentation that is informed by the model itself,” Giannone says. 

Learning from mistakes

To do this, GIFT asks the model to generate code that solves a CAD generation problem multiple times in parallel. It checks the correctness of these guesses to understand how well the model can solve this problem.

“For a model, generating CAD query code that is almost correct is not that hard, but generating code that is perfectly correct and can be executed is much more challenging for a standard VLM,” Giannone says.

For guesses that are nearly correct, GIFT adjusts them to become successful solutions. It saves these “near-misses” and successful solutions in a new dataset that can teach the model how to overcome problems that would usually trip it up.

“If we sample the model 10 times and it generates 10 correct answers to the same problem, then there is not much for it to learn. We care about the in-between cases, where the model might only solve the problem 50 percent of the time,” he says.

Using these in-between cases allows GIFT to generate data augmentations that are both model-aware and task-aware. In addition, by incorporating multiple correct solutions to the same problem, the new data expand the model’s general knowledge of CAD code generation.

This automatic system does not require human intervention to correct the model’s mistakes.

GIFT creates data augmentations from a pre-trained VLM using a process known as inference-time scaling. This process allows a static model, which has already been trained, to generate better outputs without the high computational costs of retraining the entire model. 

Using inference-time scaling, the user can determine how much computation they want to use for GIFT, tailoring it to their time and budget constraints. 

GIFT outperformed several competing techniques, generating CAD programs that were more accurate while using only about 20 percent as much computation. The CAD models generated by VLMs using GIFT were better aligned with the shapes of ground-truth models.

“With GIFT, we started with geometry because with engineering problems, if the geometry of a 3D shape is not correct, nothing else will be correct, but there are many other aspects to consider,” Giannone says.

In the future, the researchers want to expand GIFT so the framework can teach models to generate CAD programs that improve the performance and manufacturability of 3D models. They also want to apply the system to larger models and more diverse CAD generation tasks.

This research was funded, in part, by the MIT-IBM Computing Research Lab. 

3 Questions: Neural transparency and the future of AI design

Assistant Professor Pat Pataranutaporn describes a new interface that lets everyday users glimpse inside an AI's neural network before their chatbot ever says a word.

Millions of people are now designing their own personalized artificial intelligence companions, yet most have little idea how those creations will actually behave. In a new paper, MIT Media Lab Assistant Professor Pat Pataranutaporn and his graduate student researchers Anthony Baez and Sheer Karny introduce “neural transparency,” a tool that lets everyday users glimpse inside an AI’s neural network before their chatbot ever says a word. The work is being presented this week at the ACM Conference on Intelligent User Interfaces. 

In this interview, Pataranutaporn, who is the Asahi Broadcasting Corporation CD Professor of Media Arts and Sciences, explains what they found, why the stakes are higher than most users realize, and what genuinely transparent AI might look like in the future.

Q: Your paper introduces “neural transparency,” a way to let everyday users peek inside an AI’s neural networks before their chatbot ever says a word. Can you describe how that actually works, and why you focused on the design moment, rather than catching problems after a chatbot is already out in the wild?

A: Millions of people are now creating personalized AI chatbots and agents powered by large language models, turning them into collaborators, tutors, coaches, creative partners, and companions through simple text prompts. Yet most people have very little idea how those prompts will shape the AI’s behavior until they begin interacting with it. We wanted to change that.

“Neural transparency” means giving people something like a brain scan for AI. Not because AI has a human brain, but because its neural network contains internal patterns that can hint at how it may behave before it speaks. In this work, my students Anthony Baez, Sheer Karny, and I combined insights from the fields of human-AI interaction and mechanistic interpretability to make those hidden patterns accessible to everyday users.

The basic idea is simple. First, we choose behaviors we care about, such as empathy, honesty, toxicity, hallucination, or sycophancy. Then, we compare the model’s internal activations when it is prompted to exhibit one trait versus its opposite. That difference becomes a kind of “behavior direction” inside the model. When a user writes a custom system prompt — the instructions that shape their chatbot’s personality before any conversation begins — we project the model’s internal activations onto those directions and translate the results into an intuitive visualization. In our case, this is a sunburst diagram that previews the chatbot’s likely personality traits before the user starts chatting with it.

We focused on the design moment because that is where prevention is possible. Today, people often discover problems only after the chatbot has already behaved in unintended ways. Our goal was to move from reactive correction to anticipatory design by helping people identify potential risks while they are still shaping the AI.

Q: Your study turned up something pretty striking: People consistently misjudge how their personalized AI will behave, overestimating the good traits and underestimating potentially harmful ones like sycophancy. What does that tell us about the risks baked into how millions of people are currently building AI companions, and why is that blind spot so hard to close?

A: I often joke that if AI showed up looking like the Terminator, it would be much easier for us to know what to do. The real challenge is that AI often appears as a warm friend, coach, tutor, or companion. That makes it difficult to recognize when something is going wrong.

Our study suggests that people have a blind spot when designing personalized AI. People often think they know how their chatbot will behave, but in our study they incorrectly predicted its personality on 11 of the 15 traits we measured. That highlights the need for tools that help people better understand AI before they start using it.

This matters because some behaviors that feel helpful in the moment may not be healthy over time. In previous research, we documented cases of psychological harm associated with interactions with AI chatbots. An LLM [large language model] that constantly validates your opinions or never challenges your thinking can reinforce harmful decisions, unhealthy beliefs, or emotional dependency. Psychology has long shown that people are naturally drawn to affirmation, so designing AI is not only a technical challenge, but also a psychological one.

The deeper issue is that today’s AI systems remain largely black boxes: Even experts cannot always predict how a system prompt will shape an AI’s behavior over a long conversation. As AI companions become part of everyday life, we need tools that help people understand what they are building before they begin using it. AI should be supportive without becoming blindly agreeable, personalized without becoming manipulative, and transparent enough that people can make informed choices.

Q: One of your most interesting findings is that the visualization significantly increased user trust but didn’t actually change how people designed their chatbots. What will it take to close that gap, and where do you see tools like this heading as AI companions become more deeply embedded in people’s everyday lives?

A: I actually think this is one of the most interesting findings in the paper, because it shows that transparency alone is not enough. People appreciated being able to see inside the model and reported greater trust in the system, but simply presenting information did not fundamentally change how they designed their AI companions.  

In our followup work, which is currently available as a preprint, we are studying how a model’s internal neural representation changes over the course of a multi-turn conversation rather than remaining fixed from the initial prompt. We are already seeing promising results. By visualizing how these internal representations drift over time, people become significantly better at recognizing and anticipating changes in AI behavior, and are less likely to become overconfident in their understanding of the chatbot. AI companions are dynamic systems that evolve as they interact with us, so understanding those internal changes is an important next step. Nevertheless, this is still a very young research area. 

Looking further ahead, I believe these kinds of transparency tools could become as commonplace as nutrition labels are for food. As AI becomes deeply woven into education, health care, work, and personal relationships, people should be able to understand not only what an AI can do, but how it may influence their thinking, emotions, and behavior. That kind of transparency is essential if we want AI to genuinely help people flourish.

MIT Professor Susumu Tonegawa, renowned molecular biologist and Nobel laureate, dies at 86

Tonegawa made landmark discoveries about how the immune system generates antibody diversity and how the brain forms memories.

Susumu Tonegawa, the Picower Professor of Biology and Neuroscience at MIT and a Nobel laureate, died July 11 at the age of 86.

Tonegawa was a renowned molecular biologist who wielded his keen insight in a variety of fields, including immunology and neuroscience. In the early 1980s, Tonegawa discovered how the immune system generates its incredible diversity of antibodies — a breakthrough that earned him the Nobel Prize in Physiology or Medicine in 1987.

Following that landmark achievement, he turned his attention to neuroscience, where his work has helped to reveal how the brain stores memories as traces called “engrams.”

An MIT faculty member for more than 40 years, Tonegawa also served as the founding director of MIT’s Picower Institute for Learning and Memory and director of the RIKEN Brain Science Institute of Japan, and was a Howard Hughes Medical Institute Investigator.

“Few scientists have reshaped our understanding of biology as profoundly as Susumu Tonegawa,” says Myriam Heiman, director of the Picower Institute. “His intellectual fearlessness, extraordinary creativity, and relentless pursuit of fundamental questions opened entirely new frontiers in both immunology and neuroscience. His influence on science and on the people who had the privilege of working alongside him is immeasurable.” 

Drawn to molecular biology

Born in Nagoya, Japan, Tonegawa spent his early years moving between rural towns, due to his father’s job as an engineer for a textile company. When it was time for him to go to high school, his parents sent him to a school in Tokyo, where he became interested in chemistry.

He was admitted to the University of Kyoto to study chemistry, and while there, he was drawn to the nascent field of molecular biology. He began his graduate studies at the Institute for Virus Research at the University of Kyoto, but after only a couple of months, his advisor, Professor Itaru Watanabe, suggested that he apply to a school in the United States, which had more advanced molecular biology programs.

Tonegawa took that advice and was accepted at the University of California at San Diego, where he studied how a virus called phage lambda controls gene transcription. After earning his PhD in 1968, he went on to a postdoc in a lab at the Salk Institute.

In that lab, Tonegawa began studying gene expression of a virus known as SV40. However, his U.S. visa was set to expire at the end of 1970, so he soon headed for a position at the newly established Basel Institute for Immunology in Switzerland. 

At the time, Tonegawa had little background in immunology, but he soon became fascinated by the 100-year-old question of “antibody diversity” — how the body’s immune system is able to generate hundreds of millions of antibodies from a relatively small set of genes. (The entire human genome contains about 20,000 genes.) That antibody diversity is what allows the immune system to recognize so many pathogens, including those it has never seen before.

With colleagues in Basel, Tonegawa discovered that each antibody protein is not encoded by its own gene — instead, genes for different components of the antibody can be randomly recombined to generate limitless combinations.

In 1987, Tonegawa was a solo recipient of the Nobel Prize for discovering that process, known as V(D)J gene rearrangement. In announcing the award, the Nobel committee noted that Tonegawa’s discoveries “explain the genetic background allowing the enormous richness of variation amongst antibodies. Beyond deeper knowledge of the basic structure of the immune system these discoveries will have importance in improving immunological therapy of different kinds, such as for instance the enforcement of vaccinations and inhibition of reactions during transplantation.”

From antibodies to engrams

In the early 1980s, after his groundbreaking antibody discoveries, Tonegawa began to feel the urge to turn to new research directions. He also wanted to return to the United States, so in 1981, he accepted the offer of a professorship at MIT’s Center for Cancer Research (today known as the Koch Institute for Integrative Cancer Research). There, he began working on T cells and contributed to scientists’ understanding of how T cells are able to generate a large diversity of T-cell receptors.

While at the CCR, he also began to study questions in neuroscience. As he told an interviewer from the Picower Institute in 2022, he was always in search of new scientific endeavors to keep him interested in his work.

“When I decided to become a scientist, my criteria of what to do was whether the scientific problem I got to solve was interesting or not. Whether I’m curious our not. I didn’t think about other things like, Could it be too risky? Can I really develop my career by venturing into the field I am not familiar with? That never occurred to me. I just followed my curiosity and instinct,” Tonegawa said in an interview published in the summer 2022 Picower Institute newsletter.

In 1994, he was chosen as the founding director for MIT’s Center for Learning and Memory, which became the Picower Institute for Learning and Memory in 2002. Tonegawa continued to serve as the center’s director until the end of 2006. 

Professor Li-Huei Tsai, who succeeded Tonegawa as the Picower Institute’s director, calls working alongside Tonegawa “one of the greatest honors of my career.”

“His passion, boundless energy, and unwavering pursuit of the fundamental mechanisms underlying memory were contagious, inspiring generations of neuroscientists to join and advance the field. Today, we lost a giant. His scientific legacy will continue to shape neuroscience for years to come, and he will be deeply missed by all of us,” she says.

Over the past two decades, Tonegawa’s lab has made significant discoveries in the field of memory research. In 2013, he and his colleagues reported that they had identified “engrams” in the brain’s hippocampus. These engrams consist of episodic memories — memories of experiences — that are stored in specific groups of hippocampal cells. Engrams encode elements including objects, space, and time, linked to a specific experience.

At that time, the researchers also found that it was possible to implant “false memories” in mice by using optogenetics to reactivate an existing engram while the animals formed a new memory. This prompted the mice to associate a new location with the memory of an event that had actually happened in a different location.

Later work from Tonegawa’s lab showed that engrams extend beyond the hippocampus and are stored across a widely distributed complex that spans many brain circuits. More recently, he had been working on engrams of “knowledge memory” to decipher the fundamental mechanism of abstract memory. His recent work also delved into how the emotional associations of memories are encoded, and how the brain maintains a timeline of chronological events.

In addition to the Nobel Prize, Tonegawa received many other awards, including the Albert and Mary Lasker Award for Basic Research in 1987, the Bristol-Myers Award for Distinguished Achievement in Cancer Research in 1986, and the David M. Bonner Lifetime Achievement Award from the University of California at San Diego in 2010. He was also known for training many scientists who are now leaders in the field of neuroscience.

Tonegawa was a longtime fan of the Boston Red Sox, and in May 2004, he had the opportunity to throw out the ceremonial first pitch at Fenway Park, as part of the team’s tribute to the Boston area’s scientific and medical communities.

He is survived by his wife, Mayumi Tonegawa ’92, two children, Hidde Tonegawa ’09 and Hanna Tonegawa, and two grandchildren. He was predeceased by a son, Satto Tonegawa. 

Following a private funeral, his ashes will be buried in Kyoto, Japan.

3D-printed bridge points the way to greener construction

MIT researchers developed a framework that folds a printer’s real-world limits into the optimization, while revealing that better hardware could sharply reduce material use.

Concrete is the most widely used building material on Earth, and producing it is one of the largest single sources of carbon emissions. One promising way to reduce its environmental footprint is to 3D-print concrete, laying it down bead by bead like a giant icing-piping robot. This process eliminates the labor-intensive formwork of pouring it into molds, and places the material only where a structure needs it.

But many of the most efficient designs created by computers are impossible for today’s printers to build. Engineers use a technique called topology optimization to find the strongest structure that uses the least amount of material. But those mathematically ideal designs, with their intricate, spider-web shapes, don’t account for the physical limitations of large-scale concrete printers with their thick nozzles, limited turning, and need to print in one continuous motion.

Now a team of MIT researchers has developed a way to close that gap. Their framework, described in a new article in Additive Manufacturing, bakes a printer’s real fabrication limits directly into the optimization, so the design that comes out is one a machine can build and print with little or no manual redesign. They demonstrated it by designing, printing, and load-testing a 2.3-meter concrete bridge and found that today’s printing hardware, not the concrete itself, limits how light a structure can be.

“We were finding a lot of cracks you can fall through when it comes to translating these super-optimal designs into manufacturable designs,” says co-first author Hajin Kim-Tackowiak PhD ’26, a postdoc in MIT’s Department of Civil and Environmental Engineering (CEE). “Those cracks were like chasms.”

Designing for what can be built

Those conversations surfaced three key limitations: how thick each printed bead must be, how sharply the nozzle can turn, and the need to print in a single continuous line. The researchers translated each constraint directly into the mathematical rules of their framework.

Existing 3D-printed structures are typically produced with older methods that optimize the shape first, and then require “a massive amount of post-processing,” taking days to run, Kim-Tackowiak explains. By contrast, the team’s framework generated fully printable designs in about two minutes on a laptop. When the team needed to slightly reduce the bridge’s size on the day of printing, they simply reran the optimization and had an updated design five to 10 minutes later. 

“Reaching that speed at all is recent,” says co-first author Zane Schemmer, a PhD student in CEE. The math the method relies on, mixed-integer optimization, was long considered too hard to use. “You go back five, 10 years ago, the solver we used, even three years ago, could not solve these problems,” he says. “This field has been avoided, because everyone thinks that’s not an avenue we can go down. But with new algorithms and resources, it’s becoming a way we can start to frame problems.”

A bridge reveals the real limitation

To validate the framework, the researchers went back to Autodesk’s facility to print a 2.3-meter-long concrete bridge.

“The bridge took about 30 minutes to make and was built from off-the-shelf mortar,” says senior author Josephine Carstensen, the Gilbert W. Winslow (1937) Career Development Professor in Civil Engineering.

In testing, the roughly 900-pound structure held more than 2,000 pounds spread across it with virtually no measurable bending, closely matching the team’s simulations.

But the test also revealed the study’s biggest surprise. “What we found was our result was super over-engineered,” Kim-Tackowiak says. “From zero to 200,000 pounds, your design is entirely driven by these 'can I build it or not' constraints. And then, after 200,000 pounds, you can start to think about the physics.” In other words, the limits of current printing technology, not the strength of concrete, were dictating how efficient the structure could be.

A roadmap for better printers

Because the framework finds the mathematically best possible design, the researchers could measure exactly how much each hardware limitation costs in material.

“With mixed-integer optimization, we can find the global optimum, the best solution there is, as opposed to just a good solution,” Carstensen says. “Because we know we’re finding the best solution out there, we can also quantify: If we had a machine that could do other things, what would that mean for how much material we’re using?”

The single biggest lever was the width of the printed bead. The bridge used a 4 centimeter bead. The analysis showed a machine that was able to lay a 1cm bead could cut material use by as much as 76 percent while staying “well within safety margins,” Carstensen says. The result surprised her. “I thought the continuous path would be the problem, the one that had the highest effect,” she says. “But it wasn’t. It was the bead width.”

The result is a roadmap for printer-makers showing that modest hardware improvements could unlock large gains in efficiency and cut concrete’s carbon footprint.

Part of what made the bridge possible is that every piece is in compression. “With concrete, it’s really good when you push on it, really bad when you pull on it,” Schemmer says. “We're able to guarantee that every piece of concrete that you see is in compression, there’s no part that’s being pulled on.”

The savings come not only from using less material, but from skipping molds entirely, an advantage that grows for one-off shapes. Carstensen sees early promise in disaster relief, “You can quickly put up new infrastructure without needing to make formwork.”

The bridge’s compression-only nature showed itself dramatically after testing. It had held more than 2,000 pounds without budging, but when a worker lifted one corner a few inches to sweep beneath it, it broke. The failure wasn’t a design flaw so much as a demonstration of the principle behind it: Concrete is weak when pulled, and the lift put parts of the bridge in tension they were never meant to carry. “It’s optimal in one way, but it’s definitely not optimal in every way,” Kim-Tackowiak says.

That points to the team’s next step of reinforced concrete. “We know a pure concrete structure is not necessarily going to be the most optimal thing, so we’re moving it more into the world we live in today, which is reinforced concrete,” Kim-Tackowiak says, “though working out how to feed rebar into a printed concrete structure,” she adds, “is proving its own challenge.”

The work was funded by the National Science Foundation and supported by the MIT Center for Advanced Production Technologies. Joining Kim-Tackowiak, Schemmer, and Carstensen on the paper are co-authors Pittipat Wongsittikan, a PhD student in the MIT Building Technology Architecture program, and Jackson Jewett MEng ’18, PhD ’25, a former MIT postdoc.

Electric fields help guide neural activity, even from moment to moment

A new study adds evidence that electric fields in the brain help to organize and shape underlying neural activity via “ephaptic coupling.”

It’s a fact of life that the electrical activity of neurons will vary during repetitions of the same task, even when the ultimate outcome is the same. A new study shows that a lot of ongoing fluctuations in the brain’s activity could be explained by the influence local electric fields exerted on the neurons, a phenomenon called “ephaptic coupling.” The finding, published in Cerebral Cortex, adds to evidence that the brain’s electric fields act as important control signals for underlying brain function.

“The brain is a rollicking sea of electrical influences,” says study co-author Earl K. Miller, Picower Professor of Neuroscience in The Picower Institute for Learning and Memory and MIT’s Department of Brain and Cognitive Sciences. “But the traditional view of brain function focuses only on the spiking and synaptic connections among individual neurons. Now, there is growing evidence for electric field effects. For instance, in this study we show that neural variability is explained by how ephaptic effects are influencing neural activity.”

In 2022 and 2023, Miller and fellow author Dimitris Pinotsis, associate professor at City St George’s, University of London, published several studies showing that local electric fields in the brain’s cortex not only reflected the information neurons were processing better than any individual neuron did, but also that the fields actively helped to organize the underlying neural spiking that executes that processing. Like an orchestra conductor, the electric waves can conduct crowds of neurons so that they are “playing the same tune.” They further theorize that fields physically exert influence on the structure of the brain via cytoelectric coupling, in which the fields alter the cytoskeleton of neurons, optimizing them to oscillate in synchrony.

Because electric fields can be manipulated, Miller and Pinotsis argue in the new study that understanding how they influence momentary brain function could open the door to therapeutic interventions designed to improve it when it is faltering in disease. It would be difficult to adjust every neural connection, but ephaptic coupling suggests that intervening at the level of electric fields could accomplish that therapeutic end, the researchers say.

“Properly devised electric field manipulations could help patients rewire faulty circuits,” Pinotsis and Miller wrote.

In the duo’s prior studies, they analyzed signals averaged over time, documenting that in general, even though local (or “mesoscale”) electric fields in the cortex arise from the electrical activity of individual neurons, the field ultimately represents and coordinates their function. Think of it this way: Neurons are like individual citizens, and the electric fields are their government. Once the citizens establish a government with their individual votes, they are then subject to and unified by the laws the government creates and enforces. 

In the new study, the team asked whether mesoscale electric fields not only provide this ephaptic influence overall during working memory tasks, but also trial by trial. After all, that’s closer to the timescale of actual brain operations that matter both for healthy function and in disease. 

So the scientists looked anew at the data they recorded as animals played a simple video game. The animals were shown a dot in one of six positions around a screen. After the dot disappeared, the animals had to hold its former position in memory because to succeed in the game and earn a reward, they had to glance when cued to indicate the direction where the dot had appeared. Meanwhile, the scientists recorded neural electrical spiking and more collective local field potentials. Using that information, they calculated the local prevailing electric field at each moment.

In their statistical analysis of the data, they made several findings. One, as expected, was that neural activity varied sometimes quite widely trial by trial during the task. Another, using a mathematical technique called Granger Causality, showed that the direction of influence between the electric field and the neural activity was strongly in favor of the field. In other words, in the coupling between the two, the fields were dominant.

“We found that electric fields that emerge from neural activity, captured with LFPs [local field potentials], turn around and influence this activity in a top-down fashion (ephaptic coupling),” the researchers wrote.

Moreover, the team’s modeling and calculations showed that the strength of the ephaptic coupling between the field and the neural activity was proportional to the variations in the LFP power — another sign that the fields influenced the neural activity.

“The larger the variability, the more evident the top-down organizing effects,” the researchers wrote. “The emerging picture is that electric fields serve as control parameters.”

The U.K. Medical Council, the U.S. Army Research Office, the U.S. Office of Naval Research, the Freedom Together Foundation, and the Picower Institute funded the study.

Ketogenic diets may increase cancer risk in the small intestine

MIT researchers have found that a ketogenic high-fat, low-carbohydrate diet fuels the growth of intestinal tumors.

A high-fat, low-carbohydrate diet, also called a ketogenic diet, can help some people lose weight by forcing their bodies to burn fat for fuel instead of sugar. 

In recent years, scientists have been exploring how this type of diet might affect other aspects of health and disease, including cancer. While some research has shown that the diet may protect against the development of colon cancer, a new study by MIT researchers suggests that in the small intestine, a ketogenic diet may increase the risk of cancer.

“Ketogenic diets have distinct effects on different tissues even within the gastrointestinal tract. I think the message here is that we need to be very careful in generalizing the effects that these diets can have, because what might be beneficial for one tissue may be detrimental for another tissue,” says Omer Yilmaz, director of the MIT Stem Cell Initiative, an associate professor of biology at MIT, and a member of MIT’s Koch Institute for Integrative Cancer Research.

Yilmaz is the senior author of the study, which appears today in Nature. MIT postdocs Jessica Shay and Fangtao Chi are the lead authors of the paper. Researchers from the labs of Alex K. Shalek, director of MIT’s Institute for Medical Engineering and Science, and Matthew Vander Heiden, director of the Koch Institute, also contributed to the study.

Diet and cancer

Ketogenic diets, originally developed in the 1920s as a way to treat epilepsy, have been adapted in the past few decades as a strategy to lose weight or increase lifespan. The diet comprises a high percentage of fat, low percentage of carbohydrates, and normal or reduced amounts of protein.

This type of diet forces the body to burn fatty acids for energy in place of carbohydrates such as glucose. Burning these lipids produces ketone bodies — primarily β-hydroxybutyrate (BHB) and acetoacetate — as byproducts of fatty acid metabolism. These ketone bodies are also generated when people fast or follow very low-calorie diets, which force the body to burn its own fatty stores.

A 2022 Nature study suggested that ketogenic diets have a protective effect against colon cancer and that BHB — the most abundant ketone body — is responsible for this effect. In the new Nature study, the MIT team wanted to explore whether ketogenic diets might have a similar protective effect in the small intestine.

The researchers fed mice who were genetically predisposed to developing intestinal cancer either a ketogenic diet, a control diet, or a high fat/high calorie diet. They found that mice on a ketogenic diet were more likely to develop tumors of the small intestine than those on a control diet. While they did not become obese, mice on the ketogenic diet developed tumors at rates similar to or even higher than those of mice on an obesogenic high fat/high calorie diet. 

Additional studies revealed that ketone bodies did not play a role in tumor development. Instead, tumor growth was driven by how intestinal cells burn dietary fat for energy — a metabolic pathway called fatty acid oxidation. This pathway activates a family of proteins called PPARs, which signal stem cells to multiply more rapidly, increasing the chance that some become cancerous. 

This stem cell proliferation can be beneficial in certain situations, such as when the intestinal lining needs to be repaired after illness or injury. However, too much proliferation can tip cells toward becoming cancerous. 

“Having more stem cells means that when you injure the small intestine, it can repair itself better, but the downside is that having more active stem cells can lead to tumor formation,” Yilmaz says.

Opposite effects

Surprisingly, the same ketogenic diet that promoted tumors in the small intestine had the opposite effect in the colon. The researchers found, similar to the earlier Nature study back in 2022, that a ketogenic diet suppressed the development of colon tumors. However, the new findings suggest that ketone bodies are not responsible for this protective effect.

“Given how much attention has been paid to ketone bodies like BHB, both as a commercial health trend and in recent high-profile studies suggesting BHB suppresses colon cancer, we fully expected them to be the direct drivers. Instead, our experiments in genetically engineered mice revealed that these molecules are essentially metabolic bystanders. The real surprise is that tumor acceleration is driven entirely by how stem cells process and burn the heavy influx of dietary fat itself,” Yilmaz says.

The researchers now hope to further study why ketogenic diets have such different effects in the colon and the small intestine. As ketogenic diets continue to gain popularity, understanding these tissue-specific effects will be critical for guiding their use, the researchers say.

“The deeper question is why the same diet has opposite consequences in two adjacent parts of the gut. That is what we are working to understand next,” Chi says.

The findings carry practical implications. Because the diet’s effects — both the tumor acceleration in the small intestine and the protection in the colon — are driven entirely by fat metabolism rather than the ketones themselves, commercial ketone supplements or drinks would not be expected to mimic either the risks or the benefits discovered in this study. This may be especially relevant given that small intestinal tumors have been rising in incidence in recent decades, with the greatest impact on patients with inherited conditions that predispose them to intestinal cancer, such as familial adenomatous polyposis.

The research was funded, in part, by the National Institutes of Health, a Pew-Stewart Trust scholar award, the Kathy and Curt Marble cancer research award, a Koch Institute-Dana Farber/Harvard Cancer Center Bridge project grant, the American Federation for Aging Research, the MIT Stem Cell Initiative, a Damon Runyon Postdoctoral Research Fellowship, and the Koch Institute Support (core) grant from the National Cancer Institute.

Helping AI models to meet the real world

Through research and entrepreneurship, Professor Devavrat Shah is helping to design methods that can handle constant decision-making using limited computational resources.

Systems using artificial intelligence to enhance forecasting, planning, and decision-making in businesses have been proliferating in recent years, but in many cases, they lack the detailed, specific information about the organization itself, limiting the usefulness of those tools. 

Devavrat Shah, a principal investigator at MIT’s Laboratory for Information and Decision Systems (LIDS), faculty member with the department of Electrical Engineering and Computer Science (EECS), and member of the Institute for Data, Systems, and Society (IDSS), has been focused on how to design methods that can handle second-by-second decision-making using limited computational resources. 

“In a sense, with a small amount of resource, you have to do a lot of heavy lifting,” he says. As a researcher, “my interest is in the ability to develop methods that can extract information from data at scale in as effective a manner as possible.”

The Andrew (1956) and Erna Viterbi Professor has been teaching at MIT since 2005. 

In 2019, he also co-founded a spinoff company called Ikigai Labs. Ikigai built a foundation model for tabular, time series data based on years of research in Shah’s lab, which was patented and licensed by MIT to the company. This model can take input from enterprise data from varied sources, continuously and at scale, so that it learns as it goes along by testing its predictions against real outcomes.

Shah explains that the system is an extension of the kind of graphical models that are used, for example, by GPS devices to convert a sparse amount of data received from satellites into an accurate model of a position on the Earth’s surface, or by communication system like that in a digital watch that communicates at high speed in an energy-efficient manner. 

“My interest was: How does one design such graphical models for generic, tabular data?” he says.

While most AI models have been taught using text and images, this system takes tabular data as its input — structured data such as the familiar kind of row-and-column format used in spreadsheets. And then it provides the kind of real-time planning, on a vastly larger scale. 

The idea for Ikigai was to provide forecasting and decision-making technology for large businesses, such as consumer goods manufacturers and pharmaceutical companies.

Shah gives the example of how a consumer electronics company might use this system. 

“Let’s say you’re making headphones and all sorts of different things. And each of the products that you manufacture has lots of small pieces that come from different parts of the world. And once the device is sold, it needs to be supported and maintained. And you have to come up with new versions of the product, you have to market them, you have to price them … So the questions you would typically ask would be: If I were to sell these next quarter or next year, how many will be sold in different places, and what would happen to demand if I change the price, or if I introduce promotion?”

He adds that all of these processes are interdependent, and at every stage of the processes decisions have to be made that have implications over time. “At some level,” he says, “digitizing these processes and being able to do predictions and constantly optimize is what leads to ultimately better business operations.”

Ikigai was recently acquired by the international firm Celonis, where Shah is now chief scientist in addition to his roles at MIT. Ultimately, he hopes the model he developed for Ikigai will help Celonis deliver tools that can integrate with a company’s own data and business processes in order to provide real-world analyses that can help make forecasts, plans, and decisions.

Shah adds that Celonis has specialized in digitizing and automating operations for more than 1,400 large companies around the world. Now that these systems are fully digitized, they provide a platform for Ikigai’s software to take the next step, reading the data from these digitized systems in order to provide detailed models to allow simulation of different options, predict optimum strategies, and forecast the results of a given set of decisions. 

“Once the digital layer of these processes exists and this information layer exists,” Shah says, “now, on top of it, we can put the Ikigai stack to enable decision-making at a much larger scale than otherwise.”

While so many companies are working on various aspects of AI, “we are very much focused on part of the domain that the rest of the world is not paying attention to,” which is the area of structured or time-domain data. By starting from such data, he says, it provides a very cost-effective version of AI. 

“A narrower focus comes with sharper technology,” he says, “but it’s broad enough that it’s very valuable.”

Shah adds, “The recent buzzword that’s become pertinent in the modern AI popular press is a ‘world model.’ In a sense, this is trying to build the enterprise process world model, so to speak.”

Three MIT Press journals lead their fields with Clarivate No. 1 rankings

Computational Linguistics, International Security, and The Review of Economics and Statistics lead their disciplines with 2026 Clarivate impact factors.

In an increasingly crowded, for-profit landscape for scholarly research, the health of a publishing program is often measured by the influence of its publications. This year, three MIT Press journals demonstrated their stature by earning the highest impact factors in their disciplines.

Computational Linguistics ranked first in the Linguistics category, International Security led the International Relations category, and The Review of Economics and Statistics topped the Social Sciences, Mathematical Models category in Clarivate’s 2026 journal impact factor rankings.

For the MIT Press, this achievement highlights the distinctive strength of its journals program. Although relatively small compared to other commercial and university press publishers, MIT Press journals consistently publish widely cited scholarship across a broad range of disciplines, from social science and the humanities to neuroscience and artificial intelligence. 

Clarivate’s impact factors capture the previous year’s scholarly citation activity, but the influence of MIT Press journals often extends well beyond academia. In recent months, International Security articles have been cited by Foreign Policy, Foreign Affairs, The Conversation, CBC, and Brookings. The journal has also published research with significant real-world policy relevance, including a widely discussed article by MIT political scientist Caitlin Talmadge that anticipated how a limited strike on Iran could escalate into attempts to disrupt shipping through the Strait of Hormuz, triggering a broader military and economic crisis. 

“I am proud and humbled that International Security has had the number one impact factor in International Relations for two years running,” says Jacqueline Hazelton, editor of International Security. “Thanks are due to our generous reviewers, our brilliant authors, our talented editors who handle the often-thankless work of copy editing and production, and, of course, our readers. We plan to continue leading the field in IR/security studies with rigorous scholarship that challenges the conventional wisdom, identifies new threats and opportunities, engages with policy and theory, and illuminates history.”

The MIT Press journals team is small, with under 10 people working across production, sales, and marketing; but that small team collaborates with the editorial staff of 50 disparate journals to publish around 2,500 articles annually. “Some of the joy I take in editing International Security stems from working with the people at MIT Press,” Hazelton adds. “They are helpful and patient. They know what to do, and they do it.”

“The journals division at MIT Press has undergone significant change over the past decade — from business model upheaval and rapid technological advances to the ongoing challenge of competing with commercial publishers many times our size,” says Nick Lindsay, director of journals and institutional partnerships at the MIT Press. “Through it all, the journals group has adapted and evolved to meet those challenges and remains a home for experimentation and fair and equitable publishing.”

The MIT Press’ reputation for influential publishing has attracted many prestigious partners to its journals program, including Harvard University, the American Academy of Arts and Sciences, and the University of California at Berkeley. Amid this growth and development, the program continues to launch and support new journals in emerging and interdisciplinary fields while upholding the high editorial and publishing standards that have made it what it is today.

“Computational Linguistics has long stood for depth and rigor, and in a field that moves remarkably fast, our aspiration is for it to remain a home for work that lasts — scholarship the community can keep building on for years to come,” says Wei Lu, editor of Computational Linguistics. “We are very proud of this result, which reflects both the strength of the work our authors publish and the care our reviewers and editors bring to the journal. We are grateful to MIT Press for being such a steadfast partner.”

This strong performance extended well beyond the press’ three top-ranked publications. Transactions of the Association for Computational Linguistics was ranked 2nd in the Linguistics Category out of 312 journals; Global Environmental Politics was 2nd in the International Relations category out of 173 journals; and The Review of Economics and Statistics was 17th in the Economics category among 626 journals. Other highlights include Harvard Data Science Review ranking 7th in Statistics and Probability; European Societies ranking No. 13 in Sociology; and Neurobiology of Language ranking No. 13 in Psychology, Experimental.

Overall, 13 MIT Press journals earned impact factors that place them in the top quartile of their area of publishing, including: 

• Computational Linguistics 
• European Societies
• Evolutionary Computation 
• Global Environmental Politics
• Harvard Data Science Review
• International Security
• Journal of Cold War Studies
• The Journal of Interdisciplinary History
• Linguistic Inquiry
• Neurobiology of Language
• Quantitative Science Studies
• The Review of Economics and Statistics
• Transactions of the Association for Computational Linguistics

Together, these rankings point to the strong reputation that the MIT Press has built for its journals portfolio, a relatively small program that shapes conversations across the humanities, social sciences, and STEM fields.

How visual learning happens in the brain

Scientists find that learning to identify a new object subtly reshapes visual processing in the brain.

The wiring and rewiring of the brain never ends. Neural pathways are constantly being reshaped as we interact with the world and learn new things. At MIT’s McGovern Institute for Brain Research and York University in Toronto, Ontario, scientists are combining detailed analysis of brain activity with computational modeling to better understand that change.

McGovern Institute postdoc Lynn Sörensen, McGovern investigator and MIT Professor James DiCarlo, and York University Assistant Professor Kohitij Kar, worked together to compare what happened when animals and an artificial neural network with brain-like architecture were trained to visually identify the same objects. As the model’s performance improved, it reorganized itself in ways that closely paralleled changes the team detected in the animal brains.

Their open-access work, reported July 8 in the journal Nature Communications, shows how changes in visual processing support animals’ ability to learn to discriminate new kinds of objects. By modeling these changes, the researchers hope to better predict how training reshapes perception, which could one day inform educational strategies for a wide range of learners.

Subtle changes

Learning about a new object calls on many parts of the brain. Visual-processing areas work together to make sense of information taken in through the eyes, then communicate with other brain areas to give the visual information meaning and guide behavior. Multiple parts of this system likely change during learning, and the research team wanted a clearer understanding of how that change is distributed.

Neuroscientists have debated how much change occurs in the brain’s visual-processing areas when an animal learns to recognize new objects. Some suspected that visual-processing pathways remain largely unchanged during learning to avoid broadly disrupting visual perception, but others have reported changes in activity within dedicated visual-processing areas with this kind of learning in humans and other primates.  

To take a closer look, the team focused on neural activity in a key component of the brain’s visual object-processing network, the inferior temporal (IT) cortex. By the time visual information reaches the IT cortex, key object features are clearly represented — so much so that it’s possible to “decode” what object the subject is seeing and even predict what errors it’s likely to make in identifying it, simply by analyzing patterns of neural activity there.

The team recorded neural activity in the IT cortex from animals as they looked at and identified images of objects. Some of the animals were untrained, so the images they saw had little meaning to them. Others had already learned to identify similar objects, so they could usually discriminate between elephants, chairs, and other select objects, even when those objects were presented at different sizes, from different angles, or against different backgrounds than the ones they had seen before.

The broad pattern of activity in the IT cortex was largely similar in trained and untrained animals, suggesting that learning had not dramatically rewritten this high-level visual representation. Still, the group found subtle but reliable differences in the way neurons in the IT cortex responded to images in animals that had learned to recognize the kinds of objects they were shown, compared to the untrained animals.

Modeling learning

The group turned to computational models to investigate how those modest changes might contribute to learning. Sörensen trained a suite of artificial neural networks whose internal components had been mapped to the IT cortex to identify the same categories of objects the animals had seen. The models were designed to learn using gradient descent, meaning they continually improved their accuracy by adjusting their parameters in response to errors.

Only some of the animal models showed learning behavior that matched that of the subjects. In those that did, the IT-like stage changed in ways that resembled the learning-related changes the researchers had observed in the IT cortex of trained animals.

While gradient descent is commonly used to train artificial intelligence, it is generally considered biologically implausible as a direct model of how the brain learns. The researchers say the strong match in learning effects between the animals and their model demonstrates that these kinds of artificial neural networks can offer insights into biological learning at a useful level of abstraction, even if the brain does not learn in the same way.

“This shows that you can actually build in silico versions of future experiments,” Sörensen says. “I think that gives us this playground of asking ‘what if’ questions — and potentially predicting new things that go beyond the experimenter’s intuition.”

Most of the changes that allowed for learning in the model occurred outside of the IT cortex. “This tells us that there is a lot between the area we recorded from and the final behavioral readout that needs to change during this process,” Kar says. He adds that the team’s model will be useful as researchers look more deeply into how downstream brain areas contribute to learning.

The researchers stress that their study allowed more granular measurements of brain activity than would be possible in humans, and because the animal brains are organized similarly to our own, their experiments have direct relevance to human learning. They say understanding the impact of plasticity in the subjects’ IT cortex could help researchers design new learning strategies for humans.

“Our prior conceptual working model of you learning new objects was that your brain makes changes to synaptic connections that are largely downstream of your visual system, so you don’t destroy your visual system,” says DiCarlo, who is also the Peter de Florez Professor of Brain and Cognitive Sciences and director of the MIT Siegel Family Quest for Intelligence. “You wouldn’t want your whole visual system to become an elephant detector [just because you’ve learned to identify an elephant]. But this study went beyond that to say actually, when you learn ‘elephant,’ your IT does change a little bit to make it a little more relevant to elephants.”

That likely has consequences for recognizing other visual features, too. Subtle changes in the IT cortex that support elephant recognition might also make you better at identifying things other than elephants, DiCarlo says. Likewise, the same changes might make it a little harder to identify something else.

These kinds of consequences may be difficult to predict intuitively, but become obvious with computational modeling. For instance, the team’s models revealed that after learning to recognize new objects, the IT cortex contained more information about objects’ locations. By providing insights like these, models could aid the design of more effective training strategies for visual tasks, including for people with altered sensory processing, who may learn from visual information in atypical ways.

Can AI build a jet engine? JARVIS Challenge tests role of AI copilots in tough-tech engineering

MIT students designed, built, and tested a jet engine with AI copilots, assessing AI’s usefulness in developing high-performance aerospace systems.

Artificial intelligence has rapidly transformed software engineering. Generative AI and large language models (LLMs) can create huge volumes of code and documentation; machine-learning algorithms can monitor performance and detect security vulnerabilities. But when the task is to conceive, design, and make a complex physical system such as a jet engine, are those AI tools equally transformative?

This past semester, the JARVIS Challenge (Jet-engine AI Research and Validation Intensive Sprint) set out to explore whether AI can compress the design-build-test cycle, asking MIT undergraduates to discover whether AI can help them to build faster and better. 

“The JARVIS challenge showed that AI can substantially accelerate safety-critical hardware engineering, but engineering judgment remains the decisive differentiator. An AI-native engineer is not defined by using AI, but by leading it — knowing when to trust it, when to challenge it, and how to translate AI outputs into working hardware. Manufacturing — not engineering design or analysis — remained the fundamental rate-limiting step,” says Professor Zolti Spakovszky, director of the MIT Gas Turbine Laboratory.

The teams, the tools, the task

The challenge gave undergraduates four weeks to design, fabricate, assemble, and test a small gas turbine aero engine, using AI as their primary engineering partner. The objective: build a “JARVIS-class” single-spool jet engine producing 50–100 pounds of thrust, running on Jet-A, and completing five 60-second runs. Teams had total freedom over design, materials, and fabrication. 

Representing nearly every department in the School of Engineering, 31 students organized into seven teams, ranging from all first-years to senior-heavy groups. Many of the competitors initially had little experience in turbomachinery, compressible flows, or, in the case of the younger students, even thermodynamics. Many had never seen the inside of a gas turbine before signing up to build one.  

At their disposal: MIT’s machine shops and manufacturing vendors; commercial software including Concepts NREC, SolidWorks, and ABAQUS; and various test rigs for characterizing and assembling individual components.

The teams also had access to MIT Parley, a newly launched platform that aggregates frontier large language models through a single interface. Through Parley, JARVIS leads could see directly how the students were using the AI tools, including their prompts, the cost per prompt, the specific LLMs being used, and other critical information. The JARVIS leads secured early access to Parley for all participants, and with financial support from MIT Lincoln Laboratory, the Department of Mechanical Engineering, and corporate sponsors Safran, Voyager Technologies, Beehive Industries, and Boom Technology, students had access to essentially unlimited use of AI.

The sponsors were drawn by recruiting interest and genuine curiosity about how AI might reshape engineering workflows. 

“We see this as the future of engineering,” Ryan (Hal) Hefron of Voyager Technologies told the students. “You’re honing skills that are not just nice to have — they’re going to be the future baseline in the engineering workforce.”

Vincent Garnier, managing director of Safran Tech, watched the competition unfold with excitement. “JARVIS was a genuine experiment, a learning endeavor. We frankly didn’t know what to expect, from the students or from the AI models. What struck me coming from the students was: first, the enthusiasm to explore; then, as the project developed, they all came to the cool-headed realization of what AI could or could not help them with, and then almost instantly adapted for that,” he says. “It makes me confident that this generation of leading engineers will probably not fall prey to easy and shortsighted use of AI, and will do so by keeping ever more in contact with experiments — physical or thought experiments.”

The faculty leadership — professors Zachary Cordero, Zolti Spakovszky, Masha Folk, and Andreea Bobu of the Department of Aeronautics and Astronautics, along with Lincoln Laboratory engineers and a team of teaching assistants — were there to ensure safety. In weekly progress reviews, they would critically evaluate the student progress and assess how the students were using AI.

Spakovszky developed a careful technique for guiding teams in the right direction without giving away answers or providing help. After a team’s presentation, he might ask: “Do you know what a rabbet fit is? Take in the comment.”

Where AI helps and hurts

By the end of week 1, one team withdrew from the competition; the others had, with varying degrees of success, developed an initial design for their gas turbines. Different teams used AI to summarize textbooks, teach them to use design software, source vendors, create Excel sheets, answer specific questions, find references, and create comparative analysis between design decisions. One team created an agent in Parley and tasked it with serving as their project manager. 

By week 2, teams had to start working on detailed CAD designs, ordering parts, and prototyping their combustors. This is where the teams started to hit limitations in their use of AI. While Claude and ChatGPT were good at offering design alternatives and filling knowledge gaps, teams found that the hallucinations, sycophancy, and lack of physical understanding that have become notorious features of generative AI were undermining their confidence and slowing them down. 

“AI is a helpful tool, great at finding information, helping organize things, and can write well, but it can’t do design,” says Elizabeth Tupaj, a member of team 811 Crew. “The moment the engineer doesn’t know what is going on and the AI is in charge is the moment the design becomes unreliable, at least with AI at its present capabilities.”

Teaching assistant John Zhang notes, “seeing this firsthand with the students reminded me how much first impressions matter. If the students couldn’t get answers from the AI early on, they quickly grew frustrated and formed a lasting opinion that precluded them from using it later.” 

In the final weeks, the finalists hit another obstacle no AI could solve: working with vendors. “AI searches found vendors we had no rapport with, who had no interest in our tight timeline,” students reported. “The vendors who came through were the ones our team had personal relationships with.”

Of the three finalists, only Fast and Fractured achieved first-attempt ignition of their mini-combustor. The team had used AI heavily for trade studies and architecture comparisons, arriving at a viable design despite none of them having prior gas turbine experience.

“The JARVIS Challenge showed what’s possible when you combine AI-enabled design with motivated students and a culture of rapid experimentation,” says Masha Folk, the Charles Stark Draper Career Development Professor of Aeronautics and Astronautics. “The moment that stood out most was when the first student-designed combustor was installed on the test stand. It ignited flawlessly, ramped to full power, transitioned to dual-fuel operation, and then sustained stable combustion on 100 percent Jet-A fuel. This was proof that we can dramatically accelerate the cycle of design, build, and test while giving students hands-on experience with a real engineering challenge.”

At the vanguard of AI-native engineering

By the end of May, the two more senior teams – Fast and Fractured and 811 Crew – had completed full engine tests. Fast and Fractured, with their AI-assisted design, were delayed by vendor headaches week after week, but finally made it to test. Unfortunately, their hot fire was cut short when the rotor rubbed and seized against the stationary housing. Team 811 Crew, however, who had more exposure to turbomachinery and propulsion concepts going into the competition, emerged victorious. Their engine started, successfully transitioned to Jet-A, and generated net thrust. 

“As we stood there with the air-starter, hearing their engines spool up and watching them spit fire, it felt like my heart was racing out of my chest. There were so many ways it could go wrong! What these students accomplished in such a short time span is nothing short of amazing,” says PhD student Joe Chiapperi. 

The 811 team had been resistant to using AI throughout the competition, trusting instead to their fundamentals and teamwork. “We had people who were at least somewhat familiar with the design software, mechanical engineers who knew how to build anything, and aerospace engineers who had taken classes on the design of gas turbine engines specifically,” says Tupaj. 

From the start of the JARVIS Challenge, younger students used Parley more frequently and cleverly, while the juniors and seniors leveraged deeper experience. 

“JARVIS taught me that getting value from AI takes two things: enough expertise to judge what it tells you and catch it when it’s wrong, and enough curiosity to actually lean on it where it could help,” says Professor Andreea Bobu. “The team that moved fastest in the sprint was experienced and leaned heavily on AI to get there. The team that eventually won was more resistant to AI; they had the expertise, but that skepticism made them slower. The sweet spot seems to be knowing enough to stay in charge of the tool, and being eager enough to pick it up in the first place. To me, that’s the real opportunity ahead: training the next generation of engineers who have the judgment to direct these AI tools and the instinct to reach for them.”

The competition’s clearest finding: engineering experience is a multiplier, and the human factor remains a vital element. Mastering the first principles and fundamental concepts breeds good engineering judgment and the ability to navigate strings of tough decisions in the face of incomplete information. And when it comes to building safety-critical physical systems, nothing can replace human hands and human accountability. 

“JARVIS has shown that AI copilots can have a multiplicative effect on engineering productivity, with judgment and first-principles thinking serving as the key differentiators among teams,” adds teaching assistant Kyle Woody. 

But the implications of AI in aerospace are significant. If small teams using well-managed AI copilots can compress design-build-test cycles from years to weeks, the consequences for workforce structure, R&D timelines, and competitive dynamics could be substantial. The students who tackled the JARVIS Challenge are among the first engineers to grapple with those stakes not as a thought experiment, but in a machine shop, with a jet engine on the test stand.

“JARVIS highlighted the power of AI in the design of physical systems,” says Cordero, associate director of the MIT Gas Turbine Laboratory. “But it also showed that the key to unlocking that power is education, through coursework, internships, and hands-on extracurriculars like MIT Motorsports and Rocket Team. Performance in JARVIS correlated strongly with year in school. My main takeaway is that in the AI era, education is more valuable than ever.”

MIT engineers find a precise way to grow artificial blood vessels

Gently stretching and pulling a “blood vessel on a chip” encourages controlled sprouting of new vessels, for possible use in artificial tissues or organs.

Tissue engineers are finding ways to grow living organs and tissues from cells, with the aim of replacing diseased and damaged counterparts in the body. Scientists have successfully grown artificial muscles, livers, kidneys, skin, and other tissues. But there’s been no reliable way to engineer precisely patterned networks of blood vessels, some of which can be finer than a human hair. 

Without a vascular network to deliver nutrients, any artificial tissues, no matter how life-like, can’t function. 

Now MIT engineers have found they can engineer and control the growth of blood vessels by mechanically stretching them. 

The team has built a human “blood vessel on a chip,” composed of a central artery made from human endothelial cells, that is embedded in a gel that also contains a small magnet. The researchers studied how the main artery responded as they jostled the gel back and forth using an external magnet to move the magnet embedded within the gel.

They found that the simple mechanical action of repeatedly jostling the artery stimulated the artery to sprout other, smaller capillaries. By changing the direction in which the artery is jostled or stretched, the researchers could redirect the growing new vessels. And stretching the artery by various degrees influenced how many more new vessels sprouted. 

Their results, reported in the Proceedings of the National Academy of Sciences, offer scientists a new way to engineer artificial blood vessels and program the patterns in which they grow. 

“Healthy tissues depend on organized blood vessel networks, but state-of-the-art protocols don't enable fabricating such networks within engineered tissues,” says Ritu Raman, associate professor of mechanical engineering at MIT and the study’s co-lead author. “The ability to program blood vessel growth with physical cues may enable reproducible and scalable fabrication of engineered tissues that can be implanted in the body to restore function after debilitating disease or injury.”

The study’s MIT co-authors include Sina Kheiri, Jessica Shah, Shashaank Venkatesh, and Roger Kamm, along with Peiyuan Chai and Ryan Flynn at Harvard University. 

“Moving is good”

Blood vessels are tricky to grow and control using conventional fabrication techniques. While 3D printers can produce vessels at the scale of major arteries and veins, the technology is not precise enough to print intricate networks of much finer, thread-like capillaries. Scientists have had some success with growing blood vessels from individual cells, by cultivating them in Petri dishes filled with nutrients and growth factors. But controlling how and where they grow remains a challenge. 

“You can try to pattern chemical cues, like growth factors, to direct where vessels grow, but you can’t do this very precisely,” Raman says. “We thus need other types of patternable cues that can help us build tissues with organized vessels.”

She and her students wondered whether they could grow and control new blood vessels using a protocol they previously developed to grow artificial muscles and nerves. In their previous works, the team engineered a small chip filled with a gel that they infused with nutrients and growth factors. They embedded a small magnet within the gel, and then carpeted the surface of the gel with live muscle or neuron cells. They then manipulated an external magnet to pull the embedded magnet, and the cell-covered gel, back and forth. This work revealed that mechanical “exercise,” pulling the cells back and forth, directly influenced how the cells grew. 

In their new work, the team used a similar setup to see if they could grow and control new blood vessels. 

The researchers built a “blood-vessel-on-a-chip,” smaller than a postage stamp, and filled it with a similar nutrient-rich gel containing a small magnet. They poked a thin tube lengthwise through the gel to create a hollow channel, and coated the channel with live endothelial cells, which naturally grow and fuse to form blood vessels in the body. Once the cells took on the channel’s shape, they started sprouting new, capillary-like vessels in the gel. 

Placing the device under a motorized stage fitted with small, suspended magnets, the researchers moved the magnets back and forth in different directions, and by various degrees, and observed whether and how blood vessels sprouted from the central artery in response. 

“The main takeaway is: Stretching the blood vessel back and forth seems to enhance the number of new capillaries that grow,” Raman says. 

If the main artery were simply left alone in the gel, it would grow some new vessels in random locations along its length. But when the artery was jostled, significantly more vessels sprouted. When the team used the magnets to stretch the gel back and forth, by 5 percent of the gel’s total width, many new vessels grew out from the main artery. When they stretched by 15 percent, fewer vessels sprouted, but those that did grew longer. And when the team changed the direction of stretching, the new vessels followed in response, taking turns and following the pattern of the team’s mechanical stimulation.

“We’re finding that moving is good, which is always the takeaway of everything we do in our lab,” Raman says. “Mechanical forces play an important role in our bodies. That means that if you want to grow more or less vessels, or shorter or longer vessels, or vessels in certain directions, we now know how to do that.”

A gatekeeping gene

The researchers went a step further to investigate why blood vessels grow in response to mechanical forces. To do so, they looked to gene editing, and the role of one particular gene: Piezo1. 

Raman had recently attended a talk by molecular biologist Ardem Patapoutian. In 2021, Patapoutian received the Nobel Prize in Physiology or Medicine for his discovery of ion channels in cell membranes that open and close in response to mechanical pressure. These channels, named PIEZO1 and PIEZO2, act as a cell’s gatekeepers, controlling what goes in and what comes out of a cell. Both types of channels, Patapoutian found, are regulated by their respective genes, also named PIEZO1 and PIEZO2. 

After his talk, Raman showed Patapoutian her group’s experimental results, which showed a connection between blood vessel growth and mechanical stimulation. Patapoutian in turn proposed that the explanation could be the PIEZO1 channel; by mechanically exercising the central artery, Raman may have been stimulating ion channels in the artery’s cells to open, triggering new blood vessels to grow. 

To test this hypothesis, Raman looked to knock down the PIEZO1 gene. If this gene were less active, and fewer blood vessels grew as a result, then it would mean that blood vessels do indeed grow in response to mechanical stimulation, and specifically, through the activation of PIEZO1 ion channels. 

The team repeated their experiments, this time with endothelial cells that were genetically edited to suppress the PIEZO1 gene. Sure enough, they observed that significantly fewer new blood vessels sprouted, even as they mechanically exercised the central artery.

Now that the team has found a way to grow and control blood vessel growth, they plan to apply the protocol to grow organized networks of vessels to supply artificial organs and tissues. “We are now investigating how precisely patterning blood vessel growth can help improve muscle function,” says co-author Jessica Shah.

This work was supported, in part, by the U.S. Department of War Army Research Office Early Career Program and PECASE Grant, and a Department of War DURIP Program Grant.

Arthur Bahr named head of MIT’s Literature Section

A faculty member since 2007, the scholar of medieval literature explores how different parts of physical manuscripts combine to create interest and meaning.

Professor Arthur Bahr has been named head of the MIT Literature Section, effective July 1. 

“Arthur is an exceptional scholar and a proven leader. I am confident that he will guide the unit with judgment, insight, and a deep commitment to its continued success,” says Agustín Rayo, the Kenan Sahin Dean of the School of Humanities, Arts, and Social Sciences. “I very much look forward to having him join the school’s leadership team.”

Bahr’s work blends formalist and materialist approaches to find literary resonance in the physical particularities of medieval manuscripts. He joined the MIT faculty in 2007 and helped lead the Ancient and Medieval Studies program in 2009-18 and 2022-23, working with colleagues from across the Institute to strengthen and expand the program. He has also been curriculum chair and undergraduate officer of the Literature Section.

“Lit@MIT has some of the world’s most innovative literary scholars and some of the Institute’s most dedicated teachers,” Bahr says. “It has also been my home for nearly 20 years, and I feel both humbled and energized by the opportunity to help shape its future. 

“Literature creates opportunities to slow down and reflect on what really matters, and in a fast-paced, increasingly automated world, those skills are more vital than ever,” he continues.

Bahr succeeds Associate Professor Sandy Alexandre, who served as head of the unit since July 2025.

Bahr is the author of “Chasing the Pearl-Manuscript: Speculation, Shapes, Delight” (University of Chicago Press, 2025); “Fragments and Assemblages: Forming Compilations of Medieval London” (University of Chicago Press, 2013); and co-editor of “Medieval English Manuscripts: Form, Aesthetics, and the Literary Text,” a special volume of The Chaucer Review (47.4, April 2013). His essays have appeared in ELH, Studies in the Age of Chaucer, Studies in Philology, and The Chaucer Review, among others. 

Bahr has been named a SHASS Faculty Fellow for the spring 2027 semester. His next project combines his interest in manuscripts with his training as a figure skating judge to explore analogies between sheets of parchment and sheets of ice, as sites of performance, inscription, and erasure.  

Bahr was named a MacVicar Faculty Fellow in 2015. He received the James A. (’48) and Ruth Levitan Award for Excellence in Teaching in 2012.

Bahr has served MIT as chair of the Committee on the Undergraduate Program from 2019 to 2021, and served on the pandemic-era Academic Policies and Regulations Team. He was also a subcommittee chair of the Education Group of Task Force 2021 and Beyond, and member of the subsequent Refinement and Implementation Committee on the Undergraduate Program. 

Bahr earned his undergraduate degree from Amherst College and his PhD in English Language and Literature from the University of California at Berkeley.

How MIT students are helping to prevent cyberattacks

Students from the MIT Cybersecurity Clinic help local governments and other vulnerable organizations defend against digital threats.

In May 2019, the government of Baltimore, Maryland, fell into chaos. Cybercriminals had locked the city out of many of its critical files and demanded payment to decrypt them. The city refused to pay ransom. The attack incapacitated a swath of services, including real estate transactions and bill payment, and recovery costs soared into the millions.

The syllabus of class 11.074/11.274 (Cybersecurity Clinic), a course in the MIT Department of Urban Studies and Planning (DUSP), includes a case study on Baltimore’s situation as an example of increasingly common ransomware attacks on municipal governments and other public agencies. To counter such threats, Lecturer Jungwoo Chun and Ford Professor of Urban and Environmental Planning Lawrence Susskind launched the MIT Cybersecurity Clinic in 2019. They have offered the course nearly every semester since.

Much like a legal or medical clinic, the course doubles as hands-on training for students and a pro-bono service to at-risk communities. After completing instructional modules and passing a certification exam, students are assigned in teams to a client. By the end of the semester, each team creates a report assessing the client’s vulnerabilities to cyberattack and recommending steps to improve protection. So far, the clinic has provided more than 40 assessments, confidential and free of charge, primarily for New England municipalities and health-care organizations.

In 2025, the FBI’s Internet Crime Complaint Center documented an average of 2,765 cyberattacks targeting Americans every day. When these attacks strike cities and towns, the fallout goes beyond finances, says Chun: “There’s a terrifying, cascading effect on every dimension of our lives.” 

In recent years, cyberattacks targeting the kinds of client communities served by MIT’s clinic have imperiled water supplies, impeded 911 and police services, and exposed citizens’ personal data.

Despite being gateways to essential infrastructure, many small municipalities and hospitals lack in-house staff trained in cybersecurity. Demand for such experts far exceeds supply in today’s labor market, and public sector budgets rarely can match the high salaries private companies offer qualified candidates.

According to Comparitech, from 2018 to 2024, there have been 525 ransomware attacks on U.S. government entities, approximately one every five days, leading to an estimated $1.09 billion in downtime costs.  

“Underfunded public and not-for-profit bodies need to follow a self-help pathway,” Susskind says. “There are many low-cost moves that these organizations can implement with a little coaching from a free-service clinic.”

Defensive social engineering

Some might be surprised to find a university cybersecurity program housed outside the computer science department. Chun is an applied social scientist with expertise in public policy and planning, and Susskind is a leading scholar of conflict resolution and consensus building. They call the approach they’ve developed for the clinic “defensive social engineering” to emphasize that cybersecurity isn’t solely a technical challenge.

Chun acknowledges that the rapid development of artificial intelligence has created alarming new tools for criminals — “now AI can not only identify the vulnerability, but do the attack itself, which is really scary” — and an ever-evolving menu of software claims to guard against these attacks. Accordingly, the course spends considerable time on the technical aspects of cybersecurity. “But at the end of the day,” Chun says, “the biggest attack vector is still through humans.”

The term “social engineering” commonly refers to ways cybercrime victims are manipulated into compromising security (for example, by sending money to a scammer, downloading malicious code, or disclosing sensitive information). Susskind and Chun’s concept of defensive social engineering is similarly grounded in human psychology. The approach emphasizes that cybersecurity must be part of everyone’s job, technical or otherwise.

“It’s about people knowing what to do, people making the right choices,” says Chun. “It’s helping them use the resources and budget they have now on things that can be long-lasting, rather than just spending on the latest antivirus software.”

“Students with computer science backgrounds are surprised by the importance we attach to helping clients build organizational capacity,” says Susskind. “Students need to understand the leadership dynamics in their client communities. The IT director can’t just do what she or he wants. They depend on the local government for their budget. They need approval to hire new staff.”

On the other hand, Susskind says, students from planning or social science backgrounds often study smart city innovations without learning much about the technologies needed to manage the associated risks. And there are aspects of AI and advanced system design — along with cyber law and other topics critical to cybersecurity — that engineering students may not learn in their other courses. The Cybersecurity Clinic aims to round out the knowledge of students from every discipline. The course aims to broaden those students’ knowledge, too, by inviting at least half a dozen guest speakers each semester from industry, other universities and MIT academic departments, industry, and/or relevant public agencies.

This past spring, for example, the lineup of lecturers included Dan Ricci, the founder of Industrial Data Works, on the modeling of risk in energy systems within budget-constrained environments; Gus Serino, president of I&C Secure Inc., on operational-technology cybersecurity for industrial control systems; and representatives from the MassCyberCenter and the Cybersecurity Infrastructure Security Agency providing overviews of their respective state- and federal-level organizations’ programs and initiatives.

“There are highly specialized things to learn, especially about the ways AI is changing cybersecurity, that we need help teaching,” Susskind says. “The rate at which the field of cybersecurity is changing means that most academics will have a very hard time keeping up.”

A roadmap for improvement

Clinic students spend the first four weeks of the semester preparing for field assignments. A series of online modules, supplemented by class discussion, outline the scope and nature of cyberattacks against critical urban infrastructure; review the 23 risk areas most relevant to their type of clients; and provide guidance for each step of the assessment process. This includes simulations of tricky client interactions. What if clients don’t take students seriously, or fail to provide the necessary information? What if they argue to receive a more positive assessment than the facts warrant?

“I’ve never ever had a class that prepared us for such realistic scenarios before,” says Diego Contreras, a rising senior majoring in computer science and engineering who completed the course this spring.

The modules culminate in an exam students must pass on their first try to receive a field assignment. For the remainder of the semester, they’ll receive continued support via weekly class meetings and get faculty input on their drafted reports, but the onus is on students to coordinate their team’s activities and build client trust.

“You represent MIT, and that is quite the responsibility,” Contreras says. “This course has given me people skills I wouldn’t have developed in any other context.”

“The most delicate aspect of the project was balancing our assessment findings,” says Zev Moore ’26, who took the class last fall as a senior studying mathematical economics and finance. “Our approach was to provide important feedback while simultaneously validating the positive security measures our client already had in place, which ensured our report felt like a collaborative roadmap for improvement.”

Certain key recommendations show up in the majority of reports. For example, clients are advised to inventory all hardware and software tied into their network and track who has access; patch software and back up data regularly; require multi-factor authentication and frequent password updates; train employees not to open attachments from unknown parties; prepare an attack response plan that clarifies lines of authority and includes the organization’s stance on paying ransoms; and only use vendors with good cybersecurity hygiene.

“None of these items is costly,” Susskind says. “Together, they will probably avoid 80 percent or more of the possible cost and danger of cyberattacks.”

Spreading the model

To date, more than 120 students have completed the full course at MIT. The online modules that prepare students for certification are freely available to the public as a massive open online course on MITx called Cybersecurity for Critical Urban Infrastructure, which has attracted tens of thousands of learners. The modules are also used by universities with their own cybersecurity clinics — a growing cohort, thanks in part to a consortium (with 61 member institutions and counting) co-founded by MIT in 2021 with the University of California at Berkeley, Indiana University, and the University of Alabama.

Most student teams wrap up client work after finalizing their recommendations; a few have volunteered to stay on after semester’s end to advise on implementation. In either case, Susskind and Chun check in periodically with clients for at least two years following each engagement.

“We often hear of the vulnerability assessment report serving as the organization's blueprint for their short-term, mid-term, and long-term agenda to be more prepared for future attacks,” says Chun. “We primarily work with IT directors or chief technology officers, and many of them have been telling us post-engagement that they shared the MIT report with the city or town leadership and were able to convince them they needed extra budget or a specific line item. They were using the student report as leverage to say, ‘it’s not just me saying it. We have a credible team who dedicated their time and these are the findings.’

“It's really a humbling experience,” Chun adds, “when some of our past clients reach out to us again after some time to say: ‘Now we have different people, we just purchased new equipment. Can we do this all over again?’”

New method aims to keep kids safe from illegal AI-generated content

Researchers developed an auditing technique to test generative AI models for malicious capabilities, without prompting them for illegal outputs.

With the exploding popularity of generative artificial intelligence, many open-source models are now available online for anyone to adapt for their task, such as generating product renderings in a certain artistic style. 

But these models also find their way into the hands of nefarious actors who may optimize them to produce illegal content, like hate speech or child sexual abuse material (CSAM). This is a growing problem — the National Center for Missing and Exploited Children received more than 1.5 million reports of AI-generated CSAM in 2025, an increase from 67,000 in 2024.

Engineers usually test AI for harmful capabilities by prompting the model and inspecting its outputs, but this is impossible for CSAM, since it is illegal in the U.S to generate such content, regardless of intent.

To avoid this dilemma and improve AI safety, Associate Professor Ashia Wilson and her graduate student, Vinith Suriyakumar, teamed up with researchers from MIT’s Healthy ML Lab, led by Marzyeh Ghassemi, and child safety non-profit Thorn to develop a new auditing approach that determines whether a model can produce CSAM without prompting it. Thorn is a child safety nonprofit whose mission is to transform how children are protected from sexual abuse and exploitation in the digital age.

Their technique examines how the inner workings of a model have been adapted, but it never generates an output. By examining hidden representations, it can reliably infer whether a model has been specialized to produce harmful imagery.

When tested, the auditing procedure identified model variations that had been specialized to generate CSAM with 100 percent accuracy. A hosting platform could use this technique to flag unsafe models and quickly remove them or prevent them from being uploaded in the first place.

“This unlocks a new avenue for platforms that host open-source models and for law enforcement to actually test whether a model is capable of generating CSAM. Before, we had no way of measuring this. It was a huge blind spot that some people were taking advantage of. Now, we can address an AI safety problem that is having severe negative impacts,” says Vinith Suriyakumar, an MIT electrical engineering and computer science (EECS) graduate student and lead author of a paper on this technique.

Suriyakamur and Wilson, the Lister Brothers Career Develop Professor in EECS, a principal investigator in the Laboratory for Information and Decision Systems (LIDS), and senior author, are joined on the paper by Lena Stempfle, an MIT postdoc; Ghassemi, an associate professor in EECS and a member of the Institute of Medical Engineering Sciences (IMES) and LIDS; and others at Boston University and Thorn. The paper was be presented as a spotlight at the “Trustworthy AI for Good” workshop at the International Conference on Machine Learning.

Auditing adaptations

Recent techniques have made it easier for users to specialize a generative AI model for their task through a process known as fine-tuning. 

Rather than retraining the entire model on a task-specific dataset, individuals can utilize an algorithm called low-rank adaptation (LoRA) to specialize the model in a more efficient manner.

This has led to a wave of new generative AI model variants for a variety of purposes, like producing watercolor images that mimic an artistic movement. But it has also enabled malicious actors to create models that can generate high-quality CSAM and other harmful imagery.

To audit a model, engineers typically prompt it for harmful content and check its outputs, but this manual auditing procedure is not scalable. In addition, repeatedly generating heinous images can have negative psychological impacts on human evaluators. 

This evaluation method quickly falls apart when testing CSAM, which is illegal to generate for any purpose in the U.S. and many other international jurisdictions.

“We are in this very difficult situation where, based on the law itself, we cannot use the de facto means of evaluation. We had to throw out the entire toolkit and take a different approach,” Suriyakumar says.

After learning about this conundrum, the researchers joined forces with Thorn, to address this issue.

A nongenerative solution

Instead of focusing on outputs, the researchers targeted the modifications a LoRA algorithm makes during fine-tuning. 

Their technique probes these modifications, called LoRA adaptors, to determine whether a model has been specialized for a harmful capability, without generating an output.

Using a technique called Gaussian probing, the researchers feed the model a set of random data points and analyze how it manipulates those data within its multilayer internal structure. 

“We never run the model all the way to the end or prompt the model, so we never generate images,” Suriyakumar explains.

The researchers capture those modifications at multiple time points within the model’s inner structure and average them to summarize how the LoRA adaptor changed the model’s computation. They found these responses to be a strong signal of how a model had been specialized.

They tested their method on variations of three types of models, comparing the results to ground-truth data from LoRA adaptors known for generating CSAM, other harmful images, and safe content. 

Their method was 100 percent accurate in identifying models that had been adapted to generate CSAM. 

“There is a huge bucket of child safety concerns with AI, and these are real concerns that need to be addressed. A lot of children are being harmed by AI deepfakes. We’ve shown that Gaussian probing can be a very useful tool, and we hope the research community really pours more attention into this problem,” Wilson says.

Importantly, their technique is scalable and would be relatively inexpensive to implement. Since thousands of model variations are published online every month, scalability is key to help auditors remove harmful adaptations before they are widely distributed.

Gaussian probing is also more robust than some other auditing techniques, since a nefarious actor would need to carefully alter the inner workings of the base model to avoid detection.

In the future, the researchers want to evaluate their technique on a larger set of model variations and explore whether Gaussian probing can detect harmful capabilities in base models before they are adapted.

“Now we have a technological approach to partially address this concern. So much effort was poured into this collaboration, which enabled us to tackle a really hard problem that is harming so many children, nationally and around the world. Hopefully, we can have a transformative impact in this area,” Ghassemi says.

This work was supported, in part, by the Bridgewater AIA Labs Research Fellowship.

Tiny infrared chip could improve detection of gases and heat

The tunable device controls mid-infrared light for more precise thermal imaging, chemical sensing, or pollution monitoring.

Infrared cameras can be used to spot useful information that our eyes can’t see, such as gases escaping from a pipeline, chemicals in the atmosphere, or heat leaking from a building. But sensing infrared light in sophisticated ways still requires expensive and bulky systems.

Now MIT researchers have created a chip-based optical device that can dynamically control incoming infrared light, to act as a tunable lens that gathers additional information for infrared cameras. Each microscopic pixel of the device’s lens can control infrared light independently, allowing it to change its focus and help cameras detect different signals without moving parts.

The system is described in a paper published in Nature Communications. The researchers also explain how they built a lab-scale demonstration using mostly conventional manufacturing processes in a semiconductor chip factory, suggesting the approach could be implemented at industrial scales.

The technology could enable compact, tunable infrared cameras for more dynamic thermal imaging, chemical sensing, pollution monitoring, and even new kinds of optical computing.

“This could give us more information as we study space, or help with environmental protections where you want to monitor for specific compounds in the atmosphere,” explains first author Cosmin-Constantin Popescu PhD ’25. “Thermal imaging is another application, and you can think of military applications where night vision goggles are currently being used. Basically, a lot of organic molecules absorb in the mid-infrared wavelength, and you could use this system to detect them.”

Joining Popescu on the paper are MIT PhD students Maarten Robbert Anton Peters and Khoi Phuong Dao; Dynasil company scientists Oleg Maksimov and Harish Bhandari; University of Central Florida PhD candidate Kathleen Richardson and scientist Rashi Sharma; University of Washington Professor Arka Majumdar; Korea Advanced Institute of Science and Technology Associate Professor Hyun Jung Kim; MIT postdoc Rui Chen; Luigi Ranno PhD ’25; Brian Mills ’20, PhD ’26; Draper Laboratory scientist Dennis Calahan; MIT principal investigator Tian Gu; and Juejun Hu, MIT’s John F. Elliott Professor of Materials Science and Engineering.

Chip-based lenses

In recent years, researchers have developed ways to dynamically control light by etching tiny, precise patterns on transparent materials known as “metasurfaces,” which could enable more compact, programmable cameras and other advanced optical devices.

Hu’s research group at MIT has experimented with a class of metasurfaces that shift from solid to liquid after heat is applied. The phase changes can be harnessed to control how the materials interact with light. In 2021, Hu and collaborators created a miniature lens that could adjust its focus to different depths through such phase changes.

The device worked reliably, but it could only adjust focus all at once across the entire material, which is how most metasurfaces work. For their new study the researchers wanted to build on that approach to control light independently at each microscopic pixel of the material.

“Most active metasurfaces trying to do single-pixel tuning need wires going to every pixel, and how you route the wires becomes a big issue,” Hu explains. “The best approach so far has been one-dimensional pixel control with a bunch of wires.”

The researchers also wanted to create a system that worked with the mid-infrared wavelength of light, which the human eye can’t see but is useful for detecting heat signatures and molecules including methane and propane. Mid-infrared detection devices are already used to detect gas leaks and study Earth’s atmosphere, and for a number of defense and aerospace applications.

To build their system, the researchers adapted an approach commonly used in displays in which two layers of neatly packed copper wires are placed on top of each other perpendicularly. Below the wires is a layer of doped silicon that generates heat at the cross points of the wires and sits on top of the phase-change material. The silicon’s heat is used to switch each pixel of the material between crystalline and amorphous structures, which changes how the material interacts with the infrared light coming in. The silicon also includes a diode selector, which helps prevent unintended currents from leaking through neighboring pixels.

“We did some calculations showing this architecture allows us to scale to potentially millions of pixels without having any issues with the [unintended] currents,” Hu says. “The key innovation is this crossbar architecture, which creates a scalable way to increase the pixel-level switching of metasurfaces. We didn’t invent this architecture — it’s used in displays — but it’s the first time anyone’s used it for active phase-change metasurfaces to show you can get pixel-level control. People have been working toward two-dimensional pixel-level control for a long time, and it’s the first time anyone’s implemented it.”

The researchers worked with equipment in MIT.nano and with a factory that manufactures semiconductor chips, ultimately creating a two-dimensional system that featured a 6-by-6 metasurface pixel array. They tested their system and found it could switch on and off reliably.

“We found this mesh architecture to be very resilient,” Popescu says. “You don’t want these materials to switch once and not work anymore. You want it to switch a large number of times: maybe tens of thousands of times or more.”

Scaling up

The researchers say integrating part of their system’s design into existing semiconductor manufacturing should help it move beyond a research prototype.

“As you want to scale up, you need something that’s part of a consistent process, and that’s why chip foundry manufacturing becomes so important,” Hu says. “Working with a semiconductor foundry with well-defined process control is very powerful. It also allows you to implement each of the components into a single efficient process.”

The researchers are working to add more pixels to their array and develop more robust versions of their system so that it can start capturing more infrared information.

“In lots of cases when you’re taking images, you have prior knowledge of what you’re looking for,” Hu says. “You might be looking for a human in a dark room, or some specific features in an image, like a tree, and that prior information can be useful because now you can configure this system to specifically highlight those features.”

Hu also notes that researchers have used metasurfaces to emulate computational neural networks that power AI systems, though he notes that applications could be farther away from taking hold.

“This could enable more effective optical computing, where metasurfaces are used to encode network weights in neural networks,” Hu explains. “When light passes through the material, it interacts with the metasurface, and that information gets encoded in such a way that you can infer computational results. Researchers have already used this approach to emulate very complex neural networks.”

The work was supported, in part, by the U.S. Air Force, the U.S. National Science Foundation, the National Research Foundation of Korea, and the Draper Scholar Program.

Discovery could lead to brighter, more energy-efficient digital displays

Researchers found a simple solution for extending the lifespans of LEDs made from glowing microscopic particles called quantum dots.

A new study led by MIT researchers could drive the development of more energy-efficient digital displays — such as flat-screen TVs, augmented and virtual reality headsets, smartphone screens, medical imaging devices, and even large-area ambient lighting surfaces — that also generate richer, brighter colors.

The MIT scientists, in collaboration with researchers at Samsung, studied the microscopic changes that occur inside LEDs that utilize electrically excited quantum dots, which are precisely shaped nanoscale semiconductor particles that emit extremely pure colored light. 

Quantum dots are currently used in some of the computer and television displays with the best picture quality available. The efficiency of these displays could be further improved, and their manufacturing process further simplified, if the quantum dots could be electrically excited, as was first demonstrated in the quantum dot LED (QD-LED) structures over 20 years ago. 

But limitations on the operating lifespans of these QD-LEDs have prevented their widespread use in commercial applications.

The new study shows how encapsulating QD-LEDs in an acrylate-based resin can extend their lifespan by minimizing the physical degradation that would otherwise occur during QD-LED operation. 

The researchers demonstrated that encapsulating QD-LEDs with a resin layer using a simple, scalable process boosts stability and performance. In some devices, resin encapsulation enabled a 5,000-fold lifespan improvement. Importantly, their study reveals the fundamental reasons resin encapsulation is effective.

“The insights into how and why quantum dot LEDs get modified during their operation open the possibility of fixing everything that holds back commercialization of QD-LED displays. This technology can provide a light source like never before — pure in color, paper thin, and of large area, transforming how we produce both displays and general lighting,” says Vladimir Bulović, the Fariborz Maseeh (1990) Professor of Emerging Technology, principal investigator in the Research Laboratory of Electronics (RLE), director of MIT.nano, and senior author of this study.

He is joined on the paper by lead author Ruiqi Zhang, an electrical engineering and computer science graduate student; Moungi Bawendi, the Lester Wolfe Professor of Chemistry; and other colleagues at MIT and Samsung SAIT. The research appears today in Science Advances.

A blue bottleneck

This paper draws on foundational work by Bawendi, who shared the Nobel Prize in Chemistry in 2023 for discovering and synthesizing quantum dots, and engineering work by Bulović, who joined MIT in 2000, when he began collaborating with Bawendi to make efficient LED displays using quantum dots. 

Conventional LED displays utilize thousands of tiny lightbulbs that generate the red, green, and blue light needed to create the perception of any color on the visible spectrum. More advanced OLED screens, which Bulović was developing through his graduate work at Princeton University, utilize electrically excited, glowing organic molecules instead of light bulbs.

Bulović, Bawendi, and others at MIT sought to replace the organic molecules with quantum dots, which emit purer red, green, and blue light in a more energy-efficient manner.

“With quantum dots, the color quality of the screen would be more visually appealing and more optically flexible. One can mix and match those quantum dot colors more precisely to generate any color that is needed,” says Bulović.

Their collaboration generated a series of inventions on quantum dot LED technologies, leading to the launch of the startup QD Vision, which successfully commercialized the first-ever displays containing quantum dots. In 2016, QD Vision was acquired by Samsung, which incorporated a less efficient form of quantum dot technology into their “QLED” displays.

Although they are more energy-efficient, electrically excited QD-LEDs have still not been commercialized, particularly since the limited lifetime of the blue QD-LED does not meet the requirements of commercial displays.

“The blue quantum dot LEDs are 50 to 100 times less stable than their red and green counterparts. If you use them in an LED display, your TV might last for just a few months before it stops working. We wanted to understand what is different about the blue quantum dot LEDs,” Zhang says.

A nanoscale investigation

He and his collaborators developed a technique to slice a tiny QD-LED in nanoscale-thin slivers, revealing the device cross-section. They examined these cross-sections under extremely powerful microscopes at MIT.nano. This precise method allowed them to see what happens at the nanoscale to the ultrathin layers of materials stacked inside the QD-LED.

They explored the structural and chemical changes that occurred in each layer of red and blue QD-LEDs by comparing cross-sections of freshly made devices to cross-sections of devices that were operated on overdrive. The researchers found that during operation, the three core functional layers that enable blue QD-LEDs to glow are degraded, with modified morphology and reduced thickness. 

The distinct quantum dots also get merged together, losing their shape. This layer thinning and coarsening is caused, in part, by the release of extra hydrogen and oxygen during operation.

“We don’t yet know exactly where these extra elements are coming from — there are so many possibilities. But we definitely don’t want extra hydrogen and oxygen in the device,” Zhang says.

To prevent this degradation, they utilized a technique sometimes adopted by industry. They encapsulated the QD-LEDs with an acrylate-based resin.

They discovered that this encapsulation technique suppresses the release of the hydrogen and oxygen and inhibits some of the degradation that changes the morphology of the layers of the blue QD-LED. 

“For the first time, we have insights into the details of what happens inside these structures of many mixed and layered materials that form the QD-LED. No one knew this before,” Bulović says.

This encapsulation strategy, which is a cost-effective and scalable technique, led to an eightfold improvement in the lifetime of red QD-LEDs and more than a 5,000-fold lifetime improvement in blue QD-LEDs.

The researchers believe the resin prevents the formation of moisture in the cloud of gases that surrounds the quantum dot. That moisture likely causes the QD-LED to degrade. 

However, their experiments revealed that resin encapsulation does not eliminate all sources of degradation. 

The researchers are now exploring the addition of extra layers to QD-LEDs that could further improve efficiency and lifespan. They also plan to build on the lessons learned in this study to increase the stability of QD-LEDs for other applications. 

“This version of quantum dot LEDs would be better than anything that exists now — simpler to make, more efficient, and higher performing. This could open vistas into many more ways of thinking about this technology, not just for the sake of displays or lighting, but also for sensors, lasers, and so on,” says Bulović.

This work was funded by the Samsung Advanced Institute of Technology. The research was carried out, in part, using MIT.nano facilities.

New flapping robot swims and flies like a diving bird

MIT engineers’ design could lead to a new class of aerial-aquatic vehicles for ocean exploration.

Loons, gulls, puffins, and petrels are some of the 100 species of birds that can both fly and swim. These diving birds can plunge in water to swim after prey, and leap back into the air to fly away. 

Inspired by these naturally aquatic aviators, engineers at MIT and EPFL in Lausanne, Switzerland, have designed a robot that can swim underwater, then flap out of the water to continue flying through air, much like diving birds. 

The “flapping-wing aerial-aquatic vehicle,” or FAAV, weighs less than 300 grams (about half a pound) and is designed to help scientists study the mechanics that enable diving birds to fly through air and water. 

The robot has a central body, or fuselage; two flexible, flapping wings; and a steerable tail. The wings and tail can be swapped out for different sizes. In experiments carried out in a water tank and at a local lake, the engineers identified combinations of wing size, flapping frequency, and tail angle that enable the robot to smoothly transition from swimming through water to breaking through the surface to flying through the air.

Their results, which appear today in the journal Science, could help scientists understand how diving birds adapt their flight mechanics to move through air and water — mediums with very different physical properties. The design could also launch a new class of aerial-aquatic drones and vehicles. The researchers envision such winged robots could be deployed in oceanography to fly to and sample from aquatic regions that would otherwise be too dangerous for traditional ocean vessels to access.

“Our dream vision is for oceanographers, marine biologists, and members of coastal communities to launch this robot from a boat, or from shore, and it would fly close to the area of interest, such as an iceberg or a port facility, or over a pod of whales,” says Raphael Zufferey, assistant professor of mechanical engineering at MIT. “It would dive into the water to take a measurement or collect a sample, and fly back to deliver the data at a fraction of the cost of traditional methods. Then it could go back out to dive for more.” 

Zufferey is the lead author of the new study, which includes co-authors from EPFL and Northwest Indian College in Bellingham, Washington.

Flight mechanics

At MIT, Zufferey heads up the AURA Lab, where he and his students engineer aerial and aquatic vehicles inspired by biomechanics in nature. The robots they build are small in size and designed to unobtrusively explore and monitor the health of oceans and waterways. 

For their new work, the team aimed to design a vehicle that can fly in the air and underwater. Any such vehicle would have to adapt to and transition between two very different substances. Water is 1,000 times denser than air, and moving through one or the other requires very different mechanics. Or so people might assume.

“You have to do some adaptation to make that transition work. But there’s a solution that exists in nature,” Zufferey says. “Birds like puffins can fly very fast through the air, and can dive and swim through water at speeds of 3 meters per second. They’re able to do pretty amazing things. So we knew is was possible. Just no one had tried this in a mobile robotic system.”

To get an idea for how diving birds fly, the team looked through the scientific literature and pulled together available data on puffins, petrels, kingfishers, and other diving birds. They observed that smaller birds flap their wings around 10 times per second when flying through air, and around four times per second when swimming through water. Larger birds have a slightly lower flapping frequency through both air and water due to their wider wingspans. 

With the biomechanics of birds in mind, the team developed a winged robot designed to flap at similar frequencies to that of actual diving birds. 

Making the leap

The new robot roughly resembles a bird, with a body, two wings, and a tail. The body contains a battery and waterproof electric motor that drives a crankshaft, which in turn pumps the wings up and down at preset frequencies. The wings are made of thin membranes that are coated with hydrophobic nanoparticles to help wick away water. And the tail is motorized, enabling it to change its angle to help the robot fly up or dive down. 

The wings can be swapped out for different sizes. The researchers fabricated and tested three sets of wings: small (60 centimeters wide), medium (80 centimeters), and large (100 centimeters). They carried out experiments first in a small water tank, then in Lake Geneva in Switzerland.

In their tests, they placed the robot underwater, about half a meter below the surface. They programmed the wings to flap at certain frequencies and the tail to pitch at certain angles throughout the robot’s flight. They then observed under what conditions the robot successfully swam up toward the surface, out of the water and into the air. 

The robot flew multiple flights with different wing sizes, flapping frequencies, and tail angles. Overall, the team found the robot was able to reliably fly, swim, and transition between water and air when it flew with medium-sized wings. Flexibility in the wings is key; the wings need to be flexible enough to minimize flapping amplitude in water and also firm enough to keep the robot aloft in the air. 

The researchers also found the robot could swim through water at speeds of almost 1 meter per second when it flapped with a frequency of around 5 herz, or five flaps per second. The robot could fly through the air at around 6 meters per second, when flapping at a similar frequency. The speeds and flapping frequencies of the robot were similar to that of actual diving birds. 

To make the leap from water to air, they found the robot should be pitched at 70 degrees — a relatively steep angle that keeps the robot’s wingtips from touching the water’s surface as it flaps up and into the air. Any steeper, and the robot would tip back into the water.

Interestingly, this combination of wing size, flap frequency, and tail pitch enabled the robot to swim underwater, launch off the surface, and fly, without something that many diving birds require: feet. When birds such as puffins and ducks take off from the water’s surface, they paddle their feet, along with flapping their wings and pitching their tails. Surprisingly, Zufferey and his colleagues found that, at least in robotics, the act of flying out of water doesn’t necessarily require a paddling maneuver. 

“If you look at birds, most birds need to paddle at the surface to take off. And the question was, do we need the same for robots? And it turns out we don’t,” Zufferey says.

Going forward, the team is improving the design of the wings to enable them to turn in addition to flapping up and down. They will also test the robot’s performance under turbulent conditions, such as swimming out of choppy waters and flying through wind. Then, they hope to deploy the vehicle to help answer questions in ocean science.

“One of the major challenges in ocean science is collecting data both frequently and across many locations, which is something this robot could do in the future,” Zufferey says. “You could send this out not just every week, but every hour. It could fly out at high speeds, dive in fly back, deliver its data, and go back out, multiple times.”

This work was supported, in part, by a Marie Skłodowska-Curie Actions fellowship grant.

MIT-led project opens first climate shelter in Bangladesh

The Jameel Observatory-CREWSnet project opens a solar-powered “adaptation fortress” in Satkhira, a region facing severe and growing climate threats.

In southwestern Bangladesh, where extreme heat and severe tropical cyclones threaten the lives of millions of people, a new kind of climate refuge has opened its doors.

At the Baradal Aftab Uddin Collegiate School in the Satkhira district, the Jameel Observatory Climate Resilience Early Warning System Network (Jameel Observatory-CREWSnet) opened its first “adaptation fortress,” a solar-powered community shelter designed to protect residents from extreme heat and tropical storms.

A year-round refuge

When the heat arrives in southwestern Bangladesh, people have traditionally looked for relief under the shade of trees or near bodies of water. Now, during heatwaves, temperatures can reach 44 degrees Celsius (111 degrees Fahrenheit), levels at which shade is no longer enough.

A school by day and refuge from disaster, the adaptation fortress transforms the traditional concept of a cyclone shelter into a permanent year-round community resilience hub.

The facility offers residents protection from two of the region’s fastest-growing climate threats. During government-declared heat emergencies, it can host up to 200 people in four air-conditioned rooms supplied with clean drinking water. As a cyclone shelter, it can accommodate up to 500 people in additional rooms.

For the 30 million residents in southwestern Bangladesh, caught in a compounding cycle of cyclones and record-breaking heatwaves, the fortress represents something larger: a shift from reacting to disasters to preparing for them.

From forecast to fortress

That shift is the founding premise of the Jameel Observatory-CREWSnet project, which develops climate-resilience solutions that help vulnerable communities prepare for and adapt to life-altering conditions. 

The opening of the adaptation fortress marks a milestone for the project, and for MIT’s broader climate mission. Jameel Observatory-CREWSnet was one of MIT's five Climate Grand Challenges flagship projects, selected to translate climate research into tangible solutions for underserved communities facing some of the world’s most urgent climate threats. 

The project started in 2022 with Community Jameel and a research team at MIT led by Elfatih Eltahir, the H.M. King Bhumibol Professor of Hydrology and Climate in the Department of Civil and Environmental Engineering, along with John Aldridge, assistant leader of the Human Resilience Technology Group at MIT Lincoln Laboratory, and Deborah Campbell, senior staff scientist at MIT Lincoln Laboratory.  

Working in collaboration with BRAC International, a Bangladesh-founded nonprofit organization, the project combines advanced climate and socioeconomic forecasting with practical adaptation solutions. The adaptation fortress extends the project’s mission from forecasting climate threats to building permanent protection against them.

“When we launched the Jameel Observatory-CREWSnet, our goal was to close the gap between what climate science tells us is coming and what communities can actually do about it,” says Eltahir. “The adaptation fortress is that idea made concrete. Our models project more intense heatwaves for this region, and now residents of Satkhira have a place built to withstand them.”

The project’s climate modeling gives the fortress its urgency. Developed over decades in Eltahir’s research group, the models predict increasingly intense heatwaves across southwestern Bangladesh in the years ahead — dangerous heat layered on top of the cyclone risks they already endure. 

That same evidence shaped who gets through the door first. A priority access list focuses on those the heat endangers most: the elderly, people with respiratory conditions such as asthma, expectant mothers and mothers with infants, and students of the Baradal school.

Built to outlast the grid

The building was designed to weather climate shocks. A rooftop solar array powers the building as its primary energy source, with a battery backup that keeps it fully operational during grid outages. Solar grid-based air conditioning units combat extreme heat, and windows of glass encased in iron protect against breakage while sealing in the cool air.

The facility also integrates rainwater harvesting to mitigate the severe salinity that plagues local groundwater, and is designed to help cover its own upkeep. A net-metering interface allows surplus electricity generated during low-occupancy periods to be sold back to the national grid, creating a circular revenue stream that funds long-term maintenance.

The fortress is built with the community. A school committee oversees day-to-day operations and emergency protocols in partnership with BRAC, formalized through a signed memorandum of understanding to ensure long-term sustainability. The facility is supported by a comprehensive user guide translated into Bangla to empower local management.

Engineered to scale 

The Satkhira adaptation fortress is a pilot, and will be rigorously assessed. Remote sensors will track temperature, humidity, and power consumption. The findings will directly inform a second adaptation fortress planned for a secondary school in the Jashore district, where construction is scheduled to begin before the end of 2026.

If the evidence supports the model’s effectiveness, the concept could ultimately scale to as many as 1,250 fortresses across southwestern Bangladesh.

“From the start, our vision for this project has been a capability that could extend far beyond any single community,” says Campbell. “The adaptation fortress is a model we can learn from and refine in Satkhira, then carry to the many other places facing these same compounding climate threats.”

The work is supported by Community Jameel for Jameel Observatory CREWSnet, and by MIT Climate Grand Challenges.

Beyond the pitch: The founder’s journey

An MIT mechanical engineering course explores entrepreneurship through lessons and stories shared by alumni startup founders.

The path to launching and growing a startup can be full of twists and turns. For a budding entrepreneur, gaining perspective from those who have already experienced the journey can be incredibly valuable, and highly inspirational. 

“There are so many amazing entrepreneurial stories among our alumni. We want to bring those stories to our students and our community and build networks with our incredible alumni founders,” says John Hart, the Class of 1922 Professor and head of the Department of Mechanical Engineering (MechE). “Through the Founder’s Journey class and other new programs, we want to cultivate interest in entrepreneurship among our students and expand opportunities to bring MechE-born technologies to the world.” 

According to a 2015 report on MIT’s global entrepreneurial impact, there are more than 30,000 active companies founded by MIT alumni worldwide, employing some 4.6 million people. Marina Hatsopoulos SM ’93, founding CEO of Z Corp., an early market leader in 3D printing, said one of the aims of the course was to show students they don’t need to reinvent everything. “So much of this has been done before. I want them to understand that this is a well-trod path.” 

Class 2.S977/2.S979 (Founder’s Journey: Launching and Scaling Hardware Startups) explores real-life challenges of startups focused on building and scaling hardware technologies. First held in spring 2025, the inaugural class invited students to “find and activate their entrepreneurial energy” through the lens of challenges faced by founders and their teams at various stages in development of new hardware-focused companies — ranging from fundraising to supply chain development, and much more. 

Each week of the class was structured around a key challenge faced during the development and growth of a hardware startup, presented by the instructors and guest speaker. The speakers were founders of companies in robotics, energy, 3D printing, consumer products, and other frontier technologies. Students engaged through preparing questions for the speakers and participating in follow-on discussions and reflective exercises throughout the semester. 

Ken Zolot, senior lecturer at MIT, and Hatsopoulous co-led the class and developed it along with Hart. Hart, who was among the alumni speakers in the course’s first iteration, also spoke to the class about his experience as a co-founder of VulcanForms, which began through collaboration with fellow co-founder Martin Feldmann MEng ’14. 

The other alumni speakers included Mick Mountz (Kiva/Amazon); Jon Hirschtick (Solidworks/Onshape); Max Lobovsky (Formlabs); Elise Strobach (Aeroshield); Greg Mark (Markforged); Seemantini Nadkarni (Coalesenz); Eran Egozy (Harmonix); Renuka Babu (DOTS Technology); Davide Marini (Inkbit); Loewen Cavill (Amira); and Colin Angle (iRobot).

Colin Angle ’89, SM ’91, co-founder of iRobot

Colin Angle ’89, SM ’91, co-founder and former CEO of iRobot, now CEO and co-founder of Familiar Machines and Magic, identified a passion for building things early on. 

“This idea that you can create something from nothing, that you can have an idea and not just draw it, but build it and make it real, is something I’ve always loved,” he says. “MIT had such a strong, hands-on ethos, and that really, powerfully resonated.”

While living in the Alpha Delta Phi Fraternity house at MIT, Angle watched several companies get their start (by his count, five multimillion-dollar companies were started by his fraternity brothers during his time in the house). Seeing others do it helped to demystify the process. 

He started iRobot in his living room, beginning at first not with a product concept, but a grand vision. “We’re supposed to have robots. So, if not us, who? And if not now, when? It was a magical day.” 

iRobot may be best known for the Roomba, an autonomous robotic vacuum cleaner, but through the years the company also sent robots to Afghanistan (saving thousands of lives with the Pack Bot tactical mobile robot) and explored the Great Pyramid in Giza live on National Geographic. 

“The joy I have taken from my entrepreneurial journey has been the ability to build bigger things, from building teams to building a company capable of building something far beyond what I could have ever imagined doing myself … we created inventions that no one thought possible, simply because we believed we could.”

Elise Strobach SM ’17, PhD ’20, CEO and co-founder of AeroShield

Elise Strobach SM ’17, PhD ’20 is CEO and co-founder of AeroShield Materials. The company, co-founded with Kyle Wilke PhD ’19 and Aaron Baskerville-Bridges SM ’20, MBA ’20, develops super-insulating transparent window inserts with technology based on transparent silica aerogels developed by Strobach while she was completing her PhD in Professor Evelyn Wang’s lab.

“I wasn’t thinking of myself as an entrepreneur at that time, but looking back, that’s definitely where that seed was planted,” says Strobach. As entrepreneurs, she says, “We have the … freedom to find the best problem to solve and to continue to seek the best way to solve that problem.”

Aerogels, which were first invented almost 100 years ago and were first commercialized by NASA to insulate equipment in space, had a hazy blue tint that limited their use in certain applications. The aerogel material created by Strobach and her team is completely see-through, creating a variety of new everyday applications. The company recently achieved another milestone, with their work on display at the Smithsonian National Air and Space Museum in Washington.

“You don’t have to know everything to start. You just have to know that this is what you want to do and just get started.”

Maxim Lobovsky SM ’11, CEO and co-founder of FormLabs

Maxim Lobovsky SM ’11 was already working on 3D printers when he came to MIT to study at the MIT Media Lab. As he was finishing his master’s degree, he saw an opportunity to build something new.  

Lobovsky, with fellow Media Lab graduates David Cranor SM ’11 and Natan Linder SM ’11, founded Formlabs, a developer and manufacturer of 3D printing technology. The trio set out to build a professional-level 3D printer, but a significant cost reduction and one that would be easier to use than what was then available on the market. At a time when 3D printers could cost $100,000 or more, Formlabs’ product started around $3,000.

“We definitely built Formlabs in a classic, disruptive innovation path,” Lobovsky says. They achieved the cost reduction through several different ways, including replacing technology developed in the 1980s with modern consumer electronics components like the laser diodes that were developed for Blu-ray Disc players, and with “just a lot of clever engineering.” 

It was a long grind to raise the first round of funding, he says. The team participated in MIT’s 100K competition and pitched their idea to many potential investors (with limited success, initially). Their big break came in the form of an overheard conversation. 

“As someone who is naturally introverted, shy engineer … a really important lesson [was] that, sometimes, you can get lucky,” he says. “Sometimes talking loudly at a restaurant is actually a good way to get things going.” 

Lobovsky and one of his co-founders were having dinner with a potential investor at Legal Seafoods in Harvard Square. The pitch to the initial investor didn’t go well, but Mitch Kapor, the founder of Lotus Software and an early pioneer in the PC industry overheard the conversation, and he ended up leading Formlabs’ first round of funding. 

Today, Formlabs is the largest supplier of professional stereolithography and selective laser sintering 3D printers in the world. 

Jon Hirschtick ’83, SM ’83, co-founder of SolidWorks and Onshape

Jon Hirschtick ’83, SM ’83, co-founder of SolidWorks and Onshape, says the first time he can remember thinking about starting a company was when he was an undergraduate. 

“I had heard about startups, and it sounded like a lot of things that I was drawn to … a sense of being able to realize your vision, express yourself; a sense of excitement, of making money, and even the idea of a chaotic environment,” he says. 

Hirschtick has spent over four decades building computer-aided design (CAD) software, starting as an intern at MIT in 1981 and continuing that work today. “I thought, ‘hey, the world could use this software.’ It’ll be a better place with the software that I envisioned.”

He refers to CAD as a meta product design. “We’re designing a product that other people use to design products, and that’s just really cool to me.” 

“I think startups just fit me,” he says. “The excitement, the idea of trying to solve a lot of problems at the same time. MIT is a place of problem-solving ... and a startup is a place where there’s lots of problems to solve.” He adds that a lot of big companies are doing new things, but “startups are always doing things.”

He says most anything today that is a manufactured product is modeled in CAD first. “If you’re interested and excited by product development, then building a CAD system lets you get involved in the world’s product development.”

“Nobody knows for sure when they start a company whether it’s going to be successful or not. If it were, if there was a way of knowing for sure, then there wouldn’t be all these classes in entrepreneurship. They’d just tell you the secret. There’s always risk. Visions and hallucinations, they look and feel the same. You only find out which is which once you really try to realize them.”

A version of this story appears in the 2026 issue of MechE Connects, the Department of Mechanical Engineering’s magazine. 

A baseball-sized sensor can detect chemical threats

The “TOSSIT” device, developed at MIT Lincoln Laboratory, can warn service members and first responders of dangerous vapors and aerosols.

Researchers at MIT Lincoln Laboratory have designed a throwable, baseball-sized sensor that can remotely detect hazardous vapors and aerosols. 

Called the Tactical Optical Spherical Sensor for Interrogating Threats (TOSSIT), the sensor is designed to alert military service members, first responders, and law enforcement to the presence of chemical threats like nerve and blister agents, industrial chemical accidents, or fentanyl dust. 

Users can simply toss, drone-drop, or launch TOSSIT into an area of concern. To detect chemicals, the sensor samples the air and uses an internal camera to observe color changes on a removable dye card.

If certain chemicals are present, TOSSIT alerts users via an app or alarms in the sensor.

"TOSSIT fills an unmet need, providing a low-cost sensing option for vapors and solid aerosol threats — think toxic dust particles — that would otherwise not be detectable by small deployed sensor systems,” says principal investigator Richard Kingsborough.

After extensive testing in the field, the technology is being transferred to the U.S. military.

Tiny robot boats build floating structures

MIT researchers developed FloatForm, a swarm of small aquatic robots that snap together like ants forming a raft, assembling into reconfigurable structures on the water.

Most people think of the waterfront as the edge of the city. A team of MIT researchers sees it as a dynamic, Lego-like construction site.

Their new system, called “FloatForm,” is a swarm of small square robotic boats that assemble themselves into larger structures on the water, break apart, and reassemble into something new, all with minimal human direction. 

Each robot, about the size of a dinner plate at 21 centimeters square, is a self-contained vessel with its own thrusters, sensors, and magnetic latches. Together, they hint at a future in which floating infrastructure could become more adaptive: a temporary platform after an emergency, a market on a canal, or a stage that appears for a festival and dissolves when the crowd goes home.

“Our FloatForm projects envisions a future where the waterfront becomes a programmable extension of the city, where autonomous boats can self-organize into bridges, platforms, and other useful structures on demand,” says Daniela Rus, the Panasonic Professor of Electrical Engineering and Computer Science at MIT and director of MIT’s Computer Science and Artificial Intelligence Laboratory (CSAIL). “This kind of distributed robotics opens new possibilities for mobility, emergency response, public space, and infrastructure on water.”

“With FloatForm, we are essentially turning static water surfaces into dynamic, programmable spaces,” says Wei Wang, lead author of a new paper on the project and a former MIT research scientist who now leads the Marine Robotics Lab at the University of Wisconsin at Madison. “Imagine an urban environment where public space isn’t fixed, but can autonomously expand, contract, or reconfigure on demand.” 

“We see it as forming infrastructure on the water, using a modular system to create one larger system,” says Alejandro Gonzalez-Garcia, a former researcher with MIT CSAIL and the Senseable City Lab. “If there’s an emergency, you could form a new bridge to alleviate traffic in the city. Or you could create floating markets and floating stages. If you want a more livable city, you want to use the water, too.”

The open-access work, published today in Nature Communications, comes from the labs of Rus and Carlo Ratti, professor of practice of urban technologies and planning at MIT and director of the Senseable City Lab, and grows out of Roboat, their joint project with the Amsterdam Institute for Advanced Metropolitan Solutions that put full-size autonomous vessels on Amsterdam’s canals. Those canals once carried the city’s goods; today, they mostly carry tourists. 

“We explored whether the canals could be used for waste collection, or for transport, to offload some of the stress on the roads back onto the water,” says Niklas Hagemann, an MIT graduate student in architecture, CSAIL affiliate, and former Senseable City Lab researcher who has worked on the project since its early stages. “Urban areas are getting denser, so could you expand public space onto water that’s currently underutilized?”

FloatForm shrinks that vision down to tabletop scale to answer a harder question: How do you get dozens, and eventually thousands, of floating robots to organize themselves?

Lessons from the ant raft

The team found its answer in biology. Fire ants famously survive floods by linking their bodies into living rafts, with no leader choreographing the assembly. Each ant follows simple local rules, and a resilient structure emerges.

“Each ant is an independent agent,” says Gonzalez-Garcia. “We wanted each robot to have its own capabilities, the same way ant colonies form a raft.”

Most existing self-assembling robot systems, on water and elsewhere, rely on a central computer dictating every move. That approach is vulnerable to single points of failure and scales poorly: The planning math balloons as robots are added, and the swarm must assemble sequentially, with most robots idling while they wait their turn. FloatForm flips the balance. A lightweight central planner steps in only sparingly, assigning each robot a final position to perfect the lattice, a level of geometric precision that purely distributed methods struggle to guarantee. Everything else, including navigating toward the target shape, avoiding collisions, and adapting to disturbances, runs on the robots themselves, which coordinate by exchanging positions with their immediate neighbors. The whole swarm moves at once.

That parallelism is what sets the work apart. The planning complexity of FloatForms approach depends only on a robot’s local neighbors, not the total size of the swarm. “What we’re trying to do is to have minimal central intervention, and have them all move together at the same time,” says Gonzalez-Garcia.

In experiments at MIT, a fleet of eight robots repeatedly gathered from random positions into a target shape, latched into a rigid structure, broke apart on command, reassembled into a new configuration, and then drove across the pool as a single vessel, with each run taking four to eight minutes. In that final mode, called collective transport, a planner charts a trajectory for the whole structure and each robot computes its own contribution. “Every robot becomes an actuator,” Gonzalez-Garcia explains. Simulations showed the framework scaling smoothly to swarms of 64.

“The beauty of this largely decentralized approach is that the computation doesn’t get bogged down as the swarm grows,” says Wang. “Whether you are working with eight boats or 80, the entire fleet coordinates and moves simultaneously. Because the overall assembly time doesn’t significantly increase in principle, the system remains highly scalable.” 

There's a physical payoff to sticking together, too. “Our boats become more stable by joining together, like the ant raft, if you have waves or currents,” Hagemann says.

An origami handshake

The robots connect through a latching mechanism hidden entirely inside each hull. A single servo motor at the center drives an origami-inspired auxetic structure, a geometry that contracts uniformly in all directions at once, pulling permanent magnets on all four sides inward to release, or pushing them outward to grab a neighbor across gaps of 10 to 15 centimeters. The magnets are arranged with alternating polarities, so the boats reliably click into clean square lattices.

The elegant part is what the mechanism doesn’t do: consume (much) power. A 3D-printed gearbox holds the latch in either state with the motor switched off. “It uses energy to latch and de-latch, but in between those states, it doesn’t use any energy,” says Hagemann. For infrastructure that might hold a configuration for hours, that matters. “Because the robots are so small, you can only have a battery so big,” adds Gonzalez-Garcia. “If they use less energy on latching, they can use more on computation, or on actually moving.”

Getting there took some humbling engineering. Four miniature thrusters arranged in an “X” give each robot omnidirectional motion, including turning in place, but they pack large forces relative to the robots’ tiny inertia, which made early prototypes twitchy and prone to aggressive spins at low speeds. The team added stabilizing fins to increase hydrodynamic drag and tuned the controllers to stay robust across robots that, at this scale, are never quite identical. The magnets posed their own problem: They held on so well that de-latching sometimes required the robots to twist themselves free.

From the tank to the canal

Across 10 trials, the system completed its missions without human intervention 90 percent of the time with four robots and 70 percent with eight. When things did go wrong, the architecture showed its resilience: A robot that briefly lost its bearings could rejoin the structure on its own, without bringing the whole swarm to a halt, and robots stuck in formation deadlocks learned to shake themselves free and retry.

Moving from a controlled indoor tank to a real canal or harbor will take more than confidence. “There’s always a relationship between the size of a boat and the magnitude of the disturbance it can handle,” says Gonzalez-Garcia. “These boats are very small, so in very disturbed water, they cannot work.” Scaling up will mean reinforcing the latches, potentially with mechanical interlocking like the full-size Roboat used, and trading the lab’s ultrasonic indoor positioning for GPS or vision-based sensing. Helpfully, the coordination algorithm was designed to be sensor-agnostic: swap the sensors, keep the logic.

The team envisions applications well beyond city canals, from forming temporary platforms for offshore inspection and maintenance to adaptive sensor networks for studying migratory species to reconfigurable docking stations for emergency response in hard-to-reach areas. There is also potential for offshore and remote operations, from temporary construction platforms to environmental monitoring and scientific expeditions.

And the geography is wide open. “Venice, the Netherlands, Belgium, the fjords and lakes of Norway, really any city with a river can take advantage of this,” says Gonzalez-Garcia. “The project uses spaces where water is already important, but it also raises the question: Where else can water be used for something more?” 

“This is an exciting step forward in realizing distributed collective behaviors on water,” says University of Michigan Assistant Professor Steven Ceron, who wasn’t involved in the research. “Assembly, self-reconfiguration, and collective motion are difficult enough in dry environments, but achieving these behaviors in a predominantly distributed fashion on water represents a serious additional challenge, and this team has credibly overcome it. By shifting the computational burden onto the robots themselves, they have built a more resilient system that in the near future could enable robot collectives like this to be deployed in open-water environments for search operations, environmental monitoring, and reconfigurable marine infrastructure.”

Gonzalez-Garcia, Hagemann, and Wang wrote the paper with senior authors Ratti, who is also a professor at Politecnico di Milano, and Rus. Gonzalez-Garcia is additionally affiliated with the MECO Research Team at KU Leuven. The research was supported by a grant from the Amsterdam Institute for Advanced Metropolitan Solutions, with additional support from the University of Wisconsin at Madison. The team thanks MIT Sea Grant and Professor Michael Triantafyllou for providing the test tank.

Bringing the data to every sideline

PhD candidate Henry Wang works with FIFA and other organizations to advance the way sports are played, analyzed, and refereed.

With Boston serving as a host city for the FIFA World Cup, the whole Bay State has soccer fever, including Henry Wang. As a child growing up in Dallas, sports were everything to him. Today, Wang is working on research that could impact some of the biggest sporting events in the world, including future World Cups.

The first such event that made a big impression on Wang involved a different form of football.

“The first ever sports game I remember watching was Super Bowl XLII in 2008,” he says. “I was really drawn to the competition, and the way it was presented. It’s this whole big spectacle.”

Wang, a fourth-year PhD candidate in social and engineering systems within MIT’s Institute for Data, Systems, and Society, studies how data and technology can improve the way sports are played, analyzed, and refereed. Working in the MIT Sports Lab in collaboration with FIFA, he develops systems with the goals of helping referees make faster, more accurate decisions and expanding access to performance analytics across the globe.

Now in the final stretch of his doctoral program and preparing to defend his thesis at the end of this year, Wang has spent nearly a decade at MIT. After earning his undergraduate degree in 2023 with a double major in computer science, economics, and data science and business analytics, he transitioned directly into graduate school. Sports have been a constant throughout that journey.

A competitive swimmer since age 7, Wang says athletics shaped both his identity and his community.

“Athletic competition was always a really big part of my life,” he says. “It’s kind of how I made a lot of friends, around the pool, and now at school, or in the lab and office.”

Ironically, swimming entered his life not because of a burning passion for sports, but because of a doctor’s recommendation.

“I don’t really come from a huge sports family,” Wang says. When he was diagnosed with asthma as a child, his pediatrician suggested swimming to strengthen his lungs. 

His parents, both scientific researchers in radiology and medical physics, supported his growing passion. That support eventually led Wang to MIT, where he served as captain of the men’s swimming and diving team. In tandem, he continued pursuing research opportunities that merged his technical interests with his love of sports.

His first sports analytics project began with a cold email.

As a first-year student, Wang reached out to MIT Sloan School of Management Senior Lecturer Ben Shields to see if he could assist Shields with his research on sports strategy and analytics. Shields later connected Wang with a coach he knew who was interested in analyzing the two-point conversion strategy for MIT’s football team.

The project revealed that MIT could benefit from attempting two-point conversions much more frequently. The experience opened the door to the MIT Sports Lab, where Wang found mentors including Lecturer Christina Chase, Professor Anette “Peko” Hosoi, and former research scientist Ferran Vidal-Codina.

His research now focuses on two central questions: How can technology democratize access to sports data, and how can it help officials make better decisions?

Wang works with FIFA Innovation, the group within soccer’s global governing body that leads the development and testing of match technology used on the field. His research explores automatic event detection and officiating technologies designed to assist referees without disrupting the fan experience.

In one recent project, Wang helped develop a semi-automated system that uses players’ skeletal data and ball tracking to determine which player last touched the ball before it goes out of bounds. The research prototype aims to assist goal kick and corner kick decisions while minimizing interruptions to the game.

For Wang, success means that referees find the tools helpful, and fans barely notice it at all.

“A ball goes out of bounds, and we can immediately tell the referee it’s a corner kick,” he says. “The fans don’t even notice it.”

Alongside his doctoral research, Wang has gained experience across professional sports, spending two years with the Boston Red Sox’s baseball sciences team before accepting a role as a senior data scientist in basketball research and development with the Philadelphia 76ers, where he will continue working after graduation.

Despite his demanding schedule, he says the work rarely feels like work.

“I enjoy it so much,” he says. “I really don’t know what else I would be doing.”

Outside the lab, sports continue to anchor his life. Swimming at MIT provided structure and community during challenging moments.

“MIT can be pretty hard,” Wang says. “Having a consistent 5-to-7 o’clock swim practice every day definitely helped a lot.”

For Wang, sports have always been more than competition. They have shaped his friendships, inspired his research, and guided his career trajectory.

Now, as he works to build technologies that could change how billions of people experience the world’s most popular games, he is still driven by the same sense of love he felt watching sports as a child.

“I want every kid who plays sports to have the best experience possible, because I know how meaningful that can be toward someone’s life journey,” Wang says.

Ana Miljački named head of the Department of Architecture

In this leadership role, Miljački seeks to shepherd a collective commitment to transform the present into a better future.

Ana Miljački looks back at her nearly 20 years teaching in the MIT Department of Architecture and says that one thing was perfectly clear to her on arrival: the caliber of her students.

“I appreciated immediately that these were students comfortable being at the edge of the discipline, eager to push and transform it,” says Miljački. “They didn’t necessarily seek the spotlight, but understood the value of participating in important transformations.”

Transformations are forthcoming for Miljački, the Francis White Davis Professor of Architecture: She became head of the Department of Architecture for the School of Architecture and Planning (SA+P) on July 1, and the architecture department itself will move to the Metropolitan Storage Warehouse (the Met) in late summer. 

Miljački takes the reins from Nicholas de Monchaux, the Weber-Shaughness Professor, who helped significantly advance the department’s commitment to studio-based research and impact, particularly around climate resilience and sustainability. He also helped catalyze and deepen the ongoing exchange between MIT and Tuskegee University rooted in the legacy of Robert R. Taylor.

In announcing Miljački’s new role, SA+P Dean Hashim Sarkis noted that Miljački has directed two of the department’s specialized graduate degree programs: the Master of Science in Architecture Studies program (2023-25) and the Master of Architecture program (2016-20), and played a central role in shaping the department’s academic and pedagogical culture.

“Ana has led many of the department’s academic programs with dedication, advancing experimentation in pedagogy, encouraging critical thinking, and linking research and learning in a manner that is distinctly MIT,” says Sarkis. “She teaches history, theory, and design, and her work is internationally recognized for its contributions to architectural discourse, pedagogy, and institutional critique. I look forward to seeing her bring this vision to the department as a whole.”

Building a career at MIT

Having taught at Columbia University, City College in New York, and Harvard University Graduate School of Design, Miljački quickly recognized the value of being at MIT as a young faculty member. She found generous support for her research — humanities-driven historical scholarship, criticism, and her curatorial work. 

“When junior faculty are supported to produce their own work, they also support students who are helping them,” says Miljački. “That is not something I had encountered until coming to MIT. The way the Institute has historically treated young faculty is unmatched by any other institution.”

She launched a distinguished career as a scholar and curator examining the organization, politics, authorship, and cultural production of architecture from Cold War-era Eastern Europe to contemporary architectural practice. In 2014, she co-curated the U.S. Pavillion at the Venice Architecture Biennale, which featured the exhibition “OfficeUS.” 

In 2018, Miljački founded the Critical Broadcasting Lab at MIT, with a goal to cultivate tools necessary for critical practice, including the capacity to grapple with complexity, nuance, and politics of architectural production. Intervening in the world but operating from within the protections of academic life, its broadcasting and curatorial work remains insulated from special interests and its members retain the freedom to speak critically. The lab has made important contributions to São Paulo and Seoul biennials, as well as to the Great Repair exhibition in Berlin. It mounted a solo exhibition and an accompanying discussion at the Museum of Yugoslavia in Serbia in 2025. 

Last year, Miljački co-curated, with Nicholas de Monchaux and Calvin Zhong, work from the Department of Architecture that examines diverse responses to the global climate crisis. The exhibition — The Next Earth: Computation, Crisis, Cosmology — was one of the collateral events of the Venice Biennale’s 19th International Architecture Exhibition. The vibrant presentation of MIT Architecture’s work in progress highlighted both the direct and circuitous narratives that link all of the department’s research and production to our contemporary climate crisis and possible responses to it.

Criticism as a core element of education

“I Would Prefer Not To” is Miljački’s podcast, conceived and produced by the Critical Broadcasting Lab in collaboration with the Architectural League of New York, and currently in its fifth season. The series sheds light on an unexamined part of architecture: why an architect turns down a commission. For Miljački, the podcast and all of her work as a critic and curator are forms of exhibition-making. Last year, her podcast won the Architecture in Media Award from the American Institute of Architects.

Students, says Miljački, are the reason she gets up every day. Even with her new responsibilities as department head, she will continue to teach class 4.210 (Positions: Cultivating Critical Practice), the History, Theory, and Criticism course required for incoming MArch students. The course, which transforms every year to include the most urgent topics of the moment, explores the recent past of architectural discourse, enables students to locate their own concerns, and is oriented toward the future. She sees the course as less of an opportunity to deliver a fixed body of knowledge and more as a process of shaping how students engage with ideas and one another. The class “intellectually socializes” incoming students, creating a shared framework that allows them to become meaningful interlocutors for each other over time. 

“We have to think critically about this present that we occupy: how we got here. What it means to practice architecture today. How might we do it differently?” she says. “Sometimes we forget that we make the reality. It matters to what end we do it, how we understand the context in which we operate, and how it has already shaped us.”

A 19th century warehouse for the 21st century

Adding a new dimension to her tenure as department head is that, in August, the Department of Architecture moves into its new home — the Met (W41). Faculty and students have for generations worked on Building 7’s fourth floor, which skirts around the building’s dome. The fragmented space is not optimal for building community and spontaneously sharing work designed in the various studios. The Met will provide a unified home for MIT Architecture — and for most of the Department of Urban Studies and Planning — where the disciplines and their research on the built environment may overlap.

“I think it’s a really exciting moment to transform physically where we are and how we relate to one another,” says Miljački. “I have brilliant colleagues in the department, but we’ve been spending too much time circling around the dome looking for each other. The new building provides a place for us to gather, to see each other’s work, and thus truly conduct our research and teaching in each other’s presence.

“Also, importantly, we will be in a building that is a great example of adaptive reuse by the architecture firm Diller Scofidio + Renfro. Reusing, recycling, and maintaining the existing architectural stock is what we need to figure out how to do well in the field of architecture right now. To be able to didactically read this building every day will be very important. Our move will literally help guide us in teaching and learning while it also signals both internally and externally our commitment to this necessary shift in the discipline.”

Histories and timelines

Miljački sees the project of an architectural school as a collective cultivation of utopia. Over the years and in various leadership roles at MIT, she has forged how she thinks of leadership itself. 

“I now think that leadership importantly involves narrating stories in which we can all recognize ourselves,” she says. “For me, it may be primarily about fostering a sense of collective purpose in the face of an unacceptable status quo.

“Recently, I’ve been describing the school as a series of material, human, and other timelines, all unfolding at different speeds and tangling together to consequentially meet and materialize in the aging walls that surround us, in our care and labor protocols, in our pedagogies, collective and individual political investments, joys and heartaches. Cycles of global catastrophes and major weather events that arrive in the form of black and red clouds we all breathe in, connect us back to more- and less-recent forms of extraction here and elsewhere on the planet. Architectural fashions, and sometimes technical expertise, travel the same channels by which political action spreads. And importantly, learning and enabling of all sorts of action happen in many more ways that are not codified than those that are. Every school is its own version of this mesh of timelines, people, and things. I am humbled daily to take part in the MIT version of it, and to now take the helm on behalf of this collective.”

MIT engineers whip up a more breathable hydrogel

The new aerated material could enable longer-lasting bandages, implants, and wearable sensors.

Hydrogels are squishy, bio-friendly materials that are made mostly of water and a bit of polymer. The Jell-O-like substance is available in the form of medical patches, sprays, and glues, and can be stuck to the skin or implanted in the body to dress wounds, affix implants, and encapsulate and release medicine over time. 

For all their sticky, stretchy, and protective properties, hydrogels lack one key trait: breathability. If worn for too long, a bandage or patch can trap moisture and sweat, which can irritate tissues and reduce the effectiveness of any device that a hydrogel adheres.  

Now MIT engineers have come up with a recipe for a hydrogel that is both hydrated and aerated, or permeable to air. The new material is just as soft, stretchy, and robust as conventional hydrogels, but a network of tiny tunnels running through the gel allows air to pass through. 

The aerated hydrogel can be worn for longer periods of time compared to conventional hydrogels, without causing skin irritation. It can also reduce sweat buildup, even during exercise. In experiments, volunteers wore wireless heart monitors that were attached to their chest with the new breathable hydrogel. After working out regularly for 10 days, the volunteers showed no signs of skin irritation, and the heart monitors maintained clear readings.  

The results, which are reported today in the journal Nature, may enable longer-lasting hydrogel products, such as breathable bandages and dressings, cosmetic face masks, and contact lenses, along with better-performing health monitors and implants. 

“Water and oxygen are both essential for life,” says Xuanhe Zhao, the Uncas (1923) and Helen Whitaker Professor of Mechanical Engineering, and a professor of civil and environmental engineering, and medical engineering and science. “Now that we’ve added air to hydrogels, people can find broad applications.”

Zhao’s MIT co-authors on the study include Xiao-Yun Yan, Shucong Li, Won Jun Song, Runze Li, Bastien Aymon, Jingjing Wu, Gengxi Lu, Jiayi Liu, Shu Wang, Eric Lu, Hyunhee Lee, James Zhang, Casey O’Brien, and Zachary Smith, along with collaborators from multiple other institutions.

Breathing through Jello

Water makes up about 90 percent of a typical hydrogel. The rest of the material consists of polymers. When mixed with water in a chemical process known as “cross-linking,” the polymers settle into a sort of scaffold that holds the water in place, forming a gel that’s both squishy and stretchy. But because hydrogel’s composition is mainly water, it’s inherently challenging for any air to make its way through the material effectively. 

“In general, water is not breathable,” co-lead author Xiao-Yun Yan says. “Hydrogel is 80 to 90 percent water, similar to Jell-O. And you cannot breathe through Jell-O.”

Other groups have tried to design air-permeable hydrogels, mainly taking one of two approaches. The first has been to essentially puncture microscopic holes throughout the gel. Such designs are breathable, but only in air. When they are placed in liquid, the holes quickly clog up. 

Researchers have also tried mixing hydrogel with certain polymers, such as silicone, that naturally allow air through. But this approach requires adding a large amount of polymers to the hydrogel in order to create enough permeable space for air to move through the entire gel. These hydrogels end up having a greater balance of polymer to water, making them less hydrated in general. 

Zhao, who has been a leader in the development and application of hydrogels, looked to make a hydrogel that lets air through without losing its water-heavy makeup. 

“We want to have lots of tiny channels to let air through, while also maintaining lots of water in the gel,” Zhao says. “This was a significant challenge, and something that people thought was impossible to do.”

Highways for air

After several years of investigation, the team hit on an ideal recipe for a breathable hydrogel that minimizes the non-water ingredients needed to let air through. In their new study, they report that the key to the recipe is “phase separation.” A common example of this process is the interaction between oil and water. The difference in the two liquids’ phases cause them to instantly separate. When the two are mixed, oil and water glom to their own kind, while avoiding the other. 

Zhao and his colleagues took advantage of viscoelastic phase separation in concocting a breathable hydrogel. For their new design, they mixed their conventional hydrogel recipe with a very small amount of silica aerogel particles, which are essentially “solid-form” air bubbles. 

“They are like boba beads,” Yan offers. “The particles are made of silica, which is hydrophobic, meaning that water does not want to leak through them, so they are very stable in water.” 

And as it turns out, the particles are similar to oil when mixed with water. The researchers found that when they mixed just a small amount of the particles with a solution of the water-heavy hydrogel, the water molecules glommed together, essentially finding each other faster than the less abundant silica particles. This effect of viscoelastic phase separation created large pockets of water and squeezed the silica particles into skinny, interconnected tunnels. The team observed that after a few hours, this effect formed a network of thin and sturdy, silica-skinned tunnels through which air could flow.

“It’s as if the particles formed a network of connected tunnels, like air-permeable highways within the hydrated hydrogel,” says co-lead author Shucong Li. 

Once they confirmed that the network had formed, the team cross-linked the mixture, essentially freezing the gel, and its breathable network, in place. They then tested the gel’s breathability and mechanical performance over multiple experiments, including one in which they asked several volunteers to wear the gel, attached to a wireless electrocardiogram (ECG) monitor, while exercising for 20 minutes. The volunteers also wore monitors with conventional, commercial hydrogel adhesives.

Throughout the workouts, the researchers observed that the breathable hydrogel maintained a strong ECG signal, in contrast to the conventional gel which exhibited significant signal fluctuations.The researchers observed similar results in an experiment with several volunteers who wore the breathable hydrogel and ECG monitor over 10 days. 

“We reliably saw that after 10 days, the quality of the ECG signal is still pretty good, and after you take off the monitor, there were no noticeable blisters or redness on the skin,” Li says. “This indicates healthy skin conditions.”

The team also exercised the gel itself, putting it through 10,000 cycles of stretching and compression. After these tests, they found the gel still retained the network of air channels, maintaining its breathability. 

“After 10,000 cycles, there was less than a 5 percent drop in oxygen permeability,” Li says. “That matters, because even with your heartbeat, your chest continuously undergoes small strains. So we have to make sure this gel is durable for such daily activity.”

Zhao says the new study provides a novel approach for others to fabricate breathable and multifunctional hydrogels, using the concept of visoelastic phase separation as a guide. 

“We’ve discovered that this process can create these air-permeable hydrogels, and we demonstrate one application,” he says. “But we think there can be very broad applications. This is a technology platform.”

This work was carried out in part through the use of MIT.nano’s facilities. This work was supported in part by the MIT Hatsopoulos Faculty Fellowship, the Uncas and Helen Whitaker Professorship, a HEALS seed grant, the National Institutes of Health, the National Science Foundation, and the Department of Defense Congressionally Directed Medical Research Programs.

MIT researcher proposes a way to detect nuclear weapons in space

The 1967 Outer Space Treaty bans nuclear weapons in space, but there’s currently no way to verify that satellites aren’t carrying them.

In 2024, a U.S. government official warned that Russia could be developing a new satellite designed to carry nuclear weapons into space. The statement followed the launch of a suspicious Russian satellite into low-Earth orbit in 2022, just a few weeks before the country’s full-scale invasion of Ukraine.

A nuclear detonation in low-Earth orbit — the region about 100 miles to 1,200 miles above Earth’s surface — would release trillions of highly energetic electrons that would destroy many of the satellites in space, disrupting telecommunications networks, GPS, space-based internet, and more.

The 1967 Outer Space Treaty bans the placement of nuclear weapons in space, but there’s currently no way to verify satellites don’t contain nuclear weapons. In fact, no verification methods have even been proposed in unclassified, peer-reviewed literature.

Now, MIT Professor Areg Danagoulian is proposing a way to determine if a satellite orbiting Earth contains a nuclear weapon. In a new paper published in Nature, Danagoulian describes his idea for a satellite-based sensor system that could orbit close by a suspect satellite and detect neutrons generated by high-energy protons colliding with radioactive material.

In the paper, Danagoulian calculates that a sensor system the size of a large encyclopedia could detect a nuclear weapon with 99 percent accuracy if it orbited within 4,000 meters of the suspect satellite for about a week. He also estimates that the detection time could be cut to a matter of hours if multiple satellite sensors were used or the sensor satellite was able to get within 1,000 meters of the suspect satellite.

“If we eventually have some verification mechanisms for the Outer Space Treaty, that will put pressure on countries to respect the treaty or disclose what they are doing, because they know if they try to violate it, we will find out,” Danagoulian says. “I very much hope this will turn into a real system, or proof-of-concept system, but the goal right now is to get national labs to use this work for their own research, and to get policymakers to seriously consider this technology as a potential part of national technical means.”

Protecting space

In 1962, the U.S. detonated a 1.4-megaton thermonuclear warhead in space, which unintentionally destroyed many of the early satellites of the era. The blast released enormous volumes of highly energized electrons, and many became trapped in Earth’s magnetic field, where they damage any electronics in their path.

“When you have a nuclear detonation in outer space, basically the whole body of the bomb becomes ionized, and nearly every single electron in the weapon’s mass becomes free,” Danagoulian explains. “It gets injected into what’s called the inner Van Allen radiation belt. Once there, the electrons start hitting everything flying through those belts, causing ionization, radiation damage, and more. As you go further out into space, you create these thick belts around Earth populated by highly energetic protons and electrons.”

The 1967 Outer Space Treaty declared space the “province of all mankind” and banned nuclear weapons in space, among other safeguards. It has since been signed by 118 countries including the U.S., China, and Russia.

Monitoring compliance with the treaty has taken on increased urgency since Russia’s 2022 launch of a suspicious satellite, Cosmos2553, which Russia claims is used for surveillance and sensing. However, U.S. authorities believe it may carry components of a nuclear device undergoing testing, with the possible future goal of fielding an actual nuclear anti-satellite weapon. The detonation of a nuclear weapon at that orbit could destroy many of the U.S. reconnaissance satellites, international communication satellite platforms, as well as the Starlink satellites.

“The Russians launched this satellite in a very strange and unusual orbit because it goes through the most hostile environment possible around the planet,” Danagoulian explains. “No one puts satellites there because it’s highly radioactive. Why would you put a satellite in that orbit? Well, that location is likely the best point for trapping electrons if you were to detonate a thermonuclear weapon.”

Danagoulian notes most research on nuclear detection is highly classified, making it hard to know how much progress has been made in national labs. But he wanted to show that scientifically proving the presence of a nuclear weapon in space is possible.

Particle bombardment

The approach Danagoulian developed centers on a reaction known as spallation, caused by highly energetic protons in radioactive environments.

“When an energetic proton slams into elements with a high atomic number, like uranium and plutonium, each proton may knock out something like 40 neutrons,” he explains. “That’s a ridiculously large number. We’re talking about millions of protons per second per square centimeter, with many of them generating 40 neutrons. The question is can you detect some of those neutrons?”

Normal satellites wouldn’t emit nearly as many neutrons, but there are still naturally occurring protons, neutrons, and electrons in the atmosphere, especially in low-Earth orbit. Danagoulian’s concept uses two panels made up of pixels of neutron sensors known as scintillators that interact with radiation and emit light. The panels are sandwiched between synthetic crystal diamond detectors that allow the system to distinguish between neutrons coming from radioactive materials and natural protons and electrons. The two-panel construction then can be used to estimate the direction of the neutron, allowing it to differentiate between natural atmospheric neutrons and those coming from a suspected satellite. 

“Most neutron detectors are very sensitive to protons, so you have to come up with some smart ways to reject protons but keep neutrons,” Danagoulian says. “You also have to tell the difference between naturally occurring neutrons and neutron spallation from the satellite.”

He believes the system, placed inside of an inspector satellite, would be strong enough to survive the harsh environment of low-Earth orbit while also being fast enough to process the protons, electrons, and neutrons that bombard it.

Danagoulian’s calculations on how long the detector satellite would have to be near the suspect satellite give him confidence in the feasibility of the system. If a detector satellite were able to get within 1,000 meters of the suspect satellite, it could accurately detect nuclear weapons in about one hour. That would amount to a single flyby.

Danagoulian calls the paper a feasibility study of the concept.

“I say in the paper this isn’t a completely proven system,” he says. “The purpose of the paper is to show the scientific community that it’s scientifically possible to do this. But there are many more practical considerations to be made to actually build these detectors.”

Danagoulian hopes the study will stimulate further research and development. He is also working with researchers in MIT’s Center for Nuclear Security and Policy (CNSP) to understand the policy landscape around this issue.

If a version of his system is eventually developed, Danagoulian believes it could encourage the nonproliferation that has helped preserve satellites so far. He notes that while adversarial countries are naturally suspicious of each other’s claims, scientific evidence would strengthen trust.

“You can fake intelligence,” he says, “but you can’t fake physics.”

The work was supported, in part, by the National Nuclear Security Administration, the Carnegie Foundation, and Longview Philanthropy.

The brain’s internal ruler

A simple brain circuit measures objects’ distance from the body using touch signals from a rodent’s whiskers, MIT scientists find.

If you are crossing an unfamiliar room in the dark, you may grope around a bit to get a sense of your space.

But for many animals, feeling out a space comes more naturally. A mouse, for instance, can efficiently navigate in the dark just by grazing its whiskers against walls and other obstacles.

Fan Wang, a professor of brain and cognitive sciences and an investigator at the McGovern Institute for Brain Research at MIT, has discovered how neurons in a mouse’s brainstem use signals from the animal’s touch-sensitive whiskers to estimate an object’s distance from the face.

Her team’s findings, published June 25 in the journal Neuron, unlock key circuitry the brain uses to represent the space immediately surrounding the body.

Mapping space

The circuit the team discovered is part of the brain’s system for creating an egocentric map of space — that is, understanding where things are relative to one’s own body. Neuroscientists know that the brain calls on specialized circuits to understand space in this way, which are different from its system for mapping space using external landmarks.

In their study, Wang and her team explored how the brain maps the space closest to the body, known as the peripersonal space. This is the space in which we move, and it is vital that we understand where things are in relationship to our bodies so we can reach, step, avoid hazards, and otherwise interact effectively with our environment.

Wang says mice were an appealing model for investigating how the brain understands objects’ distance within the peripersonal space, because a rodent’s whiskers seem so much like a built-in set of rulers. These whiskers, which vary in length, are swept back and forth as the animals explore their environment. As whiskers bend and vibrate, the mechanical sensations are relayed to the brain by sensory neurons at their base. Those neurons fire more when a whisker bends close to the face than they do in response to contact near the whisker’s tip, communicating information about the proximity of the touch.

Wang’s team wanted to know if the brain uses these signals to build an internal ruler-like representation of distance more precise than “near” or “far.” To find out, graduate student Wenxi Xiao and Research Scientist Kyle Severson monitored neural activity in a small sensory-processing region in the brainstem where tactile signals from the whiskers first arrive in the brain. They studied what happened there as mice walked on a treadmill while brushing their whiskers against a wall that passed by at different distances.

Many neurons in the region were sensitive to the whisker bending triggered by the wall. Some behaved similarly to the sensory neurons they were getting their information from, firing more when the wall was closer to the face and thus serving as a proximity-based distance code. But other cells were tuned in to discrete distances, firing only when the distance of the wall the whiskers had touched was within a specific range.

The whiskers rule

For some neurons, activity peaked when the wall was 23 millimeters away from the face, near the tips of the longest whiskers. Others responded most when the wall was at intermediate distances. “Each of these neurons represents a specific distance, and together they span the full range reached by the longest whisker, like tick marks on the ruler,” Wang explains. “We call that the map code.”

The team wanted to know how the brain converts proximity signals from different whiskers into accurate map code of object’s distances from the head. “You cannot just listen to individual whisker neurons, because a contact at the tip of a short whisker would be in the middle of a long whisker. You need a brain circuit to build a unified distance map,” Wang says.

Through computational modeling and by exploring what happened when they manipulated neural signaling in specific ways, Wang’s team showed how distances can be calculated by comparing inputs from different sensory neurons. Their findings suggest that each brainstem neuron that makes up the map code receives both direct excitatory inputs from proximity-sensitive whisker neurons and inhibitory inputs from neurons driven by proximity-dependent whisker touch signals.

“Essentially, the inhibitory pathway allows the brainstem to compare two inputs by subtraction,” Wang explains. “If one input signals ‘this is how far it is’ and the other signals ‘this is how far I estimate it to be,’ subtracting one from the other yields an intermediate value. We think it’s a simple and elegant way to transform tactile input into a representation of discrete distance.”

Wang notes that despite their importance, the brain’s body-centered representations of space have so far received little attention from neuroscientists, who know much more about how we understand locations in space relative to landmarks (an allocentric map). She is eager to investigate how the egocentric map code her team discovered is integrated with other brain systems to guide movement, social interactions, and other behavior, and hopes the findings will further exploration from other groups.

The study was funded by grants from the National Institutes of Health.

Many black holes had past lives, new research shows

Physicists have found signs of colliding black holes that are themselves products of previous black hole smash-ups.

When a star dies, a black hole is born. This has been the textbook origin story for most black holes. At the end of a massive star’s life, its outer layers blast away in a brilliant supernova, and its core collapses into a gravitationally tight and dense region, forming a black hole.

Recent discoveries from gravitational-wave detectors have revealed hundreds of merging black holes across the universe. Many of them have been thought to come directly from exploding stars. But black holes can also come from other, smaller black holes. The products of previous black hole mergers can, in principle, merge again, creating a more massive black hole. This alternative, black-holes-birthing-black-holes pathway is known as “hierarchical merging.”

Now MIT scientists are finding that a good number of merging black holes may have indeed merged before. They carried out a new analysis of recent data from the LIGO, Virgo, and KAGRA observatories, containing 155 pairs of binary black holes, and found about 14 percent of merging black holes in the universe may in fact be second-generation black holes that formed from the previous merging of two smaller black holes. 

The results, which the team reports this week in Physical Review Letters, suggest that repeated hierarchical merging is a significant pathway by which black holes form. 

“We’re finding that, for some of these merging black holes, it’s not their first rodeo,” says the study’s first author, Cailin Plunkett, a graduate student in MIT’s Department of Physics. “Overall in the universe, black holes are merging all the time. The question of how often are they repeatedly merging was pretty uncertain. Now we’re seeing a relatively consistent picture where there’s a decent percentage of black holes that are coming from this repeated pathway.”

The study’s co-authors are Salvatore Vitale, associate professor of physics at MIT; Thomas Callister of Williams College; and Michael Zevin of Adler Planetarium and Northwestern University.

Lopsided pairs

When a massive star collapses and dies, the resulting black hole should have very little spin. In addition to losing a huge amount of mass when it explodes, the star should also lose much of its inherent spin, or angular momentum. The black hole left over should then have little to no spin. 

In contrast, when two black holes merge, the collision should create a new, wildly spinning second-generation black hole. 

“They would be spinning very fast, at about 70 percent their maximum possible spin,” Vitale says. 

Scientists suspect that hierarchical mergers occur in dense stellar environments, where stars are so tightly packed together that multiple neighboring stars could die and collapse to form black holes that are then close enough to merge with each other to form second-generation black holes. 

“You might have a ton of stars whizzing around each other, and if some are massive and explode, they become black holes. The black holes continue to whizz around, and can capture each other and merge,” Plunkett says. “This process can repeat potentially ad infinitum, by virtue of the fact that you have a ton of stars and black holes in this really dense environment.”

One sign of a hierarchical merger is that one black hole in a pair of merging black holes has a much higher spin, and higher mass, than the other. Such a lopsided duo would signal that at least one of the black holes came from the collision of two previous black holes. 

In 2024, scientists detected two such lopsided mergers in signals recorded by the LIGO, Virgo, and KAGRA observatories. The observatories detect incoming gravitational waves — incredibly small wobbles in the fabric of space and time — that are the reverberations from distant cosmic phenomena, such as colliding black holes. 

The observatories detected two gravitational-wave signals, labeled GW241011 and GW241110, each of which likely contain a black hole spinning much faster than its partner. The hierarchical mergers were discovered by analyzing each signal in detail to tease out the specific masses and spins of the black holes involved in each merger.

That work inspired Plunkett and Vitale to do a search of similar hierarchical mergers using all the gravitational-wave signals that the observatories have captured to date. 

A pattern of wobbles

For their new study, the team analyzed the LIGO-Virgo-KAGRA Gravitational Wave Transient Catalog 4.0 (GWTC-4.0), which comprises gravitational-wave detections from the observatories’ fourth observing run. Rather than analyze each gravitational-wave signal one by one, which is what scientists did for GW241011 and GW241110, Plunkett and Vitale searched for a characteristic pattern of hierarchical mergers across the data overall, to see if any matching signals popped out.

The pattern they searched for represents a range of orbital “wobbles.” Just before they merge, two black holes spiral toward each other in a disk-like, orbital plane. When the spins of the pair are perpendicular to the plane, this remains relatively steady. But when one or both spins are not perpendicular to the plane, the disk will wobble. The degree to which the whole plane wobbles, or “precesses,” can tell scientists about the balance of masses and spins between the two spiraling black holes. 

Plunkett and Vitale developed a model for the range of wobbling that should be a sign of a hierarchical merger, specifically between a first-generation and a second-generation black hole. 

The team applied the model to the entire GWTC-4.0 catalog, which comprises gravitational-wave signals from 153 black hole mergers, in addition to the signals from GW241011 and GW241110. Their analysis revealed that a number of mergers fit the pattern for orbital wobbling that was likely caused by the colliding of first- and second-generation black holes. 

Specifically, they found that roughly 14 percent of merging black holes in the universe may have merged before, and that these second-generation black holes had very particular masses: Black holes of around 10 solar masses (10 times the mass of the sun) and 30 solar masses were run-of-the-mill star-born black holes, while second-generation black holes had masses of around 20 solar masses or 40 solar masses and above. 

“One of the reasons why the 40-and-above regime is interesting is, stellar evolution theory predicts you shouldn’t be able to form black holes in that mass range at all from just a supernova,” Plunkett says. “We think supernovae from really massive stars end up being so violent that they leave no black holes at all above roughly 45 solar masses. Yet we have seen black holes that are that massive. And the question is: Where did they come from?”

The team’s new analysis provides support for the idea that black holes can form from the repeated merging of other black holes, and that this alternate origin story could explain some of the curious black holes that we can detect today. 

This work was supported, in part, by the National Science Foundation, and the Brinson Foundation.

Discovery helps explain why solid-state batteries often fail

New research could help prevent the formation of tiny seeds of lithium metal within the electrolyte, enabling batteries that charge faster and last longer.

Next generation batteries that use new electrolyte materials could achieve far higher energy density than today’s lithium-ion batteries, without many of the safety concerns. But advanced batteries, such as those that use solid or almost-solid electrolytes, have been plagued by the formation of tiny spikes of lithium metal called dendrites that cause the batteries to lose efficiency and fail.

Exactly how those dendrites form is still up for debate. While the interface between the battery’s electrolyte and electrodes has been the focus of most research, another culprit is the boundary where two grains of electrolyte in a solid material meet. Researchers know these boundaries can seed dendrites within electrolytes, although the effects have been difficult to study.

Now researchers at MIT and the Technical University of Munich have uncovered why such boundaries can lead to dendrites: Hidden electrical imbalances across the boundaries affect how the electrolyte conducts electrical charges, which influences how the ions and electrons move through the material during battery operation. In a paper published today in Nature Nanotechnology, the researchers characterized the electrical and chemical behavior of the boundaries and showed that adjusting how the electrolyte is processed enhances the movement of ions while reducing electron leakage. This adjustment can increase critical current density by more than 300 percent, which could enable solid-state batteries that charge faster and last longer.

“Grain boundaries are like the weather: Everyone talks about it, but nobody does anything about it,” says senior author Harry Tuller, a professor in MIT’s Department of Materials Science and Engineering. “In this paper, we’ve decided to do something about grain boundaries, and by doing something we’ve shown improved performance and demonstrated the importance of grain boundaries more broadly.”

Joining Tuller on the paper are first author Hyunwon Chu PhD ’25; former MIT professor Jennifer Rupp, the Electrochemical Material Professor at the Technical University of Munich (TUM), who led the study and is the corresponding author; TUM researchers Waldemar Kaiser, Lukas Wolz, Fran Kurnia, Kun Joong Kim, David Egger, and Johanna Eichhorn; Thomas Defferriere PhD ’22; Willis O’Leary PhD ’24; and University of Antwerp researchers Proloy Nandi, Johan Verbeeck, Sara Bals, and Thomas Altantzis.

Investigating grain boundaries

Rupp’s research group, which moved from MIT to TUM during this research, has spent years studying the behavior of next-generation electrolyte materials. Electrolytes in solid-state batteries are made of many tiny crystals of material packed together.

“What we call a grain, like a grain of salt, is actually a single crystal, but it might only be on the order of 1 micron in size,” explains Tuller. “Under high temperature processes, the best materials essentially consolidate to be void or pore-free and can be nearly 100 percent dense, but each of those crystallites is separated from its neighbor by a grain boundary.”

Solid-state battery researchers have increasingly focused on grain boundaries as the source of the lithium metal dendrites that cause them to short circuit. It’s been suspected that grain boundaries have different chemical and electrical properties from the grains, which interact with the ions and electrons shuttling between electrodes during battery charging and discharging. However, the exact mechanisms by which the boundaries slowed the ions down, leaked electrons, and led to dendrites was unknown.

“Grain boundaries are like defects,” Tuller says. “The boundaries have a higher level of defects than in the grains themselves, and generally that means as carriers of charge approach the boundary, whether electrons or ions, there’s some kind of blockage to overcome.”

To better understand that interference, the researchers developed a model to explain how local electrical imbalances at grain boundaries change the movement of lithium ions and electronic charge carriers. They tested the model in a common solid electrolyte material called lithium lanthanum zirconate, or LLZO, using techniques including electron microscopy, machine learning modeling, and electrochemical impedance spectroscopy, which measures how easily a charge moves through a material.

They found the cores of the boundaries carry a local electrical charge, building up local electric fields that lead to enhanced ionic resistance while causing a build-up of electrons in the boundary region, where they can reduce lithium ions, leading to lithium metal dendrite formation.

“For the last 30 years, the world has been dominated by lithium-ion batteries, but there is a growing recognition that other battery types are needed for batteries used in a variety of uses,” Rupp explains. “This work gives us the fundamental understanding of the space charge interface at the grain boundary. If understood properly, we can come up with engineering concepts to increase cycle life, transference of ions over electrons at these interfaces, and ultimately a better battery.”

Better battery materials

The researchers used their observations to adjust the material processing conditions of the LLZO electrolyte material and minimize the negative charges at the boundaries, finding they could ease the movement of lithium ions and reduce the leakage of electrons.

The modifications allowed them to create an electrolyte that had a critical current density more than 300 percent higher than a baseline sample. Higher current density allows for faster charging and discharging. It should also delay short circuiting to extend the life of batteries.

“Fires are currently a huge issue in the battery industry,” Rupp says. “By showing how to engineer these space charges in a controlled way, which is new in the field, we can have a strong impact on safety. It’s a new way to turn up the notch and get these batteries to charge faster and last longer before they break.”

The findings, along with the researchers’ engineering work, present a roadmap for battery researchers to accelerate the development of high-performance, longer lasting solid-state batteries.

“We showed we can control the initiation of these dendrites to maximize solid state batteries’ high performance,” Chu says. “In this paper, we started with a theory for how these dendrites form, then we did the material characterization to support that theory, then we did the engineering to apply the findings and actually improve battery performance.”

The work was supported, in part, by the National Science Foundation and the U.S. Department of Homeland Security.

Lerna Ekmekcioglu named head of MIT’s History Section

A faculty member since 2011, Ekmekcioglu examines how communities survive, adapt, remember, and make claims on justice under difficult political conditions.

Lerna Ekmekcioglu, the McMillan-Stewart Professor of History, has been named head of the History Section, effective July 1. 

“Lerna is an exceptional scholar and a proven leader. I am confident that she will guide the unit with thoughtfulness, wisdom, and a deep commitment to its continued success. I very much look forward to working with her in the years ahead,” says Agustín Rayo, the Kenan Sahin Dean of the School of Humanities, Arts, and Social Sciences.

Ekmekcioglu, who joined the MIT faculty in 2011, is a historian of the modern Middle East, the Ottoman Empire, and Turkey, Armenian history, gender, feminism, genocide, and minority politics. She served as director of the Program in Women’s and Gender Studies from 2022 to 2025, where she remains an affiliated faculty member.

Ekmekciouglu succeeds Malick Ghachem, who was named head of the History Section on July 1, 2023. 

“As I begin this new role, my first priority is to sustain and expand the remarkable momentum already underway in the unit. It is truly an exciting moment to be head of History,” says Ekmekciouglu. “We have ambitious new initiatives, extraordinary faculty work, and — this is not a small thing — a group of colleagues who actually like and trust one another.”

She cites the History of Now, launched in 2025, as one of several exciting initiatives underway, adding that her role will be ensuring the section’s projects are sustainable, visible, and intellectually fruitful.

“The work ahead is both practical and intellectual: supporting faculty research and teaching, sustaining new initiatives, expanding public engagement, and demonstrating why historical inquiry is indispensable to MIT’s mission,” she says.

Ekmekcioglu’s first monograph, “Recovering Armenia: The Limits of Belonging in Post-Genocide Turkey” (Stanford University Press, 2016), explored the Armenian community in Turkey after the Armenian Genocide and the limits of minority belonging in the early Turkish Republic.

It won the Der Mugrdechian Society for Armenian Studies Outstanding Book Award.

Her forthcoming book, “Feminism in Armenian: Lives and Texts Through Empire, Genocide, and Diaspora,” co-authored with Melissa Bilal of the University of California at Los Angeles, continues her long-standing work on Armenian feminist thought, activism, and archives across empire, violence, and dispersion.

Ekmekcioglu is a 2016 recipient of the the James A. and Ruth Levitan Award for excellence in teaching. She also organizes the biannual McMillan-Stewart Lecture Series on women, gender, religion, politics, and law across the Middle East and North Africa.

Ekmekcioglu earned a BA from Boğaziçi University in Istanbul 2002 and a PhD from New York University in 2010.

MIT in the media: Innovating and educating for the next 250 years of America

During a "Washington Post Live" panel discussion with ASU President Michael Crow, President Sally Kornbluth explored how universities are preparing the next generation of scientists to lead in America’s rapidly changing technological landscape.

Without federal support for curiosity-driven research, the innovation and talent pipeline that has helped ensure our nation’s prosperity and safety could run dry, warned President Sally Kornbluth during a Washington Post Live event. 

During "The Next Generation," a panel discussion moderated by Washington Post reporter Zachary Goldfarb at The Washington Post’s “Building America Summit,” Kornbluth and Arizona State University (ASU) President Michael Crow joined forces for a spirited discussion on the importance of curiosity-driven research, examining how universities are preparing the next generation of scientists to lead in America’s rapidly changing technological landscape. 

“Many of the things we have in our everyday lives, whether they be medical advances, technological advances, a lot of these things came from 30, 40, 50 years of scientists just trying to figure out how things work,” emphasized Kornbluth.

Kornbluth pointed to MIT’s curriculum that focuses on teaching foundational skills that can be applied to a myriad of technological advances, skills that will be indispensable to leading in an AI-enabled world.

“I do not think that any of our traditional subjects are now outmoded [by AI]. It’s how you approach them,” said Kornbluth. “In our new curriculum, not only are we leaning into basic STEM fields. We really feel we have to resurrect some of the old, moral and civic and ethical educational goals much more strongly because we want all these kids that are learning to be leading-edge technologists, to come at it from a moral, civic and ethical perspective.”

Artificial intelligence

Key to Kornbluth’s mission is maintaining a human-centric approach to AI. Inspired by MIT’s motto, “mens et manus” (mind and hand), she shared: “We really want students to be able to use physical AI. We want our students to still be able to build things, but use AI as an augmentation tool.”

Kornbluth expressed the importance of teaching interested faculty and students how to best use AI as a tool and her commitment to uplifting student collaboration. 

“We’re putting a big emphasis on things like teamwork. So, [students] need to be able to use these tools and come together towards goals, because you could imagine a situation that AI becomes your buddy instead of your study group. We don’t really want that to happen,” said Kornbluth. 

Using AI effectively requires writing strong prompts. Kornbluth discussed how foundational knowledge in fields like math, physics, biology and chemistry, along with teaching students how to write and communicate clearly and effectively, enables students to use AI responsibly when it comes to applying these new technologies to scientific research.

Students need to be able “to take that knowledge and think about how they can use AI to the greatest good and also learn to write the right prompts,” said Kornbluth. 

Kornbluth noted the MIT Sloan School of Management’s unique role in AI exploration. “It’s because the students are all coming with business experience and the demand out there in the field for them to have really strong AI knowledge is very high,” she said. 

The impact of frozen funds

Federal funding fuels curiosity-driven research—the groundwork of medical, technological and countless scientific breakthroughs.

“It is very difficult to make a groundbreaking discovery that’s going to revolutionize human life because you want to do that. You really have to be figuring out how things work and traditionally that sort of research in this country has been funded by the government because it does not have an immediate return,” said Kornbluth.

Discussing issues with federal funding, Kornbluth said that although money has been appropriated for universities, it has not been released to them by and large.

“We’re really trying to figure out what the funding stream is going to be going forward,” said Kornbluth. 

When asked about the consequences of these frozen funds, Kornbluth pointed to the long timeline required to develop life-saving treatments. 

As one example, Kornbluth pointed to diabetes treatments. 

“[Treatments] started with injections of insulin saving people and now it’s automated pumps and CGMs [Continuous Glucose Monitors],” said Kornbluth. “The next phase is going to be an actual functional cure, which is stem cell implantation—masking the cells so they’re not rejected by the immune system. But it takes a lot of basic work to be able to get there.”

“That [diabetes] is just one area. You can extrapolate that to cancer therapy,” said Kornbluth. 

Investment in basic research can advance treatments such as immunotherapy. 

“Immunotherapy is just in its infancy—it doesn’t work in every possible kind of cancer at this point. But all of the modifications that are being done now in basic science laboratories through to pharmaceutical companies and biotech are making it more and more broadly applicable so that pancreatic cancer is not absolutely a death sentence now,” Kornbluth emphasized.

National impact

Beyond research and AI, the president concluded by highlighting the strength of MIT’s student body, programs, and spinouts. 

Kornbluth underscored the value of an MIT education for students and the greater economy. 

Twenty percent of MIT’s class of 2029 were first-generation students. Education“is the best pathway to economic mobility,” said Kornbluth. 

She continued: “MIT has spun out north of 30,000 companies. The economic impact of MIT on this country is equivalent to the 14^th largest GDP in the world. We are having a huge impact on the economy and we’re producing the next generation of talent.”

Though MIT is highly selective, Kornbluth noted it is financially accessible through its free tuition program for students with parental incomes under $200,000. She further highlighted MIT for America, an initiative expanding access to calculus, a required course for institutions such as MIT, in under-resourced high schools nationwide.

Kornbluth and Crow concluded the panel by highlighting how their respective universities learn from one another.

“What we [ASU] learn from MIT is, where’s the edge of technology,” said Crow. “We learn how master technologists, and master scientists work in small groups.” For ASU, which has a student population of over 150,000, “ it’s instructive to learn and then operate at a different scale and in a different way. There’s a lot of back and forth,” he said.

Kornbluth expressed her hope for MIT to continue its longstanding tradition of research and education in service of the nation’s next 250 years.

“As a smaller private institution, we’re putting a much stronger footprint in how we can impact people well beyond the MIT walls,” said Kornbluth, “as well as having a scientific impact on society through our discoveries.” 

A portable ultrasound system could make reliable breast imaging more accessible

The new technology, which generates high-resolution, 3D images of breast tissue, requires no expertise to operate and could be used at home.

For people at high risk of developing breast cancer, yearly mammograms may not be enough to detect tumors early. To make earlier diagnosis easier, an MIT team has developed portable detectors based on ultrasound, which could be used much more frequently.

In a new paper, the team reports that they have improved the resolution of the images produced by their system, making it easier to spot potential tumors, as well as cysts and microcalcifications. The researchers also created a user interface that makes it simple to use the ultrasound probe, even for people with no expertise in ultrasonography.

This system, they believe, could not only enable earlier detection, but also allow for long-term monitoring following breast cancer treatment — either in a doctor’s office or at home.

“At each time interval, the computer interface guides you to position the device in exactly the same location, which is important for the longitudinal monitoring of a given tissue. It’s very intuitive and quite easy to use,” says Canan Dagdeviren, an associate professor of media arts and sciences at MIT and the senior author of the study.

Former MIT postdoc Md Osman Goni Nayeem and MIT graduate students Shrihari Viswanath and Hyeokjun Yoon are the lead authors of the paper, which appears today in Nature Communications.

Higher-quality imaging

While many people receive annual mammograms to check for breast cancer, it is possible for cancer to develop in between these annual screenings. These cancers, known as interval cancers, tend to be more aggressive, and they account for 20 to 30 percent of all breast cancer cases.

After losing an aunt to an interval breast cancer in 2015, Dagdeviren was motivated to develop a screening technique that would be more effective on women with dense breast tissue and could be performed more often than mammography. She decided on ultrasound, which uses sound waves to create images of tissue. Ultrasounds are often used to follow up on abnormal mammograms, but current ultrasound technology requires large equipment and a trained operator.

Earlier this year, Dagdeviren’s lab published a study in which they demonstrated a small ultrasound probe attached to an acquisition and processing module that is a little larger than a smartphone. This compact system can create a 3D image of the entire breast by scanning just two or three locations.

In the new Nature Communications study, the researchers reported several advances that allow for higher resolution imaging and greater ease of use.

One key advance is the addition of a “backing layer” to the ultrasound transducer. This layer helps to contain and focus the ultrasound waves, improving the resolution and quality of the resulting images. It also increases the range of soundwave frequencies that can be absorbed, and reduces both acoustical noise and electrical noise, further enhancing the images.

“With the backing layer, the device produces more accurate and sharper images, with a wider operating range of frequencies,” Nayeem says.

To further improve the quality of the images, the researchers designed an algorithm that adaptively performs a process called beamforming. This algorithm allows the system to compensate for differences in the speed at which sound waves travel through different types of tissue, such as skin and fat. 

“What we are trying to do is predict the speed of sound properties of the tissue you’re imaging, and then use that to reconstruct the image more accurately. We see up to a 10 percent improvement in the resolution just by applying this technique,” Viswanath says. 

The researchers asked 10 volunteers, who were not experts in ultrasound technology, to use the system to try to identify small micro targets embedded in a “tissue phantom” — a gel-like material engineered to mimic human tissue. Participants had a much higher success rate locating the spheres when they used the new system than when they used a traditional ultrasound probe.

A user-friendly system

For the new version of this system, the researchers also created a user interface, displayed on a computer screen, that guides the user to place the probe in the correct location. This could be especially important for tracking progression of treatments such as neoadjuvent therapy, or long-term monitoring of known abnormalities such as fibroadenomas or microcalcifications.

In a trial with seven people, the researchers found that the users were able to accurately place the probe in the correct location each time they did a scan. 

“Conventionally, you need an operator to move the probe around the breast, but we made a computer-vision interface for users to do it by themselves. This is very user-friendly and it shows live images on the screen,” Yoon says.

For future versions of this technology, the researchers hope to create an interface that could be used with a cellphone or tablet, making the system easier to carry. In addition to enabling earlier diagnosis, this type of system could make ultrasound more accessible to patients in areas where there aren’t enough trained ultrasound technicians, the researchers say.

Dagdeviren and some of her students now hope to form a company to work toward making the technology commercially available. While breast cancer diagnosis is their first target application, they hope to expand it to many others.

“The technology is so versatile that it can be used for any soft tissue imaging, from ovarian cancer to measuring endometriosis progression, or fetal monitoring,” Dagdeviren says.

The research was funded by the National Science Foundation, the 3M Non-Tenured Faculty Award, Lyda Hill Philanthropies, the MIT Media Lab Consortium, and a Tata Center Technology and Design Fellowship.

How urban design leads to better wellness

An extensive study of U.S. cities identifies walkable neighborhoods, urban greenery, and access to amenities as key contributors to residents’ health.

A new big-data analysis of the U.S. pinpoints how urban design aids the health of city residents — especially when cities provide walking opportunities, greenery, and mixed-use streets with a blend of commercial and residential activity. 

The study examines tens of thousands of urban census-bureau tracts in the U.S., seeing how city features correlate with population health measures, while accounting for socioeconomic considerations as well. 

“We found that on a very large scale, urban planning and design, such as the availability of different amenities and their spatial arrangement, plays a critical role in population health outomes,” says Winston Yap, a visiting scholar at the MIT Senseable City Lab, a postdoc at Cornell University, and co-author of a new paper outlining the study’s findings. 

While there is not one design template for all locations, short and well-connected blocks with a variety of amenities, as well as the strategic placement of parks, all help well-being — physiologically and psychologically. 

“We usually think about physical health first, but we also found a high correlation between good design and mental health,” says Fabio Duarte, an MIT researcher and co-author of the paper. “If you are walking more, it is not only a matter of physical fitness, but gives people a chance to avoid isolation, have serendipitous meetings with people, and at least see there are others around.”

The paper, “Urban motifs associated with population health,” appears today in Nature Health. The authors are Yap; Duarte, who is associate director and a principal research scientist at MIT Senseable City Lab; postdocs Yu Zheng, Kee Moon Zhang, and Peng Luo, who is also an incoming assistant professor at the University of Iowa; Paolo Vineis, a professor at Imperial College, London; Carlo Ratti, director of the MIT Senseable City Lab; and Filip Biljecki, an associate professor at the National University of Singapore.

Only connect

The researchers say they conducted the analysis not just due to an interest in cities, but out of recognition that health care systems are often swamped, and preventative health measures are ever-more important. 

“We wanted to do this study because health care systems around the world are overloaded,” Yap says. “There’s a lot of burden on health care systems, and there is a need not just for treatment but for prevention as well, for obesity, high cholesterol, depression and other mental health issues, and more.” 

To conduct the study, the researchers analyzed 28,323 census tracts, using data from the U.S. Census Bureau along with health data from the U.S. Center for Disease Control and Prevention (CDC). They then used geospatial data, including more than 8 million street view images, to see how urban form related to the health status of residents in those areas. The study accounts for socioeconomic factors and other variables in building an assessment of the relationship between design and health. The study confimed that by themselves, socioeconomic factors are associated with urban health disparities; it then examined the relative impact of differences in urban design in those different settings. 

“By bringing together open demographic, health, and environmental data, the study highlights the importance of open data accessibility for planning healthy cities,” says Ratti.

The scholars also applied a graph deep-learning model to the data, an emerging machine-learning technique they used to help understand which key factors in urban design are most connected to health outcomes. 

The research reveals that in some cases, rectangularity in city blocks, and “building spread,” meaning structures that cover the full size of their lots, can enhance wellness. Examples of this include Manhattan or Boston’s Back Bay neighborhood, where mixed-use buildings on relatively short blocks create many amenities and a variety of walking routes. That said, circular and curving street forms can also work, as long as they feature a lot of interconnectedness as well. 

Urban greenery is almost always a significant factor in urban wellness, with parks scoring high as a facet of city design that helps resident health. Beyond that, expanding the tree canopy can also help urban health outcomes. 

The presence of cultural institutions and restaurants are also linked to general health, while access to health care amenities are understandably connected to physical health improvements. In general, access to points of interest, broadly defined, whether cultural or commercial, is a significant factor in abetting better health, in cities across the country. 

“One of the major contributions of the study is that we look at not only one or two cities, but the entire United States,” Yap says. “In a large-scale study, we were trying to find patterns that were consistent across different urban contexts, as well as populations with different characteristics. Just using this data, we can predict very confidently the population health outcomes for a neighborhood.”

Knowing where to intervene

The research also provides a kind of road map for urban planners and city officials when it comes to policy decisions and local improvements. Among other things, the study suggests where cities might see the greatest return on investment in urban improvements, in health terms. Improvements in lower-income neighborhoods, on aggregate, may generate about four times the added health benefits than the same level of investment in better-off areas that already realize the benefits of good urban amenities. 

“It’s important to know where to intervene,” Yap says. 

“I think for me it shows how intertwined different policies are,” Duarte adds. “Some funding for urban development could have a direct influence on health, and could be more inexpensive than [direct spending on health].”

The researchers regard the study as just one empirical step in this domain. As they note, additional studies could observe changes over time, to further enhance our picture of the connection between urban design and health. Still, as the authors write in the paper, “we believe that our broad picture provides an overarching scaffolding for the understanding of the social and material determinants of health and can guide [further] analytical studies.” 

The research received support from the Campus for Research Excellence and Technological Enterprise (CREATE) program of the National Research Foundation Singapore; the Singapore-MIT Alliance for Research and Technology (SMART); and the MIT Senseable City Lab consortium. It is part of the Largescale 3D Geospatial Data for Urban Analytics project, supported by the National University of Singapore.

The brain’s language network is more extensive than previously thought

A new study reveals that parts of the brain located far from the canonical language-processing centers are also involved in language comprehension.

For decades, neuroscientists have known that specific regions in the brain’s left hemisphere are responsible for processing language. However, a new study by MIT researchers shows that language processing also occurs in many other parts of the brain.

Using functional magnetic resonance imaging (fMRI) data from more than 700 people, the researchers identified 17 additional regions of the brain that appear to play a role in language. These regions are scattered across the brain, including parts of the cerebellum, hippocampus, and cerebral cortex, and they make up about 5 percent of the total volume of the adult brain — about the size of a large strawberry.

“Even though there are all these distant components, it’s pretty restricted in terms of volume. You don’t need that much of the brain to do language,” says Evelina Fedorenko, an MIT associate professor of brain and cognitive sciences, a member of MIT’s McGovern Institute for Brain Research,and the senior author of the study.

Exactly how these regions contribute to language processing is still to be discovered, although the researchers have made some progress toward determining the functions of the cerebellar regions that they identified.

MIT postdoc Agata Wolna is the lead author of the paper, which appears in the Journal of Neuroscience. Other authors include Aaron Wright, a K. Lisa Yang Post-Baccalaureate Research Scholar at MIT; Colton Casto, a graduate student at Harvard University; Samuel Hutchinson, a graduate student at MIT; and Benjamin Lipkin PhD ’26.

Tracking language

The brain’s language processing centers include Broca’s area, first discovered in the 1800s, plus additional regions in the left frontal and temporal lobes of the brain. Scientists have found that some of the corresponding areas of the right hemisphere also contribute to processing language, especially the social-emotional components of language.

There have also been hints that other parts of the brain might be involved in language processing. Early in her career, Fedorenko’s language studies often showed active brain regions outside of the canonical language centers, but she says she was discouraged from including them in her papers.

“When we initially started looking at language, in the first couple of papers, I tried to be comprehensive and include anything that seemed consistent across participants, and there was a huge amount of resistance,” she says. “People would say things like, ‘Well, we know those are not language areas, so please focus on the language areas.’”

In the new study, she and Wolna wanted to revisit those brain scans and see if they could systematically identify language regions outside of the standard language-processing areas.

To do that, they analyzed data from 772 people who had been scanned in Fedorenko’s lab since 2013. Each of these participants underwent a task known as a language localizer, which is used to determine the location of language processing areas for each subject. 

During the test, participants read or listen to sentences as well as sequences of nonwords. For each person, the researchers measure the difference in strength of response when reading real sentences or nonsense sequences. The brain areas that work harder during the sentence condition are considered to be doing something relevant to language, especially if they respond while both reading and listening to sentences.

“It’s a very simple paradigm that lets you identify this core language system in individual brains,” Wolna says.

When searching for language areas, the researchers usually use a relatively strict statistical threshold. In this study, they relaxed the threshold and also used some targeted searches in subcortical areas, in hopes of finding all areas that may contribute to language processing.“We always see this frontal temporal network, but there’s quite a lot of evidence that there are other regions that are also critical for language processing,” Wolna says. “By using a laxer threshold and zooming in on areas with weak MRI signal, we tried to maximize the chances of finding small and weakly responsive regions outside of this left frontal temporal system.”

A widespread network

For about 490 of the participants, the researchers also had data on how their brain responded during a spatial working memory task — remembering the locations of flashing squares on a grid. This task engages a brain network called the multiple demand system, which does not overlap with the core language areas.

This task allowed the researchers to ask whether any of the newly identified language-sensitive regions specifically respond to language and not more general cognitive processes.

Of the 17 new language sites that were revealed by this study, five are located in the cerebellum, which is mainly involved in coordinating the body’s movement. In a study published earlier this year, researchers led by Casto found that three of those cerebellar regions also became engaged during some nonlinguistic cognitive tasks, which was also seen in the new study.

“Those areas that respond to both language and some other tasks could be really interesting and important because they may be doing something like integrating information from different cortical systems,” Fedorenko says.

They also found language-selective regions in the medial frontal cortex, the bottom surface of the left temporal lobe, the hippocampus, and the amygdala. The researchers now plan to further study how these brain regions might contribute to language processing.

“We can now test some ideas from past work, and also more rigorously characterize these regions across different kinds of language manipulations, and different kinds of nonlinguistic tasks, to try to understand what it is that they’re doing,” Fedorenko says.

The research was funded by the Simons Center for the Social Brain at MIT, the McGovern Institute, MIT’s Department of Brain and Cognitive Sciences, and the MIT Siegel Family Quest for Intelligence.

MIT-Kalaniyot program expands, with new cohort of scholars

The innovative effort bringing researchers from Israel to campus keeps growing while enhancing community connections and academic inquiry.

As a new academic year dawns, the MIT-Kalaniyot program is welcoming its second cohort of scholars to campus, expanding an innovative effort to build new connections between MIT and researchers from Israel. 

In fall 2026, MIT-Kalaniyot has 11 new scholars arriving at MIT to pursue research, collaborating with Institute faculty across a wide variety of disciplines. They consist of seven new Kalaniyot Postdoctoral Fellows and four new Kalaniyot Sabbatical Scholars, who are faculty on leave from institutions in Israel. 

It is another step forward for a program which, less than two years ago was still an idea on a drawing board. The project aims to enhance research and create stronger community ties — not only among those connected to the program, but across the MIT campus.

“The goals of the program are to build academic ties between MIT and Israel, alongside a strong, supportive community,” says Or Hen, an MIT nuclear physicist and a co-founder of MIT-Kalaniyot. “MIT has a mission that revolves around research, education, and entrepreneurship, and MIT-Kalaniyot strengthens MIT, to help meet that mission for the world.”

The scholars will be working on a wide range of topics, including mathematics, materials science, behavioral economics, architecture, modern history, chemistry, quantum computing, and computational methods for examining cellular activity.

“We designed Kalaniyot to strengthen MIT’s research and its community at the same time,” says Ernest Fraenkel, a professor of biological engineering and a co-founder of  MIT-Kalaniyot. “We now have scholars in the program working in each of MIT’s five schools. The academic breadth shows our model is working.” MIT-Kalaniyot will also feature its first teaching fellow at the Institute, hosted by MIT’s History program. 

MIT-Kalaniyot was founded by Hen and Fraenkel as a constructive response to discord over conflict in the Middle East. Hen is the Class of 1956 Associate Professor of Physics and associate director of the Laboratory for Nuclear Science; Fraenkel is the Grover M. Hermann Professor in Health Sciences and Technology.

Fraenkel and Hen credit multiple members of MIT’s community and upper administration for backing the MIT-Kalaniyot idea from the start, making it feasible for the program to launch. 

“When we first shared the idea, we were very encouraged by the response from MIT’s senior leadership,” Fraenkel says. “They understood the value of a faculty-led effort, and their constructive response gave us confidence that our approach could be successful.”

“This would be impossible to do the way we’re doing it without the administration’s support,” Hen says. “The program is faculty-led and institution-backed. That’s what you want.”

Hen adds: “I think MIT today is home to one of the most, if not the most, accepting and welcoming communities for Israelis, and I can stand by that statement very strongly. The way our community grew these past years is remarkable.”

Embedded at MIT

MIT-Kalaniyot, named for a well-known flower that grows in Israel and other parts of the region, welcomed its first cohort of scholars to the MIT campus for the 2025-26 academic year. Hen and Fraenkel also give Tal Cohen, an associate professor in MIT’s Department of Civil and Environmental Engineering, substantial credit for developing the concept. 

Scholars at Israel’s nine state-recognized universities are eligible to seek the MIT-Kalaniyot fellowships, which enable research, collaboration, and training at the Institute. The scholars come from a range of academic and personal backgrounds, including both Arab and Jewish citizens of Israel. 

The program is highly competitive, with many more applicants than positions currently available. Applicants are encouraged to identify in advance MIT faculty they would like to work with; accepted applicants then already have a “faculty host” lined up. Many of the new fellows will be working with researchers in established MIT labs, for instance. 

“When they’re here, they are treated exactly like anybody else in an academic unit at MIT and that’s really important,” Fraenkel says. “They’re embedded in these places.”

The program is also intended to generate the kinds of community connections that help scholars flourish, both professionally and personally. MIT-Kalaniyot features weekly lunches, attended by people from the larger community, where scholars can forge connections and friendship. 

The program also features informal academic talks and discussions, with the talks given by MIT researchers both within and outside of MIT-Kalaniyot. Hen, for one, has already seen the benefits of such events; one paper he has recently co-authored directly stemmed from discussions he had at a program event. 

“The range of MIT faculty who stepped forward as hosts has been one of the most gratifying parts of the program,” Fraenkel says. “It shows that this is not confined to one field or one corner of the Institute. It is becoming part of MIT’s broader academic life.”

Adds Hen: “I think it sends a very strong and important message. We’re able to move forward at MIT and build collaborative partnerships with strong ties.”

An additional facet of the program is the potential impact of MIT-based research in practical, tangible ways. One of the 2025 fellows, a leading physician, focused her MIT work on new methods of breast cancer detection, and now, back in Israel, is working to apply those findings in active medical settings. 

Plans for future growth

Having first taken root at MIT, the MIT-Kalaniyot concept is now spreading to other places. In the last two years, Columbia University, Cornell University, Dartmouth College, Harvard University, the University of Pennsylvania, and the University of Southern California have implemented the concept, with other universities in the process of adopting it as well. 

“This national movement all started by replicating the MIT model,” Hen says. “Each university then innovated in their own way. They start from the MIT approach, and then they adapt to what’s happening on their campus. They learn from us, we learn from them, and together we support a broad academic network.”

The progress at MIT and elsewhere has led Hen and Fraenkel to feel optimistic about the ongoing evolution of MIT-Kalaniyot. 

“We started at a tense time on our campus, not really knowing what the future would hold, and it’s exceeded our hopes,” Fraenkel says. “Now we want Kalaniyot to become a recognized center at MIT, funding seed grants for research that wouldn’t happen any other way.”  

While Fraenkel and Hen do not yet have a firm timetable for those developments, they regard them as being realistic. 

“Now we see Kalaniyot as a program that helps MIT well beyond our community,” Hen says. After all, he observes, simply as a vehicle for research, the program has the potential to provide added capacity for MIT, as well as the further connections to top scholars being generated by the effort. 

Indeed, Hen reflects, he is motivated the question: “How do we best support MIT in realizing its mission for the world?” Overall, he says, “I think that’s the ultimate goal of Kalaniyot. We do it in one way, other people can do it in other ways, and as long as you do net good, and support the MIT mission, we value and treasure that, and just want to be part of it.”

“I really believe this is the DNA of MIT,” Fraenkel says. “We’re all about finding practical solutions to society’s biggest problems. Kalaniyot brings extraordinary people here to do exactly that, and the whole Institute is stronger for it.”

Q&A: What is agentic AI today, and what do we want it to be?

Computer scientist Phillip Isola cuts through the hype to explain how AI agents work and what the future might hold for this rapidly advancing technology.

The deployment of automated software systems called AI agents has recently exploded. A November 2025 report by MIT Sloan School of Management and Boston Consulting Group found that 35 percent of surveyed businesses had already deployed AI agents, while another 44 percent planned to implement agentic AI soon. 

To understand the fundamentals and potential impacts of these increasingly popular tools, MIT News spoke with Phillip Isola, an associate professor in the Department of Electrical Engineering and Computer Science (EECS) and a member of the Computer Science and Artificial Intelligence Laboratory (CSAIL), who studies the intelligence AI agents possess, as well as the underlying models and mechanisms that power agentic AI systems.

Q: What is agentic AI and how is it different from generative AI models like ChatGPT and Claude?

A: Agentic AI is AI that takes actions in the world. These actions could be a physical action, like robotic manipulation, or a digital action, like booking a flight. On the other hand, we think of generative AI as making up stories, poems, art, and images, rather than taking actions for us. 

The word “agent” is just a brand name. It usually means AI that is going to help people interact with an application, a website, or the physical world. Most agents we encounter today are digital agents, like customer service agents you can talk with about product complaints. 

Most companies that offer agents use the same few AI models under the hood and give them the ability to take actions and remember what happened. An agent starts with a fundamental generative AI system, like Claude, at the core. Then companies put different wrappers around that foundation model for their product or application. Those wrappers might be specific tools that agent can use, and those tools depend on the application. Maybe the agent has access to a calculator so it can solve math problems, or maybe it has access to a more complicated hard drive and operating system so it can remember a firm’s financial data and past business negotiations. 

The biggest challenge in developing agentic AI comes from a lack of training data. If I want to create a system that can go online and book a flight for me, that seems pretty simple. But we don’t have a lot of data that spells out exactly how to do that — where to move the mouse, which buttons to click on, what to do if something goes wrong, or how to call somebody and negotiate about the price of the airline ticket. One way to train a system like this is to have the AI agent visit airline websites, try things out, and see what works and what doesn’t work. These environments are hard to model, so often the agent must learn by trial and error.

Q: What are some promising applications of agentic AI?

A: I think the area where we’ve seen the most success has been with coding agents. This is something that evolved from generative AI. People trained language models on code, and then they can predict what a human would do to solve a coding problem. In addition, an agent can learn to do this by going through a feedback loop where it tries out different solutions and checks to see if it got the answer right. As long as it can check the answer, the AI agent can perform this trial-and-error loop until it figures out a good strategy.

But there is always a balance between automating decision making versus simply assisting and informing humans. Analytical AI methods, like the systems that help predict possible outcomes of decisions, are not agentic in nature, but are very informative to human decision-makers. For cases that are either high-stakes or safety-critical, like medicine, security, high-level business policies, etc., the technology might not be ready for AI to completely automate those processes, or we might not even be comfortable with that.

Q: Are there risks we should be thinking about when using AI agents?

A: One big risk area comes from the fact that it is often very easy to get agents to do certain types of work for you. With coding agents, you can “vibe code” and just ask the agent to make a code for you, so you don’t have to do the hard work yourself. There is a big risk that, because it is so easy, people will not put enough effort into verifying that it is doing the right thing. Bugs will be introduced, private data will get leaked — this is already happening.

Agents aren’t perfect, in the sense that they might make mistakes because they are not well-trained and don’t know what to do. But even if they are very competent, if a human doesn’t use them appropriately or gives them an instruction that is too vague, the AI agent could make a mistake because the human made a mistake. If humans are less involved in thinking through all the consequences, I think we might be more prone to making those mistakes. 

An additional aspect is the risk of de-skilling. It is unclear how far this will go, but when we are relying on agents to do our homework, our coding, and our math, we might lose the ability to do that ourselves, and we might lose that ability too soon because the technology is not yet ready to fully automate those processes.

Q: What does the future hold for agentic AI?

A: What we think of now as agentic AI refers to large language models using tools to interact with digital and physical systems. One obvious limitation is that, under the hood, these have the architecture of a language model and are trained on text data. To make even more powerful AI agents, we might need to model videos, physical forces, time series, radar scans, and other modalities. We might need to have models with fundamentally different architectures that can handle continuous data, high-dimensional data, stochastic data, and so on. 

But, on the other hand, maybe an extremely good coding model could act as a puppeteer to interface with sensors, actuators, and web APIs? Perhaps, once you have a super-smart reasoning system that understands math, language, and code, you can give it a camera and a keyboard and it will figure out what to do in the spatial domain. Is the next wave of AI just going to be Claude with sensors, actuators, and tools, or is it going to be something built in a new way from the ground up? That’s the big question a lot of people in AI are grappling with right now.

Scientists find ozone depletion began decades before discovery of ozone hole

Using modern tools, they also determined that carbon tetrachloride, used as a dry-cleaning and degreasing agent as early as the 1930s, was at the root of early ozone loss.

The Antarctic ozone hole was discovered in 1985, when scientists observed a severe depletion in the Earth’s protective layer of stratospheric ozone. Industrial chemicals known as chlorofluorocarbons (CFCs), then widely used as refrigerants, propellants, foam-blowing agents, and solvents, were at the root of the ozone depletion. After concerted global effort to phase out the use of CFCs, ozone today is recovering, especially in the Antarctic. 

The discovery of the ozone hole was possible thanks, in part, to the measurement tools that were available at the time. Advances in those tools, along with satellites and other monitoring technologies, have since allowed scientists to track ozone’s recovery. 

But what if today’s tech was available much earlier? Would scientists have been able to spot even earlier signs of human-induced ozone depletion? And if so, when would those first signs have popped up, and where? 

MIT scientists now have some answers. The team, led by atmospheric chemist Susan Solomon, has carried out a thought experiment in which they consider a hypothetical world where today’s atmospheric monitoring capabilities were available throughout the last century. In this scenario, they simulated the atmosphere’s chemistry through history and discovered not only when the earliest sign of ozone depletion would have been detectable, but also where, and why. 

In a study appearing today in the Proceedings of the National Academy of Sciences, the scientists suggest that the first signs of ozone depletion appeared as early as 1957 — about 30 years before the ozone hole was discovered. And, this first signal of ozone loss popped up not in the Antarctic, but in the upper stratosphere of the tropics. What’s more, the cause of this early depletion was not due to CFCs, but to another industrial chemical: carbon tetrachloride. 

“What we’ve learned from textbooks is that CFCs result in ozone depletion,” says the study’s first author, Jian Guan, a graduate student in MIT’s Department of Earth, Atmospheric and Planetary Sciences (EAPS). “It turns out there was another compound that caused ozone depletion much earlier than CFCs. This was a big surprise.”

For Solomon, who was an early pioneer in the study of ozone’s effects on the atmosphere, and who was the first to show that CFCs were the main agent eroding Antarctic ozone, the new results were a complete shock. 

“The fact that ozone depletion would have happened as early as the late 1950s, which is much earlier than I would have thought, just absolutely blew my mind,” says Solomon, the Lee and Geraldine Martin Professor of Environmental Studies and Chemistry at MIT. “This study shows it’s really important to keep monitoring so that we can fully understand how the atmosphere responds and recovers.”

The study’s MIT co-authors include Peidong Wang, Yaowei Li, and Kane Stone; along with Benjamin Santer of the University of East Anglia; Qiang Fu of the University of Washington; Rolando Garcia, Douglas Kinnison, and Jun Zhang of the National Center for Atmospheric Research; Jean-Francois Lamarque of Climate Modeling and Analysis LLC; and Gabriel Chiodo of the Spanish National Research Council. 

Chlorine connection

Ozone is a highly reactive molecule, made from three oxygen atoms, that exists naturally in the upper layers of the atmosphere. In the stratosphere, ozone acts as a shield, absorbing the sun’s rays and reducing the harmful ultraviolet radiation that can reach the Earth’s surface. 

In the late 1980s, after scientists first observed signs of ozone depletion in the Antarctic, Solomon led expeditions to the region to measure the stratosphere’s composition. Those measurements confirmed that ozone’s agent of destruction was CFCs — the chemicals which were used globally in refrigeration, air conditioning, and aerosol propellants, among other uses. 

Specifically, Solomon measured higher-than-expected levels of chlorine dioxide in the Antarctic stratosphere. The presence of this molecule, in the same place where ozone depletion was observed, had only one chemical explanation: Ozone was being broken apart by rogue atoms of chlorine. At the time, chlorine-heavy CFCs were in wide use, and MIT chemist Mario Molina proposed that if CFCs drifted up to the stratosphere, photons from the sun could break apart the molecules and release atoms of chlorine, which would then be free to break apart ozone’s oxygen atoms. 

Molina’s work, and Solomon’s measurements, were key in showing that CFCs could deplete ozone — a discovery that earned Molina a share of the 1995 Nobel Prize in Chemistry. Soon after, nearly every country in the world signed the Montreal Protocol, which ultimately led to the successful phase-out of CFCs and other ozone-depleting substances. In recent years, as a result of that global cooperation, scientists have observed initial signs of ozone recovery.

“We know what we have now, and ozone is starting to recover,” Solomon says. “But no one has ever really documented where and when and why the first ozone depletion would have happened.”

Signal over noise

For their new study, Solomon, Guan, and their colleagues took a “what-if” approach, posing the question: What if the past had the monitoring capabilities of the present? When would we have been able to detect the earliest sign of human-induced ozone depletion? 

Today’s monitoring tools are sensitive to a certain signal to noise, meaning they can identify patterns of ozone loss that are more likely a “signal” of human-induced depletion (such as from CFCs), versus ozone loss that is due to “noise,” such as random fluctuations from weather and natural phenomena. 

With this in mind, the team looked to reproduce the chemistry of the atmosphere over the last century to see whether they could see a signal over the noise, based on the sensitivity of today’s monitoring tools. 

The team used 16 different model runs, each of which simulates varying conditions and dynamics of the atmosphere at various latitudes and altitudes, as well as the concentrations and interactions of ozone and other molecules. Ozone is affected by not only human-caused chemicals but also natural phenomena such as volcanic eruptions and El Niño weather patterns. Each model run simulates ozone’s response to these natural phenomena, which the team combined to establish a range of “noise,” or ozone depletion that likely is due to natural variability.

They added to each model the various industrial chemicals that were known to have been produced at various times over the last century. 

“Year by year, we have estimates from industry of how much of these chemicals were made and sold globally, and the emissions of all these chemicals, which the models include,” Solomon explains. “And in the case of carbon tetrachloride, the really cool thing is, we also have ice core data.”

Ice cores are drilled-out cylinders of deeply buried ice, that had formed in the Antarctic and Arctic from the falling and layering of snow over hundreds of years. Ice cores contain the remnants of snow, as well as whatever trace chemicals in the atmosphere the snow originally fell through. Scientists can therefore use ice cores to estimate the composition of the atmosphere through history. 

“We actually see in the ice cores that carbon tetrachloride starts increasing already by the 1940s,” Solomon notes. 

The team incorporated industrial and ice core data into their models, then looked to see whether a signal of human-induced ozone loss stood out from the noise of natural fluctuations. Their analysis revealed that a signal did appear, as early as 1957. Not only did they see when the signal appeared, but also where: in the tropics, rather than the Antarctic. 

The researchers say that human-induced ozone loss was likely occurring globally, but was easier to spot in the tropical upper stratosphere, since that is the region where the range of natural fluctuations is the smallest, and therefore where a signal can stand out better.

Finally, the analysis indicated that carbon tetrachloride, and not CFCs, was the cause of the earliest ozone depletion. 

“That’s the only ozone-depleting substance that was increasing that early,” Solomon says. “We started using carbon tetrachloride in the 1930s as a dry-cleaning agent, and as a degreasing solvent. We didn’t start using CFCs until quite a bit later.”

Carbon tetrachloride has since been phased out of use in most of the world, initially due to its health concerns; the chemical can cause nervous system disorders with prolonged exposure and is a suspected carcinogen. Since the Montreal Protocol began to tightly limit its use in the 1990s, the molecule’s concentrations in the atmosphere have been on a decline. Still, Solomon says the new study highlights the need for vigilance in monitoring carbon tetrachloride, CFCs, and other ozone-depleting substances that may have been phased out but can still linger for decades.

“We’ve gone through a big effort to get rid of these chemicals,” Solomon says. “Don’t we have an obligation to keep monitoring to make sure the atmosphere responds the way we think it should?”

This research was supported, in part, by the National Science Foundation, the National Oceanic and Atmospheric Administration, and the European Commission.

Graphene can hold multiple states of superconductivity, a new study finds

What’s more, the superconducting states get stronger under conditions expected to kill them.

The ordinary graphite in pencil lead is proving to be surprisingly multifaceted at the microscale. 

In a study appearing today in the journal Nature, MIT researchers report that a certain microscopic structure found in natural graphite can host multiple superconducting states. Superconductivity is an electronic state of matter in which electrons pair up and glide through a material with zero resistance. 

While there are thousands of materials that are known to be superconductors, it is rare for one material to host multiple forms of superconductivity. 

The researchers discovered the multiple superconducting states in atomically thin exfoliations of graphite, known as graphene. Specifically, graphene is a single-atom-thin sheet of carbon atoms arranged precisely in a microscopic lattice. The team made its discoveries in samples of rhombohedral graphene, which is a natural structure within graphite consisting of a stack of four or five graphene layers. 

Interestingly, the researchers found that several of the new superconducting states in rhombohedral graphene are able to persist in the presence of a magnetic field, which normally kills superconductivity. 

And in a further surprise, these superconducting states even get stronger when exposed to a magnetic field. 

Overall, the findings reveal a new family of unconventional superconducting states in one seemingly simple material. 

“People might assume that this is a simple, boring carbon material,” says Long Ju, the Lawrence C. and Sarah W. Biedenharn Associate Professor of Physics at MIT. “But we can control this material by tuning certain experimental ‘knobs,’ such as electrical voltages. This is how a simple physical material can exhibit so many different superconducting properties.” 

It’s still unclear exactly how each of the multiple superconducting states arise, or how they are able to persist under a magnetic field, when normally superconductivity should fade.

“From a fundamental physics point of view, it’s very exotic that a magnetic field doesn’t kill superconductivity, and instead it boosts it,” Ju says. “We have provided a lot of experimental results and provided the nutrition that people can absorb to try to think about what’s going on here.” 

The study’s MIT co-authors include co-first authors Junseok Seo and Shenyong Ye, together with Tonghang Han, Zhenghan Wu, Wei Xu, Jixiang Yang, Emily Aitken, Prayoga Liong, Phatthanon Pattanakanvijit, Zach Hadjri, and Mingda Li. External collaborators are co-first author Armel Cotten and members of Dominik Zumbuhl’s group at the University of Basel in Switzerland, plus others at Florida State University, the University of Florida, Gainesville, and the National Institute for Materials Science in Japan. 

Natural steps

Graphene and other atomically thin, two-dimensional materials can exhibit unexpected electronic, magnetic, thermal, and physical properties. And when two or more sheets of graphene are stacked and twisted at precise orientations, the “magic-angle” structure can suddenly host weird and exotic phenomena. 

Ju’s group has been probing the exceptional properties of graphene. But rather than artificially stacking and twisting layers, they have looked for interesting behavior in naturally occurring graphene structures. In recent years, they have unearthed surprising electronic properties in rhombohedral graphene. This particular configuration consists of graphene layers stacked on top of each other, each one slightly offset from the last, similar to the steps in a staircase. 

Rhombohedral graphene can be found naturally in ordinary graphite. But to find it first requires exfoliating a block of graphite (usually with Scotch tape), then searching the exfoliated sample for the telltale staircase-like pattern, which researchers can then isolate for further experimentation. 

Using this approach, Ju and his colleagues have been able to isolate and probe samples of four- and five-layer rhombohedral graphene. They have so far discovered that the structure can host a rare, “chiral” form of superconductivity, as well as fractional electron charge, among other behavior. 

In the flow

For their new study, the team took a slightly different approach in studying rhombohedral graphene. Previously, they electrically “doped” their samples, progressively adding electrons as they passed a separate electric current into the material. They then measured the voltage, or essentially the force that pushes the current through the material, and looked for instances when the voltage dropped to zero, indicating that the current was passing through without resistance.

In this way, the team has observed superconductivity when adding electrons to rhombohedral graphene. So they wondered: What might happen if they did the opposite, and took electrons away? 

In their new study, the team looked for signs of superconductivity as they carefully removed electrons from rhombohedral graphene, progressively lowering the material’s electron density, as they applied a separate, external electric current to measure the electrical resistance. In these experiments, they also applied external magnetic field along directions parallel and perpendicular to the graphene plane. These experiments were carried out in collaboration with Zumbuhl’s group in Switzerland, who provided access to a laboratory setup in which graphene samples could be exposed to high magnetic fields and ultracold temperatures. 

In these experiments, the researchers found that at certain electron densities, four different superconducting states emerged. What’s more, three of the states persisted in the presence of a relatively high magnetic field. 

Normally, magnets destroy superconductivity by severing the bond between the paired electrons gliding through the material. 

But in Ju’s experiments, the team observed three superconducting states that survived in a magnetic field up to around 9 tesla, which is about 180,000 times stronger than the Earth’s magnetic field. In these instances, the magnetic field they applied was in a parallel orientation with respect to the plane of the material. When they switched the magnetic field to a perpendicular orientation, they discovered another surprise: At a certain electron density, superconductivity not only persisted, but increased. The material was able to continue superconducting, at higher temperatures than predicted. 

Every superconducting material has a critical temperature below which electrons can conduct without resistance, and above which superconductivity cannot persist. But the team found that, at a certain electron density, and in the presence of a perpendicular magnetic field, superconductivity in rhombohedral graphene was able to survive beyond the material’s critical temperature that corresponds to zero magnetic field. 

“The superconductivity actually is enhanced, as in, the transition temperature goes from 55 millikelvin to probably 90 millikelvin,” Ju explains. “At the same time, the material can take another 50 or 60 percent extra current before superconductivity gets destroyed. And that is very unusual.”

The researchers are unsure of what microscopic behavior is enabling multiple and unconventional superconducting states, though they propose one idea. Conventional superconductivity emerges when electrons pair up. These “Cooper pairs” consist of electrons with opposite spin, and it’s thought that a magnetic field can pull the spins out of their opposite configurations, and as a result, break up superconductivity. 

Instead, the team proposes that perhaps in rhombohedral graphene, and at certain electron densities, electrons can pair up with aligned spins. Any magnetic field would still pull on the spins, but in the same direction, preserving their alignment, and their superconductivity. 

The researchers acknowledge that the idea needs much more investigation, both experimentally and theoretically. For now, they see the results as a demonstration of what new and exotic phenomena can emerge in a seemingly simple material, with the right measurements and controls. 

“We can control the simplest chemical and structural material— crystalline carbon— as part of the fun,” says lead author Junseok Seo, who is a graduate student in Ju’s group. “We’re not only dealing with what nature gives us, but we’re applying additional controls to change it to something that nature does not give us, but that can exist in the same material.”

This work was supported, in part, by the U.S. Office of Naval Research. Device fabrication was carried out, in part, at MIT.nano.

How data centers can better manage energy use

A new study suggests flexibility in the timing of electricity consumption could lower consumer costs.

The number of U.S. data centers is growing, largely to power artificial intelligence programs. That has led to concern about the environmental consequences of data centers — and their impact on the energy grid itself. What will happen if scores of new data centers come online? 

A new study by MIT researchers indicates that the impact of data centers could vary significantly, depending on how their energy use is structured.

Specifically, if data centers move a significant portion of their energy consumption to non-peak hours, it might actually help lower average energy costs. The environmental impact, in terms of type of energy consumed, would differ by location, with some places likely seeing a greater buildout of renewables and others experiencing a relative increase in fossil fuel use. 

“The key with data centers is: How can we add them to the network without adding a lot to our peak usage?” says Christopher Knittel, an economist in the MIT Sloan School of Management and co-author of a new paper detailing the study. “One way for data centers to do that — to add to average usage but not the peak usage — is if they provide some grid flexibility during those high-cost periods. And that’s what we’ve been interested in understanding.”

Specifically, the paper finds that a flexible arrangement for data-center energy consumption, compared to an inflexible one, would produce cost savings of up to 5 percent in Texas, 4 percent in the Mid-Atlantic region, and 2 percent in the western U.S. states. To achieve that, data centers would have to move more than 20 percent of their consumption — sometimes more like 50 percent — to non-peak hours. 

The paper is titled “Flexible Data Centers Reduce Power System Costs But Can Increase Emissions,” and appears today in the journal iScience. The authors are Juan Ramon L. Senga, a postdoc in MIT’s Center for Energy and Environmental Policy Research; Shen Wang, a postdoc in MIT’s Center for Energy and Environmental Policy Research; and Knittel, who is the George P. Schultz Professor at MIT Sloan and the associate dean for climate and sustainability at MIT. 

The 20 percent solution

The expansion of data centers has raised questions about additional stress for the U.S. grid, the global effects of increased fossil-fuel consumption, and the local environmental effects of data centers. The current study examines the first two of these issues. 

To conduct the research, the scholars extensively simulated scenarios in which data centers expand, using the so-called “Gen X” model of the U.S. power grid, for a year’s worth of energy use. 

The study focused on the grid systems in three areas: Texas, the Mid-Atlantic region, and the “Western Interconnect,” comprising the 11 large western states in the lower 48 states of the U.S. The researchers studied these regions because they collectively host most of the country’s data centers — about 82 percent of U.S. data centers by 2030, according to one analysis. 

A bit counterintuitively, the researchers found that adding data centers could lower energy costs in some scenarios. Typically, about 60 percent of grid expenses are fixed costs, like power lines, while about 40 percent consists of energy costs. Adding data centers to the grid could, in effect, apportion the fixed costs over a higher volume of energy use. 

“It’s really just math,” Knittel says. 

But there is a catch. Lower costs might only happen if data centers increase their average consumption faster than their peak-hours consumption, when energy is most expensive. As it happens, most data centers do have flexibility built into their energy-use patterns, since they usually run at about 80 percent capacity.

In the study’s modeling, that flexibility often consists of shifting use from early-morning and early-evening peaks, to more midday energy consumption, when the energy load is lower and solar is at full capacity. The simulations show this makes a difference.

“There are two dimensions that data centers have to make decisions about,” Knittel says. “One is how much of their load in any one time period is flexible. And two, how many hours, plus or minus, can they move that computation?”

Pretty soon, real money

Additionally, data centers have different amounts of flexibility based on the types of AI-related computation they host. Data centers being used for AI training data tend to consume energy at a steady rate, but as a result could provide more flexibility for shifting power loads compared to inference data centers, which are used more for online search queries. In the latter case, consumption is driven more by end-user Internet habits.

Overall, Knittel emphasizes, the magnitude of cost savings suggested by the study, ranging from 2 percent to 7 percent, is significant. 

“Three percent is a big number,” Knittel says. “When you’re talking about the grid, 3 percent or 6 percent doesn’t sound like a lot. But when you’re multiplying it by 100 billion dollars, it becomes real money.”

When it comes to environmental impact, the modeling finds that the projected level of data center growth by 2030 would be very significant in terms of carbon dioxide emissions. Compared to a world with no data center growth, the study finds those emissions would rise by 58 percent in Texas, 20 percent in the Mid-Atlantic region, and by 24 percent in the western U.S. That underscores the need to be strategic about data center consumption. 

But the modeling also finds that the implications of data center buildout for clean-energy use vary by region. In Texas, where 54 percent of grid power is wind energy, having more data centers with flexible patterns of energy use could reduce emissions, by increasing demand for wind energy. The study finds that in this scenario, there could be 40 percent fewer CO₂ emissions. 

However, in the Mid-Atlantic region, where there is a reasonable amount of solar energy but relatively less wind power, more data centers with flexible consumption patterns could increase both renewable energy and fossil-fuel energy consumption.  Here the modeling suggests an increase in CO₂ emissions system-wide of 3 percent. 

“When data centers provide some flexibility in that latter scenario, the data centers actually move hours to when sun and wind energy production is slowing, and that allows a coal plant to stay on,” Knittel observes. “So it doesn’t necessarily attract more renewable investment. It attracts more coal investment.”

“That’s why we have policy”

For any of this to happen, however, the data centers would have to implement the flexible energy-use schedules modeled in the study. And it’s not clear that companies using data centers would be motivated to do that. To Knittel, this suggests officials might have to craft regulations in this area. 

“That’s why we have policy,” Knittel says.

More specifically, he adds, there is one big policy lever officials could use to achieve this goal: offering quicker initial hookups to the grid in return for time-of-use flexibility. 

“One big concern about these data centers now is how long it takes for them to connect to the grid,” Knittel says. “One way to provide flexibility now is what’s called ‘connect and manage,’ which is, connecting you faster to the grid if you agree to provide flexibility. Tech firms would take that deal. They would rather connect a year earlier, and throttle down computation a few hours a day, than to have to wait. We do this with power plants too.”

Certainly, Knittel adds, as firms competing with each other, “Tech companies say they won’t provide flexibility alone. But if everyone in the industry has to, it’s okay.” 

The current study is the first to examine the “end-to-end” implications of the centers for costs and emissions. The results, the scholars feel, bear further evaluation — and it is a topic they are continuing to model. 

“Those are two dimensions I think we should all be considering here,” Knittel says. “The end result is really up to us, and up to policy.” 

The research received support from the Future Energy Systems Center of the MIT Energy Initiative. 

Listening for the echoes of black holes

By analyzing X-ray reverberations and other astrophysical data, Erin Kara seeks to understand the most extreme objects in the universe.

Black holes are often misunderstood to be just that: dark and mysterious voids that are somehow akin to Alice in Wonderland’s mind-bending rabbit hole. 

But rather than a tunnel of nothing, a black hole is actually something — and a lot of it. The densest objects in the universe, black holes exert tremendous gravitational pull, gathering in the surrounding fabric of space and time, and generating huge disks of matter that whirl toward a black hole before falling in, past the point of no return. 

In recent years, as astronomers have been able to train more telescopes on the sky, for longer stretches of time, they have captured a surprising range of black hole behavior.

“It used to be that we didn’t have eyes on systems all the time,” says Erin Kara, an associate professor of physics at MIT. “Now we’re seeing that they can turn on and off at rates that are much faster than we ever thought possible. We see things are getting sucked in toward black holes faster than we thought, perhaps due to stars whipping around and getting trapped in a black hole’s accretion disk.”

Kara and her group in MIT’s Kavli Institute for Astrophysics and Space Research are at the forefront of black hole physics. She is using data from telescopes in space and on the ground to study the properties of black holes, especially supermassive black holes — the ultradense giants at the centers of galaxies. Supermassive black holes are the engines of galaxy formation. Kara, who recently earned tenure at MIT, seeks to connect the extreme physics of black holes with how galaxies such as our own Milky Way come to be.

“It’s amazing that we as humans can know anything about what’s happening billions of light years away,” Kara says. “There’s a lot of new open puzzles about supermassive black holes that I’m excited about.” 

Early impact

Kara was born and raised in Bethlehem, Pennsylvania, as the youngest of four. Her mother was a nurse, and her father a doctor, so it felt only natural for Kara to follow their lead. She set out on a premed track at Barnard College of Columbia University. As part of the program that first year, she took an introductory physics class and was instantly drawn to the subject’s concrete, fundamental descriptions of the physical world, from the quantum to cosmic scales. 

“Physics was always the class that explained things at the ground level,” Kara recalls. “And I thought, wow, this is cool. I have to keep going with this.”

In class, she kept asking questions and wanting to know more. Her professor, astronomer Reshmi Mukherjee, took note and invited Kara to join her research group as a summer intern. The team would be working on new data from a telescope that was readying for launch. That summer, in June 2008, NASA launched the Fermi Gamma-Ray Space Telescope into low-Earth orbit, with the purpose of surveying the sky for sources of gamma rays — high-energy radiation that is produced by black holes, neutron stars, and other extreme astrophysical objects. 

When the telescope started sending back data, Mukherjee assigned Kara a project: to characterize two of the telescope’s unidentified gamma-ray signals. Both signals were bright, and the question was whether they came from nearby, within the Milky Way galaxy, or much further away. If the latter was the case, it would mean the sources were possibly quasars — a type of extremely active supermassive black hole that at the time was a rarity in astronomy observations. 

Kara got to work on the data and soon confirmed that both sources were indeed quasars. 

“It was a small discovery, but it felt awesome,” Kara says. “And I love that about astronomy, that there are so many unanswered questions, and even early on in your career, you can make an impact.”

Needless to say, Kara caught the astronomy bug, and soon opted to switch from premed to physics, though the new path was not always smooth. On Barnard’s all-women’s campus, introductory classes in physics were small, and professors were encouraging and approachable. In contrast, upper-level courses were held at Columbia, where Kara was one of a much larger, co-ed cohort. 

“It’s a very unique experience to be with all women in a physics environment, and then to see how my feelings about my own abilities changed, just based on the environment,” Kara reflects. “I went to Columbia and all of a sudden felt like I couldn’t do this. All these guys were much more confident and outwardly understanding of the material. In the end, I did well there too. And that juxtaposition helped me gain confidence and know, yeah, I belong here.”

Black hole reverb

After graduating with a major in physics and a minor in art history, Kara went abroad, to the Institute of Astronomy at Cambridge University. She earned a scholarship there to pursue a one-year master’s degree in physics, but she ended up staying to complete a PhD on a topic that was just starting to grow roots: black hole X-ray reverberation. 

In 2009, her thesis advisor, Andy Fabian, and his team were looking through archival data from an X-ray telescope and noticed curious time delays in signals coming from around a black hole. They interpreted the signals as X-ray echoes, or reverberations. It was the first evidence of X-ray echoes around a black hole, and it helped to resolve a debate in the field over the source of the radiation. 

Her advisor determined that the reverb was a result of X-rays generated from the black hole’s corona — a crown-shaped aura of high-energy radiation immediately surrounding the black hole — that then bounced, or reverberated, off the swirling disk of gas and dust that circles a black hole, known as an accretion disk. 

“They had only found these echoes in one black hole. But the archive was full of data of these reverberation signals that no one had analyzed in this particular way,” Kara explains. “So I had my whole PhD to kind of play with this archive, and it felt very discovery-driven.”

Since that initial exploration, Kara has worked to advance the study of X-ray reverberation as a technique to map regions around black holes and other extreme astrophysical objects. 

A pivotal disruption

After earning a PhD in physics, Kara returned to the U.S. for postdoctoral work at the University of Maryland and NASA’s Goddard Space Flight Center. She intended to work on data from a new satellite, Hitomi — a Japanese mission that would detect far-off X-rays to help scientists map the large-scale structure and evolution of the universe. After 40 days, the scientists lost control of the satellite, which ultimately began spinning uncontrollably and broke apart in orbit. Before it failed, the telescope sent back one clean signal.

“It got one really good observation, which was unlike any spectrum we had ever seen before,” Kara recalls. 

The data confirmed that the satellite’s detector — a microcalorimeter that was developed at NASA — was sound. That technology is now at the heart of Hitomi’s successor, the X-ray Imaging and Spectroscopy Mission, or XRISM, which has been successfully taking data since its launch in 2023. Today, Kara leads a science group as part of the XRISM mission to analyze X-ray signals from supermassive black holes. 

Back then, however, with the end of Hitomi, she had to pivot. She started working with a new group at NASA Goddard that was gearing up for the launch of another telescope — the Neutron Star Interior Composition Explorer, or NICER. In 2017, the telescope, which was developed and built by MIT researchers, was launched and attached to the International Space Station, where it measured the timing of incoming X-rays from astrophysical sources in deep space. 

The group Kara joined was analyzing NICER data for signs of tidal disruption events, which are instances when a black hole tears apart a nearby star. This was some of her earliest work on these dynamic sources, and she has since incorporated tidal disruption events — and data from NICER — as a main research area. 

At the hub

In 2019, Kara accepted a junior faculty position in MIT’s Department of Physics — a decision that to her was a “no-brainer.” 

“X-ray astronomy has its history at MIT,” Kara says. “Bruno Rossi, Hale Bradt, George Clark, Claude Canizares — it all started here. It was always a place that felt like a hub. And that was the draw.”

Today, she and her students regularly analyze data from various satellites and telescopes such as XRISM and NICER to better understand black holes and how they grow, evolve, and affect the galaxies around them. She continues to advance X-ray reverberation mapping, which has helped scientists map the extreme regions immediately surrounding a black hole. Her group is also studying signals from other extreme X-ray sources, including tidal disruption events, quasiperiodic eruptions, and galactic black hole outbursts. 

Kara also plans to explore data from future observatories, including the Ultraviolet Transiet Astronomy Satellite (ULTRASAT), which will continuously scan the entire sky for hot, ultraviolet sources; and the Laser Interferometer Space Antenna (LISA), a space telescope that will detect low-frequency gravitational waves from sources such as pairs of lopsided, David-and-Goliath black holes. 

And she’s also found time for a bit of black hole fun: In 2022, Kara collaborated with educators and music anthropologists at MIT to convert a black hole’s X-ray echoes to audible sound. As a musician herself — she sings and plays the violin — she was curious how a black hole’s cosmic energy might “sound.” The effect was otherworldly, to say the least. 

“One of the reasons that I love black holes is that they are very extreme, and feel very sci-fi crazy, and things don’t make sense, and physics breaks down around them. And at the same time, they’re super foundational to even why we’re here,” Kara says. “For reasons we don’t fully understand, the distribution of stars and gas and dust in a galaxy is dictated in part by the supermassive black hole at its center. Our sun is one of those stars. It’s all intertwined. And untangling some of that is what motivates me.”

MIT in the media: Exploring how curiosity-driven science is an essential ingredient in America’s success

“Scientific American” showcases the history and future of America’s scientific engine, highlighting promising young scientists and icons at MIT and beyond.

Over the past 80 years, America’s bold, sustained investment in scientific research, and the discoveries, ideas and innovations that flowed from it made America a world leader. The nation’s scientific leadership has been essential to our shared prosperity and national security, and delivered real benefits for all Americans.

On June 16, Scientific American released a special section, “The Young American Scientists,” which celebrates early-career professionals actively engaged in scientific research, and features commentary from MIT faculty on why they continue to be so devoted to curiosity-driven science, demonstrating how their hard work and dedication make Americans safer, healthier, and more prosperous. Among the section’s profiles are many MIT faculty, students, and alumni, who share their advice for young scientists and their reasons for optimism in uncertain times.

President Sally Kornbluth emphasizes the importance of curiosity-driven research, noting that discovery “is part of our American DNA and has yielded vast returns to the citizens of this country and the world.” She adds, “what’s needed is a rededication to public investment in American science. Even if I were not the leader of a premier scientific institution, this is what I’d say. Investing in American science is not a gamble; if you look back in time, there is no question about the benefits.”

Adds Institute Prof. Robert Langer: “What American science has done over the past 50, 100 years has been remarkable.”

Scientific American notes that at MIT, that commitment to discovery is reflected in initiatives such as Curiosity on a Mission and the Generative AI Impact Consortium, which are aimed at finding “solutions to real-world problems in a way that is beneficial to society.” “On one hand, we’re at a time, technologically, where things could not be more exciting [and] our science [could not be] more cutting-edge. At the same time, we’ve never seen a situation where people felt so uncertain about the continuity of science funding, particularly when it comes to the basic discovery science that fuels the economy and will fuel societal impact a decade or two from now,” says Kornbluth.

The first sparks

Witnessing invention can spark a lifelong fascination with science. After the launch of Sputnik, the world’s first artificial satellite, Prof. Alan Lightman “became entranced with the idea of building a rocket” of his own. In his essay “My childhood in science,” Lightman describes how these early scientific memories and experiments have shaped him to be a well-rounded writer and physicist.

“Now more than ever, when much of the world, including the U.S., has lost its moral compass, leading to a dog-eat-dog mentality, we need science combined with literature, philosophy, history and art. We need to discover not only the physical world but also our own humanity,” writes Lightman.

Likewise, Prof. John Urschel, a former NFL player, emphasizes the importance of collaboration and having a wide range of interests. 

“A lot of good research happens when people can draw on tools, techniques and insights from different areas, disciplines and even fields. I hope we can encourage promising young scientists to establish strong, broad backgrounds and to communicate frequently with those outside their particular areas,” says Urschel.

Invention and discovery

Scientific American highlights students and alumni looking to better the world by doing everything from investigating neurological disease to securing our energy future. 

At MIT, Visiting Scientist Alice Stanton developed miBrain, a 3D tissue model of the human brain, to help scientists develop personalized treatments for Alzheimer’s and Parkinson’s. Stanton has developed a miniature version of miBrain, a brain-on-a-chip, to better test therapeutics.

Stanton notes “the road to effective treatments is long and bumpy,” compounded by cuts to federal funding. “When we have a loved one who gets sick, we want a treatment—we want something to cure them. It doesn’t come out of thin air,” she explains.

Bob Mumgaard PhD ‘08, CEO of Commonwealth Fusion Systems is working to commercialize fusion power. “Whether in areas such as fusion—or in drugs by design for diseases such as Alzheimer’s and Parkinson’s or in [the creation of] materials we never thought possible—our ability to use new tools to tackle some of these big, meaty problems is super exciting,” Mumgaard emphasizes. 

Graduate student Alex Zhang tackles context rot: the phenomenon when AI language models degrade as they produce more information. To solve this issue, Zhang develops recursive language models (RLMs) that enable the model to work with itself to reevaluate reasoning.

“The types of research that I want to work on are things that I think should be shared for the benefit of people in general,” says Zhang. 

The benefits of scientific collaboration 

What happens when scientific disciplines join forces at MIT?

Prof. Emery Brown highlighted the MIT Health and Life Sciences Collaborative (HEALS), noting that the effort brings together scientists and engineers from a variety of backgrounds to tackle the most pressing health challenges of our times.  

Brown explains that with President Kornbluth’s support, HEALS encourages “faculty to look more deeply into solving health care problems. The enthusiasm for HEALS has been contagious across the campus.”  

MIT alumna Lucy Jones PhD ‘81, who is known for her work advancing public safety during earthquakes and for developing the first American earthquake drill called the Great ShakeOut, shared the necessity of collaboration in developing scientific solutions for pressing real-world problems.

 “Solutions have to be done in collaboration, which means spending time with policymakers,” says Jones. 

Jones also shares how scientific advances in computing have helped make Americans around the country safer when the ground starts to shake.

“My first year in grad school, I was reading paper seismograms. Now everything is computerized. We used to do field deployments; now we have permanent networks. We’re starting to use fiber‑optic cables as seismometers,” says Jones. “Computers have changed everything, including science.”

The state of American science 

Within the profiles, interviewees were asked what needs to change in American science right now. Many expressed concerns with federal funding. 

“I’m fortunate to work with extraordinary students and postdocs, but the infrastructure that lets them do their best work is under real stress: funding instability at the National Institutes of Health and the National Science Foundation, immigration uncertainty for international scientists and an erosion of public trust in expertise,” says Prof. Feng Zhang.

Zhang developed CRISPR-based genome editing tools, which could increase our understanding human diseases and lead to new treatments. “We can lose the lead rapidly if we do not protect our innovation ecosystem,” he says.

Positive developments include the progress Prof. Alan Guth has witnessed in cosmology. 

“With new techniques, we’re able to unravel, to make sense out of, what we’re observing,” says Guth. “A lot of progress has been made on those lines, so in terms of the physics of the field, I think things are going great. But to me, the real problem is the prospects for future funding.”

Langer shares his faith in the durability and strength of America’s science and innovation ecosystem. 

“I look at the history of American innovation and education over the past 250 years, and it’s been spectacular,” says Langer. “Plenty of times there’ve been setbacks. We’ve had world wars, you know, we’ve had depressions, and people keep persisting and keep learning. They keep discovering and they keep inventing. So that gives me a lot of cause for hope. This is not the worst time by any means.”

Summer 2026 recommended reading from MIT

Enjoy these recent titles from Institute faculty and staff.

Summer is the perfect time to curl up with a good book — and MIT authors have had much to offer in the past year. The following titles represent a selection of books published in the past 12 months by MIT faculty and staff.  In addition to links for each book from its publisher, the MIT Libraries has compiled a helpful list of the titles held in its collections.

Looking for more literary works from the MIT community? Enjoy our book lists from 2025,  2024, 2023, 2022, and 2021.

Happy reading!

Fiction and poetry

“We (the People of the United States)” (Penguin Books, 2026)
By Joshua Bennett, the Distinguished Chair of the Humanities at MIT and professor of literature

Bennett marks the 250th anniversary of the founding of the U.S. with a book-length work of poetry about the country and some of its distinctive figures. The piece features remarkable people or inventions from each of the 50 states, meditating on their place in the nation’s cultural fabric.

“The Race for Daphne” (Finishing Line Press, 2026)
By Sarah C. Beckmann, communications and marketing associate in the MIT Media Lab

A poetry collection structured as a crew race exploring girlhood, womanhood, and motherhood through the experiences of a rower and writer. These poems subvert the historical dominance of male heroes by celebrating ordinary female heroism, while examining love, home, and what it means to be an American woman today.

“Jezelle: Thief of Forks” (Self-published, 2026)
By Scott Austin Tirrell, director of administration and finance in the Art, Technology, and Culture Program

Abandoned by her father and raised by the streets of Grafton Notch, Jezelle survives by trusting no one. When a strange magic awakens within her, it offers more than escape — it offers power. But in a city that preys on broken children, power makes her valuable, dangerous, and hunted. To claim the life stolen from her, Jezelle must decide what she is willing to become.

Science and Engineering

“Phenomenal Moments: Revealing the Hidden Science Around Us” (Candlewick Press, 2025)
By Felice Frankel, research scientist in the Department of Chemical Engineering

Enlisting readers to “be the scientist” through vivid fine-art photographs, science photographer Felice Frankel zooms in and out on beautiful and brilliant moments all around us to reveal the chemical, natural, or physical processes — from viscosity and venation to chlorophyll and capillary action — behind scientific phenomena.

“Syntax: A Cognitive Approach” (MIT Press, 2025)
By Edward A. F. Gibson, professor of brain and cognitive sciences

This book lays out the grammar of a language from the perspective of a cognitive scientist, outlining the components of language structure and the model of syntax that Gibson advocates: dependency grammar, in which a word is connected to another word via a dependency arc to form a larger compositional meaning. This formalism can explain numerous aspects of word order universals across languages.

“Birds Up Close: An Engineer Explores Their Hidden Wonders” (MIT Press, 2026)
By Lorna J. Gibson, professor post-tenure in the Department of Materials Science and Engineering 

A renowned engineer and lifelong birder, Gibson explores the hidden microscopic structures and engineering principles that keep birds aloft and alive — how an egg forms, how a bird generates lift, how woodpeckers safely drill their holes, and much more. She also considers the longer view of birds in their habitats and natural history. Her up-close look at avian mysteries provides a perspective like no other, for the expert ornithologist and curious observer alike.

“Carbon Removal” (MIT Press, 2025)
By Howard J. Herzog, senior research engineer at the MIT Energy Initiative, and Niall Mac Dowell

In “Carbon Removal,” Herzog and MacDowell discuss how technology and policy can come together to help us reach “net-zero” climate targets. The authors explore the rapidly evolving world of carbon dioxide removal (CDR), presenting the technological pathways of enhancing the land sink, biomass-based carbon capture and storage, engineered removal methods, and ocean-based carbon removal. They also discuss barriers facing CDR as well as ethical implications of this process. 

“Climate Change, Drinking Water Security, and Public Health: Global Challenges and Solutions” (Springer Nature, 2026)
Chapters by Libby Hsu, associate director of academics at MIT D-Lab

In her chapter, “Drinking Water Status Around the World and Its Effect on Health,” Hsu discusses the Earth’s water resources, which are found in a variety of settings. In her chapter, “Waterless and Low-Water Sanitation Technologies that Improve Quality of Life and Conserve Water Resources,” she shares her experience with sanitation challenges in the Global South and how that has reinforced the value of waterless and low-water sanitation technologies that are suitable for scaling around the world.

“A Pox on Fools: The True Believers, Grifters, and Cynics Who Convinced Us to Reject Vaccines” (Penguin Random House, 2026)
By Thomas Levenson, professor of science writing in MIT Comparative Media Studies/Writing

In his latest book, Levenson searches for the origins of the most common arguments against vaccines: that they are unnatural; that they are more dangerous than the illnesses they claim to prevent; and that they are an affront to freedom. “A Pox on Fools” explores the human impulse to question and wonder — sometimes past the point at which the very act of questioning turns deadly.

“The Shape of Wonder: How Scientists Think, Work, and Live” (Penguin Random House, 2025)
By Alan Lightman, professor of the practice of the humanities in MIT Comparative Media Studies/Writing, and Martin Rees

Lightman and Rees pull back the curtain on the field of science, revealing that scientists are driven by the same sense of curiosity, wonder, and responsibility toward a future that shapes us all. They guide us through the fascinating lives and minds of scientists around the world and throughout time, and provide an inside peek at what makes scientists tick — their daily lives, passions, and concerns about the societies they live in.

“Uncertainty in Climate Change Research: An Integrated Approach” (Springer Nature, 2025)
Chapter by Jennifer Morris, principal research scientist at the MIT Center for Sustainability Science and Strategy and the MIT Energy Initiative, and John Reilly, senior lecturer in the MIT Sloan School of Management

Understanding future emissions scenarios is essential for preparing for climate change. The chapter “Emissions and Concentration Scenarios” examines how socioeconomic uncertainty contributes to overall climate change projections, and identifies key drivers of greenhouse gas emissions. It reviews the history of emissions scenarios and compares various approaches, including IPCC methods and formal uncertainty analysis techniques. The chapter concludes with lessons learned from over 40 years of socioeconomic scenario development for climate research.

“The Headache: The Science of a Most Confounding Affliction — and a Search for Relief” (Harper Collins, 2025)
By Tom Zeller Jr., managing editor of Undark, published by the Knight Science Journalism Program at MIT

From blinding migraines to severe headache disorders known as “clusters,” chronic head pain affects 40 percent of the population, many of them suffering in silence. Finally, “The Headache” reveals the science behind a group of disorders that is as much a curse as a cultural punchline, and leads to key insights into the nature of pain itself. Guided by his own decades-long struggle with cluster headaches, Zeller’s journey into headache science is at once intimate and panoramic.

Culture, humanities, and social sciences

“The People Can Fly: American Promise, Black Prodigies, and the Greatest Miracle of All Time” (Little, Brown, and Company, 2026)
By Joshua Bennett, the Distinguished Chair of the Humanities at MIT and professor of literature

In this work, Bennett offers a series of profiles, carefully wrought to see how some prominent figures were able to flourish from childhood forward. He closely reads their works for indications about how they understood the shape of their own lives. In so doing, Bennett underscores the significance of the social settings that prodigious talents grow up in. He also offers reflections on his own career trajectory and encounters with these artists, driving home their influence and meaning.

“Thinking Historically: A Guide to Statecraft and Strategy” (Yale University Press, 2025)
By Francis J. Gavin, research affiliate of the MIT Security Studies Program 

It seems obvious that we should use history to improve policy. If we have a good understanding of the past, it should enable better decisions in the present, especially in the highly consequential worlds of statecraft and strategy. But how do we gain that knowledge? How should history be used? In this book, Gavin explains the many ways historical knowledge can help us understand and navigate the complex, often confusing world around us. 

“The Economic Consequences of the Second Trump Administration: A Preliminary Assessment” (Centre for Economic Policy Research, 2025)
Edited by Gary Gensler, professor of the practice of global economics and management and finance in the MIT Sloan School of Management; Simon Johnson, the Ronald A. Kurtz (1954) Professor of Entrepreneurship and professor of global economics and management at MIT Sloan; Ugo Panizza; and Beatrice Weder di Mauro

How might the economic and geopolitical positions of the Trump administration affect growth, trade, investment, inflation, stability, and the role of the U.S. dollar? This volume offers evidence-based, expert analysis to help decision makers understand the impact of tariffs, breaks in global alliances, government downsizing, deregulation, threats to the rule of law, and more.

“The Colony and the Company: Haiti after the Mississippi Bubble” (Princeton University Press, 2025)
By Malick W. Ghachem, professor of history

Many things account for Haiti’s modern troubles. A good perspective on them comes from going back in time to 1715 or so — and grappling with a far-flung narrative involving the French monarchy, a financial speculator named John Law, and a stock-market crash called the “Mississippi Bubble.” In "The Colony and the Company," Ghachem examines the economic transformations and multi-sided power struggles of that time.

“Retrench, Defend, Compete: Securing America’s Future Against a Rising China” (Cornell University Press, 2025)
By Charles L. Glaser, senior fellow in the MIT Security Studies Program 

Many believe China’s ascent will drive it to war with the United States. Yet this is far from inevitable; geography and nuclear weapons should ensure U.S. security. The real danger, Glaser contends, lies in East Asia’s territorial disputes, especially over Taiwan. To reduce the risk of war, Glaser makes a bold case for ending U.S. security commitments to Taiwan and carefully calibrating its policies on protecting South China Sea maritime features. 

“Trade in War: Economic Cooperation Across Enemy Lines” (Cornell University Press, 2025)
By Mariya Grinberg, associate professor of political science and MIT Security Studies Program affiliate

“Trade in War” is an urgent, insightful study of a puzzling wartime phenomenon: states doing business with their enemies. To explain why states trade with their enemies, Grinberg examines the wartime commercial policies of major powers during the Crimean War, the two World Wars, and several post-1989 wars.

“Constructing Economic Nationalisms in Brazil and India” (Cambridge University Press, 2026)
By Jason Jackson, associate professor in political economy and urban planning in the Department of Urban Studies and Planning

Conventional approaches cite India’s leftist “socialism” and Brazil’s right-wing authoritarianism to explain why India resisted foreign direct investment (FDI) while Brazil welcomed foreign firms. However, this ignores puzzling industry-level variation: India restricted FDI in auto manufacturing but allowed multinationals in oil, while Brazil welcomed foreign auto companies but prohibited FDI in oil. This book argues that FDI policies were shaped by contrasting colonial experiences that generated distinct economic nationalisms and patterns of industrialization in both countries. 

“Traders, Speculators, and Captains of Industry: How Capitalist Legitimacy Shaped Foreign Investment Policy in India” (Harvard University Press, 2025)
By Jason Jackson, associate professor in political economy and urban planning in the Department of Urban Studies and Planning

Is foreign capital an agent of economic growth in developing countries or a vehicle of extraction? Examining how Indian elites wrestled with this question in the late colonial and postcolonial periods, Jackson argues that it reflects a false binary. Instead of simply choosing between domestic and foreign capital, Indian policymakers have long considered the business ethics of individual firms. Indian economic nationalism, in other words, has never been characterized by a straightforward preference for domestic over foreign capital.

“The Handbook of Social Protection: Evidence and New Directions for Low- and Middle-Income Countries” (MIT Press, 2026)
Edited by Benjamin A. Olken, the TEPCO Professor of Economics in the Department of Economics, and Rema Hanna

Over the past several decades, social protection programs that provide financial assistance to the poor and insure against shocks for the vulnerable have become widespread in low- and middle-income countries. These programs can play a critical role in society. This book provides an overview of what we know about the differing aspects of social protection and highlights the open questions for research for the future. 

“Argumentation: The Key Concepts” (Routledge, 2026)
By Edward Schiappa, the John E. Burchard Professor of Humanities in MIT Comparative Media Studies/Writing

In this book, Schiappa delves into the identification and analysis of fallacies, the evaluation of evidence, and the crucial roles of context, audience adaptation, and argumentative style. It explores the ethical dimensions of argument, the impact of cognitive bias, and the influence of cultural and discourse communities.

“American Independence in verse” (Pentameter Press, 2025)
By Brad Skow, the Laurence S. Rockefeller Professor in the Department of Linguistics and Philosophy

“American Independence in verse,” published by Pentameter Press, traces a story of America’s origins through a collection of vignettes featuring some well-known characters, like politician and orator Patrick Henry, alongside some lesser-known but no less important ones, like royalist and former chief justice of North Carolina Martin Howard. Each is rendered in blank verse, a nursery-style rhyme, or free verse.

“Rwanda’s Genocide Heritage: Between Justice and Sovereignty” (Duke University Press, 2025)
By Delia Wendel, associate professor of urban studies and international development in the Department of Urban Studies and Planning

Drawing from oral histories and a visual archive of memory work after the 1994 genocide in Rwanda, Wendel explores the human rights and government priorities that preserved killing sites and victims’ remains for public display. Rwanda’s genocide memorials exemplify a global phenomenon that Wendel terms “trauma heritage,” wherein hidden or unrecognized violence is made visible in public space to demand justice and recognition. Wendel argues that trauma heritage innovates on the form histories take by “writing” them into landscapes, constituting a reparative historiography from the Global South. 

Technology and society 

“Computing in the Age of Decolonization: India’s Lost Technological Revolution” (Princeton University Press, 2026)
By Dwaipayan Banerjee, associate professor of science, technology, and society

In this book, Banerjee examines India’s pursuit of technological self-sufficiency, and the global forces that prevailed against this vision. He describes why the nation is “the world’s leading provider of inexpensive outsourcing and offshoring services, yet enjoys minimal benefits from more profitable advances in research, manufacturing, and development.”

“Auditing AI” (MIT Press, 2026)
By Karrie G. Karahalios, professor of media arts and sciences at the MIT Media Lab; Marc Aidinoff PhD ’22; Nathan Matias SM ’13, PhD ’17; Christian Sandvig; Alondra Nelson; Kristen Vaccaro; Esha Bhandari; Ellery Roberts Biddle; Lena Armstrong; Motahhare Eslami; and Danaé Metaxa

This book serves as a first-of-its-kind roadmap for auditing artificial intelligence systems to prevent decision-making failures in health care, policing, and employment. Using canonical examples of AI gone wrong — from misidentified facial recognition to biased hiring algorithms — this book explains why robust audits are essential and how they drive concrete policy and corporate change.

“Shape Computation: Fifty Years, 1972-2022” (Springer Nature, 2025)
Edited by Sotirios Kotsopoulos SM ’00, PhD ’05, a research affiliate in the Department of Architecture, with a chapter by Terry W. Knight, the William and Emma Rogers Professor of Design and Computation in the Department of Architecture

This book provides a panorama of “shape computation” and “shape grammars,” a computational theory that has, from its inception 50 years ago, been directed toward the “how” of design. Knight’s chapter, “How is that? Computing the Temporality of Drawing,” describes how process and time are key to studying, appreciating, designing, and making things. She notes that in creative production it is not only important to ask, “What is that?” but also “How is that?” — in other words, how did or how can a thing come to be? As a process carried out over time, computation offers a means for rethinking, representing, and elevating the “how” in designing and making activities. 

“The Remote Revolution: Drones and Modern Statecraft” (Cornell University Press, 2025)
By Erik Lin-Greenberg, associate professor in the Department of Political Science

In “The Remote Revolution,” Erik Lin-Greenberg shows that drones are rewriting the rules of international security — but not in ways one would expect. Leveraging diverse types of evidence from original wargames, survey experiments, and cases of U.S. and Israeli drone operations, Lin-Greenberg explores how drone operations lower risks of escalation. 

“The Comedy of Computation: Or, How I Learned to Stop Worrying and Love Obsolescence” (Stanford University Press, 2025)
By Benjamin Mangrum, associate professor of literature

We often deal with our doubts and fears about computing through humor, whether reconciling ourselves to machines or critiquing them. In fact, this dynamic turns up throughout modern culture, in movies, television, fiction, and the theater. Mangrum analyzes this phenomenon in “The Comedy of Computation,” digging into several facets of modern culture and technology.

“Rubrique Technologie / Tech Section” (Printed Matter, 2026)
By Nick Montfort, professor of digital media in MIT Comparative Media Studies/Writing, and Patsy Baudoin

This work is based on a text generator that produces French and English news items that imagine some of the ways technology will impact us in the near future. Most of the generated news involves people getting struck by autonomous vehicles or even aircraft. Others describe labor disputes, hostile takeover attempts, inventions, and the termination of online services. What is imagined in “RT/TS” is not apocalyptic or discontinuous but actually features many of the same problems we face today; the methods of producing the texts are today’s as well.

“Shared Wisdom: Cultural Evolution in the Age of AI” (MIT Press, 2025)
By Alex “Sandy” Pentland, the Toshiba Professor of Media Arts and Sciences and professor of information technology in the MIT Media Lab

How can we build a flourishing society by using human nature to design technology rather than letting technology shape society? Pentland explores how cultural inventions — from civilizations to the Enlightenment — accelerated innovation and collective wisdom. He argues that understanding these key factors in cultural evolution is essential for solving global challenges like climate change and pandemics, and shows how AI and digital media can aid rather than replace human deliberation.

“Priority Technologies: Ensuring US Security and Shared Prosperity” (MIT Press, 2026)
Edited by Elisabeth B. Reynolds, professor of the practice of urban studies and planning, with a foreword by Simon Johnson, the Ronald A. Kurtz (1954) Professor of Entrepreneurship and professor of global economics and management

A new world order is emerging, and within it, U.S. priorities are shifting. For the country to flourish as well as defend and secure its interests, it must build on its decades of experience in developing frontier technologies and globally competitive industries through investments into priority technologies for the 21st century. This volume presents an introduction to some of the key areas where the U.S. must lead in order to ensure both national and economic security: critical minerals, semiconductors, biomanufacturing, quantum computing, drones, and advanced manufacturing.

Education, work, finance, and social impact

“The Meritocracy Paradox: Where Talent Management Strategies Go Wrong and How to Fix Them” (Columbia University Press, 2025)
By Emilio J. Castilla, the NTU Professor of Management and professor of work and organization studies in the MIT Sloan School of Management

Organizations often hail meritocracy as a fair and efficient way to identify, advance, and reward talent. But efforts to create a level playing field can be held back by talent management systems that confer rewards based on individual performance evaluations. In practice, these merit-based systems “may actually reinforce or create advantages for certain groups,” Castilla contends.

“The Art of Monetary Policy: Lessons from Sun Tzu for Central Banks” (MIT Press, 2026)
By Kristin J. Forbes, the Jerome and Dorothy Lemelson Professor of Management and professor of global economics and management in the MIT Sloan School of Management

Central banks are navigating a world of higher debt, tightly interconnected markets, and rising geopolitical tensions. How might they respond effectively? In “The Art of Monetary Policy,” Forbes draws on the writings of Chinese military strategist Sun Tzu to suggest modern principles for central banks, including preparing for the next financial battle, establishing a strong tactical position, combining weapons and methods, and modifying and varying tactics to maintain flexibility.

“Launching from the Lab: Building a Deep-Tech Startup” (MIT Press, 2026)
By Lita Nelsen, former director of the MIT Technology Licensing Office, and Maureen Stancik Boyce, mentor for the MIT Sandbox program

“Launching from the Lab” provides a much-needed framework for new entrepreneurs who are founding companies based on “deep technology” — groundbreaking innovations rising from new discoveries in fundamental research. Nelsen and Stancik Boyce cover the steps to launch and fund such companies, beginning with emergence from the laboratory and acquiring intellectual property through the intensive research of customer needs, building a team, and raising capital.

“There’s Got to Be a Better Way: How to Deliver Results and Get Rid of the Stuff That Gets in the Way of Real Work” (Hachette, 2025)
By Nelson Repenning, professor of management, and Donald Kieffer

The chaos of everyday business forces people into an exhausting, ineffective, seemingly never-ending cycle of work-arounds, firefighting, and Whac-a-Mole. The irritatingly urgent crowds out the lastingly important. In this book, Repenning and Kieffer describe the game-changing discipline of dynamic work design, which improves productivity, reduces costs, and increases efficiency, ensuring that all parts of a company can work in concert.

“Bayesian Entrepreneurship” (MIT Press, 2026)
Edited by Erin L. Scott, senior lecturer of technological innovation, entrepreneurship, and strategic management in the MIT Sloan School of Management; and Scott Stern, the David Sarnoff Professor of Management of Technology and professor of technological innovation, entrepreneurship, and strategic management at MIT Sloan

This edited volume introduces and explores the concept of Bayesian entrepreneurship, a novel framework for understanding entrepreneurial decision-making under uncertainty. It brings together contributions from leading scholars to examine how entrepreneurs form beliefs about opportunities, learn through experimentation, and make strategic decisions.

“Disciplined Entrepreneurship for Climate and Energy Ventures: 24 Steps to Build Solutions for People and the Planet” (Wiley, 2025)
By Ben Soltoff, entrepreneur in residence at MIT Sloan; Bill Aulet, Ethernet Inventors Professor of the Practice; Tod Hynes, senior lecturer of climate and energy ventures; Francis O’Sullivan, senior lecturer in technological innovation, entrepreneurship, and strategic management; and Libby Wayman, senior lecturer of climate and energy ventures

Climate and energy entrepreneurs face challenges that traditional startup playbooks don’t address. Their ventures can require massive capital and take years to reach market, all while striving to achieve a positive impact on people, planet, and profit. This book adapts the MIT-born “Disciplined Entrepreneurship” framework specifically for climate and energy ventures, recognizing that founders in this space need their own approach.

Arts and design, architecture, urban studies and planning

“Tiny Gardens Everywhere: The Past, Present, and Future of the Self-Provisioning City” (W.W. Norton, 2026)
By Kate Brown, the Thomas M. Siebel Distinguished Professor in History of Science

Nurturing health, hope, and community, gardeners in cities and suburbs are reclaiming lost commons, transforming vacant lots into vibrant plots, turning waste into compost, and recreating what was once the most productive agriculture in recorded human history. In a book with global scope, ranging from Estonia to Amsterdam and Washington, Brown contends that urban gardening has many positive spillover effects, from health and environmental benefits to community-building — apart from periods of pushback when others are trying to eliminate it.

“Small-Town Renaissance: Bridging Technology, Heritage, and Planning in Shrinking Italy” (Springer Nature, 2025)
Edited by Brent D. Ryan, vice provost and professor of urban design and public policy in the Department of Urban Studies and Planning; Carmelo Ignaccolo PhD ’24; and Giovanna Fossa

This book explores the transformative power of digitization in rural regions — where technology isn’t just a tool, but a lifeline for local culture, economic resilience, and future development. Born from a unique research collaboration between the MIT and Politecnico di Milano, this book brings together scholarly work on shrinking towns, economic development, and digital innovation. The project tackled some of the most pressing challenges facing rural Italy — from population decline to economic stagnation — through the lens of digital transformation. 

“Blanking: An Annotated Archive of Projects and Thoughts on Architecture” (Park Books / University of Chicago Press, 2026)
By Rosalyn Shieh, assistant professor in the Department of Architecture, and Troy Schaum

Based on the work and vision of their architecture firm Schaum/Shieh, this book shares what is said and what can be heard in a studio. So much of architectural thinking and knowledge is presented, formulated, and traded in spoken words: pinups, meetings, walkthroughs. Those exchanges inform this book, in which ideas and knowledge that are usually only spoken are made accessible to readers.

“Design Before Disaster: Japan’s Culture of Preparedness” (University of Virginia Press, 2026)
By Miho Mazereeuw, associate professor in the departments of Architecture and Urban Studies and Planning

Few countries have faced as many environmental disasters as Japan, which has endured typhoons, cyclones, floods, earthquakes, volcanic eruptions, and tsunamis. Japanese residents have responded to their precarious circumstances by developing a unique culture of disaster preparedness, equipping the island nation to plan for future emergencies and to greatly reduce their impact. Mazereeuw offers a detailed framework to design and prepare for anticipated disasters and describes effective interventions in urban landscape and architecture. 

“Reconstruction as Violence in Assad’s Syria” (American University in Cairo Press, 2025)
Edited by Nasser Rabbat, professor of architecture and director of the Aga Khan Program for Islamic Architecture at MIT, and Deen Sharp, with a foreword by Hashim Sarkis, dean of the MIT School of Architecture and Planning

This book delves into the complex interplay of post-conflict reconstruction in Syria, challenging the traditionally held dichotomy between the end of violence and the commencement of rebuilding. The contributors to this volume — architects, urbanists, geographers, and historians — employ critical concepts such as urbicide, domicide, and “civilian crisis architecture” to argue against the conventional theoretical frameworks that support a neat separation of phases.

How architecture influences political activity

In Ghana, semi-communal “compound houses” affect how much people vote and participate in political activity, new research shows.

Could the precise architectural form of your residence influence how much you participate in politics? 

A new study by MIT scholars finds this to be exactly the case — at least in Accra, Ghana, where many people live in semi-communal structures known as “compound houses,” often sharing kitchens, bathrooms, and common living-room spaces, while having private bedrooms.

The detailed study of homes in Ghana’s capital finds that residents of compound houses are more likely to vote, attend rallies, and take part in political campaigns, compared to people with more private forms of housing. 

“The overarching pattern we find is that if you compare people who live in compound houses to residents of other housing types, like single-family homes or self-contained apartments, there is a pretty big difference in political actions,” says Noah Nathan, an MIT political scientist and co-author of a newly published paper detailing the study’s results. “People seem to vote more, and there are more other types of political behavior, like going to rallies, participating in campaigns, and contacting politicians.”

While those differences could stem from factors other than housing, the highly granular study suggests the architecture itself really matters. The researchers examined the specific floor plans of compound houses and found variations in people’s political information and social connections — key factors that existing studies show predict political activity — that map to differences in where people live within compound houses.

“We show that those kinds of social relationships and exchanges of political information seem to vary systematically with people’s individual locations within the layouts of the buildings they live in,” says Nathan, an associate professor in MIT’s Department of Political Science. “That’s consistent with architectural design leading you to have different levels of political participation.”

The open-access paper, “Vernacular Architecture and Grassroots Urban Politics: How Politics Is Embedded in Residential Design,” appears in the American Political Science Review. Nathan’s co-author is Paige Bollen PhD ’23, an assistant professor of political science at Ohio State University.

Compound effects

Compound houses are a common form of residence in Ghana, much of West Africa, and some other parts of the world. They tend to house lower-income people who construct them out of inexpensive local materials. Trying to understand their effects is part of taking seriously the idea that place, and space, influence how people live. 

“Rather than just thinking of cities as big agglomerations of people, we should evaluate cities through their actual built forms and designs,” Nathan says. “Space affects politics because people interact with each other in space. It’s not just that people are near each other, but the designs force them to interact or talk in ways that affect how information is exchanged and how social networks form, and that can aggregate up into politics in terms of action and cooperation.”

To conduct the study, Nathan and Bollen used three forms of data to draw out the effects of compound houses on politics. Through pre-existing administrative and electoral data, they first show that polling stations in neighborhoods with a high proportion of compound houses have better electoral turnout than neighborhoods with fewer compound houses. And from existing national survey data, the researchers determined that residents of compound houses actively participate in politics more often. 

The researchers then conducted an original research survey of 1,272 residents in 391 compound houses in 30 neighborhoods of Accra, combined with mapping that showed the layout of those compound houses and where the survey participants lived within each one. In this way, they showed the effects of compound houses more precisely: Living in parts of them with especially high exposure to other people actually increases the amount of social network ties people report, as well as the amount of political information they obtain.

Quantitatively, changes in the centrality of people’s locations within compound houses seem to make a bigger difference in political engagement than other fundamental non-housing factors, such as changes in employment or measures of socioeconomic status. 

“We leverage that variation to show that even within compound houses, the people with more exposures to neighbors have different social network ties and different forms of information than neighbors who live in more private locations,” Nathan notes. 

Encouraging participation

As the scholars discuss in the paper, the effects of architecture on civic involvement are hardly immutable, but likely depend very much on the type of political state in question. 

“We think under different conditions, this kind of architecture could have different effects,” Nathan says. “If you live in an authoritarian regime with an active police state, inhabiting an architecture in which you’re constantly on display to your neighbors is probably going to have the exact opposite implications from what we find in the study.”

However, he adds, since Ghana has a generally healthy democracy and is not a repressive state, “In this context, where there are not such high costs to participating in politics, we think these effects are going to break in the direction of more political participation.”

The study itself is an outgrowth of long-running, overlapping research interests on the part of Nathan and Bollen. Nathan is currently developing a book project about urban form, architecture, and politics both in Ghana, where he has conducted research for many years, and in other cities across the African continent. Bollen conducted her PhD research at MIT on public spaces, interactions, and political dynamics in Ghana and South Africa; her advisor was MIT Professor Evan Lieberman.

Sociologists, management experts, architects, and planners have all studied the effects of building design on human behavior, but have often focused on issues such as workplace productivity. Some political scientists, including MIT Associate Professor Bernardo Zacka, have also highlighted the salience of architecture to politics. But few political scientists have undertaken quantitative empirical studies of the subject. If they do, Nathan thinks, the results might surprise some people. 

“There’s a famous idea that cities can be anonymizing,” Nathan says. “I think that’s actually not true. When you go to urban Ghana, people know each other, and there is a great deal of social capital and social connections. And I think part of the reason is that many people live in architectures that are not anonymizing.”

Improving the speed and energy-efficiency of AI agents

A new system, known as Murakkab, optimizes the design and deployment of multistep workflows that power AI applications.

Agentic workflows are artificial intelligence-powered software systems that chain together multiple models and external tools to tackle complicated tasks, like analyzing a video and answering questions about it.

But the way these highly fragmented systems are designed and deployed often causes inefficiencies that can lead to wasted computation, energy, and cost. 

To improve efficiency, researchers from MIT and Microsoft developed an intelligent system that streamlines the process of designing agentic workflows and automatically optimizes how those workflows are implemented. 

With this new method, a developer can describe what they want the agentic workflow to do in plain language, without needing to specify all the details of their application in advance. 

The system automatically figures out the best models and tools to use, as well as the ideal hardware configuration and computational resource allocation when the workflow is executed by a cloud provider.

It adjusts those configurations on the fly based on each user’s priorities, such as minimizing costs or maximizing speed.

When tested on several agentic workloads, this new system reduced the number of computational units needed for deployment, significantly cutting energy requirements and costs compared to traditional approaches without hampering performance.

“Agentic workflows are getting very complicated and quickly becoming the backbone of what cloud providers are doing. Energy usage is a huge concern, so we need to be very careful about how efficient these workflows are. It is very easy to over-allocate resources, wasting energy and money. Enabling a cloud provider to intelligently make these workflows more resource-optimal is a win for everyone involved,” says Gohar Chaudhry, an electrical engineering and computer science (EECS) graduate student and lead author of a paper on this system.

He is joined on the paper by Adam Belay, an associate professor of EECS and a member of the MIT Computer Science and Artificial Intelligence Laboratory; senior author Ricardo Bianchini, technical fellow and corporate vice president at Microsoft Azure; and others at Microsoft Azure. The paper will be presented at the USENIX Symposium on Operating Systems Design and Implementation.

A configuration conundrum

An agentic workflow is a system composed of several autonomous AI agents that collaboratively use various models and tools, like databases or Python programs, to dynamically complete a multi-step task, such data processing or code generation. 

These workflows can serve as behind-the-scenes processes that power user-facing applications.

Typically, developers must hard-code all technical choices upfront. They need to define which AI agents, models, and tools to use, and the order in which to use them. They also must specify the hardware that runs the workflow and how to balance tradeoffs like speed versus cost. 

This is especially challenging because agentic workflows bring together multiple black-box models and diverse tools, each with their own configuration options, which may be offered by different companies. 

If a new AI model is released that would improve the application’s accuracy or efficiency, the developer would need to start from scratch to implement it.

“Even if you wanted to do all this manually, it is unlikely that you’ll be able to configure the workflow optimally because the space of possible configurations is so large,” Chaudhry says. 

In addition, the cloud data center that deploys the application for customers can’t see inside the workflow to allocate its hardware resources in the most efficient manner at the time of the user’s request. 

With this new system, called Murakkab (an Urdu word that means a composition of things), the researchers sought to optimize the entire agentic workflow process.

Dynamic decision-making

First, Murakkab enables developers to create an agentic workflow by describing their intent for the application in high-level terms, rather than detailing how the many components of that workflow should be combined. 

For instance, a developer might describe a video Q&A application that extracts key frames, generates a transcript, and then answers user queries about the video. 

“There are many ways to do this, and all these different models and tools have implications on how fast the application can finish the task,” he says. 

Murakkab takes the developer’s straightforward specifications and automatically identifies the best existing models and tools to put together into the workflow. 

It also determines which components need to run sequentially and which can be run in parallel to boost performance. 

“The platform makes configuration decisions dynamically over time, so if a new model or GPU accelerator comes out tomorrow, the developer doesn’t need to worry about that,” he says.

When the cloud provider deploys that application for a customer, Murakkab optimizes the workflow by configuring its components to meet the user’s constraints, such as prioritizing accuracy while meeting a latency requirement. 

It adaptively identifies ideal hardware allocations and deployment schedules to maximize efficiency in real time, then generates a workflow that is ready for the cloud provider to execute.

“Our system also gives cloud providers visibility into multiple workloads, so the provider can share computational resources in the most efficient manner while satisfying the constraints of users,” he says.

When tested on diverse agentic workflows for video Q&A and code generation, Murakkab met user requirements while using only about 35 percent of the computation required by other methods. It consumed only about 27 percent as much energy for less than 25 percent of the cost.

The dynamic nature of Murakkab also enables users to balance tradeoffs. In one instance, the system lowered energy consumption of an agentic workflow by more than an order of magnitude with only about a 2 percent drop in accuracy for the customer.

The system was also able to identify an unexpectedly ideal configuration for a model that selects video frames, optimizing performance for a video Q&A task. This type of optimization would be nearly impossible for a developer to do manually, Chaudhry says. 

Next, the researchers plan to expand their system to more complex workflows and larger computing clusters while exploring opportunities to optimize new agentic applications. 

“There is a lot of potential to make these workflows more resource-optimal so they consume far less energy, but we need to be thinking about this at the scale of major cloud platforms,” says Chaudhry.

This research was supported, in part, by the Semiconductor Research Corporation and the U.S. Defense Advanced Research Projects Agency.

What happens when environmental change outpaces life’s ability to adapt?

A new model links Earth’s mass extinctions to mismatches between rates of environmental change and biological adaptation.

When an animal’s environment changes faster than the animal can adapt, its chances of survival can flat-line. The same is true for populations, and even entire species. 

Now, scientists at MIT and the University of Leicester have found that this connection between evolutionary adaptation and the pace of environmental change holds up at the global scale as well — and can determine life’s susceptibility to mass extinction. The researchers developed a theoretical model of this phenomenon, which they present in a paper appearing today in Physical Review Letters.

The team compared the model with available data from past major mass extinctions, including how fast the global environment changed at the time of each event. The model successfully predicted the severity of most mass extinctions in Earth’s history, or the fraction of life that was unable to adapt, and therefore went extinct. 

Interestingly, the researchers found that the range of adaptation rates across animal groups is broadly similar to the range of rates at which the environment can change.

“What we’re beginning to see is a certain level of organization, and ways in which life behaves that are consistent with the ways in which the environment behaves,” says study author Daniel Rothman, professor of geophysics and co-director of the Lorenz Center at MIT. “It may be that life has evolved so that its range of adaptabilities matches the range of stresses that it meets.”

Rothman’s study co-author is Sergei Petrovskii, professor of applied mathematics at the University of Leicester in England.

A catastrophizing connection

The connection between extinction and environmental change is not new. In the late 18th century, the French naturalist Georges Cuvier, who is often referred to as the founding father of paleontology, was the first to propose the concept of “catastrophism.” He had discovered fossil bones near Paris that didn’t match any animal known to exist at the time. Cuvier concluded that the bones were from a group of giant mammals that existed at one time but was no longer around. He proposed, then, that an entire species could disappear, or go extinct, likely due to a widespread catastrophe. 

“That itself was a major idea, that a species could go extinct,” Rothman says. “And he had suggested it was an environmental catastrophe that had caused it.”

The concept of catastrophism later gave way to the view that Earth’s history was shaped mainly by slow, gradual processes. But in the mid-20th century the American geologist Norman Newell revisited the problem. In seeking the cause of extinctions, he proposed what Rothman and Petrovskii call the “rate-mismatch” hypothesis, the notion that extinction occurs when the rate of environmental change is higher than the rate at which a species can evolve to adapt. 

Biologists have since observed Newell’s hypothesis play out in many cases where changes in the environment have driven the extinction of individual species. Rothman and Petrovskii wondered: Could the hypothesis also apply at the global scale?

“We know that individual species go extinct when environmental change outpaces their ability to adapt,” Rothman notes. “But it hasn’t been clear whether this same idea applies at the scale of global extinction events.”

Finding a mismatch

For their new study, the researchers looked to test the rate mismatch hypothesis at the global scale. They wanted to see whether mass extinction events in history can be explained by a mismatch between the rate of global environmental change and the rate at which life around the world can adapt. 

To do so, at least in theory, they would have to compare two sources of data: the rates at which the global environment has changed over time and the rates at which different groups of organisms adapt to environmental change. The first can be found in geological records, which scientists have used extensively to infer how the Earth’s climate changed through history. The second, however, is almost impossible to record.

“We’re talking about the rates at which organisms adapt to major environmental change at effectively geologic timescales, from thousands to millions of years,” Rothman says. “And that doesn’t lend itself to direct observation.”

In place of actual data, the researchers aimed to construct a general mathematical theory to describe the range of adaptation rates across animal groups around the world. In this context, “adaptation” refers to any change within a species, over time periods that are much longer than a generation, that enable the species to persist as its environment changes. 

It is generally understood in evolutionary theory that a species can successfully adapt only when multiple conditions are met. For instance, there needs to be variation in the population, these variations must be heritable, some variations enable an organism to adapt better than others, and the organisms that adapt better should leave more offspring. If all these conditions are met, the entire species should be able to adapt to a given environmental change. However, if any one condition fails, the population will go extinct. 

Rothman and Petrovskii recognized that in this case, a species’ probability of successfully adapting multiplies with every condition that it meets. And it turns out that this pattern can be described mathematically as a very simple, bell-shaped curve. Such a curve essentially describes what fraction of the world’s animals can adapt at given rates, from the slowest to the fastest adapters, and how this fraction changes nonlinearly with the rate of adaptation. This curve generally shows that most animal groups can adapt at intermediate rates, while fewer animal groups adapt at the slowest and fastest rates. 

After they established this general pattern of adaptation rates, the researchers looked to see how this pattern compares to recorded rates of environmental change, and how these two rates match, or don’t match, at times of mass extinction. 

To do so, they considered paleontological and geochemical data from 27 episodes over the last 450 million years where the carbon cycle experienced significant change — a measure that is generally understood to reflect global environmental change. They then compared rates of environmental change with the fraction of animal groups that went extinct during each episode — numbers that were established previously in a well-regarded study by paleobiologist John Alroy. 

In the end, Rothman and Petrovskii observed that indeed, for almost every mass extinction event in the last 450 million years, there was a mismatch in the rates at which the environment changed and at which animals could adapt; mass extinctions occurred when a significant fraction of animals could not adapt fast enough to match the changing environment. Their results confirm that the rate mismatch hypothesis applies at the global scale.

What’s more, this mismatch in rates could predict the severity of extinction events, or the fraction of animal life that went extinct given the rate at which the environment changed. 

In the case of the end-Permian extinction, it’s likely that the rapid acidification of the ocean outpaced organisms’ ability to evolve adequate protections, leading to the extinction of over 80 percent of the world’s marine species. 

The team’s work focuses on applying the new model to past extinction events. But the work could also provide a framework for understanding modern extinction risk. 

“Carbon dioxide levels in the ocean are increasing today at a rate which, when appropriately re-scaled, is similar to rates of carbon-cycle change that are just lower than those associated with major extinction events in the past,” Rothman says. “It suggests that modern environmental change may be approaching rates beyond which adaptation becomes increasingly difficult.” 

This research is supported, in part, by Schmidt Sciences, LLC; the MIT Climate Grand Challenges; the U.S. National Science Foundation; the European Space Agency; and the London Mathematical Society.

Computer model could enable bridges and buildings that use less material

MIT researchers developed an approach for generating more buildable structures, bridging the gap between optimized design and real-world construction.

In 2022, global production of construction materials accounted for more than 7 percent of total carbon emissions. But how many of those materials were truly necessary to build houses, buildings, and bridges?

A technique called topology optimization can design structures that reduce the amount of material used, in some cases by as much as 90 percent, which would represent a multi-gigaton reduction in building emissions. Unfortunately, topology optimization is mostly used by researchers for applications like 3D printing rather than by engineers designing at the scale of buildings and bridges.

That’s because topology optimization doesn’t create structures that can easily be built on time and budget, which are the things builders really care about.

Now MIT researchers have created a way to make topology optimization designs more buildable. Their framework, described in a new paper in Automation in Construction today, allows users to apply constraints to algorithmically generated structures to limit their complexity. For instance, the approach allows users to limit how many components meet at each point of their design and how small they want their smallest parts. It also builds on previous work by designing structures with multiple materials and taking into account materials’ properties to distribute load and specify part connections.

“There’s an interplay between the materials you’re using, the constructability of designs, and the optimization of the structure,” says senior author Josephine Carstensen, MIT’s Gilbert W. Winslow (1937) Career Development Professor in Civil Engineering. “You need to be able to address all three at the same time. That’s what we tried to do here.”

The researchers used their approach to design steel, wood, and multimaterial truss structures that support loads in buildings and bridges, showing the carbon emissions associated with materials changed significantly when different constraints were applied. They hope their framework will move topology optimization closer to being used in real-world construction.

“In the literature, there’s sometimes been a disconnect between the carbon savings you can achieve on a computer and the realistic carbon savings you can achieve for built structures — especially when it comes to design technologies like topology optimization,” Carstensen says. “The problem lies in the lack of constructability of designs. These designs have been perceived as too difficult to make with conventional methods, so they are never even attempted. That’s what is exciting about our approach: We can add constraints so that you will never be in a situation where the design that comes out is too hard to make.”

Joining Carstensen on the paper is first author and civil and environmental engineering PhD student Zane Schemmer.

More buildable designs

Computer-based topology optimization has been around for decades. It uses computer programs to optimally distribute material in a given space, for instance creating the strongest possible structures at the lowest weight. The resulting designs are often complex, spider web-like structures that would be a challenge for even the most capable engineers to build.

“A big question Josephine and I were asking is why isn’t industry using it?” Schemmer recalls. “What are the obstacles that prevent industry from designing things more efficiently, and how can we fill the gaps between research and real life?”

In recent years, several researchers have developed ways to make topology optimization easier to use. For their study, Schemmer and Carstensen wanted to bring those approaches together and add new capabilities, like creating designs that use multiple materials, which has been another challenge in the field.

“A big aspect of sustainability going forward will be not only using less material, but also implementing materials efficiently based on considerations like where you are in the world, your access to materials, and each of their associated carbon costs,” Schemmer says.

To build their framework, they used a class of equations called mixed integer algorithms that help make binary decisions about things like materials and connections.

“You can’t have a part that’s 72 percent timber and 28 percent steel,” Schemmer says. “Instead, it says, ‘This truss or cable is going to be made out of this,’ and then based on that decision, how do we make sure all of these connections meet their strength standards?”

The system’s decisions also take into account material properties. For instance, steel struts can withstand compressive loads, but steel cables cannot. The model also has more realistic modeling of how parts connect than previous approaches.

“In 3D printing, the way things come together is easy,” Carstensen says. “In construction, that’s not the case. If you’re building with timber there’s a certain rule set, versus steel has a different rule set.”

Users can also decide how complex they want their design to be by specifying the maximum number of connections at each joint and the minimum angle between connected components. The model also creates minimum size limits for parts, further improving its constructability.

“It’s tough to give a contractor these complex, intricate designs because it’s going to be super difficult to build,” Schemmer says. “A lot of times contractors won’t pick up a project like that to begin with.”

The researchers compared structures designed with their approach to structures designed with conventional topology optimization, showing dramatic differences in final designs that transformed how the structures would be built. Using the Lockport “Upside-Down Bridge” near Buffalo, New York, as an example, they applied individual constraints, like a minimum angle on part connections or minimum part sizes, to the bridge’s truss design, to better understand how each constraint impacted final designs.

Finally, they made truss designs that used wood only, steel only, and combined wood and steel, showing how different projects offered tradeoffs with respect to environmental impact and constructability.

“We saw how the system knew that you could design a bridge of pure steel, but that might not be best from a carbon standpoint,” Schemmer says. “Or you could design a bridge out of purely timber, but that might not be the strongest. But these materials can work together, so you use timber for the carbon savings and steel where you need extra strength, and there’s a balance you can find in these structures.”

From research to industry

The researchers say their approach is more computationally intensive than some others, but they were able to use a MacBook Pro to run the programs in their experiment, and they believe it’s practical for most civil engineering firms.

“It’s computationally a little tougher to solve, but there’s a lot of tools coming out nowadays that make these problems a lot more feasible,” Schemmer says. “This approach has been avoided by industry in the past, but now we think it’s a practical way to solve problems dealing with variable constraints.”

If users have more computational resources, the researchers say their approach could work with a long list of materials and far bigger structures than homes, small buildings, and bridges.

Moving forward, Carstensen says the team plans to build scaled-down structures designed by the model to further validate its predictions. They also want to add constraints to their model to make it even more seamless for civil engineers to use when designing the world’s infrastructure.

“As a structural engineer by training, I was never taught how to design for low-carbon,” Schemmer says. “To tackle a problem as big as climate change, addressing the built environment is a great place to start. One of the most tangible things we can do is work at the layer of construction, at the design stage, because that’s a fundamental step that we can control. There’s a lot of decisions we make early on that lead us to use extra material we don’t need.”

The work was funded by the MIT Morningside Academy for Design.