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Astrocyte diversity across space and time

A new atlas charts the diversity of an influential cell type in the brains of mice and marmosets.

When it comes to brain function, neurons get a lot of the glory. But healthy brains depend on the cooperation of many kinds of cells. The most abundant of the brain’s non-neuronal cells are astrocytes, star-shaped cells with a lot of responsibilities. Astrocytes help shape neural circuits, participate in information processing, and provide nutrient and metabolic support to neurons. Individual cells can take on new roles throughout their lifetimes, and at any given time, the astrocytes in one part of the brain will look and behave differently than the astrocytes somewhere else.

After an extensive analysis by researchers at MIT, neuroscientists now have an atlas detailing astrocytes’ dynamic diversity. Its maps depict the regional specialization of astrocytes across the brains of both mice and marmosets — two powerful models for neuroscience research — and show how their populations shift as brains develop, mature, and age. 

The open-access study, reported in the Nov. 20 issue of the journal Neuron, was led by Guoping Feng, the James W. (1963) and Patricia T. Poitras Professor of Brain and Cognitive Sciences at MIT. This work was supported by the Hock E. Tan and K. Lisa Yang Center for Autism Research, part of the Yang Tan Collective at MIT, and the National Institutes of Health’s BRAIN Initiative.

“It’s really important for us to pay attention to non-neuronal cells’ role in health and disease,” says Feng, who is also the associate director of the McGovern Institute for Brain Research and the director of the Hock E. Tan and K. Lisa Yang Center for Autism Research at MIT. And indeed, these cells — once seen as mere supporting players — have gained more of the spotlight in recent years. Astrocytes are known to play vital roles in the brain’s development and function, and their dysfunction seems to contribute to many psychiatric disorders and neurodegenerative diseases. “But compared to neurons, we know a lot less — especially during development,” Feng adds.

Probing the unknown

Feng and Margaret Schroeder, a former graduate student in his lab, thought it was important to understand astrocyte diversity across three axes: space, time, and species. They knew from earlier work in the lab, done in collaboration with Steve McCarroll’s lab at Harvard University and led by Fenna Krienen in his group, that in adult animals, different parts of the brain have distinctive sets of astrocytes.

“The natural question was, how early in development do we think this regional patterning of astrocytes starts?” Schroeder says.

To find out, she and her colleagues collected brain cells from mice and marmosets at six stages of life, spanning embryonic development to old age. For each animal, they sampled cells from four different brain regions: the prefrontal cortex, the motor cortex, the striatum, and the thalamus.

Then, working with Krienen, who is now an assistant professor at Princeton University, they analyzed the molecular contents of those cells, creating a profile of genetic activity for each one. That profile was based on the mRNA copies of genes found inside the cell, which are known collectively as the cell’s transcriptome. Determining which genes a cell is using, and how active those genes are, gives researchers insight into a cell’s function and is one way of defining its identity.

Dynamic diversity

After assessing the transcriptomes of about 1.4 million brain cells, the group focused in on the astrocytes, analyzing and comparing their patterns of gene expression. At every life stage, from before birth to old age, the team found regional specialization: astrocytes from different brain regions had similar patterns of gene expression, which were distinct from those of astrocytes in other brain regions.

This regional specialization was also apparent in the distinct shapes of astrocytes in different parts of the brain, which the team was able to see with expansion microscopy, a high-resolution imaging method developed by McGovern colleague Edward Boyden that reveals fine cellular features.

Notably, the astrocytes in each region changed as animals matured. “When we looked at our late embryonic time point, the astrocytes were already regionally patterned. But when we compare that to the adult profiles, they had completely shifted again,” Schroeder says. “So there’s something happening over postnatal development.” The most dramatic changes the team detected occurred between birth and early adolescence, a period during which brains rapidly rewire as animals begin to interact with the world and learn from their experiences.

Feng and Schroeder suspect that the changes they observed may be driven by the neural circuits that are sculpted and refined as the brain matures. “What we think they’re doing is kind of adapting to their local neuronal niche,” Schroeder says. “The types of genes that they are up-regulating and changing during development points to their interaction with neurons.” Feng adds that astrocytes may change their genetic programs in response to nearby neurons, or alternatively, they might help direct the development or function of local circuits as they adopt identities best suited to support particular neurons.

Both mouse and marmoset brains exhibited regional specialization of astrocytes and changes in those populations over time. But when the researchers looked at the specific genes whose activity defined various astrocyte populations, the data from the two species diverged. Schroeder calls this a note of caution for scientists who study astrocytes in animal models, and adds that the new atlas will help researchers assess the potential relevance of findings across species.

Beyond astrocytes

With a new understanding of astrocyte diversity, Feng says his team will pay close attention to how these cells are impacted by the disease-related genes they study and how those effects change during development. He also notes that the gene expression data in the atlas can be used to predict interactions between astrocytes and neurons. “This will really guide future experiments: how these cells’ interactions can shift with changes in the neurons or changes in the astrocytes,” he says.

The Feng lab is eager for other researchers to take advantage of the massive amounts of data they generated as they produced their atlas. Schroeder points out that the team analyzed the transcriptomes of all kinds of cells in the brain regions they studied, not just astrocytes. They are sharing their findings so researchers can use them to understand when and where specific genes are used in the brain, or dig in more deeply to further to explore the brain’s cellular diversity.

MIT affiliates named 2025 Schmidt Sciences AI2050 Fellows

Postdoc Zongyi Li, Associate Professor Tess Smidt, and seven additional alumni will be supported in the development of AI against difficult problems.

Two current MIT affiliates and seven additional alumni are among those named to the 2025 cohort of AI2050 Fellows.  

Zongyi Li, a postdoc in the MIT Computer Science and Artificial Intelligence Lab, and Tess Smidt ’12, an associate professor of electrical engineering and computer science (EECS), were both named as AI2050 Early Career Fellows. 

Seven additional MIT alumni were also honored. AI2050 Early Career Fellows include Brian Hie SM '19, PhD '21; Natasha Mary Jaques PhD '20; Martin Anton Schrimpf PhD '22; Lindsey Raymond SM '19, PhD '24, who will join the MIT faculty in EECS, the Department of Economics, and the MIT Schwarzman College of Computing in 2026; and Ellen Dee Zhong PhD ’22. AI2050 Senior Fellows include Surya Ganguli ’98, MNG ’98; and Luke Zettlemoyer SM ’03, PhD ’09. 

AI2050 Fellows are announced annually by Schmidt Sciences, a nonprofit organization founded in 2024 by Eric and Wendy Schmidt that works to accelerate scientific knowledge and breakthroughs with the most promising, advanced tools to support a thriving planet. The organization prioritizes research in areas poised for impact including AI and advanced computing, astrophysics, biosciences, climate, and space — as well as supporting researchers in a variety of disciplines through its science systems program. 

Li is postdoc in CSAIL working with associate professor of EECS Kaiming He. Li's research focuses on developing neural operator methods to accelerate scientific computing. He received his PhD in computing and mathematical sciences from Caltech, where he was advised by Anima Anandkumar and Andrew Stuart. He holds undergraduate degrees in computer science and mathematics from Washington University in St. Louis. 

Li's work has been supported by a Kortschak Scholarship, PIMCO Fellowship, Amazon AI4Science Fellowship, Nvidia Fellowship, and MIT-Novo Nordisk AI Fellowship. He has also completed three summer internships at Nvidia. Li will join the NYU Courant Institute of Mathematical Sciences as an assistant professor of mathematics and data science in fall 2026.

Smidt, associate professor of electrical engineering and computer science (EECS), is the principal investigator of the Atomic Architects group at the Research Laboratory of Electronics (RLE), where she works at the intersection of physics, geometry, and machine learning to design algorithms that aid in the understanding of physical systems under physical and geometric constraints, with applications to the design both of new materials and new molecules. She has a particular focus on symmetries present in 3D physical systems, such as rotation, translation, and reflection.

Smidt earned her BS in physics from MIT in 2012 and her PhD in physics from the University of California at Berkeley in 2018. Prior to joining the MIT EECS faculty in 2021, she was the 2018 Alvarez Postdoctoral Fellow in Computing Sciences at Lawrence Berkeley National Laboratory, and a software engineering intern on the Google Accelerated Sciences team, where she developed Euclidean symmetry equivariant neural networks that naturally handle 3D geometry and geometric tensor data. Besides the AI2050 fellowship, she has received an Air Force Office of Scientific Research Young Investigator Program award, the EECS Outstanding Educator Award, and a Transformative Research Fund award.

Conceived and co-chaired by Eric Schmidt and James Manyika, AI2050 is a philanthropic initiative aimed at helping to solve hard problems in AI. Within their research, each fellow will contend with the central motivating question of AI2050: “It’s 2050. AI has turned out to be hugely beneficial to society. What happened? What are the most important problems we solved and the opportunities and possibilities we realized to ensure this outcome?” 

Prognostic tool could help clinicians identify high-risk cancer patients

Using a versatile problem-solving framework, researchers show how early relapse in lymphoma patients influences their chance for survival.

Aggressive T-cell lymphoma is a rare and devastating form of blood cancer with a very low five-year survival rate. Patients often relapse after receiving initial therapy, making it especially challenging for clinicians to keep this destructive disease in check.

In a new study, researchers from MIT, in collaboration with researchers involved in the PETAL consortium at Massachusetts General Hospital, identified a practical and powerful prognostic marker that could help clinicians identify high-risk patients early, and potentially tailor treatment strategies to improve survival.

The team found that, when patients relapse within 12 months of initial therapy, their chances of survival decline dramatically. For these patients, targeted therapies might improve their chances for survival, compared to traditional chemotherapy, the researchers say.

According to their analysis, which used data collected from thousands of patients all over the world, the finding holds true across patient subgroups, regardless of the patient’s initial therapy or their score in a commonly used prognostic index.

A causal inference framework called Synthetic Survival Controls (SSC), developed as part of MIT graduate student Jessy (Xinyi) Han’s thesis, was central to this analysis. This versatile framework helps to answer “when-if” questions — to estimate how the timing of outcomes would shift under different interventions — while overcoming the limitations of inconsistent and biased data.

The identification of novel risk groups could guide clinicians as they select therapies to improve overall survival. For instance, a clinician might prioritize early-phase clinical trials over canonical therapies for this cohort of patients. The results could inform inclusion criteria for some clinical trials, according to the researchers.

The causal inference framework for survival analysis can also be applied more broadly. For instance, the MIT researchers have used it in areas like criminal justice to study how structural factors drive recidivism.

“Often we don’t only care about what will happen, but when the target event will happen. These when-if problems have remained under the radar for a long time, but they are common in a lot of domains. We’ve shown here that, to answer these questions with data, you need domain experts to provide insight and good causal inference methods to close the loop,” says Devavrat Shah, the Andrew and Erna Viterbi Professor in Electrical Engineering and Computer Science at MIT, a member of Institute for Data, Systems and Society (IDSS) and of the Laboratory for Information and Decision Systems (LIDS), and co-author of the study.

Shah is joined on the paper by many co-authors, including Han, who is co-advised by Shah and Fotini Christia, the Ford International Professor of the Social Sciences in the Department of Political Science and director of IDSS; and corresponding authors Mark N. Sorial, a clinical pharmacist and investigator at the Dana-Farber Cancer Institute, and Salvia Jain, a clinician-investigator at the Massachusetts General Hospital Cancer Center, founder of the global PETAL consortium, and an assistant professor of medicine at Harvard Medical School. The research appears today in the journal Blood.

Estimating outcomes

The MIT researchers have spent the past few years developing the Synthetic Survival Control causal inference framework, which enables them to answer complex “when-if” questions when using available data is statistically challenging. Their approach estimates when a target event happens if a certain intervention is used.

In this paper, the researchers investigated an aggressive cancer called nodal mature T-cell lymphoma, and whether a certain prognostic marker led to worse outcomes. The marker, TTR12, signifies that a patient relapsed within 12 months of initial therapy.

They applied their framework to estimate when a patient will die if they have TTR12, and how their survival trajectory would be different if they do not have this prognostic marker.

“No experiment can answer that question because we are asking about two outcomes for the same patient. We have to borrow information from other patients to estimate, counterfactually, what a patient’s survival outcome would have been,” Han explains.

Answering these types of questions is notoriously difficult due to biases in the available observational data. Plus, patient data gathered from an international cohort bring their own unique challenges. For instance, a clinical dataset often contains some historical data about a patient, but at some point the patient may stop treatment, leading to incomplete records.

In addition, if a patient receives a specific treatment, that might impact how long they will survive, adding to the complexity of the data. Plus, for each patient, the researchers only observe one outcome on how long the patient survives — limiting the amount of data available.

Such issues lead to suboptimal performance of many classical methods.

The Synthetic Survival Control framework can overcome these challenges. Even though the researchers don’t know all the details for each patient, their method stitches information from multiple other patients together in such a way that it can estimate survival outcomes.

Importantly, their method is robust to specific modeling assumptions, making it broadly applicable in practice. 

The power of prognostication

The researchers’ analysis revealed that TTR12 patients consistently had much greater risk of death within five years of initial therapy than patients without the marker. This was true no matter the initial therapy the patients received or which subgroup they fell into.

“This tells us that early relapse is a very important prognosis. This acts as a signal to clinicians so they can think about tailored therapies for these patients that can overcome resistance in second-line or third-line,” Han says.

Moving forward, the researchers are looking to expand this analysis to include high-dimensional genomics data. This information could be used to develop bespoke treatments that can avoid relapse within 12 months.

“Based on our work, there is already a risk calculation tool being used by clinicians. With more information, we can make it a richer tool that can provide more prognostic details,” Shah says.

They are also applying the framework to other domains.

For instance, in a paper recently presented at the Conference on Neural Information Processing Systems, the researchers identified a dramatic difference in the recidivism rate among prisoners of different races that begins about seven months after release. A possible explanation is the different access to long-term support by different racial groups. They are also investigating individuals’ decisions to leave insurance companies, while exploring other domains where the framework could generate actionable insights.

“Partnering with domain experts is crucial because we want to demonstrate that our methods are of value in the real world. We hope these tools can be used to positively impact individuals across society,” Han says.

This work was funded, in part, by Daiichi Sankyo, Secure Bio, Inc., Acrotech Biopharma, Kyowa Kirin, the Center for Lymphoma Research, the National Cancer Institute, Massachusetts General Hospital, the Reid Fund for Lymphoma Research, the American Cancer Society, and the Scarlet Foundation.

NIH Director Jay Bhattacharya visits MIT

In a conversation with Rep. Jake Auchincloss, Bhattacharya focused on the agency’s policy goals and funding practices.

National Institutes of Health (NIH) Director Jay Bhattacharya visited MIT on Friday, engaging in a wide-ranging discussion about policy issues and research aims at an event also featuring Rep. Jake Auchincloss MBA ’16 of Massachusetts.

The forum consisted of a dialogue between Auchincloss and Bhattacharya, followed by a question-and-answer session with an audience that included researchers from the greater Boston area. The event was part of a daylong series of stops Bhattacharya and Auchincloss made around Boston, a world-leading hub of biomedical research.

“I was joking with Dr. Bhattacharya that when the NIH director comes to Massachusetts, he gets treated like a celebrity, because we do science, and we take science very seriously here,” Auchincloss quipped at the outset.

Bhattacharya said he was “delighted” to be visiting, and credited the thousands of scientists who participate in peer review for the NIH. “The reason why the NIH succeeds is the willingness and engagement of the scientific community,” he said.

In response to an audience question, Bhattacharya also outlined his overall vision of the NIH’s portfolio of projects.

“You both need investments in ideas that are not tested, just to see if something works. You don’t know in advance,” he said. “And at the same time, you need an ecosystem that tests those ideas rigorously and winnows those ideas to the ones that actually work, that are replicable. A successful portfolio will have both elements in it.”

MIT President Sally A. Kornbluth gave opening remarks at the event, welcoming Bhattacharya and Auchincloss to campus and noting that the Institute’s earliest known NIH grant on record dates to 1948. In recent decades, biomedical research at MIT has boomed, expanding across a wide range of frontier fields.

Indeed, Kornbluth noted, MIT’s federally funded research projects during U.S. President Trump’s first term include a method for making anesthesia safer, especially for children and the elderly; a new type of expanding heart valve for children that eliminates the need for repeated surgeries; and a noninvasive Alzheimer’s treatment using sound and light stimulation, which is currently in clinical trials.

“Today, researchers across our campus pursue pioneering science on behalf of the American people, with profoundly important results,” Kornbluth said.

“The hospitals, universities, startups, investors, and companies represented here today have made greater Boston an extraordinary magnet for talent,” Kornbluth added. “Both as a force for progress in human health and an engine of economic growth, this community of talent is a precious national asset. We look forward to working with Dr. Bhattacharya to build on its strengths.”

The discussion occurred amid uncertainty about future science funding levels and pending changes in the NIH’s grant-review processes. The NIH has announced a “unified strategy” for reviewing grant applications that may lead to more direct involvement in grant decisions by directors of the 27 NIH institutes and centers, along with other changes that could shift the types of awards being made.

Auchincloss asked multiple questions about the ongoing NIH changes; about 10 audience members from a variety of institutions also posed a range of questions to Bhattacharya, often about the new grant-review process and the aims of the changes.

“The unified funding strategy is a way to allow institute direcors to look at the full range of scoring, including scores on innovation, and pick projects that look like they are promising,” Bhattacharya said in response to one of Auchincloss’ queries.

One audience member also emphasized concerns about the long-term effects of funding uncertainties on younger scientists in the U.S.

“The future success of the American biomedical enterprise depends on us training the next generation of scientists,” Bhattacharya acknowledged.

Bhattacharya is the 18th director of the NIH, having been confirmed by the U.S. Senate in March. He has served as a faculty member at Stanford University, where he received his BA, MA, MD, and PhD, and is currently a professor emeritus. During his career, Bhattacharya’s work has often examined the economics of health care, though his research has ranged broadly across topics, in over 170 published papers. He has also served as director of the Center on the Demography and Economics of Health and Aging at Stanford University.

Auchincloss is in his third term as the U.S. Representative to Congress from the 4th district in Massachusetts, having first been elected in 2020. He is also a major in the Marine Corps Reserve, and received his MBA from the MIT Sloan School of Management.

Ian Waitz, MIT’s vice president for research, concluded the session with a note of thanks to Auchincloss and Bhattacharya for their “visit to the greater Boston ecosystem which has done so much for so many and contributed obviously to the NIH mission that you articulated.” He added: “We have such a marvelous history in this region in making such great gains for health and longevity, and we’re here to do more to partner with you.”

When companies “go green,” air quality impacts can vary dramatically

Cutting air travel and purchasing renewable energy can lead to different effects on overall air quality, even while achieving the same CO2 reduction, new research shows.

Many organizations are taking actions to shrink their carbon footprint, such as purchasing electricity from renewable sources or reducing air travel.

Both actions would cut greenhouse gas emissions, but which offers greater societal benefits?

In a first step toward answering that question, MIT researchers found that even if each activity reduces the same amount of carbon dioxide emissions, the broader air quality impacts can be quite different.

They used a multifaceted modeling approach to quantify the air quality impacts of each activity, using data from three organizations. Their results indicate that air travel causes about three times more damage to air quality than comparable electricity purchases.

Exposure to major air pollutants, including ground-level ozone and fine particulate matter, can lead to cardiovascular and respiratory disease, and even premature death.

In addition, air quality impacts can vary dramatically across different regions. The study shows that air quality effects differ sharply across space because each decarbonization action influences pollution at a different scale. For example, for organizations in the northeast U.S., the air quality impacts of energy use affect the region, but the impacts of air travel are felt globally. This is because associated pollutants are emitted at higher altitudes.

Ultimately, the researchers hope this work highlights how organizations can prioritize climate actions to provide the greatest near-term benefits to people’s health.

“If we are trying to get to net zero emissions, that trajectory could have very different implications for a lot of other things we care about, like air quality and health impacts. Here we’ve shown that, for the same net zero goal, you can have even more societal benefits if you figure out a smart way to structure your reductions,” says Noelle Selin, a professor in the MIT Institute for Data, Systems, and Society (IDSS) and the Department of Earth, Atmospheric and Planetary Sciences (EAPS); director of the Center for Sustainability Science and Strategy; and senior author of the study.

Selin is joined on the paper by lead author Yuang (Albert) Chen, an MIT graduate student; Florian Allroggen, a research scientist in the MIT Department of Aeronautics and Astronautics; Sebastian D. Eastham, an associate professor in the Department of Aeronautics at Imperial College of London; Evan Gibney, an MIT graduate student; and William Clark, the Harvey Brooks Research Professor of International Science at Harvard University. The research was published Friday in Environmental Research Letters.

A quantification quandary

Climate scientists often focus on the air quality benefits of national or regional policies because the aggregate impacts are more straightforward to model.

Organizations’ efforts to “go green” are much harder to quantify because they exist within larger societal systems and are impacted by these national policies.

To tackle this challenging problem, the MIT researchers used data from two universities and one company in the greater Boston area. They studied whether organizational actions that remove the same amount of CO2 from the atmosphere would have an equivalent benefit on improving air quality.

“From a climate standpoint, CO2 has a global impact because it mixes through the atmosphere, no matter where it is emitted. But air quality impacts are driven by co-pollutants that act locally, so where those emissions occur really matters,” Chen says.

For instance, burning fossil fuels leads to emissions of nitrogen oxides and sulfur dioxide along with CO2. These co-pollutants react with chemicals in the atmosphere to form fine particulate matter and ground-level ozone, which is a primary component of smog.

Different fossil fuels cause varying amounts of co-pollutant emissions. In addition, local factors like weather and existing emissions affect the formation of smog and fine particulate matter. The impacts of these pollutants also depend on the local population distribution and overall health.

“You can’t just assume that all CO2-reduction strategies will have equivalent near-term impacts on sustainability. You have to consider all the other emissions that go along with that CO2,” Selin says.

The researchers used a systems-level approach that involved connecting multiple models. They fed the organizational energy consumption and flight data into this systems-level model to examine local and regional air quality impacts.

Their approach incorporated many interconnected elements, such as power plant emissions data, statistical linkages between air quality and mortality outcomes, and aviation emissions associated with specific flight routes. They fed those data into an atmospheric chemistry transport model to calculate air quality and climate impacts for each activity.

The sheer breadth of the system created many challenges.

“We had to do multiple sensitivity analyses to make sure the overall pipeline was working,” Chen says.

Analyzing air quality

At the end, the researchers monetized air quality impacts to compare them with the climate impacts in a consistent way. Monetized climate impacts of CO2 emissions based on prior literature are about $170 per ton (expressed in 2015 dollars), representing the financial cost of damages caused by climate change.

Using the same method as used to monetize the impact of CO2, the researchers calculated that air quality damages associated with electricity purchases are an additional $88 per ton of CO2, while the damages from air travel are an additional $265 per ton.

This highlights how the air quality impacts of a ton of emitted CO2 depend strongly on where and how the emissions are produced.

“A real surprise was how much aviation impacted places that were really far from these organizations. Not only were flights more damaging, but the pattern of damage, in terms of who is harmed by air pollution from that activity, is very different than who is harmed by energy systems,” Selin says.

Most airplane emissions occur at high altitudes, where differences in atmospheric chemistry and transport can amplify their air quality impacts. These emissions are also carried across continents by atmospheric winds, affecting people thousands of miles from their source.

Nations like India and China face outsized air quality impacts from such emissions due to the higher level of existing ground-level emissions, which exacerbates the formation of fine particulate matter and smog.

The researchers also conducted a deeper analysis of short-haul flights. Their results showed that regional flights have a relatively larger impact on local air quality than longer domestic flights.

“If an organization is thinking about how to benefit the neighborhoods in their backyard, then reducing short-haul flights could be a strategy with real benefits,” Selin says.

Even in electricity purchases, the researchers found that location matters.

For instance, fine particulate matter emissions from power plants caused by one university are in a densely populated region, while emissions caused by the corporation fall over less populated areas.

Due to these population differences, the university’s emissions resulted in 16 percent more estimated premature deaths than those of the corporation, even though the climate impacts are identical.

“These results show that, if organizations want to achieve net zero emissions while promoting sustainability, which unit of CO2 gets removed first really matters a lot,” Chen says.

In the future, the researchers want to quantify the air quality and climate impacts of train travel, to see whether replacing short-haul flights with train trips could provide benefits.

They also want to explore the air quality impacts of other energy sources in the U.S., such as data centers.

This research was funded, in part, by Biogen, Inc., the Italian Ministry for Environment, Land, and Sea, and the MIT Center for Sustainability Science and Strategy. 

Paula Hammond named dean of the School of Engineering

A chemical engineer who now serves as executive vice provost, Hammond will succeed Anantha Chandrakasan.

Paula Hammond ’84, PhD ’93, an Institute Professor and MIT’s executive vice provost, has been named dean of MIT’s School of Engineering, effective Jan. 16. She will succeed Anantha Chandrakasan, the Vannevar Bush Professor of Electrical Engineering and Computer Science, who was appointed MIT’s provost in July.

Hammond, who was head of the Department of Chemical Engineering from 2015 to 2023, has also served as MIT’s vice provost for faculty. She will be the first woman to hold the role of dean of MIT’s School of Engineering.

“From the rigor and creativity of her scientific work to her outstanding record of service to the Institute, Paula Hammond represents the very best of MIT,” says MIT President Sally Kornbluth. “Wise, thoughtful, down-to-earth, deeply curious, and steeped in MIT’s culture and values, Paula will be a highly effective leader for the School of Engineering. I’m delighted she accepted this new challenge.”

Hammond, who is also a member of MIT’s Koch Institute for Integrative Cancer Research, has earned many accolades for her work developing polymers and nanomaterials that can be used for applications including drug delivery, regenerative medicine, noninvasive imaging, and battery technology.

Chandrakasan announced Hammond’s appointment today in an email to the MIT community, writing, “Ever since enrolling at MIT as an undergraduate, Paula has built a remarkable record of accomplishment in scholarship, teaching, and service. Faculty, staff, and students across the Institute praise her wisdom, selflessness, and kindness, especially when it comes to enabling others’ professional growth and success.”

“Paula is a scholar of extraordinary distinction. It is hard to overstate the value of the broad contributions she has made in her field, which have significantly expanded the frontiers of knowledge,” Chandrakasan told MIT News. “Any one of her many achievements could stand as the cornerstone of an outstanding academic career. In addition, her investment in mentoring the next generation of scholars and building community is unparalleled.”

Chandrakasan also thanked Professor Maria Yang, who has served as the school’s interim dean in recent months. “In a testament to her own longstanding contributions to the School of Engineering, Maria took on the deanship even while maintaining leadership roles with the Ideation Lab, D-Lab, and Morningside Academy for Design. For her excellent service and leadership, Maria deserves our deep appreciation,” he wrote to the community.

Building a sense of community

Throughout her career at MIT, Hammond has helped to create a supportive environment in which faculty and students can do their best work. As vice provost for faculty, a role Hammond assumed in 2023, she developed and oversaw new efforts to improve faculty recruitment and retention, mentoring, and professional development. Earlier this year, she took on additional responsibilities as executive vice provost, providing guidance and oversight for a number of Institute-wide initiatives.

As head of the Department of Chemical Engineering, Hammond worked to strengthen the department’s sense of community and initiated a strategic planning process that led to more collaborative research between faculty members. Under her leadership, the department also launched a major review of its undergraduate curriculum and introduced more flexibility into the requirements for a chemical engineering degree.

Another major priority was ensuring that faculty had the support they needed to pursue new research goals. To help achieve that, she established and raised funds for a series of Faculty Research Innovation Fund grants for mid-career faculty who wanted to explore fresh directions.

“I really enjoyed enabling faculty to explore new areas, finding ways to resource them, making sure that they had the right mentoring early in their career and the ‘wind beneath their wings’ that they needed to get where they wanted to go,” she says. “That, to me, was extremely fulfilling.”

Before taking on her official administrative roles, Hammond served the Institute through her work chairing committees that contributed landmark reports on gender and race at MIT: the Initiative for Faculty Race and Diversity and the Academic and Organizational Relationships Working Group.

In her new role as dean, Hammond plans to begin by consulting with faculty across the School of Engineering to learn more about their needs.

“I like to start with conversations,” she says. “I’m very excited about the idea of visiting each of the departments, finding out what’s on the minds of the faculty, and figuring out how we can meaningfully address their needs and continue to build and grow an excellent engineering program.”

One of her goals is to promote greater cross-disciplinarity in MIT’s curriculum, in part by encouraging and providing resources for faculty to develop more courses that bridge multiple departments.

“There are some barriers that exist between departments, because we all need to teach our core requirements,” she says. “I am very interested in collaborating with departments to think about how we can lower barriers to allow faculty to co-teach, or to perhaps look at different course structures that allow us to teach a core component and then have it branch to a more specialized component.”

She also hopes to guide MIT’s engineering departments in finding ways to incorporate artificial intelligence into their curriculum, and to give students greater opportunity for relevant hands-on experiences in engineering.

“I am particularly excited to build from the strong cross-disciplinary efforts and the key strategic initiatives that Anantha launched during his time as dean,” Hammond says. “I believe we have incredible opportunities to build off these critical areas at the interfaces of science, engineering, the humanities, arts, design, and policy, and to create new emergent fields. MIT should be the leader in providing educational foundations that prepare our students for a highly interdisciplinary and AI-enabled world, and a setting that enables our researchers and scholars to solve the most difficult and urgent problems of the world.”

A pioneer in nanotechnology

Hammond grew up in Detroit, where her father was a PhD biochemist who ran the health laboratories for the city of Detroit. Her mother founded a nursing school at Wayne County Community College, and both parents encouraged her interest in science. As an undergraduate at MIT, she majored in chemical engineering with a focus on polymer chemistry.

After graduating in 1984, Hammond spent two years working as a process engineer at Motorola, then earned a master’s degree in chemical engineering from Georgia Tech. She realized that she wanted to pursue a career in academia, and returned to MIT to earn a PhD in polymer science technology. After finishing her degree in 1993, she spent a year and a half as a postdoc at Harvard University before joining the MIT faculty in 1995.

She became a full professor in 2006, and in 2021, she was named an Institute Professor, the highest honor bestowed by MIT. In 2010, Hammond joined MIT’s Koch Institute for Integrative Cancer Research, where she leads a lab that is developing novel nanomaterials a variety of applications, with a primary focus on treatments and diagnostics for ovarian cancer.

Early in her career, Hammond developed a technique for generating functional thin-film materials by stacking layers of charged polymeric materials. This approach can be used to build polymers with highly controlled architectures by alternately exposing a surface to positively and negatively charged particles.

She has used this layer-by-layer assembly technique to build ultrathin batteries, fuel cell electrodes, and drug delivery nanoparticles that can be specifically targeted to cancer cells. These particles can be tailored to carry chemotherapy drugs such as cisplatin, immunotherapy agents, or nucleic acids such as messenger RNA.

In recognition of her pioneering research, Hammond was awarded the 2024 National Medal of Technology and Innovation. She was also the 2023-24 recipient of MIT’s Killian Award, which honors extraordinary professional achievements by an MIT faculty member. Her many other awards include the Benjamin Franklin Medal in Chemistry in 2024, the ACS Award in Polymer Science in 2018, the American Institute of Chemical Engineers Charles M. A. Stine Award in Materials Engineering and Science in 2013, and the Ovarian Cancer Research Program Teal Innovator Award in 2013.

Hammond has also been honored for her dedication to teaching and mentoring. As a reflection of her excellence in those areas, she was awarded the Irwin Sizer Award for Significant Improvements to MIT Education, the Henry Hill Lecturer Award in 2002, and the Junior Bose Faculty Award in 2000. She also co-chaired the recent Ad Hoc Committee on Faculty Advising and Mentoring, and has been selected as a “Committed to Caring” honoree for her work mentoring students and postdocs in her research group.

Hammond has served on the President’s Council of Advisors on Science and Technology, as well as the U.S. Secretary of Energy Scientific Advisory Board, the NIH Center for Scientific Review Advisory Council, and the Board of Directors of the American Institute of Chemical Engineers. Additionally, she is one of a small group of scientists who have been elected to the National Academies of Engineering, Sciences, and Medicine.

MADMEC winners develop spray-on coating to protect power lines from ice

Placing first in the MADMEC innovation contest, the MITten team aims to curb costly power outages during winter storms.

A spray-on coating to keep power lines standing through an ice storm may not be the obvious fix for winter outages — but it’s exactly the kind of innovation that happens when MIT students tackle a sustainability challenge.

“The big threat to the power line network is winter icing that causes huge amounts of downed lines every year,” says Trevor Bormann, a graduate student in MIT’s Department of Materials Science and Engineering (DMSE) and member of MITten, the winning team in the 2025 MADMEC innovation contest. Fixing those outages is hugely carbon-intensive, requiring diesel-powered equipment, replacement materials, and added energy use. And as households switch to electric heat pumps, the stakes of a prolonged outage rise.

To address the challenge, the team developed a specialized polymer coating that repels water and can be sprayed onto aluminum power lines. The coating contains nanofillers — particles hundreds of times smaller than a human hair — that give the surface a texture that makes water bead and drip off.

The effect is known as “superhydrophobicity,” says Shaan Jagani, a graduate student in the Department of Aeronautics and Astronautics. “And what that really means is water does not stay on the surface, and therefore water will not have the opportunity to nucleate down into ice.”

MITten — pronounced “mitten” — won the $10,000 first prize in the contest, hosted by DMSE on Nov. 10 at MIT, where audience presentations and poster sessions capped months of design and experimentation. Since 2007, MADMEC (the Making and Designing Materials Engineering Contest), funded by Dow and Saint-Gobain, has given students a chance to tackle real-world sustainability challenges, with each team receiving $1,000 to build and test their projects. Judges evaluated the teams’ work from conception to prototype.

MADMEC winners have gone on to succeed in major innovation competitions such as MassChallenge, and at least six startups — including personal cooling wristband maker Embr and vehicle-motion-control company ClearMotion — trace their roots to the contest.

Cold inspiration

The idea for the MITten project came in part from Bormann’s experience growing up in South Dakota, where winter outages were common. His home was heated by natural gas, but if grid-reliant heat pumps had warmed it in negative-zero winter months, a days-long outage would have been “really rough.”

“I love the part of sustainability that is focused on developing all these new technologies for electricity generation and usage, but also the distribution side of it shouldn’t be neglected, either,” Bormann says. “It’s important for all those to be growing synergistically, and to be paying attention to all aspects of it.”

And there’s an opportunity to make distribution infrastructure more durable: An estimated 50,000 miles of new power lines are planned over the next decade in the northern United States, where icing is a serious risk.

To test their coating, the team built an icing chamber to simulate rain and freezing conditions, comparing coated versus uncoated aluminum samples at –10 degrees Celsius (14 degrees Fahrenheit). They also dipped samples in liquid nitrogen to evaluate performance in extreme cold and simulated real-world stresses such as lines swaying in windstorms.

“We basically coated aluminum substrates and then bent them to demonstrate that the coating itself could accommodate very long strains,” Jagani says.

The team ran simulations to estimate that a typical outage affecting 20 percent of a region could cost about $7 million to repair. “But if you fully coat, say, 1,000 kilometers of line, you actually can save $1 million in just material costs,” says DMSE grad student Matthew Michalek. The team hopes to further refine the coating with more advanced materials and test them in a professional icing chamber.

Amber Velez, a graduate student in the Department of Mechanical Engineering, stressed the parameters of the contest — working within a $1,000 budget.

“I feel we did quite good work with quite a lot of legitimacy, but I think moving on, there is a lot of space that we could have more play in,” she says. “We’ve definitely not hit the ceiling yet, and I think there’s a lot of room to keep growing.”

Compostable electrodes, microwavable ceramics

The second-place, $6,000 prize went to Electrodiligent, which is designing a biodegradable, compostable alternative to electrodes used for heart monitoring. Their prototype uses a cellulose paper backing and a conductive gel made from gelatin, glycerin, and sodium chloride to carry the electric signal.

Comparing electrocardiogram (ECG) results, the team found their electrodes performed similarly to the 3M Red Dot standard. “We’re very optimistic about this result,” says Ethan Frey, a DMSE graduate student.

The invention aims to cut into the 3.6 tons of medical waste produced each day, but judges noted that adhesive electrodes are almost always incinerated for health and safety reasons, making the intended application a tough fit.

“But there’s a whole host of other directions the team could go in,” says Mike Tarkanian, senior lecturer in DMSE and coordinator of MADMEC.

The $4,000 third prize went to Cerawave, a team made up of mostly undergraduates and a member the team jokingly called a “token grad student,” working to make ceramics in an ordinary kitchen microwave. Traditional ceramic manufacturing requires high-temperature kilns, a major source of energy use and carbon emissions. Cerawave added silicon carbide to their ceramic mix to help it absorb microwave energy and fuse into a durable final product.

“We threw it on the ground a few times, and it didn’t break,” says Merrill Chiang, a junior in DMSE, drawing laughs from the audience. The team now plans to refine their recipe and overall ceramic-making process so that hobbyists — and even users in environments like the International Space Station — could create ceramic parts “without buying really expensive furnaces.”

The power of student innovation

Although it didn’t earn a prize, the contest’s most futuristic project was ReForm Designs, which aims to make reusable children’s furniture — expensive and quickly outgrown — from modular blocks made of mycelium, the root-like, growth-driving part of a mushroom. The team showed they could successfully produce mycelium blocks, but slow growth and sensitivity to moisture and temperature meant they didn’t yet have full furniture pieces to show judges.

The project still impressed DMSE senior David Miller, who calls the blocks “really intriguing,” with potential applications beyond furniture in manufacturing, construction, and consumer products.

“They adapt to the way we consume products, where a lot of us use products for one, two, three years before we throw them out,” Miller says. “Their capacity to be fully biodegradable and molded into any shape fills the need for certain kinds of additive manufacturing that requires certain shapes, while also being extremely sustainable.”

While the contest has produced successful startups, Tarkanian says MADMEC’s original goal — giving students a chance to get their hands dirty and pursue their own ideas — is thriving 18 years on, especially at a time when research budgets are being cut and science is under scrutiny.

“It gives students an opportunity to make things that are real and impactful to society,” he says. “So when you can build a prototype and say, ‘This is going to save X millions of dollars or X million pounds of waste,’ that value is obvious to everyone.”

Attendee Jinsung Kim, a postdoc in mechanical engineering, echoed Tarkanian’s comments, emphasizing the space set aside for innovative thinking.

“MADMEC creates the rare environment where students can experiment boldly, validate ideas quickly, and translate core scientific principles into solutions with real societal impact. To move society forward, we have to keep pushing the boundaries of technology and fundamental science,” he says.

MIT researchers “speak objects into existence” using AI and robotics

The speech-to-reality system combines 3D generative AI and robotic assembly to create objects on demand.

Generative AI and robotics are moving us ever closer to the day when we can ask for an object and have it created within a few minutes. In fact, MIT researchers have developed a speech-to-reality system, an AI-driven workflow that allows them to provide input to a robotic arm and “speak objects into existence,” creating things like furniture in as little as five minutes.  

With the speech-to-reality system, a robotic arm mounted on a table is able to receive spoken input from a human, such as “I want a simple stool,” and then construct the objects out of modular components. To date, the researchers have used the system to create stools, shelves, chairs, a small table, and even decorative items such as a dog statue.

“We’re connecting natural language processing, 3D generative AI, and robotic assembly,” says Alexander Htet Kyaw, an MIT graduate student and Morningside Academy for Design (MAD) fellow. “These are rapidly advancing areas of research that haven’t been brought together before in a way that you can actually make physical objects just from a simple speech prompt.”  

The idea started when Kyaw — a graduate student in the departments of Architecture and Electrical Engineering and Computer Science — took Professor Neil Gershenfeld’s course, “How to Make Almost Anything.” In that class, he built the speech-to-reality system. He continued working on the project at the MIT Center for Bits and Atoms (CBA), directed by Gershenfeld, collaborating with graduate students Se Hwan Jeon of the Department of Mechanical Engineering and Miana Smith of CBA.

The speech-to-reality system begins with speech recognition that processes the user’s request using a large language model, followed by 3D generative AI that creates a digital mesh representation of the object, and a voxelization algorithm that breaks down the 3D mesh into assembly components.

After that, geometric processing modifies the AI-generated assembly to account for fabrication and physical constraints associated with the real world, such as the number of components, overhangs, and connectivity of the geometry. This is followed by creation of a feasible assembly sequence and automated path planning for the robotic arm to assemble physical objects from user prompts.

By leveraging natural language, the system makes design and manufacturing more accessible to people without expertise in 3D modeling or robotic programming. And, unlike 3D printing, which can take hours or days, this system builds within minutes.

“This project is an interface between humans, AI, and robots to co-create the world around us,” Kyaw says. “Imagine a scenario where you say ‘I want a chair,’ and within five minutes a physical chair materializes in front of you.”

The team has immediate plans to improve the weight-bearing capability of the furniture by changing the means of connecting the cubes from magnets to more robust connections. 

“We’ve also developed pipelines for converting voxel structures into feasible assembly sequences for small, distributed mobile robots, which could help translate this work to structures at any size scale,” Smith says.

The purpose of using modular components is to eliminate the waste that goes into making physical objects by disassembling and then reassembling them into something different, for instance turning a sofa into a bed when you no longer need the sofa.

Because Kyaw also has experience using gesture recognition and augmented reality to interact with robots in the fabrication process, he is currently working on incorporating both speech and gestural control into the speech-to-reality system.

Leaning into his memories of the replicator in the “Star Trek” franchise and the robots in the animated film “Big Hero 6,” Kyaw explains his vision.

“I want to increase access for people to make physical objects in a fast, accessible, and sustainable manner,” he says. “I’m working toward a future where the very essence of matter is truly in your control. One where reality can be generated on demand.”

The team presented their paper “Speech to Reality: On-Demand Production using Natural Language, 3D Generative AI, and Discrete Robotic Assembly” at the Association for Computing Machinery (ACM) Symposium on Computational Fabrication (SCF ’25) held at MIT on Nov. 21. 

Cultivating confidence and craft across disciplines

Professors Rohit Karnik and Nathan Wilmers are honored as “Committed to Caring.”

Both Rohit Karnik and Nathan Wilmers personify the type of mentorship that any student would be fortunate to receive — one rooted in intellectual rigor and grounded in humility, empathy, and personal support. They show that transformative academic guidance is not only about solving research problems, but about lifting up the people working on them.

Whether it’s Karnik’s quiet integrity and commitment to scientific ethics, or Wilmers’ steadfast encouragement of his students in the face of challenges, both professors cultivate spaces where students are not only empowered to grow as researchers, but affirmed as individuals. Their mentees describe feeling genuinely seen and supported; mentored not just in theory or technique, but in resilience. It’s this attention to the human element that leaves a lasting impact.

Professors Karnik and Wilmers are two of the 2023–25 Committed to Caring cohort who are cultivating confidence and craft across disciplines. For MIT graduate students, the Committed to Caring program recognizes those who go above and beyond.

Rohit Karnik: Rooted in rigor, guided by care

Rohit Karnik is Abdul Latif Jameel Professor in the Department of Mechanical Engineering at MIT, where he leads the Microfluidics and Nanofluidics Research Group and serves as director of the Abdul Latif Jameel Water and Food Systems Lab (J-WAFS). His research explores the physics of micro- and nanofluidic flows and systems. Applications of his work include the development of water filters, portable diagnostic tools, and sensors for environmental monitoring. 

Karnik is genuinely excited about his students’ ideas, and open to their various academic backgrounds. He validates students by respecting their research, encouraging them to pursue their interests, and showing enthusiasm for their exploration within mechanical engineering and beyond.

One student reflected on the manner in which Karnik helped them feel more confident in their academic journey. When a student from a non-engineering field joined the mechanical engineering graduate program, Karnik never viewed their background as a barrier to success. The student wrote, “from the start, he was enthusiastic about my interdisciplinarity and the perspective I could bring to the lab.”

He allowed the student to take remedial undergraduate classes to learn engineering basics, provided guidance on leveraging their previous academic background, and encouraged them to write grants and apply for fellowships that would support their interdisciplinary work. In addition to these concrete supports, Karnik also provided the student with the freedom to develop their own ideas, offering constructive, realistic feedback on what was attainable. 

“This transition took time, and Karnik honored that, prioritizing my growth in a completely new field over getting quick results,” the nominator reflected. Ultimately, Karnik’s mentorship, patience, and thoughtful encouragement led the student to excel in the engineering field.

Karnik encourages his advisees to explore their interests in mechanical engineering and beyond. This holistic approach extends beyond academics and into Karnik’s view of his students as whole individuals. One student wrote that he treats them as complete humans, with ambitions, aspirations, and passions worthy of his respect and consideration — and remains truly selfless in his commitment to their growth and success.

Karnik emphasizes that “it’s important to have dreams,” regularly encouraging his mentees to take advantage of opportunities that align with their goals and values. This sentiment is felt deeply by his students, with one nominator sharing that Karnik “encourag[ed] me to think broadly and holistically about my life, which has helped me structure and prioritize my time at MIT.”

Nathan Wilmers: Cultivating confidence, craft, and care

Nathan Wilmers is the Sarofim Family Career Development Associate Professor of Work and Organizations at MIT Sloan School of Management. His research spans wage and earnings inequality, economic sociology, and the sociology of labor. He is also affiliated with the Institute for Work and Employment Research, and the Economic Sociology program at Sloan. Wilmers studies wage and earnings inequality, economic sociology, and the sociology of labor, bringing insights from economic sociology to the study of labor markets and the wage structure.

A remarkable mentor, Wilmers is known for guiding his students through different projects while also teaching them more broadly about the system of academia. As one nominator illustrates, “he … helped me learn the ‘tacit’ knowledge to understand how to write a paper,” while also emphasizing the learning process of the PhD as a whole, and never reprimanding any mistakes along the way. 

Students say that Wilmers “reassures us that making mistakes is a natural part of the learning process and encourages us to continuously check, identify, and rectify them.” He welcomes all questions without judgment, and generously invests his time and patience in teaching students.

Wilmers is a strong advocate for his students, both academically and personally. He emphasizes the importance of learning, growth, and practical experience, rather than solely focusing on scholarly achievements and goals. Students feel this care, describing “an environment that maximizes learning opportunities and fosters the development of skills,” allowing them to truly collaborate rather than simply aim for the “right” answers.

In addition to his role in the classroom and lab, Wilmers also provides informal guidance to advisees, imparting valuable knowledge about the academic system, emphasizing the significance of networking, and sharing insider information. 

“Nate’s down-to-earth nature is evident in his accessibility to students,” expressed one nominator, who wrote that “sometimes we can freely approach his office without an appointment and receive valuable advice on both work-related and personal matters.” Moreover, Wilmers prioritizes his advisees’ career advancement, dedicating a substantial amount of time to providing feedback on thesis projects, and even encouraging students to take a lead in publishing research.

True mentorship often lies in the patient, careful transmission of craft — the behind-the-scenes work that forms the backbone of rigorous research. “I care about the details,” says Wilmers, reflecting a philosophy shaped by his own graduate advisors. Wilmers’ mentors instilled in him a deep respect for the less-glamorous but essential elements of scholarly work: data cleaning, thoughtful analysis, and careful interpretation. These technical and analytical skills are where real learning happens, he believes. 

By modeling this approach with his own students, Wilmers creates a culture where precision and discipline are valued just as much as innovation. His mentorship is grounded in the belief that becoming a good researcher requires not just vision, but also an intimate understanding of process — of how ideas are sharpened through methodical practice, and how impact comes from doing the small things well. His thoughtful, detail-oriented mentorship leaves a lasting impression on his students.

A nominator acclaimed, “Nate’s strong enthusiasm for my research, coupled with his expressed confidence and affirmation of its value, served as a significant source of motivation for me to persistently pursue my ideas.”

Robots that spare warehouse workers the heavy lifting

Founded by MIT alumni, the Pickle Robot Company has developed machines that can autonomously load and unload trucks inside warehouses and logistic centers.

There are some jobs human bodies just weren’t meant to do. Unloading trucks and shipping containers is a repetitive, grueling task — and a big reason warehouse injury rates are more than twice the national average.

The Pickle Robot Company wants its machines to do the heavy lifting. The company’s one-armed robots autonomously unload trailers, picking up boxes weighing up to 50 pounds and placing them onto onboard conveyor belts for warehouses of all types.

The company name, an homage to The Apple Computer Company, hints at the ambitions of founders AJ Meyer ’09, Ariana Eisenstein ’15, SM ’16, and Dan Paluska ’97, SM ’00. The founders want to make the company the technology leader for supply chain automation.

The company’s unloading robots combine generative AI and machine-learning algorithms with sensors, cameras, and machine-vision software to navigate new environments on day one and improve performance over time. Much of the company’s hardware is adapted from industrial partners. You may recognize the arm, for instance, from car manufacturing lines — though you may not have seen it in bright pickle-green.

The company is already working with customers like UPS, Ryobi Tools, and Yusen Logistics to take a load off warehouse workers, freeing them to solve other supply chain bottlenecks in the process.

“Humans are really good edge-case problem solvers, and robots are not,” Paluska says. “How can the robot, which is really good at the brute force, repetitive tasks, interact with humans to solve more problems? Human bodies and minds are so adaptable, the way we sense and respond to the environment is so adaptable, and robots aren’t going to replace that anytime soon. But there’s so much drudgery we can get rid of.”

Finding problems for robots

Meyer and Eisenstein majored in computer science and electrical engineering at MIT, but they didn’t work together until after graduation, when Meyer started the technology consultancy Leaf Labs, which specializes in building embedded computer systems for things like robots, cars, and satellites.

“A bunch of friends from MIT ran that shop,” Meyer recalls, noting it’s still running today. “Ari worked there, Dan consulted there, and we worked on some big projects. We were the primary software and digital design team behind Project Ara, a smartphone for Google, and we worked on a bunch of interesting government projects. It was really a lifestyle company for MIT kids. But 10 years go by, and we thought, ‘We didn’t get into this to do consulting. We got into this to do robots.’”

When Meyer graduated in 2009, problems like robot dexterity seemed insurmountable. By 2018, the rise of algorithmic approaches like neural networks had brought huge advances to robotic manipulation and navigation.

To figure out what problem to solve with robots, the founders talked to people in industries as diverse as agriculture, food prep, and hospitality. At some point, they started visiting logistics warehouses, bringing a stopwatch to see how long it took workers to complete different tasks.

“In 2018, we went to a UPS warehouse and watched 15 guys unloading trucks during a winter night shift,” Meyer recalls. “We spoke to everyone, and not a single person had worked there for more than 90 days. We asked, ‘Why not?’ They laughed at us. They said, ‘Have you tried to do this job before?’”

It turns out warehouse turnover is one of the industry’s biggest problems, limiting productivity as managers constantly grapple with hiring, onboarding, and training.

The founders raised a seed funding round and built robots that could sort boxes because it was an easier problem that allowed them to work with technology like grippers and barcode scanners. Their robots eventually worked, but the company wasn’t growing fast enough to be profitable. Worse yet, the founders were having trouble raising money.

“We were desperately low on funds,” Meyer recalls. “So we thought, ‘Why spend our last dollar on a warm-up task?’”

With money dwindling, the founders built a proof-of-concept robot that could unload trucks reliably for about 20 seconds at a time and posted a video of it on YouTube. Hundreds of potential customers reached out. The interest was enough to get investors back on board to keep the company alive.

The company piloted its first unloading system for a year with a customer in the desert of California, sparing human workers from unloading shipping containers that can reach temperatures up to 130 degrees in the summer. It has since scaled deployments with multiple customers and gained traction among third-party logistics centers across the U.S.

The company’s robotic arm is made by the German industrial robotics giant KUKA. The robots are mounted on a custom mobile base with an onboard computing systems so they can navigate to docks and adjust their positions inside trailers autonomously while lifting. The end of each arm features a suction gripper that clings to packages and moves them to the onboard conveyor belt.

The company’s robots can pick up boxes ranging in size from 5-inch cubes to 24-by-30 inch boxes. The robots can unload anywhere from 400 to 1,500 cases per hour depending on size and weight. The company fine tunes pre-trained generative AI models and uses a number of smaller models to ensure the robot runs smoothly in every setting.

The company is also developing a software platform it can integrate with third-party hardware, from humanoid robots to autonomous forklifts.

“Our immediate product roadmap is load and unload,” Meyer says. “But we’re also hoping to connect these third-party platforms. Other companies are also trying to connect robots. What does it mean for the robot unloading a truck to talk to the robot palletizing, or for the forklift to talk to the inventory drone? Can they do the job faster? I think there’s a big network coming in which we need to orchestrate the robots and the automation across the entire supply chain, from the mines to the factories to your front door.”

“Why not us?”

The Pickle Robot Company employs about 130 people in its office in Charlestown, Massachusetts, where a standard — if green — office gives way to a warehouse where its robots can be seen loading boxes onto conveyor belts alongside human workers and manufacturing lines.

This summer, Pickle will be ramping up production of a new version of its system, with further plans to begin designing a two-armed robot sometime after that.

“My supervisor at Leaf Labs once told me ‘No one knows what they’re doing, so why not us?’” Eisenstein says. “I carry that with me all the time. I’ve been very lucky to be able to work with so many talented, experienced people in my career. They all bring their own skill sets and understanding. That’s a massive opportunity — and it’s the only way something as hard as what we’re doing is going to work.”

Moving forward, the company sees many other robot-shaped problems for its machines.

“We didn’t start out by saying, ‘Let’s load and unload a truck,’” Meyers says. “We said, ‘What does it take to make a great robot business?’ Unloading trucks is the first chapter. Now we’ve built a platform to make the next robot that helps with more jobs, starting in logistics but then ultimately in manufacturing, retail, and hopefully the entire supply chain.”

Alternate proteins from the same gene contribute differently to health and rare disease

New findings may help researchers identify genetic mutations that contribute to rare diseases, by studying when and how single genes produce multiple versions of proteins.



MIT School of Engineering faculty and staff receive awards in summer 2025

Faculty members and researchers were honored in recognition of their scholarship, service, and overall excellence.

Each year, faculty and researchers across the MIT School of Engineering are recognized with prestigious awards for their contributions to research, technology, society, and education. To celebrate these achievements, the school periodically highlights select honors received by members of its departments, institutes, labs, and centers. The following individuals were recognized in summer 2025:

Iwnetim Abate, the Chipman Career Development Professor and assistant professor in the Department of Materials Science and Engineering, was honored as one of MIT Technology Review’s 2025 Innovators Under 35. He was recognized for his research on sodium-ion batteries and ammonia production.

Daniel G. Anderson, the Joseph R. Mares (1924) Professor in the Department of Chemical Engineering and the Institute of Medical Engineering and Science (IMES), received the 2025 AIChE James E. Bailey Award. The award honors outstanding contributions in biological engineering and commemorates the pioneering work of James Bailey.

Regina Barzilay, the School of Engineering Distinguished Professor for AI and Health in the Department of Electrical Engineering and Computer Science (EECS), was named to Time’s AI100 2025 list, recognizing her groundbreaking work in AI and health.

Richard D. Braatz, the Edwin R. Gilliland Professor in the Department of Chemical Engineering, received the 2025 AIChE CAST Distinguished Service Award. The award recognizes exceptional service and leadership within the Computing and Systems Technology Division of AIChE.

Rodney Brooks, the Panasonic Professor of Robotics, Emeritus in the Department of Electrical Engineering and Computer Science, was elected to the National Academy of Sciences, one of the highest honors in scientific research.

Arup K. Chakraborty, the John M. Deutch (1961) Institute Professor in the Department of Chemical Engineering and IMES, received the 2025 AIChE Alpha Chi Sigma Award. This award honors outstanding accomplishments in chemical engineering research over the past decade.

Connor W. Coley, the Class of 1957 Career Development Professor and associate professor in the departments of Chemical Engineering and EECS, received the 2025 AIChE CoMSEF Young Investigator Award for Modeling and Simulation. The award recognizes outstanding research in computational molecular science and engineering. Coley was also one of 74 highly accomplished, early-career engineers selected to participate in the Grainger Foundation Frontiers of Engineering Symposium, a signature activity of the National Academy of Engineering.

Henry Corrigan-Gibbs, the Douglas Ross (1954) Career Development Professor of Software Technology and associate professor in the Department of EECS, received the Google ML and Systems Junior Faculty Award, presented to assistant professors who are leading the analysis, design and implementation of efficient, scalable, secure, and trustworthy computing systems.

Christina Delimitrou, the KDD Career Development Professor in Communications and Technology and associate professor in the Department of EECS, received the Google ML and Systems Junior Faculty Award. The award supports assistant professors advancing scalable and trustworthy computing systems for machine learning and cloud computing. Delimitrou also received the Google ML and Systems Junior Faculty Award, presented to assistant professors who are leading the analysis, design, and implementation of efficient, scalable, secure, and trustworthy computing systems.

Priya Donti, the Silverman (1968) Family Career Development Professor and assistant professor in the Department of EECS, was named to Time’s AI100 2025 list, which honors innovators reshaping the world through artificial intelligence.

Joel Emer, a professor of the practice in the Department of EECS, received the Alan D. Berenbaum Distinguished Service Award from ACM SIGARCH. He was honored for decades of mentoring and leadership in the computer architecture community.

Roger Greenwood Mark, the Distinguished Professor of Health Sciences and Technology, Emeritus in IMES, received the IEEE Biomedical Engineering Award for leadership in ECG signal processing and global dissemination of curated biomedical and clinical databases, thereby accelerating biomedical research worldwide.

Ali Jadbabaie, the JR East Professor and head of the Department of Civil and Environmental Engineering, received the 2025 Multidisciplinary University Research Initiative (MURI) award for research projects in areas of critical importance to national defense.

Yoon Kim, associate professor in the Department of EECS, received the Google ML and Systems Junior Faculty Award, presented to assistant professors who are leading the analysis, design, and implementation of efficient, scalable, secure, and trustworthy computing systems.

Mathias Kolle, an associate professor in the Department of Mechanical Engineering, received the 2025 Multidisciplinary University Research Initiative (MURI) award for research projects in areas of critical importance to national defense.

Muriel Médard, the NEC Professor of Software Science and Engineering in the Department of EECS, was elected an International Fellow of the United Kingdom's Royal Academy of Engineering. The honor recognizes exceptional contributions to engineering and technology across sectors.

Pablo Parrilo, the Joseph F. and Nancy P. Keithley Professor in Electrical Engineering in the Department of EECS, received the 2025 INFORMS Computing Society Prize. The award honors outstanding contributions at the interface of computing and operations research. Parrilo was recognized for pioneering work on accelerating gradient descent through stepsize hedging, introducing concepts such as Silver Stepsizes and recursive gluing.

Nidhi Seethapathi, the Frederick A. (1971) and Carole J. Middleton Career Development Professor of Neuroscience and assistant professor in the Department of EECS, was named to MIT Technology Review’s “2025 Innovators Under 35” list. The honor celebrates early-career scientists and entrepreneurs driving real-world impact.

Justin Solomon, an associate professor in the Department of EECS, was named a 2025 Schmidt Science Polymath. The award supports novel, early-stage research across disciplines, including acoustics and climate simulation.

Martin Staadecker, a research assistant in the Sustainable Supply Chain Lab, received the MIT-GE Vernova Energy and Climate Alliance Technology and Policy Program Project Award. The award recognizes his work on Scope 3 emissions and sustainable supply chain practices.

Antonio Torralba, the Delta Electronics Professor and faculty head of AI+D in the Department of EECS, received the 2025 Multidisciplinary University Research Initiative (MURI) award for research projects in areas of critical importance to national defense.

Ryan Williams, a professor in the Department of EECS, received the Best Paper Award at STOC 2025 for his paper “Simulating Time With Square-Root Space,” recognized for its technical merit and originality. Williams was also selected as a Member of the Institute for Advanced Study for the 2025–26 academic year. This prestigious fellowship recognizes the significance of these scholars' work, and it is an opportunity to advance their research and exchange ideas with scholars from around the world.

Gioele Zardini, the Rudge (1948) and Nancy Allen Career Development Professor in the Department of Civil and Environmental Engineering, received the 2025 DARPA Young Faculty Award. The award supports rising stars among early-career faculty, helping them develop research ideas aligned with national security needs.

Revisiting a revolution through poetry

In “American Independence in verse,” MIT philosopher Brad Skow uses poems to explore the American Revolution from multiple perspectives.

There are several narratives surrounding the American Revolution, a well-traveled and -documented series of events leading to the drafting and signing of the Declaration of Independence and the war that followed. 

MIT philosopher Brad Skow is taking a new approach to telling this story: a collection of 47 poems about the former American colonies’ journey from England’s imposition of the Stamp Act in 1765 to the war for America’s independence that began in 1775.

When asked why he chose poetry to retell the story, Skow, the Laurence S. Rockefeller Professor in the Department of Linguistics and Philosophy, said he “wanted to take just the great bits of these speeches and writings, while maintaining their intent and integrity.” Poetry, Skow argues, allows for that kind of nuance and specificity.

“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. 

The book is divided into three segments: “Taxation Without Representation,” “Occupation and Massacre,” and “War and Independence.” Themes like freedom, government, and authority, rendered in a style of writing and oratory seldom seen today, lent themselves to being reimagined as poems. “The options available with poetic license offer opportunities for readers that might prove more difficult with prose,” Skow reports.

Skow based each of the poems on actual speeches, letters, pamphlets, and other printed materials produced by people on both sides of the debate about independence. “While reviewing a variety of primary sources for the book, I began to see the poetry in them,” he says. 

In the poem “Everywhere, the spirit of equality prevails,” during an “Interlude” between the “Occupation and Massacre” and “War and Independence” sections of the book, British commissioner of customs Henry Hulton, writing to Robert Nicholson in Liverpool, England, describes the America he experienced during a trip with his wife:

The spirit of equality prevails.

Regarding social differences, they’ve no

Notion of rank, and will show more respect

To one another than to those above them.

They’ll ask a thousand strange impertinent 

Questions, sit down when they should wait at a table,

React with puzzlement when you do not

Invite your valet to come share your meal.

Here, Skow, using Hulton’s words, illustrates the tension between agreed-upon social conventions — remnants of the Old World — and the society being built in the New World that animates a portion of the disconnect leading both toward war. “These writings are really powerful, and poetry offers a way to convey that power,” Skow says.

The journey to the printed page 

Skow’s interest in exploring the American Revolution came, in part, from watching the Tony Award-winning play “Hamilton.” The book ends where the play begins. “It led me to want to learn more,” he says of the play and his experience watching it. “Its focus on the Revolution made the era more exciting for me.”

While conducting research for another poetry project, Skow read an interview with American diplomat, inventor, and publisher Benjamin Franklin in the House of Commons conducted in 1766. “There were lots of amazing poetic moments in the interview,” he says. Skow began reading additional pamphlets, letters, and other writings, disconnecting his work as a philosopher from the research that would yield the book.

“I wanted to remove my philosopher hat with this project,” he says. “Poetry can encourage ambiguity and, unlike philosophy, can focus on emotional and non-rational connections between ideas.” 

Although eager to approach the work as a poet and author, rather than a philosopher, Skow discovered that more primary sources than he expected were themselves often philosophical treatises. “Early in the resistance movement there were sophisticated arguments, often printed in newspapers, that it was unjust to tax the colonies without granting them representation in Parliament,” he notes. 

A series of new perspectives and lessons

Skow made some discoveries that further enhanced his passion for the project. “Samuel Adams is an important figure who isn’t as well-known as he should be,” he says. “I wanted to raise his profile.”

Skow also notes that American separatists used strong-arm tactics to “encourage” support for independence, and that prevailing narratives regarding America and its eventual separation from England are more complex and layered than we might believe. “There were arguments underway about legitimate forms of government and which kind of government was right,” he says, “and many Americans wanted to retain the existing relationship with England.”

Skow says the American Revolution is a useful benchmark when considering subsequent political movements, a notion he hopes readers will take away from the book. “The book is meant to be fun and not just a collection of dry, abstract ideas,” he believes. 

“There’s a simple version of the independence story we tell when we’re in a hurry; and there is the more complex truth, printed in long history books,” he continues. “I wanted to write something that was both short and included a variety of perspectives.”

Skow believes the book and its subjects are a testament to ideas he’d like to see return to political and practical discourse. “The ideals around which this country rallied for its independence are still good ideals, and the courage the participants exhibited is still worth admiring,” he says.

What’s the best way to expand the US electricity grid?

A study by MIT researchers illuminates choices about reliability, cost, and emissions.

Growing energy demand means the U.S. will almost certainly have to expand its electricity grid in coming years. What’s the best way to do this? A new study by MIT researchers examines legislation introduced in Congress and identifies relative tradeoffs involving reliability, cost, and emissions, depending on the proposed approach.

The researchers evaluated two policy approaches to expanding the U.S. electricity grid: One would concentrate on regions with more renewable energy sources, and the other would create more interconnections across the country. For instance, some of the best untapped wind-power resources in the U.S. lie in the center of the country, so one type of grid expansion would situate relatively more grid infrastructure in those regions. Alternatively, the other scenario involves building more infrastructure everywhere in roughly equal measure, which the researchers call the “prescriptive” approach. How does each pencil out?

After extensive modeling, the researchers found that a grid expansion could make improvements on all fronts, with each approach offering different advantages. A more geographically unbalanced grid buildout would be 1.13 percent less expensive, and would reduce carbon emissions by 3.65 percent compared to the prescriptive approach. And yet, the prescriptive approach, with more national interconnection, would significantly reduce power outages due to extreme weather, among other things.

“There’s a tradeoff between the two things that are most on policymakers’ minds: cost and reliability,” says Christopher Knittel, an economist at the MIT Sloan School of Management, who helped direct the research. “This study makes it more clear that the more prescriptive approach ends up being better in the face of extreme weather and outages.”

The paper, “Implications of Policy-Driven Transmission Expansion on Costs, Emissions and Reliability in the United States,” is published today in Nature Energy.

The authors are Juan Ramon L. Senga, a postdoc in the MIT Center for Energy and Environmental Policy Research; Audun Botterud, a principal research scientist in the MIT Laboratory for Information and Decision Systems; John E. Parson, the deputy director for research at MIT’s Center for Energy and Environmental Policy Research; Drew Story, the managing director at MIT’s Policy Lab; and Knittel, who is the George P. Schultz Professor at MIT Sloan, and associate dean for climate and sustainability at MIT.

The new study is a product of the MIT Climate Policy Center, housed within MIT Sloan and committed to bipartisan research on energy issues. The center is also part of the Climate Project at MIT, founded in 2024 as a high-level Institute effort to develop practical climate solutions.

In this case, the project was developed from work the researchers did with federal lawmakers who have introduced legislation aimed at bolstering and expanding the U.S. electric grid. One of these bills, the BIG WIRES Act, co-sponsored by Sen. John Hickenlooper of Colorado and Rep. Scott Peters of California, would require each transmission region in the U.S. to be able to send at least 30 percent of its peak load to other regions by 2035.

That would represent a substantial change for a national transmission scenario where grids have largely been developed regionally, without an enormous amount of national oversight.

“The U.S. grid is aging and it needs an upgrade,” Senga says. “Implementing these kinds of policies is an important step for us to get to that future where we improve the grid, lower costs, lower emissions, and improve reliability. Some progress is better than none, and in this case, it would be important.”

To conduct the study, the researchers looked at how policies like the BIG WIRES Act would affect energy distribution. The scholars used a model of energy generation developed at the MIT Energy Initiative — the model is called “Gen X” — and examined the changes proposed by the legislation.

With a 30 percent level of interregional connectivity, the study estimates, the number of outages due to extreme cold would drop by 39 percent, for instance, a substantial increase in reliability. That would help avoid scenarios such as the one Texas experienced in 2021, when winter storms damaged distribution capacity.

“Reliability is what we find to be most salient to policymakers,” Senga says.

On the other hand, as the paper details, a future grid that is “optimized” with more transmission capacity near geographic spots of new energy generation would be less expensive.

“On the cost side, this kind of optimized system looks better,” Senga says.

A more geographically imbalanced grid would also have a greater impact on reducing emissions. Globally, the levelized cost of wind and solar dropped by 89 percent and 69 percent, respectively, from 2010 to 2022, meaning that incorporating less-expensive renewables into the grid would help with both cost and emissions.

“On the emissions side, a priori it’s not clear the optimized system would do better, but it does,” Knittel says. “That’s probably tied to cost, in the sense that it’s building more transmission links to where the good, cheap renewable resources are, because they’re cheap. Emissions fall when you let the optimizing action take place.”

To be sure, these two differing approaches to grid expansion are not the only paths forward. The study also examines a hybrid approach, which involves both national interconnectivity requirements and local buildouts based around new power sources on top of that. Still, the model does show that there may be some tradeoffs lawmakers will want to consider when developing and considering future grid legislation.

“You can find a balance between these factors, where you’re still going to still have an increase in reliability while also getting the cost and emission reductions,” Senga observes.

For his part, Knittel emphasizes that working with legislation as the basis for academic studies, while not generally common, can be productive for everyone involved. Scholars get to apply their research tools and models to real-world scenarios, and policymakers get a sophisticated evaluation of how their proposals would work.

“Compared to the typical academic path to publication, this is different, but at the Climate Policy Center, we’re already doing this kind of research,” Knittel says. 

A smarter way for large language models to think about hard problems

This new technique enables LLMs to dynamically adjust the amount of computation they use for reasoning, based on the difficulty of the question.

To make large language models (LLMs) more accurate when answering harder questions, researchers can let the model spend more time thinking about potential solutions.

But common approaches that give LLMs this capability set a fixed computational budget for every problem, regardless of how complex it is. This means the LLM might waste computational resources on simpler questions or be unable to tackle intricate problems that require more reasoning.

To address this, MIT researchers developed a smarter way to allocate computational effort as the LLM solves a problem. Their method enables the model to dynamically adjust its computational budget based on the difficulty of the question and the likelihood that each partial solution will lead to the correct answer.

The researchers found that their new approach enabled LLMs to use as little as one-half the computation as existing methods, while achieving comparable accuracy on a range of questions with varying difficulties. In addition, their method allows smaller, less resource-intensive LLMs to perform as well as or even better than larger models on complex problems.

By improving the reliability and efficiency of LLMs, especially when they tackle complex reasoning tasks, this technique could reduce the energy consumption of generative AI systems and enable the use of LLMs in more high-stakes and time-sensitive applications.

“The computational cost of inference has quickly become a major bottleneck for frontier model providers, and they are actively trying to find ways to improve computational efficiency per user queries. For instance, the recent GPT-5.1 release highlights the efficacy of the ‘adaptive reasoning’ approach our paper proposes. By endowing the models with the ability to know what they don’t know, we can enable them to spend more compute on the hardest problems and most promising solution paths, and use far fewer tokens on easy ones. That makes reasoning both more reliable and far more efficient,” says Navid Azizan, the Alfred H. and Jean M. Hayes Career Development Assistant Professor in the Department of Mechanical Engineering and the Institute for Data, Systems, and Society (IDSS), a principal investigator of the Laboratory for Information and Decision Systems (LIDS), and the senior author of a paper on this technique.

Azizan is joined on the paper by lead author Young-Jin Park, a LIDS/MechE graduate student; Kristjan Greenewald, a research scientist in the MIT-IBM Watson AI Lab; Kaveh Alim, an IDSS graduate student; and Hao Wang, a research scientist at the MIT-IBM Watson AI Lab and the Red Hat AI Innovation Team. The research is being presented this week at the Conference on Neural Information Processing Systems.

Computation for contemplation

A recent approach called inference-time scaling lets a large language model take more time to reason about difficult problems.

Using inference-time scaling, the LLM might generate multiple solution attempts at once or explore different reasoning paths, then choose the best ones to pursue from those candidates.

A separate model, known as a process reward model (PRM), scores each potential solution or reasoning path. The LLM uses these scores to identify the most promising ones.     

Typical inference-time scaling approaches assign a fixed amount of computation for the LLM to break the problem down and reason about the steps.

Instead, the researchers’ method, known as instance-adaptive scaling, dynamically adjusts the number of potential solutions or reasoning steps based on how likely they are to succeed, as the model wrestles with the problem.

“This is how humans solve problems. We come up with some partial solutions and then decide, should I go further with any of these, or stop and revise, or even go back to my previous step and continue solving the problem from there?” Wang explains.

To do this, the framework uses the PRM to estimate the difficulty of the question, helping the LLM assess how much computational budget to utilize for generating and reasoning about potential solutions.

At every step in the model’s reasoning process, the PRM looks at the question and partial answers and evaluates how promising each one is for getting to the right solution. If the LLM is more confident, it can reduce the number of potential solutions or reasoning trajectories to pursue, saving computational resources.

But the researchers found that existing PRMs often overestimate the model’s probability of success.

Overcoming overconfidence

“If we were to just trust current PRMs, which often overestimate the chance of success, our system would reduce the computational budget too aggressively. So we first had to find a way to better calibrate PRMs to make inference-time scaling more efficient and reliable,” Park says.

The researchers introduced a calibration method that enables PRMs to generate a range of probability scores rather than a single value. In this way, the PRM creates more reliable uncertainty estimates that better reflect the true probability of success.

With a well-calibrated PRM, their instance-adaptive scaling framework can use the probability scores to effectively reduce computation while maintaining the accuracy of the model’s outputs.

When they compared their method to standard inference-time scaling approaches on a series of mathematical reasoning tasks, it utilized less computation to solve each problem while achieving similar accuracy.

“The beauty of our approach is that this adaptation happens on the fly, as the problem is being solved, rather than happening all at once at the beginning of the process,” says Greenewald.

In the future, the researchers are interested in applying this technique to other applications, such as code generation and AI agents. They are also planning to explore additional uses for their PRM calibration method, like for reinforcement learning and fine-tuning.

“Human employees learn on the job — some CEOs even started as interns — but today’s agents remain largely static pieces of probabilistic software. Work like this paper is an important step toward changing that: helping agents understand what they don’t know and building mechanisms for continual self-improvement. These capabilities are essential if we want agents that can operate safely, adapt to new situations, and deliver consistent results at scale,” says Akash Srivastava, director and chief architect of Core AI at IBM Software, who was not involved with this work.

This work was funded, in part, by the MIT-IBM Watson AI Lab, the MIT-Amazon Science Hub, the MIT-Google Program for Computing Innovation, and MathWorks. 

MIT engineers design an aerial microrobot that can fly as fast as a bumblebee

With insect-like speed and agility, the tiny robot could someday aid in search-and-rescue missions.

In the future, tiny flying robots could be deployed to aid in the search for survivors trapped beneath the rubble after a devastating earthquake. Like real insects, these robots could flit through tight spaces larger robots can’t reach, while simultaneously dodging stationary obstacles and pieces of falling rubble.

So far, aerial microrobots have only been able to fly slowly along smooth trajectories, far from the swift, agile flight of real insects — until now.

MIT researchers have demonstrated aerial microrobots that can fly with speed and agility that is comparable to their biological counterparts. A collaborative team designed a new AI-based controller for the robotic bug that enabled it to follow gymnastic flight paths, such as executing continuous body flips.

With a two-part control scheme that combines high performance with computational efficiency, the robot’s speed and acceleration increased by about 450 percent and 250 percent, respectively, compared to the researchers’ best previous demonstrations.

The speedy robot was agile enough to complete 10 consecutive somersaults in 11 seconds, even when wind disturbances threatened to push it off course.

“We want to be able to use these robots in scenarios that more traditional quad copter robots would have trouble flying into, but that insects could navigate. Now, with our bioinspired control framework, the flight performance of our robot is comparable to insects in terms of speed, acceleration, and the pitching angle. This is quite an exciting step toward that future goal,” says Kevin Chen, an associate professor in the Department of Electrical Engineering and Computer Science (EECS), head of the Soft and Micro Robotics Laboratory within the Research Laboratory of Electronics (RLE), and co-senior author of a paper on the robot.

Chen is joined on the paper by co-lead authors Yi-Hsuan Hsiao, an EECS MIT graduate student; Andrea Tagliabue PhD ’24; and Owen Matteson, a graduate student in the Department of Aeronautics and Astronautics (AeroAstro); as well as EECS graduate student Suhan Kim; Tong Zhao MEng ’23; and co-senior author Jonathan P. How, the Ford Professor of Engineering in the Department of Aeronautics and Astronautics and a principal investigator in the Laboratory for Information and Decision Systems (LIDS). The research appears today in Science Advances.

An AI controller

Chen’s group has been building robotic insects for more than five years.

They recently developed a more durable version of their tiny robot, a microcassette-sized device that weighs less than a paperclip. The new version utilizes larger, flapping wings that enable more agile movements. They are powered by a set of squishy artificial muscles that flap the wings at an extremely fast rate.

But the controller — the “brain” of the robot that determines its position and tells it where to fly — was hand-tuned by a human, limiting the robot’s performance.

For the robot to fly quickly and aggressively like a real insect, it needed a more robust controller that could account for uncertainty and perform complex optimizations quickly.

Such a controller would be too computationally intensive to be deployed in real time, especially with the complicated aerodynamics of the lightweight robot.

To overcome this challenge, Chen’s group joined forces with How’s team and, together, they crafted a two-step, AI-driven control scheme that provides the robustness necessary for complex, rapid maneuvers, and the computational efficiency needed for real-time deployment.

“The hardware advances pushed the controller so there was more we could do on the software side, but at the same time, as the controller developed, there was more they could do with the hardware. As Kevin’s team demonstrates new capabilities, we demonstrate that we can utilize them,” How says.

For the first step, the team built what is known as a model-predictive controller. This type of powerful controller uses a dynamic, mathematical model to predict the behavior of the robot and plan the optimal series of actions to safely follow a trajectory.

While computationally intensive, it can plan challenging maneuvers like aerial somersaults, rapid turns, and aggressive body tilting. This high-performance planner is also designed to consider constraints on the force and torque the robot could apply, which is essential for avoiding collisions.

For instance, to perform multiple flips in a row, the robot would need to decelerate in such a way that its initial conditions are exactly right for doing the flip again.

“If small errors creep in, and you try to repeat that flip 10 times with those small errors, the robot will just crash. We need to have robust flight control,” How says.

They use this expert planner to train a “policy” based on a deep-learning model, to control the robot in real time, through a process called imitation learning. A policy is the robot’s decision-making engine, which tells the robot where and how to fly.

Essentially, the imitation-learning process compresses the powerful controller into a computationally efficient AI model that can run very fast.

The key was having a smart way to create just enough training data, which would teach the policy everything it needs to know for aggressive maneuvers.

“The robust training method is the secret sauce of this technique,” How explains.

The AI-driven policy takes robot positions as inputs and outputs control commands in real time, such as thrust force and torques.

Insect-like performance

In their experiments, this two-step approach enabled the insect-scale robot to fly 447 percent faster while exhibiting a 255 percent increase in acceleration. The robot was able to complete 10 somersaults in 11 seconds, and the tiny robot never strayed more than 4 or 5 centimeters off its planned trajectory.

“This work demonstrates that soft and microrobots, traditionally limited in speed, can now leverage advanced control algorithms to achieve agility approaching that of natural insects and larger robots, opening up new opportunities for multimodal locomotion,” says Hsiao.

The researchers were also able to demonstrate saccade movement, which occurs when insects pitch very aggressively, fly rapidly to a certain position, and then pitch the other way to stop. This rapid acceleration and deceleration help insects localize themselves and see clearly.

“This bio-mimicking flight behavior could help us in the future when we start putting cameras and sensors on board the robot,” Chen says.

Adding sensors and cameras so the microrobots can fly outdoors, without being attached to a complex motion capture system, will be a major area of future work.

The researchers also want to study how onboard sensors could help the robots avoid colliding with one another or coordinate navigation.

“For the micro-robotics community, I hope this paper signals a paradigm shift by showing that we can develop a new control architecture that is high-performing and efficient at the same time,” says Chen.

“This work is especially impressive because these robots still perform precise flips and fast turns despite the large uncertainties that come from relatively large fabrication tolerances in small-scale manufacturing, wind gusts of more than 1 meter per second, and even its power tether wrapping around the robot as it performs repeated flips,” says Sarah Bergbreiter, a professor of mechanical engineering at Carnegie Mellon University, who was not involved with this work.

“Although the controller currently runs on an external computer rather than onboard the robot, the authors demonstrate that similar, but less precise, control policies may be feasible even with the more limited computation available on an insect-scale robot. This is exciting because it points toward future insect-scale robots with agility approaching that of their biological counterparts,” she adds.

This research is funded, in part, by the National Science Foundation (NSF), the Office of Naval Research, Air Force Office of Scientific Research, MathWorks, and the Zakhartchenko Fellowship.

Staying stable

Whether they walk on two, four, or six legs, animals maintain stability by monitoring their body position and correcting errors with every step.

With every step we take, our brains are already thinking about the next one. If a bump in the terrain or a minor misstep has thrown us off balance, our stride may need to be altered to prevent a fall. Our two-legged posture makes maintaining stability particularly complex, which our brains solve in part by continually monitoring our bodies and adjusting where we place our feet.

Now, scientists at MIT have determined that animals with very different bodies likely use a shared strategy to balance themselves when they walk.

Nidhi Seethapathi, the Frederick A. and Carole J. Middleton Career Development Assistant Professor in Brain and Cognitive Sciences and Electrical Engineering and Computer Science at MIT, and K. Lisa Yang ICoN Center Fellow Antoine De Comite found that humans, mice, and fruit flies all use an error-correction process to guide foot placement and maintain stability while walking. Their findings, published Oct. 21 in the journal PNAS, could inform future studies exploring how the brain achieves stability during locomotion — bridging the gap between animal models and human balance.

Corrective action

Information must be integrated by the brain to keep us upright when we walk or run. Our steps must be continually adjusted according to the terrain, our desired speed, and our body’s current velocity and position in space.

“We rely on a combination of vestibular, proprioceptive, and visual information to build an estimate of our body’s state, determining if we are about to fall. Once we know the body’s state, we can decide which corrective actions to take,” explains Seethapathi, who is also an associate investigator at the McGovern Institute for Brain Research.

While humans are known to adjust where they place their feet to correct for errors, it is not known whether animals whose bodies are more stable do this, too.

To find out, Seethapathi and De Comite, who is a postdoc in Seethapathi’s and Guoping Feng's lab at the McGovern Institute, turned to locomotion data from mice, fruit flies, and humans shared by other labs, enabling an analysis across species that is otherwise challenging. Importantly, Seethapathi notes, all the animals they studied were walking in everyday natural environments, such as around a room — not on a treadmill or over unusual terrain.

Even in these ordinary circumstances, missteps and minor imbalances are common, and the team’s analysis showed that these errors predicted where all of the animals placed their feet in subsequent steps, regardless of whether they had two, four, or six legs.

One foot in front of another

By tracking the animals’ bodies and the step-by-step placement of their feet, Seethapathi and De Comite were able to find a measure of error that informs each animal’s next step. “By taking this comparative approach, we’ve forced ourselves to come up with a definition of error that generalizes across species,” Seethapathi says. “An animal moves with an expected body state for a particular speed. If it deviates from that ideal state, that deviation — at any given moment — is the error.”

“It was surprising to find similarities across these three species, which, at first sight, look very different,” says DeComite. “The methods themselves are surprising because we now have a pipeline to analyze foot placement and locomotion stability in any legged species,” explains DeComite, “which could lead similar analyses in even more species in the future.”

The team’s data suggest that in all of the species in the study, placement of the feet is guided both by an error-correction process and the speed at which an animal is traveling. Steps tend to lengthen and feet spend less time on the ground as animals pick up their pace, while the width of each step seems to change largely to compensate for body-state errors.

Now, Seethapathi says, we can look forward to future studies to explore how the dual control systems might be generated and integrated in the brain to keep moving bodies stable.

Studying how brains help animals move stably may also guide the development of more-targeted strategies to help people improve their balance and, ultimately, prevent falls.

“In elderly individuals and individuals with sensorimotor disorders, minimizing fall risk is one of the major functional targets of rehabilitation,” says Seethapathi. “A fundamental understanding of the error correction process that helps us remain stable will provide insight into why this process falls short in populations with neural deficits,” she says. 

New bioadhesive strategy can prevent fibrous encapsulation around device implants on peripheral nerves

Inspired by traditional acupuncture, the approach has potential to impact all implantable bioelectronic devices, enabling applications such as hypertension mitigation.

Peripheral nerves — the network connecting the brain, spinal cord, and central nervous system to the rest of the body — transmit sensory information, control muscle movements, and regulate automatic bodily functions. Bioelectronic devices implanted on these nerves offer remarkable potential for the treatment and rehabilitation of neurological and systemic diseases. However, because the body perceives these implants as foreign objects, they often trigger the formation of dense fibrotic tissue at bioelectronic device–tissue interfaces, which can significantly compromise device performance and longevity.

New research published in the journal Science Advances presents a robust bioadhesive strategy that establishes non-fibrotic bioelectronic interfaces on diverse peripheral nerves — including the occipital, vagus, deep peroneal, sciatic, tibial, and common peroneal nerves — for up to 12 weeks.

“We discovered that adhering the bioelectrodes to peripheral nerves can fully prevent the formation of fibrosis on the interfaces,” says Xuanhe Zhao, the Uncas and Helen Whitaker Professor, and professor of mechanical engineering and civil engineering at MIT. “We further demonstrated long-term, drug-free hypertension mitigation using non-fibrotic bioelectronics over four weeks, and ongoing.”

The approach inhibits immune cell infiltration at the device-tissue interface, thereby preventing the formation of fibrous capsules within the inflammatory microenvironment. In preclinical rodent models, the team demonstrated that the non-fibrotic, adhesive bioelectronic device maintained stable, long-term regulation of blood pressure.

“Our long-term blood pressure regulation approach was inspired by traditional acupuncture,” says Hyunmin Moon, lead author of the study and a postdoc in the Department of Mechanical Engineering. “The lower leg has long been used in hypertension treatment, and the deep peroneal nerve lies precisely at an acupuncture point. We were thrilled to see that stimulating this nerve achieved blood pressure regulation for the first time. The convergence of our non-fibrotic, adhesive bioelectronic device with this long-term regulation capability holds exciting promise for translational medicine.”

Importantly, after 12 weeks of implantation with continuous nerve stimulation, only minimal macrophage activity and limited deposition of smooth muscle actin and collagen were detected, underscoring the device’s potential to deliver long-term neuromodulation without triggering fibrosis. “The contrast between the immune response of the adhered device and that of the non-adhered control is striking,” says Bastien Aymon, a study co-author and a PhD candidate in mechanical engineering. “The fact that we can observe immunologically pristine interfaces after three months of adhesive implantation is extremely encouraging for future clinical translation.”

This work offers a broadly applicable strategy for all implantable bioelectronic systems by preventing fibrosis at the device interface, paving the way for more effective and long-lasting therapies such as hypertension mitigation.

Hypertension is a major contributor to cardiovascular diseases, the leading cause of death worldwide. Although medications are effective in many cases, more than 50 percent of patients remain hypertensive despite treatment — a condition known as resistant hypertension. Traditional carotid sinus or vagus nerve stimulation methods are often accompanied by side effects including apnea, bradycardia, cough, and paresthesia.

“In contrast, our non-fibrotic, adhesive bioelectronic device targeting the deep peroneal nerve enables long-term blood pressure regulation in resistant hypertensive patients without metabolic side effects,” says Moon.

Noninvasive imaging could replace finger pricks for people with diabetes

MIT engineers show they can accurately measure blood glucose by shining near-infrared light on the skin.

A noninvasive method for measuring blood glucose levels, developed at MIT, could save diabetes patients from having to prick their fingers several times a day.

The MIT team used Raman spectroscopy — a technique that reveals the chemical composition of tissues by shining near-infrared or visible light on them — to develop a shoebox-sized device that can measure blood glucose levels without any needles.

In tests in a healthy volunteer, the researchers found that the measurements from their device were similar to those obtained by commercial continuous glucose monitoring sensors that require a wire to be implanted under the skin. While the device presented in this study is too large to be used as a wearable sensor, the researchers have since developed a wearable version that they are now testing in a small clinical study.

“For a long time, the finger stick has been the standard method for measuring blood sugar, but nobody wants to prick their finger every day, multiple times a day. Naturally, many diabetic patients are under-testing their blood glucose levels, which can cause serious complications,” says Jeon Woong Kang, an MIT research scientist and the senior author of the study. “If we can make a noninvasive glucose monitor with high accuracy, then almost everyone with diabetes will benefit from this new technology.”

MIT postdoc Arianna Bresci is the lead author of the new study, which appears today in the journal Analytical Chemistry. Other authors include Peter So, director of the MIT Laser Biomedical Research Center (LBRC) and an MIT professor of biological engineering and mechanical engineering; and Youngkyu Kim and Miyeon Jue of Apollon Inc., a biotechnology company based in South Korea.

Noninvasive glucose measurement

While most diabetes patients measure their blood glucose levels by drawing blood and testing it with a glucometer, some use wearable monitors, which have a sensor that is inserted just under the skin. These sensors provide continuous glucose measurements from the interstitial fluid, but they can cause skin irritation and they need to be replaced every 10 to 15 days.

In hopes of creating wearable glucose monitors that would be more comfortable for patients, researchers in MIT’s LBRC have been pursuing noninvasive sensors based on Raman spectroscopy. This type of spectroscopy reveals the chemical composition of tissue or cells by analyzing how near-infrared light is scattered, or deflected, as it encounters different kinds of molecules.

In 2010, researchers at the LBRC showed that they could indirectly calculate glucose levels based on a comparison between Raman signals from the interstitial fluid that bathes skin cells and a reference measurement of blood glucose levels. While this approach produced reliable measurements, it wasn’t practical for translating to a glucose monitor.

More recently, the researchers reported a breakthrough that allowed them to directly measure glucose Raman signals from the skin. Normally, this glucose signal is too small to pick out from all of the other signals generated by molecules in tissue. The MIT team found a way to filter out much of the unwanted signal by shining near-infrared light onto the skin at a different angle from which they collected the resulting Raman signal.

The researchers obtained those measurements using equipment that was around the size of a desktop printer, and since then, they have been working on further shrinking the footprint of the device.

In their new study, they were able to create a smaller device by analyzing just three bands — spectral regions that correspond to specific molecular features — in the Raman spectrum.

Typically, a Raman spectrum may contain about 1,000 bands. However, the MIT team found that they could determine blood glucose levels by measuring just three bands — one from the glucose plus two background measurements. This approach allowed the researchers to reduce the amount and cost of equipment needed, allowing them to perform the measurement with a cost-effective device about the size of a shoebox.

“By refraining from acquiring the whole spectrum, which has a lot of redundant information, we go down to three bands selected from about 1,000,” Bresci says. “With this new approach, we can change the components commonly used in Raman-based devices, and save space, time, and cost.”

Toward a wearable sensor

In a clinical study performed at the MIT Center for Clinical Translation Research (CCTR), the researchers used the new device to take readings from a healthy volunteer over a four-hour period. As the subject rested their arm on top of the device, a near-infrared beam shone through a small glass window onto the skin to perform the measurement.

Each measurement takes a little more than 30 seconds, and the researchers took a new reading every five minutes.

During the study, the subject consumed two 75-gram glucose drinks, allowing the researchers to monitor significant changes in blood glucose concentration. They found that the Raman-based device showed accuracy levels similar to those of two commercially available, invasive glucose monitors worn by the subject.

Since finishing that study, the researchers have developed a smaller prototype, about the size of a cellphone, that they’re currently testing at the MIT CCTR as a wearable monitor in healthy and prediabetic volunteers. Next year, they plan to run a larger study working with a local hospital, which will include people with diabetes.

The researchers are also working on making the device even smaller, about the size of a watch. Additionally, they are exploring ways to ensure that the device can obtain accurate readings from people with different skin tones.

The research was funded by the National Institutes of Health, the Korean Technology and Information Promotion Agency for SMEs, and Apollon Inc.

MIT chemists synthesize a fungal compound that holds promise for treating brain cancer

Preliminary studies find derivatives of the compound, known as verticillin A, can kill some types of glioma cells.

For the first time, MIT chemists have synthesized a fungal compound known as verticillin A, which was discovered more than 50 years ago and has shown potential as an anticancer agent.

The compound has a complex structure that made it more difficult to synthesize than related compounds, even though it differed by only a couple of atoms.

“We have a much better appreciation for how those subtle structural changes can significantly increase the synthetic challenge,” says Mohammad Movassaghi, an MIT professor of chemistry. “Now we have the technology where we can not only access them for the first time, more than 50 years after they were isolated, but also we can make many designed variants, which can enable further detailed studies.”

In tests in human cancer cells, a derivative of verticillin A showed particular promise against a type of pediatric brain cancer called diffuse midline glioma. More tests will be needed to evaluate its potential for clinical use, the researchers say.

Movassaghi and Jun Qi, an associate professor of medicine at Dana-Farber Cancer Institute/Boston Children’s Cancer and Blood Disorders Center and Harvard Medical School, are the senior authors of the study, which appears today in the Journal of the American Chemical Society. Walker Knauss PhD ’24 is the lead author of the paper. Xiuqi Wang, a medicinal chemist and chemical biologist at Dana-Farber, and Mariella Filbin, research director in the Pediatric Neurology-Oncology Program at Dana-Farber/Boston Children’s Cancer and Blood Disorders Center, are also authors of the study.

A complex synthesis

Researchers first reported the isolation of verticillin A from fungi, which use it for protection against pathogens, in 1970. Verticillin A and related fungal compounds have drawn interest for their potential anticancer and antimicrobial activity, but their complexity has made them difficult to synthesize.

In 2009, Movassaghi’s lab reported the synthesis of (+)-11,11'-dideoxyverticillin A, a fungal compound similar to verticillin A. That molecule has 10 rings and eight stereogenic centers, or carbon atoms that have four different chemical groups attached to them. These groups have to be attached in a way that ensures they have the correct orientation, or stereochemistry, with respect to the rest of the molecule.

Once that synthesis was achieved, however, synthesis of verticillin A remained challenging, even though the only difference between verticillin A and (+)-11,11'-dideoxyverticillin A is the presence of two oxygen atoms.

“Those two oxygens greatly limit the window of opportunity that you have in terms of doing chemical transformations,” Movassaghi says. “It makes the compound so much more fragile, so much more sensitive, so that even though we had had years of methodological advances, the compound continued to pose a challenge for us.”

Both of the verticillin A compounds consist of two identical fragments that must be joined together to form a molecule called a dimer. To create (+)-11,11'-dideoxyverticillin A, the researchers had performed the dimerization reaction near the end of the synthesis, then added four critical carbon-sulfur bonds.

Yet when trying to synthesize verticillin A, the researchers found that waiting to add those carbon-sulfur bonds at the end did not result in the correct stereochemistry. As a result, the researchers had to rethink their approach and ended up creating a very different synthetic sequence.

“What we learned was the timing of the events is absolutely critical. We had to significantly change the order of the bond-forming events,” Movassaghi says.

The verticillin A synthesis begins with an amino acid derivative known as beta-hydroxytryptophan, and then step-by-step, the researchers add a variety of chemical functional groups, including alcohols, ketones, and amides, in a way that ensures the correct stereochemistry.

A functional group containing two carbon-sulfur bonds and a disulfide bond were introduced early on, to help control the stereochemistry of the molecule, but the sensitive disulfides had to be “masked” and protected as a pair of sulfides to prevent them from breakdown under subsequent chemical reactions. The disulfide-containing groups were then regenerated after the dimerization reaction.

“This particular dimerization really stands out in terms of the complexity of the substrates that we’re bringing together, which have such a dense array of functional groups and stereochemistry,” Movassaghi says.

The overall synthesis requires 16 steps from the beta-hydroxytryptophan starting material to verticillin A.

Killing cancer cells

Once the researchers had successfully completed the synthesis, they were also able to tweak it to generate derivates of verticillin A. Researchers at Dana-Farber then tested these compounds against several types of diffuse midline glioma (DMG), a rare brain tumor that has few treatment options.

The researchers found that the DMG cell lines most susceptible to these compounds were those that have high levels of a protein called EZHIP. This protein, which plays a role in the methylation of DNA, has been previously identified as a potential drug target for DMG.

“Identifying the potential targets of these compounds will play a critical role in further understanding their mechanism of action, and more importantly, will help optimize the compounds from the Movassaghi lab to be more target specific for novel therapy development,” Qi says.

The verticillin derivatives appear to interact with EZHIP in a way that increases DNA methylation, which induces the cancer cells to undergo programmed cell death. The compounds that were most successful at killing these cells were N-sulfonylated (+)-11,11'-dideoxyverticillin A and N-sulfonylated verticillin A. N-sulfonylation — the addition of a functional group containing sulfur and oxygen — makes the molecules more stable.

“The natural product itself is not the most potent, but it’s the natural product synthesis that brought us to a point where we can make these derivatives and study them,” Movassaghi says.

The Dana-Farber team is now working on further validating the mechanism of action of the verticillin derivatives, and they also hope to begin testing the compounds in animal models of pediatric brain cancers.

“Natural compounds have been valuable resources for drug discovery, and we will fully evaluate the therapeutic potential of these molecules by integrating our expertise in chemistry, chemical biology, cancer biology, and patient care. We have also profiled our lead molecules in more than 800 cancer cell lines, and will be able to understand their functions more broadly in other cancers,” Qi says.

The research was funded by the National Institute of General Medical Sciences, the Ependymoma Research Foundation, and the Curing Kids Cancer Foundation.

Inaugural UROP mixer draws hundreds of students eager to gain research experience

The Institute will commit up to $1 million in new funding to increase supply of UROPs.

More than 600 undergraduate students crowded into the Stratton Student Center on Oct. 28, for MIT’s first-ever Institute-wide Undergraduate Research Opportunities Program (UROP) mixer.

“At MIT, we believe in the transformative power of learning by doing, and there’s no better example than UROP,” says MIT President Sally Kornbluth, who attended the mixer with Provost Anantha Chandrakasan and Chancellor Melissa Nobles. “The energy at the inaugural UROP mixer was exhilarating, and I’m delighted that students now have this easy way to explore different paths to the frontiers of research.”

The event gave students the chance to explore internships and undergraduate research opportunities — in fields ranging from artificial intelligence to the life sciences to the arts, and beyond — all in one place, with approximately 150 researchers from labs available to discuss the projects and answer questions in real time. The offices of the Chancellor and Provost co-hosted the event, which the UROP office helped coordinate. 

First-year student Isabell Luo recently began a UROP project in the Living Matter lab led by Professor Rafael Gómez-Bombarelli, where she is benchmarking machine-learned interatomic potentials that simulate chemical reactions at the molecular level and exploring fine-tuning strategies to improve their accuracy. She’s passionate about AI and machine learning, eco-friendly design, and entrepreneurship, and was attending the UROP mixer to find more “real-world” projects to work on.

“I’m trying to dip my toes into different areas, which is why I’m at the mixer,” said Luo. “On the internet it would be so hard to find the right opportunities. It’s nice to have a physical space and speak to people from so many disciplines.”

More than nine out of every 10 members of MIT’s class of 2025 took part in a UROP before graduating. In recent years, approximately 3,200 undergraduates have participated in a UROP project each year. To meet the strong demand for UROPs, the Institute will commit up to $1 million in funding this year to create more of them. The funding will come from MIT’s schools and Office of the Provost. 

“UROPs have become an indispensable part of the MIT undergraduate education, providing hands-on experience that really helps students learn new ways to problem-solve and innovate,” says Chandrakasan. “I was thrilled to see so many students at the mixer — it was a testament to their willingness to roll up their sleeves and get to work on really tough challenges.”

Arielle Berman, a postdoc in the Raman Lab, was looking to recruit an undergraduate researcher for a project on sensor integration for muscle actuators for biohybrid robots — robots that include living parts. She spoke about how her own research experience as an undergraduate had shaped her career.

“It’s a really important event because we’re able to expose undergraduates to research,” says Berman. “I’m the first PhD in my family, so I wasn’t aware that research existed, or could be a career. Working in a research lab as an undergraduate student changed my life trajectory, and I’m happy to pass it forward and help students have experiences they wouldn’t have otherwise.”

The event drew students with interests as varied as the projects available. For first-year Nate Black, who plans to major in mechanical engineering, “I just wanted something to develop my interest in 3D printing and additive manufacturing.” First-year Akpandu Ekezie, who expects to major in Course 6-5 (Electrical Engineering with Computing), was interested in photonic circuits. “I’m looking mainly for EE-related things that are more hands-on,” he explained. “I want to get more physical experience.”

Nobles has a message for students considering a UROP project: Just go for it. “There’s a UROP for every student, regardless of experience,” she says. “Find something that excites you and give it a try.” She encourages students who weren’t able to attend the mixer, as well as those who did attend but still have questions, to get in touch with the UROP office.

First-year students Ruby Mykkanen and Aditi Deshpande attended the mixer together. Both were searching for UROP projects they could work on during Independent Activities Period in January. Deshpande also noted that the mixer was helpful for understanding “what research is being done at MIT.”

Said Mykkanen, “It’s fun to have it all in one place!”

New control system teaches soft robots the art of staying safe

MIT CSAIL and LIDS researchers developed a mathematically grounded system that lets soft robots deform, adapt, and interact with people and objects, without violating safety limits.

Imagine having a continuum soft robotic arm bend around a bunch of grapes or broccoli, adjusting its grip in real time as it lifts the object. Unlike traditional rigid robots that generally aim to avoid contact with the environment as much as possible and stay far away from humans for safety reasons, this arm senses subtle forces, stretching and flexing in ways that mimic more of the compliance of a human hand. Its every motion is calculated to avoid excessive force while achieving the task efficiently. In MIT Computer Science and Artificial Intelligence Laboratory (CSAIL) and Laboratory for Information and Decisions Systems (LIDS) labs, these seemingly simple movements are the culmination of complex mathematics, careful engineering, and a vision for robots that can safely interact with humans and delicate objects.

Soft robots, with their deformable bodies, promise a future where machines move more seamlessly alongside people, assist in caregiving, or handle delicate items in industrial settings. Yet that very flexibility makes them difficult to control. Small bends or twists can produce unpredictable forces, raising the risk of damage or injury. This motivates the need for safe control strategies for soft robots. 

“Inspired by advances in safe control and formal methods for rigid robots, we aim to adapt these ideas to soft robotics — modeling their complex behavior and embracing, rather than avoiding, contact — to enable higher-performance designs (e.g., greater payload and precision) without sacrificing safety or embodied intelligence,” says lead senior author and MIT Assistant Professor Gioele Zardini, who is a principal investigator in LIDS and the Department of Civil and Environmental Engineering, and an affiliate faculty with the Institute for Data, Systems, and Society (IDSS). “This vision is shared by recent and parallel work from other groups.”

Safety first

The team developed a new framework that blends nonlinear control theory (controlling systems that involve highly complex dynamics) with advanced physical modeling techniques and efficient real-time optimization to produce what they call “contact-aware safety.” At the heart of the approach are high-order control barrier functions (HOCBFs) and high-order control Lyapunov functions (HOCLFs). HOCBFs define safe operating boundaries, ensuring the robot doesn’t exert unsafe forces. HOCLFs guide the robot efficiently toward its task objectives, balancing safety with performance.

“Essentially, we’re teaching the robot to know its own limits when interacting with the environment while still achieving its goals,” says MIT Department of Mechanical Engineering PhD student Kiwan Wong, the lead author of a new paper describing the framework. “The approach involves some complex derivation of soft robot dynamics, contact models, and control constraints, but the specification of control objectives and safety barriers is rather straightforward for the practitioner, and the outcomes are very tangible, as you see the robot moving smoothly, reacting to contact, and never causing unsafe situations.”

“Compared with traditional kinematic CBFs — where forward-invariant safe sets are hard to specify — the HOCBF framework simplifies barrier design, and its optimization formulation accounts for system dynamics (e.g., inertia), ensuring the soft robot stops early enough to avoid unsafe contact forces,” says Worcester Polytechnic Institute Assistant Professor and former CSAIL postdoc Wei Xiao.

“Since soft robots emerged, the field has highlighted their embodied intelligence and greater inherent safety relative to rigid robots, thanks to passive material and structural compliance. Yet their “cognitive” intelligence — especially safety systems — has lagged behind that of rigid serial-link manipulators,” says co-lead author Maximilian Stölzle, a research intern at Disney Research and formerly a Delft University of Technology PhD student and visiting researcher at MIT LIDS and CSAIL. “This work helps close that gap by adapting proven algorithms to soft robots and tailoring them for safe contact and soft-continuum dynamics.”

The LIDS and CSAIL team tested the system on a series of experiments designed to challenge the robot’s safety and adaptability. In one test, the arm pressed gently against a compliant surface, maintaining a precise force without overshooting. In another, it traced the contours of a curved object, adjusting its grip to avoid slippage. In yet another demonstration, the robot manipulated fragile items alongside a human operator, reacting in real time to unexpected nudges or shifts. “These experiments show that our framework is able to generalize to diverse tasks and objectives, and the robot can sense, adapt, and act in complex scenarios while always respecting clearly defined safety limits,” says Zardini.

Soft robots with contact-aware safety could be a real value-add in high-stakes places, of course. In health care, they could assist in surgeries, providing precise manipulation while reducing risk to patients. In industry, they might handle fragile goods without constant supervision. In domestic settings, robots could help with chores or caregiving tasks, interacting safely with children or the elderly — a key step toward making soft robots reliable partners in real-world environments. 

“Soft robots have incredible potential,” says co-lead senior author Daniela Rus, director of CSAIL and a professor in the Department of Electrical Engineering and Computer Science. “But ensuring safety and encoding motion tasks via relatively simple objectives has always been a central challenge. We wanted to create a system where the robot can remain flexible and responsive while mathematically guaranteeing it won’t exceed safe force limits.”

Combining soft robot models, differentiable simulation, and control theory

Underlying the control strategy is a differentiable implementation of something called the Piecewise Cosserat-Segment (PCS) dynamics model, which predicts how a soft robot deforms and where forces accumulate. This model allows the system to anticipate how the robot’s body will respond to actuation and complex interactions with the environment. “The aspect that I most like about this work is the blend of integration of new and old tools coming from different fields like advanced soft robot models, differentiable simulation, Lyapunov theory, convex optimization, and injury-severity–based safety constraints. All of this is nicely blended into a real-time controller fully grounded in first principles,” says co-author Cosimo Della Santina, who is an associate professor at Delft University of Technology. 

Complementing this is the Differentiable Conservative Separating Axis Theorem (DCSAT), which estimates distances between the soft robot and obstacles in the environment that can be approximated with a chain of convex polygons in a differentiable manner. “Earlier differentiable distance metrics for convex polygons either couldn’t compute penetration depth — essential for estimating contact forces — or yielded non-conservative estimates that could compromise safety,” says Wong. “Instead, the DCSAT metric returns strictly conservative, and therefore safe, estimates while simultaneously allowing for fast and differentiable computation.” Together, PCS and DCSAT give the robot a predictive sense of its environment for more proactive, safe interactions.

Looking ahead, the team plans to extend their methods to three-dimensional soft robots and explore integration with learning-based strategies. By combining contact-aware safety with adaptive learning, soft robots could handle even more complex, unpredictable environments. 

“This is what makes our work exciting,” says Rus. “You can see the robot behaving in a human-like, careful manner, but behind that grace is a rigorous control framework ensuring it never oversteps its bounds.”

“Soft robots are generally safer to interact with than rigid-bodied robots by design, due to the compliance and energy-absorbing properties of their bodies,” says University of Michigan Assistant Professor Daniel Bruder, who wasn’t involved in the research. “However, as soft robots become faster, stronger, and more capable, that may no longer be enough to ensure safety. This work takes a crucial step towards ensuring soft robots can operate safely by offering a method to limit contact forces across their entire bodies.”

The team’s work was supported, in part, by The Hong Kong Jockey Club Scholarships, the European Union’s Horizon Europe Program, Cultuurfonds Wetenschapsbeurzen, and the Rudge (1948) and Nancy Allen Chair. Their work was published earlier this month in the Institute of Electrical and Electronics Engineers’ Robotics and Automation Letters.

MIT researchers demonstrate ship hull modifications to cut fuel use

Wedge-shaped vortex generators reduce drag in ship hulls, which could advance decarbonization for the shipping industry.

Researchers at MIT have demonstrated that wedge-shaped vortex generators attached to a ship’s hull can reduce drag by up to 7.5 percent, which reduces overall ship emissions and fuel expenses. The paper, “Net Drag Reduction in High Block Coefficient Ships and Vehicles Using Vortex Generators,” was presented at the Society of Naval Architects and Marine Engineers 2025 Maritime Convention in Norfolk, Virginia.

The work offers a promising path toward decarbonization, addressing the pressing need to meet the International Maritime Organization (IMO) goal to reduce carbon intensity of international shipping by at least 40 percent by 2030, compared to 2008 levels. Achieving such ambitious emissions reduction will require a coordinated approach, employing multiple methods, from redesigning ship hulls, propellers, and engines to using novel fuels and operational methods.

The researchers — José del Águila Ferrandis, Jack Kimmeth, and Michael Triantafyllou of MIT Sea Grant and the Department of Mechanical Engineering, along with Alfonso Parra Rubio and Neil Gershenfeld of the Center for Bits and Atoms — determined the optimized vortex generator shape and size using a combination of computational fluid dynamics (CFD) and experimental methods guided by AI optimization methods. 

The team first established parametric trends through extensive CFD analysis, and then tested multiple hulls through rapid prototyping to validate the results experimentally. Scale models of an axisymmetric hull with a bare tail, a tail with delta wing vortex generators, and a tail with wedge vortex generators were produced and tested. The team identified wedge-like vortex generators as the key shape that could achieve this level of drag reduction. 

Through flow visualization, the researchers could see that drag was reduced by delaying turbulent flow separation, helping water flow more smoothly along the ship’s hull, shrinking the wake behind the vessel. This also allows the propeller and rudder to work more efficiently in a uniform flow. “We document for the first time experimentally a reduction in fuel required by ships using vortex generators, relatively small structures in the shape of a wedge attached at a specific point of the ship’s hull,” explains Michael Triantafyllou, professor of mechanical engineering and director of MIT Sea Grant. 

Vortex generators have long been used in aircraft-wing design to maintain lift and delay stalling. This study is the first to show that the vortex generators can be applied for drag reduction in commercial ships.

The modular adaptability of the wedge vortex generators would allow integration into a broad range of hull forms, including bulk carriers and tankers, and the devices can synergize with, or even replace, existing technologies like pre-swirl stators (fixed fins mounted in front of propellers), improving overall system performance. As an example case, the researchers estimate that installing the vortex generators on a 300-meter Newcastlemax bulk carrier operating at 14.5 knots over a cross-Pacific route would result in significantly reduced emissions and approximately $750,000 in fuel savings per year.

The findings offer a practical, cost-effective solution that could be implemented efficiently across existing fleets. This study was supported through the CBA Consortium, working with Oldendorff Carriers, which operates about 700 bulk carriers around the world. An extension of this research is supported by the MIT Maritime Consortium, led by MIT professors Themis Sapsis and Fotini Christia. The Maritime Consortium was formed in 2025 to address critical gaps in the modernization of the commercial fleet through interdisciplinary research and collaboration across academia, industry, and regulatory agencies.

MIT Sea Grant students explore the intersection of technology and offshore aquaculture in Norway

AquaCulture Shock program, in collaboration with MIT-Scandinavia MISTI, offers international internships for AI and autonomy in aquaculture

Norway is the world’s largest producer of farmed Atlantic salmon and a top exporter of seafood, while the United States remains the largest importer of these products, according to the Food and Agriculture Organization. Two MIT students recently traveled to Trondheim, Norway to explore the cutting-edge technologies being developed and deployed in offshore aquaculture. 

Beckett Devoe, a senior in artificial intelligence and decision-making, and Tony Tang, a junior in mechanical engineering, first worked with MIT Sea Grant through the Undergraduate Research Opportunities Program (UROP). They contributed to projects focusing on wave generator design and machine learning applications for analyzing oyster larvae health in hatcheries. While near-shore aquaculture is a well-established industry across Massachusetts and the United States, open-ocean farming is still a nascent field here, facing unique and complex challenges. 

To help better understand this emerging industry, MIT Sea Grant created a collaborative initiative, AquaCulture Shock, with funding from an Aquaculture Technologies and Education Travel Grant through the National Sea Grant College Program. Collaborating with the MIT-Scandinavia MISTI (MIT International Science and Technology Initiatives) program, MIT Sea Grant matched Devoe and Tang with aquaculture-related summer internships at SINTEF Ocean, one of the largest research institutes in Europe. 

“The opportunity to work on this hands-on aquaculture project, under a world-renowned research institution, in an area of the world known for its innovation in marine technology — this is what MISTI is all about,” says Madeline Smith, managing director for MIT-Scandinavia. “Not only are students gaining valuable experience in their fields of study, but they’re developing cultural understanding and skills that equip them to be future global leaders.” Both students worked within SINTEF Ocean’s Aquaculture Robotics and Autonomous Systems Laboratory (ACE-Robotic Lab), a facility designed to develop and test new aquaculture technologies. 

“Norway has this unique geography where it has all of these fjords,” says Sveinung Ohrem, research manager for the Aquaculture Robotics and Automation Group at SINTEF Ocean. “So you have a lot of sheltered waters, which makes it ideal to do sea-based aquaculture.” He estimates that there are about a thousand fish farms along Norway’s coast, and walks through some of the tools being used in the industry: decision-making systems to gather and visualize data for the farmers and operators; robots for inspection and cleaning; environmental sensors to measure oxygen, temperature, and currents; echosounders that send out acoustic signals to track where the fish are; and cameras to help estimate biomass and fine-tune feeding. “Feeding is a huge challenge,” he notes. “Feed is the largest cost, by far, so optimizing feeding leads to a very significant decrease in your cost.”

During the internship, Devoe focused on a project that uses AI for fish feeding optimization. “I try to look at the different features of the farm — so maybe how big the fish are, or how cold the water is ... and use that to try to give the farmers an optimal feeding amount for the best outcomes, while also saving money on feed,” he explains. “It was good to learn some more machine learning techniques and just get better at that on a real-world project.” 

In the same lab, Tang worked on the simulation of an underwater vehicle-manipulator system to navigate farms and repair damage on cage nets with a robotic arm. Ohrem says there are thousands of aquaculture robots operating in Norway today. “The scale is huge,” he says. “You can’t have 8,000 people controlling 8,000 robots — that’s not economically or practically feasible. So the level of autonomy in all of these robots needs to be increased.”

The collaboration between MIT and SINTEF Ocean began in 2023 when MIT Sea Grant hosted Eleni Kelasidi, a visiting research scientist from the ACE-Robotic Lab. Kelasidi collaborated with MIT Sea Grant director Michael Triantafyllou and professor of mechanical engineering Themistoklis Sapsis developing controllers, models, and underwater vehicles for aquaculture, while also investigating fish-machine interactions. 

“We have had a long and fruitful collaboration with the Norwegian University of Science and Technology (NTNU) and SINTEF, which continues with important efforts such as the aquaculture project with Dr. Kelasidi,” Triantafyllou says. “Norway is at the forefront of offshore aquaculture and MIT Sea Grant is investing in this field, so we anticipate great results from the collaboration.”

Kelasidi, who is now a professor at NTNU, also leads the Field Robotics Lab, focusing on developing resilient robotic systems to operate in very complex and harsh environments. “Aquaculture is one of the most challenging field domains we can demonstrate any autonomous solutions, because everything is moving,” she says. Kelasidi describes aquaculture as a deeply interdisciplinary field, requiring more students with backgrounds both in biology and technology. “We cannot develop technologies that are applied for industries where we don’t have biological components,” she explains, “and then apply them somewhere where we have a live fish or other live organisms.” 

Ohrem affirms that maintaining fish welfare is the primary driver for researchers and companies operating in aquaculture, especially as the industry continues to grow. “So the big question is,” he says, “how can you ensure that?” SINTEF Ocean has four research licenses for farming fish, which they operate through a collaboration with SalMar, the second-largest salmon farmer in the world. The students had the opportunity to visit one of the industrial-scale farms, Singsholmen, on the island of Hitra. The farm has 10 large, round net pens about 50 meters across that extend deep below the surface, each holding up to 200,000 salmon. “I got to physically touch the nets and see how the [robotic] arm might be able to fix the net,” says Tang. 

Kelasidi emphasizes that the information gained in the field cannot be learned from the office or lab. “That opens up and makes you realize, what is the scale of the challenges, or the scale of the facilities,” she says. She also highlights the importance of international and institutional collaboration to advance this field of research and develop more resilient robotic systems. “We need to try to target that problem, and let’s solve it together.”

MIT Sea Grant and the MIT-Scandinavia MISTI program are currently recruiting a new cohort of four MIT students to intern in Norway this summer with institutes advancing offshore farming technologies, including NTNU’s Field Robotics Lab in Trondheim. Students interested in autonomy, deep learning, simulation modeling, underwater robotic systems, and other aquaculture-related areas are encouraged to reach out to Lily Keyes at MIT Sea Grant.

Exploring how AI will shape the future of work

For PhD student Benjamin Manning, the future of work means grasping AI’s role on our behalf while transforming and accelerating social scientific discovery.

“MIT hasn’t just prepared me for the future of work — it’s pushed me to study it. As AI systems become more capable, more of our online activity will be carried out by artificial agents. That raises big questions: How should we design these systems to understand our preferences? What happens when AI begins making many of our decisions?”

These are some of the questions MIT Sloan School of Management PhD candidate Benjamin Manning is researching. Part of his work investigates how to design and evaluate artificial intelligence agents that act on behalf of people, and how their behavior shapes markets and institutions. 

Previously, he received a master’s degree in public policy from the Harvard Kennedy School and a bachelor’s in mathematics from Washington University in St. Louis. After working as a research assistant, Manning knew he wanted to pursue an academic career.

“There’s no better place in the world to study economics and computer science than MIT,” he says. “Nobel and Turing award winners are everywhere, and the IT group lets me explore both fields freely. It was my top choice — when I was accepted, the decision was clear.” 

After receiving his PhD, Manning hopes to secure a faculty position at a business school and do the same type of work that MIT Sloan professors — his mentors — do every day.

“Even in my fourth year, it still feels surreal to be an MIT student. I don’t think that feeling will ever fade. My mom definitely won’t ever get over telling people about it.”

Of his MIT Sloan experience, Manning says he didn’t know it was possible to learn so much so quickly. “It’s no exaggeration to say I learned more in my first year as a PhD candidate than in all four years of undergrad. While the pace can be intense, wrestling with so many new ideas has been incredibly rewarding. It’s given me the tools to do novel research in economics and AI — something I never imagined I’d be capable of.”

As an economist studying AI simulations of humans, for Manning, the future of work not only means understanding how AI acts on our behalf, but also radically improving and accelerating social scientific discovery.

“Another part of my research agenda explores how well AI systems can simulate human responses. I envision a future where researchers test millions of behavioral simulations in minutes, rapidly prototyping experimental designs, and identifying promising research directions before investing in costly human studies. This isn’t about replacing human insight, but amplifying it: Scientists can focus on asking better questions, developing theory, and interpreting results while AI handles the computational heavy lifting.”

He’s excited by the prospect: “We are possibly moving toward a world where the pace of understanding may get much closer to the speed of economic change.”

Artificial tendons give muscle-powered robots a boost

The new design from MIT engineers could pump up many biohybrid builds.

Our muscles are nature’s actuators. The sinewy tissue is what generates the forces that make our bodies move. In recent years, engineers have used real muscle tissue to actuate “biohybrid robots” made from both living tissue and synthetic parts. By pairing lab-grown muscles with synthetic skeletons, researchers are engineering a menagerie of muscle-powered crawlers, walkers, swimmers, and grippers.

But for the most part, these designs are limited in the amount of motion and power they can produce. Now, MIT engineers are aiming to give bio-bots a power lift with artificial tendons.

In a study appearing today in the journal Advanced Science, the researchers developed artificial tendons made from tough and flexible hydrogel. They attached the rubber band-like tendons to either end of a small piece of lab-grown muscle, forming a “muscle-tendon unit.” Then they connected the ends of each artificial tendon to the fingers of a robotic gripper.

When they stimulated the central muscle to contract, the tendons pulled the gripper’s fingers together. The robot pinched its fingers together three times faster, and with 30 times greater force, compared with the same design without the connecting tendons.

The researchers envision the new muscle-tendon unit can be fit to a wide range of biohybrid robot designs, much like a universal engineering element.

“We are introducing artificial tendons as interchangeable connectors between muscle actuators and robotic skeletons,” says lead author Ritu Raman, an assistant professor of mechanical engineering (MechE) at MIT. “Such modularity could make it easier to design a wide range of robotic applications, from microscale surgical tools to adaptive, autonomous exploratory machines.”

The study’s MIT co-authors include graduate students Nicolas Castro, Maheera Bawa, Bastien Aymon, Sonika Kohli, and Angel Bu; undergraduate Annika Marschner; postdoc Ronald Heisser; alumni Sarah J. Wu ’19, SM ’21, PhD ’24 and Laura Rosado ’22, SM ’25; and MechE professors Martin Culpepper and Xuanhe Zhao.

Muscle’s gains

Raman and her colleagues at MIT are at the forefront of biohybrid robotics, a relatively new field that has emerged in the last decade. They focus on combining synthetic, structural robotic parts with living muscle tissue as natural actuators.

“Most actuators that engineers typically work with are really hard to make small,” Raman says. “Past a certain size, the basic physics doesn’t work. The nice thing about muscle is, each cell is an independent actuator that generates force and produces motion. So you could, in principle, make robots that are really small.”

Muscle actuators also come with other advantages, which Raman’s team has already demonstrated: The tissue can grow stronger as it works out, and can naturally heal when injured. For these reasons, Raman and others envision that muscly droids could one day be sent out to explore environments that are too remote or dangerous for humans. Such muscle-bound bots could build up their strength for unforeseen traverses or heal themselves when help is unavailable. Biohybrid bots could also serve as small, surgical assistants that perform delicate, microscale procedures inside the body.

All these future scenarios are motivating Raman and others to find ways to pair living muscles with synthetic skeletons. Designs to date have involved growing a band of muscle and attaching either end to a synthetic skeleton, similar to looping a rubber band around two posts. When the muscle is stimulated to contract, it can pull the parts of a skeleton together to generate a desired motion.

But Raman says this method produces a lot of wasted muscle that is used to attach the tissue to the skeleton rather than to make it move. And that connection isn’t always secure. Muscle is quite soft compared with skeletal structures, and the difference can cause muscle to tear or detach. What’s more, it is often only the contractions in the central part of the muscle that end up doing any work — an amount that’s relatively small and generates little force.

“We thought, how do we stop wasting muscle material, make it more modular so it can attach to anything, and make it work more efficiently?” Raman says. “The solution the body has come up with is to have tendons that are halfway in stiffness between muscle and bone, that allow you to bridge this mechanical mismatch between soft muscle and rigid skeleton. They’re like thin cables that wrap around joints efficiently.”

“Smartly connected”

In their new work, Raman and her colleagues designed artificial tendons to connect natural muscle tissue with a synthetic gripper skeleton. Their material of choice was hydrogel — a squishy yet sturdy polymer-based gel. Raman obtained hydrogel samples from her colleague and co-author Xuanhe Zhao, who has pioneered the development of hydrogels at MIT. Zhao’s group has derived recipes for hydrogels of varying toughness and stretch that can stick to many surfaces, including synthetic and biological materials.

To figure out how tough and stretchy artificial tendons should be in order to work in their gripper design, Raman’s team first modeled the design as a simple system of three types of springs, each representing the central muscle, the two connecting tendons, and the gripper skeleton. They assigned a certain stiffness to the muscle and skeleton, which were previously known, and used this to calculate the stiffness of the connecting tendons that would be required in order to move the gripper by a desired amount.

From this modeling, the team derived a recipe for hydrogel of a certain stiffness. Once the gel was made, the researchers carefully etched the gel into thin cables to form artificial tendons. They attached two tendons to either end of a small sample of muscle tissue, which they grew using lab-standard techniques. They then wrapped each tendon around a small post at the end of each finger of the robotic gripper — a skeleton design that was developed by MechE professor Martin Culpepper, an expert in designing and building precision machines.

When the team stimulated the muscle to contract, the tendons in turn pulled on the gripper to pinch its fingers together. Over multiple experiments, the researchers found that the muscle-tendon gripper worked three times faster and produced 30 times more force compared to when the gripper is actuated just with a band of muscle tissue (and without any artificial tendons). The new tendon-based design also was able to keep up this performance over 7,000 cycles, or muscle contractions.

Overall, Raman saw that the addition of artificial tendons increased the robot’s power-to-weight ratio by 11 times, meaning that the system required far less muscle to do just as much work.

“You just need a small piece of actuator that’s smartly connected to the skeleton,” Raman says. “Normally, if a muscle is really soft and attached to something with high resistance, it will just tear itself before moving anything. But if you attach it to something like a tendon that can resist tearing, it can really transmit its force through the tendon, and it can move a skeleton that it wouldn’t have been able to move otherwise.”

The team’s new muscle-tendon design successfully merges biology with robotics, says biomedical engineer Simone Schürle-Finke, associate professor of health sciences and technology at ETH Zürich.

“The tough-hydrogel tendons create a more physiological muscle–tendon–bone architecture, which greatly improves force transmission, durability, and modularity,” says Schürle-Finke, who was not involved with the study. “This moves the field toward biohybrid systems that can operate repeatably and eventually function outside the lab.”

With the new artificial tendons in place, Raman’s group is moving forward to develop other elements, such as skin-like protective casings, to enable muscle-powered robots in practical, real-world settings.

This research was supported, in part, by the U.S. Department of Defense Army Research Office, the MIT Research Support Committee, and the National Science Foundation.

Researchers discover a shortcoming that makes LLMs less reliable

Large language models can learn to mistakenly link certain sentence patterns with specific topics — and may then repeat these patterns instead of reasoning.

Large language models (LLMs) sometimes learn the wrong lessons, according to an MIT study.

Rather than answering a query based on domain knowledge, an LLM could respond by leveraging grammatical patterns it learned during training. This can cause a model to fail unexpectedly when deployed on new tasks.

The researchers found that models can mistakenly link certain sentence patterns to specific topics, so an LLM might give a convincing answer by recognizing familiar phrasing instead of understanding the question.

Their experiments showed that even the most powerful LLMs can make this mistake.

This shortcoming could reduce the reliability of LLMs that perform tasks like handling customer inquiries, summarizing clinical notes, and generating financial reports.

It could also have safety risks. A nefarious actor could exploit this to trick LLMs into producing harmful content, even when the models have safeguards to prevent such responses.

After identifying this phenomenon and exploring its implications, the researchers developed a benchmarking procedure to evaluate a model’s reliance on these incorrect correlations. The procedure could help developers mitigate the problem before deploying LLMs.

“This is a byproduct of how we train models, but models are now used in practice in safety-critical domains far beyond the tasks that created these syntactic failure modes. If you’re not familiar with model training as an end-user, this is likely to be unexpected,” says Marzyeh Ghassemi, an associate professor in the MIT Department of Electrical Engineering and Computer Science (EECS), a member of the MIT Institute of Medical Engineering Sciences and the Laboratory for Information and Decision Systems, and the senior author of the study.

Ghassemi is joined by co-lead authors Chantal Shaib, a graduate student at Northeastern University and visiting student at MIT; and Vinith Suriyakumar, an MIT graduate student; as well as Levent Sagun, a research scientist at Meta; and Byron Wallace, the Sy and Laurie Sternberg Interdisciplinary Associate Professor and associate dean of research at Northeastern University’s Khoury College of Computer Sciences. A paper describing the work will be presented at the Conference on Neural Information Processing Systems.

Stuck on syntax

LLMs are trained on a massive amount of text from the internet. During this training process, the model learns to understand the relationships between words and phrases — knowledge it uses later when responding to queries.

In prior work, the researchers found that LLMs pick up patterns in the parts of speech that frequently appear together in training data. They call these part-of-speech patterns “syntactic templates.”

LLMs need this understanding of syntax, along with semantic knowledge, to answer questions in a particular domain.

“In the news domain, for instance, there is a particular style of writing. So, not only is the model learning the semantics, it is also learning the underlying structure of how sentences should be put together to follow a specific style for that domain,” Shaib explains.   

But in this research, they determined that LLMs learn to associate these syntactic templates with specific domains. The model may incorrectly rely solely on this learned association when answering questions, rather than on an understanding of the query and subject matter.

For instance, an LLM might learn that a question like “Where is Paris located?” is structured as adverb/verb/proper noun/verb. If there are many examples of sentence construction in the model’s training data, the LLM may associate that syntactic template with questions about countries.

So, if the model is given a new question with the same grammatical structure but nonsense words, like “Quickly sit Paris clouded?” it might answer “France” even though that answer makes no sense.

“This is an overlooked type of association that the model learns in order to answer questions correctly. We should be paying closer attention to not only the semantics but the syntax of the data we use to train our models,” Shaib says.

Missing the meaning

The researchers tested this phenomenon by designing synthetic experiments in which only one syntactic template appeared in the model’s training data for each domain. They tested the models by substituting words with synonyms, antonyms, or random words, but kept the underlying syntax the same.

In each instance, they found that LLMs often still responded with the correct answer, even when the question was complete nonsense.

When they restructured the same question using a new part-of-speech pattern, the LLMs often failed to give the correct response, even though the underlying meaning of the question remained the same.

They used this approach to test pre-trained LLMs like GPT-4 and Llama, and found that this same learned behavior significantly lowered their performance.

Curious about the broader implications of these findings, the researchers studied whether someone could exploit this phenomenon to elicit harmful responses from an LLM that has been deliberately trained to refuse such requests.

They found that, by phrasing the question using a syntactic template the model associates with a “safe” dataset (one that doesn’t contain harmful information), they could trick the model into overriding its refusal policy and generating harmful content.

“From this work, it is clear to me that we need more robust defenses to address security vulnerabilities in LLMs. In this paper, we identified a new vulnerability that arises due to the way LLMs learn. So, we need to figure out new defenses based on how LLMs learn language, rather than just ad hoc solutions to different vulnerabilities,” Suriyakumar says.

While the researchers didn’t explore mitigation strategies in this work, they developed an automatic benchmarking technique one could use to evaluate an LLM’s reliance on this incorrect syntax-domain correlation. This new test could help developers proactively address this shortcoming in their models, reducing safety risks and improving performance.

In the future, the researchers want to study potential mitigation strategies, which could involve augmenting training data to provide a wider variety of syntactic templates. They are also interested in exploring this phenomenon in reasoning models, special types of LLMs designed to tackle multi-step tasks.

“I think this is a really creative angle to study failure modes of LLMs. This work highlights the importance of linguistic knowledge and analysis in LLM safety research, an aspect that hasn’t been at the center stage but clearly should be,” says Jessy Li, an associate professor at the University of Texas at Austin, who was not involved with this work.

This work is funded, in part, by a Bridgewater AIA Labs Fellowship, the National Science Foundation, the Gordon and Betty Moore Foundation, a Google Research Award, and Schmidt Sciences.

MIT scientists debut a generative AI model that could create molecules addressing hard-to-treat diseases

BoltzGen generates protein binders for any biological target from scratch, expanding AI’s reach from understanding biology toward engineering it.

More than 300 people across academia and industry spilled into an auditorium to attend a BoltzGen seminar on Thursday, Oct. 30, hosted by the Abdul Latif Jameel Clinic for Machine Learning in Health (MIT Jameel Clinic). Headlining the event was MIT PhD student and BoltzGen’s first author Hannes Stärk, who had announced BoltzGen just a few days prior.

Building upon Boltz-2, an open-source biomolecular structure prediction model predicting protein binding affinity that made waves over the summer, BoltzGen (officially released on Sunday, Oct. 26.) is the first model of its kind to go a step further by generating novel protein binders that are ready to enter the drug discovery pipeline.

Three key innovations make this possible: first, BoltzGen’s ability to carry out a variety of tasks, unifying protein design and structure prediction while maintaining state-of-the-art performance. Next, BoltzGen’s built-in constraints are designed with feedback from wetlab collaborators to ensure the model creates functional proteins that don’t defy the laws of physics or chemistry. Lastly, a rigorous evaluation process tests the model on “undruggable” disease targets, pushing the limits of BoltzGen’s binder generation capabilities.

Most models used in industry or academia are capable of either structure prediction or protein design. Moreover, they’re limited to generating certain types of proteins that bind successfully to easy “targets.” Much like students responding to a test question that looks like their homework, as long as the training data looks similar to the target during binder design, the models often work. But existing methods are nearly always evaluated on targets for which structures with binders already exist, and end up faltering in performance when used on more challenging targets.

“There have been models trying to tackle binder design, but the problem is that these models are modality-specific,” Stärk points out. “A general model does not only mean that we can address more tasks. Additionally, we obtain a better model for the individual task since emulating physics is learned by example, and with a more general training scheme, we provide more such examples containing generalizable physical patterns.”

The BoltzGen researchers went out of their way to test BoltzGen on 26 targets, ranging from therapeutically relevant cases to ones explicitly chosen for their dissimilarity to the training data. 

This comprehensive validation process, which took place in eight wetlabs across academia and industry, demonstrates the model’s breadth and potential for breakthrough drug development.

Parabilis Medicines, one of the industry collaborators that tested BoltzGen in a wetlab setting, praised BoltzGen’s potential: “we feel that adopting BoltzGen into our existing Helicon peptide computational platform capabilities promises to accelerate our progress to deliver transformational drugs against major human diseases.”

While the open-source releases of Boltz-1, Boltz-2, and now BoltzGen (which was previewed at the 7th Molecular Machine Learning Conference on Oct. 22) bring new opportunities and transparency in drug development, they also signal that biotech and pharmaceutical industries may need to reevaluate their offerings. 

Amid the buzz for BoltzGen on the social media platform X, Justin Grace, a principal machine learning scientist at LabGenius, raised a question. “The private-to-open performance time lag for chat AI systems is [seven] months and falling,” Grace wrote in a post. “It looks to be even shorter in the protein space. How will binder-as-a-service co’s be able to [recoup] investment when we can just wait a few months for the free version?” 

For those in academia, BoltzGen represents an expansion and acceleration of scientific possibility. “A question that my students often ask me is, ‘where can AI change the therapeutics game?’” says senior co-author and MIT Professor Regina Barzilay, AI faculty lead for the Jameel Clinic and an affiliate of the Computer Science and Artificial Intelligence Laboratory (CSAIL). “Unless we identify undruggable targets and propose a solution, we won’t be changing the game,” she adds. “The emphasis here is on unsolved problems, which distinguishes Hannes’ work from others in the field.” 

Senior co-author Tommi Jaakkola, the Thomas Siebel Professor of Electrical Engineering and Computer Science who is affiliated with the Jameel Clinic and CSAIL, notes that "models such as BoltzGen that are released fully open-source enable broader community-wide efforts to accelerate drug design capabilities.”

Looking ahead, Stärk believes that the future of biomolecular design will be upended by AI models. “I want to build tools that help us manipulate biology to solve disease, or perform tasks with molecular machines that we have not even imagined yet,” he says. “I want to provide these tools and enable biologists to imagine things that they have not even thought of before.”

The unsung role of logistics in the US military

The MIT Supply Chain Management master's program hosts three Military Fellows each year from the US Army, reflecting the long-standing relationship between the military and the logistics industry.

The U.S. military is mighty, formidable, and singular in influence, stationed in at least 128 overseas bases across 51 countries. Concealed beneath the United States’ biggest investment is a surprise: The military was responsible for the birth of an industry. Today, that industry is essential for its operations.

“If you think about it, logistics started as a military function,” says Chris Caplice, executive director of the MIT Center for Transportation and Logistics (CTL). “The idea of getting supplies, ammunition, food, all the material you need to the front line was the core of logistics, and really supply chain came out of that over the last decades or centuries.”

For Caplice and the leadership at MIT CTL, a collaboration with the U.S. military seemed inevitable. In 2006, MIT CTL launched the Military Fellows program, wherein three military logistics officers participate in the MIT Supply Chain Management master’s program. “The education goes two ways: One is that these people who have been in the service for more than 20 years step out of their silo and see all the research we’re doing that’s more focused on the private sector, and is cutting-edge. On the other side, you have students who may have never interacted with the military are able to learn from them,” reflects Caplice.

This year’s cohort holds 80 years of combined military service. It comprises Lukas Toth from the Army Reserve, Duston Mullen from the South Dakota Army National Guard, and Charles Greene from the Active Army. Though they work in different components of the U.S. Army, they all agree that their experience in the program so far has been humbling. 

“All of the MIT SCM students have strong academic backgrounds and are exceptionally better at math than us,” Toth laughs. “If you’re coming to this program, you’re sharp and you want to make a difference, not just in your life, but you want to make a difference in the world. Getting to sit in a room with 40 young people who want to make change happen and want to solve complex problems has been super rewarding.”

No strangers to being challenged, adversity is what called the fellows to become logistics officers in the first place. “It comes down to a quote I heard: Operations is easy, fighting the war is easy, but logistics is hard,” says Mullen. 

As logistics officers are responsible for everything from feeding soldiers to fixing trucks to warehousing and distribution, they must perform these functions at varying scales, and with varying threats to their operations. “Our work is: How do we enable the war fighter to be able to deliver when the nation requires? We’re looking at the supply chain and ensuring that we can deliver at the right time, right place, and in the right quantity with precision and accuracy,” says Greene. 

Although companies focus extensively on optimizing their supply chains for cost and efficiency, logistics officers in the military have an additional obstacle. “That last mile could be a contested mile, and the enemy gets a vote,” adds Toth. “At the end of the day, the civilian industry’s consumer has a product they want, and at the end of the day, our war fighters have a product that they want, but we have the added challenge of having to overcome a competitor who may go so far as to destroy us.”

Despite the fellows’ rich practical experience in the military, their academic experience still brings applicable use in terms of introduction to new technologies with which they hope to engage senior military leaders, insight into industry problem-solving to reduce overall military spending and influence decision-making, and, above all, communication. In the military, the stakes are higher than in the private sector, making communication rife with consequence. 

“This experience is helping us better communicate with industry and build an industry and logistics network so that if a challenge does come our country's way, we can better communicate with everybody to solve those challenges,” reflects Toth. 

Unlocking ammonia as a fuel source for heavy industry

Four MIT alumni say their startup, Amogy, has the technology to help decarbonize maritime shipping, power generation, manufacturing, and more.

At a high level, ammonia seems like a dream fuel: It’s carbon-free, energy-dense, and easier to move and store than hydrogen. Ammonia is also already manufactured and transported at scale, meaning it could transform energy systems using existing infrastructure. But burning ammonia creates dangerous nitrous oxides, and splitting ammonia molecules to create hydrogen fuel typically requires lots of energy and specialized engines.

The startup Amogy, founded by four MIT alumni, believes it has the technology to finally unlock ammonia as a major fuel source. The company has developed a catalyst it says can split — or “crack” — ammonia into hydrogen and nitrogen up to 70 percent more efficiently than state-of-the-art systems today. The company is planning to sell its catalysts as well as modular systems including fuel cells and engines to convert ammonia directly to power. Those systems don’t burn or combust ammonia, and thus bypass the health concerns related to nitrous oxides.

Since Amogy’s founding in 2020, the company has used its ammonia-cracking technology to create the world’s first ammonia-powered drone, tractor, truck, and tugboat. It has also attracted partnerships with industry leaders including Samsung, Saudi Aramco, KBR, and Hyundai, raising more than $300 million along the way.

“No one has showcased that ammonia can be used to power things at the scale of ships and trucks like us,” says CEO Seonghoon Woo PhD ’15, who founded the company with Hyunho Kim PhD ’18, Jongwon Choi PhD ’17, and Young Suk Jo SM ’13, PhD ’16. “We’ve demonstrated this approach works and is scalable.”

Earlier this year, Amogy completed a research and manufacturing facility in Houston and announced a pilot deployment of its catalyst with the global engineering firm JGC Holdings Corporation. Now, with a manufacturing contract secured with Samsung Heavy Industries, Amogy is set to start delivering more of its systems to customers next year. The company will deploy a 1-megawatt ammonia-to-power pilot project with the South Korean city of Pohang in 2026, with plans to scale up to 40 megawatts at that site by 2028 or 2029. Woo says dozens of other projects with multinational corporations are in the works.

Because of the power density advantages of ammonia over renewables and batteries, the company is targeting power-hungry industries like maritime shipping, power generation, construction, and mining for its early systems.

“This is only the beginning,” Woo says. “We’ve worked hard to build the technology and the foundation of our company, but the real value will be generated as we scale. We’ve proved the potential for ammonia to decarbonize heavy industry, and now we really want to accelerate adoption of our technology. We’re thinking long term about the energy transition.”

Unlocking a new fuel source

Woo and Choi completed their PhDs in MIT’s Department of Materials Science and Engineering before their eventual co-founders, Kim and Jo, completed their PhDs in MIT’s Department of Mechanical Engineering. Jo worked on energy science and ran experiments to make engines run more efficiently as part of his PhD.

“The PhD programs at MIT teach you how to think deeply about solving technical problems using systems-based approaches,” Woo says. “You also realize the value in learning from failures, and that mindset of iteration is similar to what you need to do in startups.”

In 2020, Woo was working in the semiconductor industry when he reached out to his eventual co-founders asking if they were working on anything interesting. At that time, Jo was still working on energy systems based on hydrogen and ammonia while Kim was developing new catalysts to create ammonia fuel.

“I wanted to start a company and build a business to do good things for society,” Woo recalls. “People had been talking about hydrogen as a more sustainable fuel source, but it had never come to fruition. We thought there might be a way to improve ammonia catalyst technology and accelerate the hydrogen economy.”

The founders started experimenting with Jo’s technology for ammonia cracking, the process in which ammonia (NH₃) molecules split into their nitrogen (N₂) and hydrogen (H₂) constituent parts. Ammonia cracking to date has been done at huge plants in high-temperature reactors that require large amounts of energy. Those high temperatures limited the catalyst materials that could be used to drive the reaction.

Starting from scratch, the founders were able to identify new material recipes that could be used to miniaturize the catalyst and work at lower temperatures. The proprietary catalyst materials allow the company to create a system that can be deployed in new places at lower costs.

“We really had to redevelop the whole technology, including the catalyst and reformer, and even the integration with the larger system,” Woo says. “One of the most important things is we don’t combust ammonia — we don’t need pilot fuel, and we don’t generate any nitrogen gas or CO₂.”

Today Amogy has a portfolio of proprietary catalyst technologies that use base metals along with precious metals. The company has proven the efficiency of its catalysts in demonstrations beginning with the first ammonia-powered drone in 2021. The catalyst can be used to produce hydrogen more efficiently, and by integrating the catalyst with hydrogen fuel cells or engines, Amogy also offers modular ammonia-to-power systems that can scale to meet customer energy demands.

“We’re enabling the decarbonization of heavy industry,” Woo says. “We are targeting transportation, chemical production, manufacturing, and industries that are carbon-heavy and need to decarbonize soon, for example to achieve domestic goals. Our vision in the longer term is to enable ammonia as a fuel in a variety of applications, including power generation, first at microgrids and then eventually full grid-scale.”

Scaling with industry

When Amogy completed its facility in Houston, one of their early visitors was MIT Professor Evelyn Wang, who is also MIT’s vice president for energy and climate. Woo says other people involved in the Climate Project at MIT have been supportive.

Another key partner for Amogy is Samsung Heavy Industries, which announced a multiyear deal to manufacturing Amogy’s ammonia-to-power systems on Nov. 12.

“Our strategy is to partner with the existing big players in heavy industry to accelerate the commercialization of our technology,” Woo says. “We have worked with big oil and gas companies like BHP and Saudi Aramco, companies interested in hydrogen fuel like KBR and Mitsubishi, and many more industrial companies.”

When paired with other clean energy technologies to provide the power for its systems, Woo says Amogy offers a way to completely decarbonize sectors of the economy that can’t electrify on their own.

“In heavy transport, you have to use high-energy density liquid fuel because of the long distances and power requirements,” Woo says. “Batteries can’t meet those requirements. It’s why hydrogen is such an exciting molecule for heavy industry and shipping. But hydrogen needs to be kept super cold, whereas ammonia can be liquid at room temperature. Our job now is to provide that power at scale.”

Josh Randolph: Taking care of others as an EMT and ROTC leader

“I always wanted to be in public service, serve my community, and serve my country,” says the MIT mechanical engineering major.

In April, MIT senior Josh Randolph will race 26.2 miles across Concord, Massachusetts, and neighboring towns, carrying a 50-lb backpack. The race, called the Tough Ruck, honors America’s fallen military and first responders. For Randolph, it is one of the most rewarding experiences he’s done in his time at MIT, and he’s never missed a race.

“I want to do things that are challenging and push me to learn more about myself,” says Randolph, a Nebraska native. “As soon as I found out about the Tough Ruck, I knew I was going to be a part of it.”

Carrying on tradition and honoring those before him is a priority for Randolph. Both of his grandfathers served in the United States Air Force, and now he’s following in their footsteps through leadership in the U.S. Air Force Reserve Officers’ Training Corps (AFROTC) at MIT. His work with MIT Emergency Medical Services (EMS) has inspired him to aim for medical school so he could join the Air Force as a doctor.

“I always wanted to be in public service, serve my community, and serve my country,” Randolph says.

Getting attached to medicine

Randolph was particularly close with his grandfather, who worked with electronics in the Air Force and later became an engineer.

“I’ve always seen him as a big role model of mine. He’s very proud of his service,” Randolph says. A mechanical engineering major, he shares his grandfather’s interest in the scientific and technical side of the military.

But Randolph hasn’t let his commitment to the Air Force narrow his experiences at MIT.

He signed up for MIT EMS in his sophomore year as a way to push out of his comfort zone. Although he didn’t have a strong interest in medicine at the time, he was excited about being responsible for providing essential services to his community.

“If somebody’s in need on campus, they call 911, and we’re entrusted with the responsibility to help them out and keep them safe. I didn’t even know that was something you could do in college,” Randolph says.

Getting late-night calls and handling high-pressure situations took some getting used to, but he loved that he was helping.

“It feels a little uncomfortable at first, but then the more calls you run, the more experience you get and the more comfortable you feel with it, and then the more you want to do,” Randolph says.

Since joining in his second year, Randolph has responded to more than 100 911 calls and now holds the rank of provincial crew chief, meaning he provides basic life support patient care and coordinates on-scene operations.

His experiences interacting with patients and racing around Cambridge, Massachusetts, to help his community made him realize he would regret not pursuing medicine. In his final year at MIT, he set his sights on medical school. “Even though it was pretty late, I decided to make that switch and put my all into medicine,” Randolph says.

After serving as class officer during his junior year, helping to oversee the EMT certification process, Randolph became the director of professional development in his senior year. In this role, he oversees the training and development of service members as well as the quality of patient care. “It’s great to see how new students integrate and gain bigger roles and become more involved with the services,” Randolph says. “It’s really rewarding to contribute a little bit toward their development within EMS and then also just as people.”

Leadership in the ROTC

Randolph knew he would be a part of Air Force ROTC since early in high school. He later earned the Air Force ROTC Type 1 scholarship that gave him a tuition-free spot at MIT. It was through AFROTC that he became further committed to helping and honoring those around him, including through the Tough Ruck.

“Pretty often there are family members of fallen servicemembers who make tags with their loved one’s name on it and they hand them out for people to carry on their rucks, which is pretty cool, Randolph said of the race. “Overall, it is a really supportive environment, and I try to give as many people high fives and as much encouragement as I can, but at some point I get too tired and need to focus on running.”

His parents come out to watch every year.

In previous semesters, Randolph has served as flight commander and group commander within AFROTC’s Detachment 365, which is based at MIT and also hosts cadets from Harvard University, Tufts University, and Wellesley College. Currently, as squadron commander, he leads one of the 20-cadet units that makes up the detachment. He has co-organized three Leadership Laboratories dedicated to training over 70 cadets.

Randolph has earned the AFROTC Field Training Superior Performance Award, the AFROTC Commendation Award, the AFROTC Achievement Award, and the Military Order of the World Wars Bronze Award. He has also received the AFROTC Academic Honors Award five times, the Physical Fitness Award four times, and the Maximum AFROTC Physical Fitness Assessment Award two times. 

He keeps his activities and schoolwork straight through to-do lists and calendar items, but he admits the workload can still be tough.

“One thing that has helped me is trying to prioritize and figure out what things need my attention immediately or what things will be very important. If it is something that is important and will affect or benefit a lot of people, I try and devote my energy toward that to make the most of my time and implement meaningful things,” Randolph says.

A human-centered direction

For the last two years, Randolph worked in the Pappalardo Laboratory as an apprentice and undergraduate assistant, helping students design, fabricate, and test robots they were building for a class design challenge. He has also conducted linguistics research with Professor Suzanne Flynn and worked in the labs of professor of nuclear science and engineering Michael Short and professor of biological and mechanical engineering Domatilla Del Vecchio.

Randolph has also volunteered his time through English for Speakers of Other Languages, where he worked as a volunteer to help MIT employees improve their English speaking and writing skills.

For now, he is excited to enter a more human-centered field through his studies in medicine. After watching his father survive two bouts of cancer, thanks in part to robotically assisted surgery, he hopes to develop robotic health care applications.

“I want to have a deeper and more tangible connection to people. Compassion and empathy are things that I really want to try and live by,” Randolph says. “I think being the most empathetic and compassionate with the people you take care of is always a good thing.”

Faces of MIT: Brian Hanna

MIT Venture Mentoring Service Operations Manager Brian Hanna matches entrepreneurs with industry professionals who help take their ventures to the next level through personalized mentorship and expert advice.

Brian Hanna, operations manager of MIT Venture Mentoring Service (VMS), connects skilled volunteer mentors with MIT entrepreneurs looking to launch, expand, and enhance their vision.  

MIT VMS is a free service, supporting innovation across the Institute, available to all current MIT students, staff members, faculty members, or alums of a degree-granting program living in the Greater Boston area. If a community member has an idea that they’d like help developing, Hanna and his team will match them with a team of mentors who can provide practical, as-needed expertise and knowledge to guide your venture. 

VMS is part of the MIT ecosystem for entrepreneurs. VMS mentors are selected for their experience in areas relevant to entrepreneurs’ needs and assist with a range of business challenges, including marketing, finance, and product development. As the program celebrates its 25th anniversary of serving MIT’s entrepreneurial community, it has supported more than 3,500 ventures and mentored over 4,800 participants. 

When Hanna began working at VMS in 2023, he was new to the program but not to the Institute. Prior to joining VMS, he served as the employer relations coordinator in Career Advising and Professional Development (CAPD), where he worked with companies interested in recruiting MIT talent. His responsibilities included organizing career fairs, scheduling interviews, and building relationships with various local employers. After two years at CAPD, Hanna transitioned to the role of center coordinator at the McGovern Institute for Brain Research. While Hanna does not claim to be a neuroscientist, his organizational skills proved valuable as he supported six different research centers at McGovern, with research ranging from autism to bionics.  

As the VMS operations manager, Hanna supervises staff members who run events and boot camps and schedule an average of 50 mentoring sessions a month. Whether it’s a first-time entrepreneur who comes up with an idea on their morning commute or an industry veteran with licensing and a patent in place, Hanna strategically matches them with mentors who can help them build their skill set and grow their business. Hanna also provides oversight to over 200 volunteer VMS mentors, half of which are MIT alumni. 

In addition to processing all incoming applications (about 25 per month), Hanna also oversees a monthly mentor meeting centered around strengthening the VMS mentor community. During the meeting, the VMS team shares announcements, discusses upcoming events, hosts guest speakers, and invites a group of current ventures to give four-minute pitches for additional advice. These pitches allow mentees to receive input from the entire mentor network, rather than just their mentor team.   

The relationship between mentees, mentors, and VMS does not have an expiration date. Hanna notes that a saying in the office is, “we are VMS for life.” This rings true, as some ventures and mentors have been a part of the program for most of its 25-year existence. 

When a mentee is ready to meet with their mentors for the first time, VMS aims to schedule an in-person meeting to create a strong relationship. After that, the program embraces the flexibility of meeting via Zoom to help make scheduling easier. One of the most valuable resources outside of the mentoring sessions is the theme-specific boot camps sprinkled throughout the year. These sessions are four- or five-hour events led by mentors who cover topics such as marketing, business-to-business sales, or building an IP portfolio. They serve as crash courses where mentees can learn the basics of important aspects of entrepreneurship. Another resource offered to active mentees is office hours with experts in areas such as human resources, legal, and accounting. 

In December, VMS will celebrate its 25th anniversary with an event honoring current and former mentors. The event will look back on 25 years of impact and look ahead to the future of the program. 

Scientists get a first look at the innermost region of a white dwarf system

X-ray observations reveal surprising features of the dying star’s most energetic environment.

Some 200 light years from Earth, the core of a dead star is circling a larger star in a macabre cosmic dance. The dead star is a type of white dwarf that exerts a powerful magnetic field as it pulls material from the larger star into a swirling, accreting disk. The spiraling pair is what’s known as an “intermediate polar” — a type of star system that gives off a complex pattern of intense radiation, including X-rays, as gas from the larger star falls onto the other one.

Now, MIT astronomers have used an X-ray telescope in space to identify key features in the system’s innermost region — an extremely energetic environment that has been inaccessible to most telescopes until now. In an open-access study published in the Astrophysical Journal, the team reports using NASA’s Imaging X-ray Polarimetry Explorer (IXPE) to observe the intermediate polar, known as EX Hydrae.

The team found a surprisingly high degree of X-ray polarization, which describes the direction of an X-ray wave’s electric field, as well as an unexpected direction of polarization in the X-rays coming from EX Hydrae. From these measurements, the researchers traced the X-rays back to their source in the system’s innermost region, close to the surface of the white dwarf.

What’s more, they determined that the system’s X-rays were emitted from a column of white-hot material that the white dwarf was pulling in from its companion star. They estimate that this column is about 2,000 miles high — about half the radius of the white dwarf itself and much taller than what physicists had predicted for such a system. They also determined that the X-rays are reflected off the white dwarf’s surface before scattering into space — an effect that physicists suspected but hadn’t confirmed until now.

The team’s results demonstrate that X-ray polarimetry can be an effective way to study extreme stellar environments such as the most energetic regions of an accreting white dwarf.

“We showed that X-ray polarimetry can be used to make detailed measurements of the white dwarf's accretion geometry,” says Sean Gunderson, a postdoc in MIT’s Kavli Institute for Astrophysics and Space Research, who is the study’s lead author. “It opens the window into the possibility of making similar measurements of other types of accreting white dwarfs that also have never had predicted X-ray polarization signals.”

 

Gunderson’s MIT Kavli co-authors include graduate student Swati Ravi and research scientists Herman Marshall and David Huenemoerder, along with Dustin Swarm of the University of Iowa, Richard Ignace of East Tennessee State University, Yael Nazé of the University of Liège, and Pragati Pradhan of Embry Riddle Aeronautical University.

A high-energy fountain

All forms of light, including X-rays, are influenced by electric and magnetic fields. Light travels in waves that wiggle, or oscillate, at right angles to the direction in which the light is traveling. External electric and magnetic fields can pull these oscillations in random directions. But when light interacts and bounces off a surface, it can become polarized, meaning that its vibrations tighten up in one direction. Polarized light, then, can be a way for scientists to trace the source of the light and discern some details about the source’s geometry.

The IXPE space observatory is NASA’s first mission designed to study polarized X-rays that are emitted by extreme astrophysical objects. The spacecraft, which launched in 2021, orbits the Earth and records these polarized X-rays. Since launch, it has primarily focused on supernovae, black holes, and neutron stars.

The new MIT study is the first to use IXPE to measure polarized X-rays from an intermediate polar — a smaller system compared to black holes and supernovas, that nevertheless is known to be a strong emitter of X-rays.

“We started talking about how much polarization would be useful to get an idea of what’s happening in these types of systems, which most telescopes see as just a dot in their field of view,” Marshall says.

An intermediate polar gets its name from the strength of the central white dwarf’s magnetic field. When this field is strong, the material from the companion star is directly pulled toward the white dwarf’s magnetic poles. When the field is very weak, the stellar material instead swirls around the dwarf in an accretion disk that eventually deposits matter directly onto the dwarf’s surface.

In the case of an intermediate polar, physicists predict that material should fall in a complex sort of in-between pattern, forming an accretion disk that also gets pulled toward the white dwarf’s poles. The magnetic field should lift the disk of incoming material far upward, like a high-energy fountain, before the stellar debris falls toward the white dwarf’s magnetic poles, at speeds of millions of miles per hour, in what astronomers refer to as an “accretion curtain.” Physicists suspect that this falling material should run up against previously lifted material that is still falling toward the poles, creating a sort of traffic jam of gas. This pile-up of matter forms a column of colliding gas that is tens of millions of degrees Fahrenheit and should emit high-energy X-rays.

An innermost picture

By measuring any polarized X-rays emitted by EX Hydrae, the team aimed to test the picture of intermediate polars that physicists had hypothesized. In January 2025, IXPE took a total of about 600,000 seconds, or about seven days’ worth, of X-ray measurements from the system.

“With every X-ray that comes in from the source, you can measure the polarization direction,” Marshall explains. “You collect a lot of these, and they’re all at different angles and directions which you can average to get a preferred degree and direction of the polarization.”

Their measurements revealed an 8 percent polarization degree that was much higher than what scientists had predicted according to some theoretical models. From there, the researchers were able to confirm that the X-rays were indeed coming from the system’s column, and that this column is about 2,000 miles high.

“If you were able to stand somewhat close to the white dwarf’s pole, you would see a column of gas stretching 2,000 miles into the sky, and then fanning outward,” Gunderson says.

The team also measured the direction of EX Hydrae’s X-ray polarization, which they determined to be perpendicular to the white dwarf’s column of incoming gas. This was a sign that the X-rays emitted by the column were then bouncing off the white dwarf’s surface before traveling into space, and eventually into IXPE’s telescopes.

“The thing that’s helpful about X-ray polarization is that it’s giving you a picture of the innermost, most energetic portion of this entire system,” Ravi says. “When we look through other telescopes, we don’t see any of this detail.”

The team plans to apply X-ray polarization to study other accreting white dwarf systems, which could help scientists get a grasp on much larger cosmic phenomena.

“There comes a point where so much material is falling onto the white dwarf from a companion star that the white dwarf can’t hold it anymore, the whole thing collapses and produces a type of supernova that’s observable throughout the universe, which can be used to figure out the size of the universe,” Marshall offers. “So understanding these white dwarf systems helps scientists understand the sources of those supernovae, and tells you about the ecology of the galaxy.”

This research was supported, in part, by NASA.

The cost of thinking

MIT neuroscientists find a surprising parallel in the ways humans and new AI models solve complex problems.

Large language models (LLMs) like ChatGPT can write an essay or plan a menu almost instantly. But until recently, it was also easy to stump them. The models, which rely on language patterns to respond to users’ queries, often failed at math problems and were not good at complex reasoning. Suddenly, however, they’ve gotten a lot better at these things.

A new generation of LLMs known as reasoning models are being trained to solve complex problems. Like humans, they need some time to think through problems like these — and remarkably, scientists at MIT’s McGovern Institute for Brain Research have found that the kinds of problems that require the most processing from reasoning models are the very same problems that people need take their time with. In other words, they report today in the journal PNAS, the “cost of thinking” for a reasoning model is similar to the cost of thinking for a human.

The researchers, who were led by Evelina Fedorenko, an associate professor of brain and cognitive sciences and an investigator at the McGovern Institute, conclude that in at least one important way, reasoning models have a human-like approach to thinking. That, they note, is not by design. “People who build these models don’t care if they do it like humans. They just want a system that will robustly perform under all sorts of conditions and produce correct responses,” Fedorenko says. “The fact that there’s some convergence is really quite striking.”

Reasoning models

Like many forms of artificial intelligence, the new reasoning models are artificial neural networks: computational tools that learn how to process information when they are given data and a problem to solve. Artificial neural networks have been very successful at many of the tasks that the brain’s own neural networks do well — and in some cases, neuroscientists have discovered that those that perform best do share certain aspects of information processing in the brain. Still, some scientists argued that artificial intelligence was not ready to take on more sophisticated aspects of human intelligence.

“Up until recently, I was among the people saying, ‘These models are really good at things like perception and language, but it’s still going to be a long ways off until we have neural network models that can do reasoning,” Fedorenko says. “Then these large reasoning models emerged and they seem to do much better at a lot of these thinking tasks, like solving math problems and writing pieces of computer code.”

Andrea Gregor de Varda, a K. Lisa Yang ICoN Center Fellow and a postdoc in Fedorenko’s lab, explains that reasoning models work out problems step by step. “At some point, people realized that models needed to have more space to perform the actual computations that are needed to solve complex problems,” he says. “The performance started becoming way, way stronger if you let the models break down the problems into parts.”

To encourage models to work through complex problems in steps that lead to correct solutions, engineers can use reinforcement learning. During their training, the models are rewarded for correct answers and penalized for wrong ones. “The models explore the problem space themselves,” de Varda says. “The actions that lead to positive rewards are reinforced, so that they produce correct solutions more often.”

Models trained in this way are much more likely than their predecessors to arrive at the same answers a human would when they are given a reasoning task. Their stepwise problem-solving does mean reasoning models can take a bit longer to find an answer than the LLMs that came before — but since they’re getting right answers where the previous models would have failed, their responses are worth the wait.

The models’ need to take some time to work through complex problems already hints at a parallel to human thinking: if you demand that a person solve a hard problem instantaneously, they’d probably fail, too. De Varda wanted to examine this relationship more systematically. So he gave reasoning models and human volunteers the same set of problems, and tracked not just whether they got the answers right, but also how much time or effort it took them to get there.

Time versus tokens

This meant measuring how long it took people to respond to each question, down to the millisecond. For the models, Varda used a different metric. It didn’t make sense to measure processing time, since this is more dependent on computer hardware than the effort the model puts into solving a problem. So instead, he tracked tokens, which are part of a model’s internal chain of thought. “They produce tokens that are not meant for the user to see and work on, but just to have some track of the internal computation that they’re doing,” de Varda explains. “It’s as if they were talking to themselves.”

Both humans and reasoning models were asked to solve seven different types of problems, like numeric arithmetic and intuitive reasoning. For each problem class, they were given many problems. The harder a given problem was, the longer it took people to solve it — and the longer it took people to solve a problem, the more tokens a reasoning model generated as it came to its own solution.

Likewise, the classes of problems that humans took longest to solve were the same classes of problems that required the most tokens for the models: arithmetic problems were the least demanding, whereas a group of problems called the “ARC challenge,” where pairs of colored grids represent a transformation that must be inferred and then applied to a new object, were the most costly for both people and models.

De Varda and Fedorenko say the striking match in the costs of thinking demonstrates one way in which reasoning models are thinking like humans. That doesn’t mean the models are recreating human intelligence, though. The researchers still want to know whether the models use similar representations of information to the human brain, and how those representations are transformed into solutions to problems. They’re also curious whether the models will be able to handle problems that require world knowledge that is not spelled out in the texts that are used for model training.

The researchers point out that even though reasoning models generate internal monologues as they solve problems, they are not necessarily using language to think. “If you look at the output that these models produce while reasoning, it often contains errors or some nonsensical bits, even if the model ultimately arrives at a correct answer. So the actual internal computations likely take place in an abstract, non-linguistic representation space, similar to how humans don’t use language to think,” he says.

New AI agent learns to use CAD to create 3D objects from sketches

The virtual VideoCAD tool could boost designers’ productivity and help train engineers learning computer-aided design.

Computer-Aided Design (CAD) is the go-to method for designing most of today’s physical products. Engineers use CAD to turn 2D sketches into 3D models that they can then test and refine before sending a final version to a production line. But the software is notoriously complicated to learn, with thousands of commands to choose from. To be truly proficient in the software takes a huge amount of time and practice.

MIT engineers are looking to ease CAD’s learning curve with an AI model that uses CAD software much like a human would. Given a 2D sketch of an object, the model quickly creates a 3D version by clicking buttons and file options, similar to how an engineer would use the software.

The MIT team has created a new dataset called VideoCAD, which contains more than 41,000 examples of how 3D models are built in CAD software. By learning from these videos, which illustrate how different shapes and objects are constructed step-by-step, the new AI system can now operate CAD software much like a human user.

With VideoCAD, the team is building toward an AI-enabled “CAD co-pilot.” They envision that such a tool could not only create 3D versions of a design, but also work with a human user to suggest next steps, or automatically carry out build sequences that would otherwise be tedious and time-consuming to manually click through.

“There’s an opportunity for AI to increase engineers’ productivity as well as make CAD more accessible to more people,” says Ghadi Nehme, a graduate student in MIT’s Department of Mechanical Engineering.

“This is significant because it lowers the barrier to entry for design, helping people without years of CAD training to create 3D models more easily and tap into their creativity,” adds Faez Ahmed, associate professor of mechanical engineering at MIT.

Ahmed and Nehme, along with graduate student Brandon Man and postdoc Ferdous Alam, will present their work at the Conference on Neural Information Processing Systems (NeurIPS) in December.

Click by click

The team’s new work expands on recent developments in AI-driven user interface (UI) agents — tools that are trained to use software programs to carry out tasks, such as automatically gathering information online and organizing it in an Excel spreadsheet. Ahmed’s group wondered whether such UI agents could be designed to use CAD, which encompasses many more features and functions, and involves far more complicated tasks than the average UI agent can handle.

In their new work, the team aimed to design an AI-driven UI agent that takes the reins of the CAD program to create a 3D version of a 2D sketch, click by click. To do so, the team first looked to an existing dataset of objects that were designed in CAD by humans. Each object in the dataset includes the sequence of high-level design commands, such as “sketch line,” “circle,” and “extrude,” that were used to build the final object.

However, the team realized that these high-level commands alone were not enough to train an AI agent to actually use CAD software. A real agent must also understand the details behind each action. For instance: Which sketch region should it select? When should it zoom in? And what part of a sketch should it extrude? To bridge this gap, the researchers developed a system to translate high-level commands into user-interface interactions.

“For example, let’s say we drew a sketch by drawing a line from point 1 to point 2,” Nehme says. “We translated those high-level actions to user-interface actions, meaning we say, go from this pixel location, click, and then move to a second pixel location, and click, while having the ‘line’ operation selected.”

In the end, the team generated over 41,000 videos of human-designed CAD objects, each of which is described in real-time in terms of the specific clicks, mouse-drags, and other keyboard actions that the human originally carried out. They then fed all this data into a model they developed to learn connections between UI actions and CAD object generation.

Once trained on this dataset, which they dub VideoCAD, the new AI model could take a 2D sketch as input and directly control the CAD software, clicking, dragging, and selecting tools to construct the full 3D shape. The objects ranged in complexity from simple brackets to more complicated house designs. The team is training the model on more complex shapes and envisions that both the model and the dataset could one day enable CAD co-pilots for designers in a wide range of fields.

“VideoCAD is a valuable first step toward AI assistants that help onboard new users and automate the repetitive modeling work that follows familiar patterns,” says Mehdi Ataei, who was not involved in the study, and is a senior research scientist at Autodesk Research, which develops new design software tools. “This is an early foundation, and I would be excited to see successors that span multiple CAD systems, richer operations like assemblies and constraints, and more realistic, messy human workflows.”

A new take on carbon capture

Mantel, founded by MIT alumni, developed a system that captures CO2 from factories and power plants while delivering steam to customers.

If there was one thing Cameron Halliday SM ’19, MBA ’22, PhD ’22 was exceptional at during the early days of his PhD at MIT, it was producing the same graph over and over again. Unfortunately for Halliday, the graph measured various materials’ ability to absorb CO2 at high temperatures over time — and it always pointed down and to the right. That meant the materials lost their ability to capture the molecules responsible for warming our climate.

At least Halliday wasn’t alone: For many years, researchers have tried and mostly failed to find materials that could reliably absorb CO2 at the super-high temperatures of industrial furnaces, kilns, and boilers. Halliday’s goal was to find something that lasted a little longer.

Then in 2019, he put a type of molten salt called lithium-sodium ortho-borate through his tests. The salts absorbed more than 95 percent of the CO2. And for the first time, the graph showed almost no degradation over 50 cycles.  The same was true after 100 cycles. Then 1,000.

“I honestly don’t know if we ever expected to completely solve the problem,” Halliday says. “We just expected to improve the system. It took another two months to figure out why it worked.”

The researchers discovered the salts behave like a liquid at high temperatures, which avoids the brittle cracking responsible for the degradation of many solid materials.

“I remember walking home over the Mass Ave bridge at 5 a.m. with all the morning runners going by me,” Halliday recalls. “That was the moment when I realized what this meant. Since then, it’s been about proving it works at larger scales. We’ve just been building the next scaled-up version, proving it still works, building a bigger version, proving that out, until we reach the ultimate goal of deploying this everywhere.”

Today, Halliday is the co-founder and CEO of Mantel, a company building systems to capture carbon dioxide at large industrial sites of all types. Although a lot of people think the carbon capture industry is a dead end, Halliday doesn’t give up so easily, and he’s got a growing corpus of performance data to keep him encouraged.

Mantel’s system can be added on to the machines of power stations and factories making cement, steel, paper and pulp, oil and gas, and more, reducing their carbon emissions by around 95 percent. Instead of being released into the atmosphere, the emitted CO2 is channeled into Mantel’s system, where the company’s salts are sprayed out from something that looks like a shower head. The CO2 diffuses through the molten salts in a reaction that can be reversed through further temperature increases, so the salts boil off pure CO2 that can be transported for use or stored underground.

A key difference from other carbon capture methods that have struggled to be profitable is that Mantel uses the heat from its process to generate steam for customers by combining it with water in another part of its system. Mantel says delivering steam, which is used to drive many common industrial processes, lets its system work with just 3 percent of the net energy that state-of-the-art carbon capture systems require.

“We’re still consuming energy, but we get most of it back as steam, whereas the incumbent technology only consumes steam,” says Halliday, who co-founded Mantel with Sean Robertson PhD ’22 and Danielle Rapson. “That steam is a useful revenue stream, so we can turn carbon capture from a waste management process into a value creation process for our customer’s core business — whether that’s a power station using steam to make electricity, or oil and gas refineries. It completely changes the economics of carbon capture.”

From science to startup

Halliday’s first exposure to MIT came in 2016 when he cold emailed Alan Hatton, MIT’s Ralph Landau Professor of Chemical Engineering Practice, asking if he could come to his lab for the summer and work on research into carbon capture.

“He invited me, but he didn’t put me on that project,” Halliday recalls. “At the end of the summer he said, ‘You should consider coming back and doing a PhD.’”

Halliday enrolled in a joint PhD-MBA program the following year.

“I really wanted to work on something that had an impact,” Halliday says. “The dual PhD-MBA program has some deep technical academic elements to it, but you also work with a company for two months, so you use a lot of what you learn in the real world.”

Halliday worked on a few different research projects in Hatton’s lab early on, all three of which eventually turned into companies. The one that he stuck with explored ways to make carbon capture more energy efficient by working at the high temperatures common at emissions-heavy industrial sites.

Halliday ran into the same problems as past researchers with materials degrading at such extreme conditions.

“It was the big limiter for the technology,” Halliday recalls.

Then Halliday ran his successful experiment with molten borate salts in 2019. The MBA portion of his program began soon after, and Halliday decided to use that time to commercialize the technology. Part of that occurred in Course 15.366 (Climate and Energy Ventures), where Halliday met his co-founders. As it happens, alumni of the class have started more than 150 companies over the years. Halliday also received support from the MIT Energy Initiative.

“MIT tries to pull these great ideas out of academia and get them into the world so they can be valued and used,” Halliday says. “For the Climate and Energy Ventures class, outside speakers showed us every stage of company-building. The technology roadmap for our system is shoebox-sized, shipping container, one-bedroom house, and then the size of a building. It was really valuable to see other companies and say, ‘That’s what we could look like in three years, or six years.”

From startup to scale up

When Mantel was officially founded in 2022 the founders had their shoebox-sized system. After raising early funding, the team built its shipping container-sized system at The Engine, an MIT-affiliated startup incubator. That system has been operational for almost two years.

Last year, Mantel announced a partnership with Kruger Inc. to build the next version of its system at a factory in Quebec, which will be operational next year. The plant will run in a two-year test phase before scaling across Kruger’s other plants if successful.

“The Quebec project is proving the capture efficiency and proving the step-change improvement in energy use of our system,” Halliday says. “It’s a derisking of the technology that will unlock a lot more opportunities.”

Halliday says Mantel is in conversations with close to 100 industrial partners around the world, including the owners of refineries, data centers, cement and steel plants, and oil and gas companies. Because it’s a standalone addition, Halliday says Mantel’s system doesn’t have to change much to be used in different industries.

Mantel doesn’t handle CO2 conversion or sequestration, but Halliday says capture makes up the bulk of the costs in the CO2 value chain. It also generates high-quality CO2 that can be transported in pipelines and used in industries including the food and beverage industry — like the CO2 that makes your soda bubbly.

“This is the solution our customers are dreaming of,” Halliday says. “It means they don’t have to shut down their billion-dollar asset and reimagine their business to address an issue that they all appreciate is existential. There are questions about the timeline, but most industries recognize this is a problem they’ll have to grapple with eventually. This is a pragmatic solution that’s not trying to reshape the world as we dream of it. It’s looking at the problem at hand today and fixing it.”

MIT researchers use CT scans to unravel mysteries of early metal production

The team adapted the medical technique to study slag waste that was a byproduct of ancient copper smelting.

Around 5,000 years ago, people living in what is now Iran began extracting copper from rock by processing ore, an activity known as smelting. This monumental shift gave them a powerful new technology and may have marked the birth of metallurgy. Soon after, people in different parts of the world were using copper and bronzes (alloys of copper and tin, or copper and arsenic) to produce decorative objects, weapons, tools, and more.

Studying how humans produced such objects is challenging because little evidence still exists, and artifacts that have survived are carefully guarded and preserved.

In a paper published in PLOS One, MIT researchers demonstrated a new approach to uncovering details of some of the earliest metallurgical processes. They studied 5,000-year-old slag waste, a byproduct of smelting ore, using techniques including X-ray computed tomography, also known as CT scanning. In their paper, they show how this noninvasive imaging technique, which has primarily been used in the medical field, can reveal fine details about structures within the pieces of ancient slag.

“Even though slag might not give us the complete picture, it tells stories of how past civilizations were able to refine raw materials from ore and then to metal,” says postdoc Benjamin Sabatini. “It speaks to their technological ability at that time, and it gives us a lot of information. The goal is to understand, from start to finish, how they accomplished making these shiny metal products.”

In the paper, Sabatini and senior author Antoine Allanore, a professor of metallurgy and the Heather N. Lechtman Professor of Materials Science and Engineering, combined CT scanning with more traditional methods of studying ancient artifacts, including cutting the samples for further analysis. They demonstrated that CT scanning could be used to complement those techniques, revealing pores and droplets of different materials within samples. This information could shed light on the materials used by and the technological sophistication of some of the first metallurgists on Earth.

“The Early Bronze Age is one of the earliest reported interactions between mankind and metals,” says Allanore, who is also director of MIT’s Center for Materials Research in Archaeology and Ethnology. “Artifacts in that region at that period are extremely important in archaeology, yet the materials themselves are not very well-characterized in terms of our understanding of the underlying materials and chemical processes. The CT scan approach is a transformation of traditional archaeological methods of determining how to make cuts and analyze samples.”

A new tool in archaeology

Slag is produced as a molten hot liquid when ores are heated to produce metal. The slag contains other constituent minerals from the ore, as well as unreacted metals, which are commonly mixed with additives like limestone. In the mixture, the slag is less dense than the metal, so it can rise and be removed, solidifying like lava as it cools.

“Slag waste is chemically complex to interpret because in our modern metallurgical practices it contains everything not desired in the final product — in particular, arsenic, which is a key element in the original minerals for copper,” says Allanore. “There’s always been a question in archaeometallurgy if we can use arsenic and similar elements in these remains to learn something about the metal production process. The challenge here is that these minerals, especially arsenic, are very prone to dissolution and leaching, and therefore their environmental stability creates additional problems in terms of interpreting what this object was when it was being made 6,000 years ago.”

For the study, the researchers used slag from an ancient site known as Tepe Hissar in Iran. The slag has previously been dated to the period between 3100 and 2900 BCE and was loaned by the Penn Museum to Allanore for study in 2022.

“This region is often brought up as one of the earliest places where evidence of copper processing and object production might have happened,” Allanore explains. “It is very well-preserved, and it’s an early example of a site with long-distance trade and highly organized society. That’s why it’s so important in metallurgy.”

The researchers believe this is the first attempt to study ancient slag using CT scanning, partly because medical-grade scanners are expensive and primarily located in hospitals. The researchers overcame these challenges by working with a local startup in Cambridge that makes industrial CT scanners. They also used the CT scanner on MIT’s campus.

“It was really out of curiosity to see if there was a better way to study these objects,” Sabatini said.

In addition to the CT scans, the researchers used more conventional archaeological analytical methods such as X-ray fluorescence, X-ray diffraction, and optical and scanning electron microscopy. The CT scans provided a detailed overall picture of the internal structure of the slag and the location of interesting features like pores and bits of different materials, augmenting the conventional techniques to impart more complete information about the inside of samples.

They used that information to decide where to section their sample, noting that researchers often guess where to section samples, unsure even which side of the sample was originally facing up or down.

“My strategy was to zero in on the high-density metal droplets that looked like they were still intact, since those might be most representative of the original process,” Sabatini says. “Then I could destructively analyze the samples with a single slice. The CT scanning shows you exactly what is most interesting, as well as the general layout of things you need to study.”

Finding stories in slag

In previous studies, some slag samples from the Tepe Hissar site contained copper and thus seemed to fit the narrative that they resulted from the production of copper, while others showed no evidence of copper at all.

The researchers found that CT scanning allowed them to characterize the intact droplets that contained copper. It also allowed them to identify where gases evolved, forming voids that hold information about how the slags were produced.

Other slags at the site had previously been found to contain small metallic arsenide compounds, leading to disagreements about the role of arsenic in early metal production. The MIT researchers found that arsenic existed in different phases across their samples and could move within the slag or even escape the slag entirely, making it complicated to infer metallurgical processes from the study of arsenic alone.

Moving forward, the researchers say CT scanning could be a powerful tool in archaeology to unravel complex ancient materials and processes.

“This should be an important lever for more systematic studies of the copper aspect of smelting, and also for continuing to understand the role of arsenic,” Allanore says. “It allows us to be cognizant of the role of corrosion and the long-term stability of the artifacts to continue to learn more. It will be a key support for people who want to investigate these questions.”

This work was supported, in part, by the MIT Human Insight Collaborative (MITHIC). The X-ray CT system is supported by MIT's Center for Advanced Production Technologies.

Ultrasonic device dramatically speeds harvesting of water from the air

The system can be paired with any atmospheric water harvesting material to shake out drinking water in minutes instead of hours.

Feeling thirsty? Why not tap into the air? Even in desert conditions, there exists some level of humidity that, with the right material, can be soaked up and squeezed out to produce clean drinking water. In recent years, scientists have developed a host of promising sponge-like materials for this “atmospheric water harvesting.”

But recovering the water from these materials usually requires heat — and time. Existing designs rely on heat from the sun to evaporate water from the materials and condense it into droplets. But this step can take hours or even days. 

Now, MIT engineers have come up with a way to quickly recover water from an atmospheric water harvesting material. Rather than wait for the sun to evaporate water out, the team uses ultrasonic waves to shake the water out.

The researchers have developed an ultrasonic device that vibrates at high frequency. When a water-harvesting material, known as a “sorbent,” is placed on the device, the device emits ultrasound waves that are tuned to shake water molecules out of the sorbent. The team found that the device recovers water in minutes, versus the tens of minutes or hours required by thermal designs.

Unlike heat-based designs, the device does require a power source. The team envisions that the device could be powered by a small solar cell, which could also act as a sensor to detect when the sorbent is full. It could also be programmed to automatically turn on whenever a material has harvested enough moisture to be extracted. In this way, a system could soak up and shake out water from the air over many cycles in a single day.

“People have been looking for ways to harvest water from the atmosphere, which could be a big source of water particularly for desert regions and places where there is not even saltwater to desalinate,” says Svetlana Boriskina, principal research scientist in MIT’s Department of Mechanical Engineering. “Now we have a way to recover water quickly and efficiently.”

Boriskina and her colleagues report on their new device in a study appearing today in the journal Nature Communications. The study’s first author is Ikra Iftekhar Shuvo, an MIT graduate student in media arts and sciences, along with Carlos Díaz-Marín, Marvin Christen, Michael Lherbette, and Christopher Liem.

Precious hours

Boriskina’s group at MIT develops materials that interact with the environment in novel ways. Recently, her group explored atmospheric water harvesting (AWH), and ways that materials can be designed to efficiently absorb water from the air. The hope is that, if they can work reliably, AWH systems would be of most benefit to communities where traditional sources of drinking water — and even saltwater — are scarce.

Like other groups, Boriskina’s lab had generally assumed that an AWH system in the field would absorb moisture during the night, and then use the heat from the sun during the day to naturally evaporate the water and condense it for collection.

“Any material that’s very good at capturing water doesn’t want to part with that water,” Boriskina explains. “So you need to put a lot of energy and precious hours into pulling water out of the material.”

She realized there could be a faster way to recover water after Ikra Shuvo joined her group. Shuvo had been working with ultrasound for wearable medical device applications. When he and Boriskina considered ideas for new projects, they realized that ultrasound could be a way to speed up the recovery step in atmospheric water harvesting.

“It clicked: We have this big problem we’re trying to solve, and now Ikra seemed to have a tool that can be used to solve this problem,” Boriskina recalls.

Water dance

Ultrasound, or ultrasonic waves, are acoustic pressure waves that travel at frequencies of over 20 kilohertz (20,000 cycles per second). Such high-frequency waves are not visible or audible to humans. And, as the team found, ultrasound vibrates at just the right frequency to shake water out of a material.

“With ultrasound, we can precisely break the weak bonds between water molecules and the sites where they’re sitting,” Shuvo says. “It’s like the water is dancing with the waves, and this targeted disturbance creates momentum that releases the water molecules, and we can see them shake out in droplets.”

Shuvo and Boriskina designed a new ultrasonic actuator to recover water from an atmospheric water harvesting material. The heart of the device is a flat ceramic ring that vibrates when voltage is applied. This ring is surrounded by an outer ring that is studded with tiny nozzles. Water droplets that shake out of a material can drop through the nozzle and into collection vessels attached above and below the vibrating ring.

They tested the device on a previously designed atmospheric water harvesting material. Using quarter-sized samples of the material, the team first placed each sample in a humidity chamber, set to various humidity levels. Over time, the samples absorbed moisture and became saturated. The researchers then placed each sample on the ultrasonic actuator and powered it on to vibrate at ultrasonic frequencies. In all cases, the device was able to shake out enough water to dry out each sample in just a few minutes.

The researchers calculate that, compared to using heat from the sun, the ultrasonic design is 45 times more efficient at extracting water from the same material.

“The beauty of this device is that it’s completely complementary and can be an add-on to almost any sorbent material,” says Boriskina, who envisions a practical, household system might consist of a fast-absorbing material and an ultrasonic actuator, each about the size of a window. Once the material is saturated, the actuator would briefly turn on, powered by a solar cell, to shake out the water. The material would then be ready to harvest more water, in multiple cycles throughout a single day.

“It’s all about how much water you can extract per day,” she says. “With ultrasound, we can recover water quickly, and cycle again and again. That can add up to a lot per day.”

This work was supported, in part, by the MIT Abdul Latif Jameel Water and Food Systems Lab and the MIT-Israel Zuckerman STEM Fund.

This work was carried out in part by using MIT.nano and ISN facilities at MIT.

Bigger datasets aren’t always better

MIT researchers developed a way to identify the smallest dataset that guarantees optimal solutions to complex problems.

Determining the least expensive path for a new subway line underneath a metropolis like New York City is a colossal planning challenge — involving thousands of potential routes through hundreds of city blocks, each with uncertain construction costs. Conventional wisdom suggests extensive field studies across many locations would be needed to determine the costs associated with digging below certain city blocks.

Because these studies are costly to conduct, a city planner would want to perform as few as possible while still gathering the most useful data for making an optimal decision.

With almost countless possibilities, how would they know where to start?

A new algorithmic method developed by MIT researchers could help. Their mathematical framework provably identifies the smallest dataset that guarantees finding the optimal solution to a problem, often requiring fewer measurements than traditional approaches suggest.

In the case of the subway route, this method considers the structure of the problem (the network of city blocks, construction constraints, and budget limits) and the uncertainty surrounding costs. The algorithm then identifies the minimum set of locations where field studies would guarantee finding the least expensive route. The method also identifies how to use this strategically collected data to find the optimal decision.

This framework applies to a broad class of structured decision-making problems under uncertainty, such as supply chain management or electricity network optimization.

“Data are one of the most important aspects of the AI economy. Models are trained on more and more data, consuming enormous computational resources. But most real-world problems have structure that can be exploited. We’ve shown that with careful selection, you can guarantee optimal solutions with a small dataset, and we provide a method to identify exactly which data you need,” says Asu Ozdaglar, Mathworks Professor and head of the MIT Department of Electrical Engineering and Computer Science (EECS), deputy dean of the MIT Schwarzman College of Computing, and a principal investigator in the Laboratory for Information and Decision Systems (LIDS).

Ozdaglar, co-senior author of a paper on this research, is joined by co-lead authors Omar Bennouna, an EECS graduate student, and his brother Amine Bennouna, a former MIT postdoc who is now an assistant professor at Northwestern University; and co-senior author Saurabh Amin, co-director of Operations Research Center, a professor in the MIT Department of Civil and Environmental Engineering, and a principal investigator in LIDS. The research will be presented at the Conference on Neural Information Processing Systems.

An optimality guarantee

Much of the recent work in operations research focuses on how to best use data to make decisions, but this assumes these data already exist.

The MIT researchers started by asking a different question — what are the minimum data needed to optimally solve a problem? With this knowledge, one could collect far fewer data to find the best solution, spending less time, money, and energy conducting experiments and training AI models.

The researchers first developed a precise geometric and mathematical characterization of what it means for a dataset to be sufficient. Every possible set of costs (travel times, construction expenses, energy prices) makes some particular decision optimal. These “optimality regions” partition the decision space. A dataset is sufficient if it can determine which region contains the true cost.

This characterization offers the foundation of the practical algorithm they developed that identifies datasets that guarantee finding the optimal solution.

Their theoretical exploration revealed that a small, carefully selected dataset is often all one needs.

“When we say a dataset is sufficient, we mean that it contains exactly the information needed to solve the problem. You don’t need to estimate all the parameters accurately; you just need data that can discriminate between competing optimal solutions,” says Amine Bennouna.

Building on these mathematical foundations, the researchers developed an algorithm that finds the smallest sufficient dataset.

Capturing the right data

To use this tool, one inputs the structure of the task, such as the objective and constraints, along with the information they know about the problem.

For instance, in supply chain management, the task might be to reduce operational costs across a network of dozens of potential routes. The company may already know that some shipment routes are especially costly, but lack complete information on others.

The researchers’ iterative algorithm works by repeatedly asking, “Is there any scenario that would change the optimal decision in a way my current data can't detect?” If yes, it adds a measurement that captures that difference. If no, the dataset is provably sufficient.

This algorithm pinpoints the subset of locations that need to be explored to guarantee finding the minimum-cost solution.

Then, after collecting those data, the user can feed them to another algorithm the researchers developed which finds that optimal solution. In this case, that would be the shipment routes to include in a cost-optimal supply chain.

“The algorithm guarantees that, for whatever scenario could occur within your uncertainty, you’ll identify the best decision,” Omar Bennouna says.

The researchers’ evaluations revealed that, using this method, it is possible to guarantee an optimal decision with a much smaller dataset than would typically be collected.

“We challenge this misconception that small data means approximate solutions. These are exact sufficiency results with mathematical proofs. We’ve identified when you’re guaranteed to get the optimal solution with very little data — not probably, but with certainty,” Amin says.

In the future, the researchers want to extend their framework to other types of problems and more complex situations. They also want to study how noisy observations could affect dataset optimality.

“I was impressed by the work’s originality, clarity, and elegant geometric characterization. Their framework offers a fresh optimization perspective on data efficiency in decision-making,” says Yao Xie, the Coca-Cola Foundation Chair and Professor at Georgia Tech, who was not involved with this work.

Four from MIT named 2026 Rhodes Scholars

Vivian Chinoda ’25, Alice Hall, Sofia Lara, and Sophia Wang ’24 will begin postgraduate studies at Oxford University next fall.

Vivian Chinoda ’25, Alice Hall, Sofia Lara, and Sophia Wang ’24 have been selected as 2026 Rhodes Scholars and will begin fully funded postgraduate studies at the University of Oxford in the U.K. next fall. Hall, Lara, and Wang, are U.S. Rhodes Scholars; Chinoda was awarded the Rhodes Zimbabwe Scholarship.

The scholars were supported by Associate Dean Kim Benard and the Distinguished Fellowships team in Career Advising and Professional Development. They received additional mentorship and guidance from the Presidential Committee on Distinguished Fellowships.

“MIT students never cease to amaze us with their creativity, vision, and dedication,” says Professor Taylor Perron, who co-chairs the committee along with Professor Nancy Kanwisher. “This is especially true of this year’s Rhodes scholars. It’s remarkable how they are simultaneously so talented in their respective fields and so adept at communicating their goals to the world. I look forward to seeing how these outstanding young leaders shape the future. It’s an honor to work with such talented students.”

Vivian Chinoda ’25

Vivian Chinoda, from Harare, Zimbabwe, was named a Rhodes Zimbabwe Scholar on Oct. 10. Chinoda graduated this spring with a BS in business analytics. At Oxford, she hopes to pursue the MSc in social data science and a master’s degree in public policy.  Chinoda aims to foster economic development and equitable resource access for Zimbabwean communities by promoting social innovation and evidence-based policy.

At MIT, Chinoda researched the impacts of the EU’s General Data Protection Regulation on stakeholders and key indicators, such as innovation, with the Institute for Data, Systems, and Society. She supported the Digital Humanities Lab and MIT Ukraine in building a platform to connect and fundraise for exiled Ukrainian scientists. With the MIT Office of Sustainability, Chinoda co-led the plan for a campus transition to a fully electric vehicle fleet, advancing the Institute’s Climate Action Plan.

Chinoda’s professional experience includes roles as a data science and research intern at Adaviv (a controlled-environment agriculture startup) and a product manager at Red Hat, developing AI tools for open-source developers.

Beyond academics, Chinoda served as first-year outreach chair and vice president of the African Students’ Association, where she co-founded the Impact Fund, raising over $30,000 to help members launch social impact initiatives in their countries. She was a scholar in the Social and Ethical Responsibilities of Computing (SERC) program, studying big-data ethics across sectors like criminal justice and health care, and a PKG social impact internship participant. Chinoda also enjoys fashion design, which she channeled into reviving the MIT Black Theatre Guild, earning her the 2025 Laya and Jerome B. Wiesner Student Art Award.

Alice Hall

Alice Hall is a senior from Philadelphia studying chemical engineering with a minor in Spanish. At Oxford, she will earn a DPhil in engineering, focusing on scaling sustainable heating and cooling technologies. She is passionate about bridging technology, leadership, and community to address the climate crisis.

Hall’s research journey began in the Lienhard Group, developing computational and techno-economic models of electrodialysis for nutrient reclamation from brackish groundwater. She then worked in the Langer Lab, investigating alveolar-capillary barrier function to enhance lung viability for transplantation. During a summer in Madrid, she collaborated with the European Space Agency to optimize surface treatments for satellite materials.

Hall’s current research in the Olivetti Group, as part of the MIT Climate Project, examines the manufacturing scalability of early-stage clean energy solutions. Hall has gained industry experience through internships with Johnson and Johnson and Procter and Gamble.

Hall represents the student body as president of MIT’s Undergraduate Association. She also serves on the Presidential Advisory Cabinet, the executive boards of the Chemical Engineering Undergraduate Student Advisory Board and MIT’s chapter of the American Institute of Chemical Engineers, the Corporation Joint Advisory Committee, the Compton Lectures Advisory Committee, and the MIT Alumni Association Board of Directors as an invited guest.

She is an active member of the Gordon-MIT Engineering Leadership Program, the Black Students’ Union, and the National Society of Black Engineers. As a member of the varsity basketball team, she earned both NEWMAC and D3hoops.com Region 2 Rookie of the Year honors in 2023.

Sofia Lara

Hailing from Los Angeles, Sofia Lara is a senior majoring in biological engineering with a minor in Spanish. As a Rhodes Scholar at Oxford, she will pursue a DPhil in clinical medicine, leveraging UK biobank data to develop sex-stratified dosing protocols and safety guidelines for the NHS.

Lara aspires to transform biological complexity from medicine’s blind spots into a therapeutic superpower where variability reveals hidden possibilities and precision medicine becomes truly precise.

At the Broad Institute of MIT and Harvard, Lara investigates the cGAS-STING immune pathway in cancer. Her thesis, a comprehensive genome-wide association study illuminating the role of STING variation in disease pathology, aims to expand understanding of STING-linked immune disorders.

Lara co-founded the MIT-Harvard Future of Biology Conference, convening multidisciplinary researchers to interrogate vulnerabilities in cancer biology. As president of MIT Baker House, she steered community initiatives and executed the legendary Piano Drop, mobilizing hundreds of students in an enduring ritual of collective resilience. Lara captains the MIT Archery Team, serves as music director for MIT Catholic Community, and channels empathy through hand-stitched crocheted octopuses for pediatric patients at the Massachusetts General Hospital.

Sophia Wang ’24

Sophia Wang, from Woodbridge, Connecticut, graduated with a BS in aerospace engineering and a concentration in the design of highly autonomous systems. At Oxford, she will pursue an MSc in mathematical and theoretical physics, followed by an MSc in global governance and diplomacy.

As an undergraduate, Wang conducted research with the MIT Space Telecommunications Astronomy Radiation (STAR) Lab and the MIT Media Lab’s Tangible Media Group and Center for Bits and Atoms. She also interned at the NASA Jet Propulsion Laboratory, working on engineering projects for exoplanet detection missions, the Mars Sample Return mission, and terrestrial proofs-of-concept for self-assembly in space.

Since graduating from MIT, Wang has been engaged in a number of projects. In Bhutan, she contributes to national technology policy centered on mindful development. In Japan, she is a founding researcher at the Henkaku Center, where she is creating an international network of academic institutions. As a venture capitalist, she recently worked with commercial space stations on the effort to replace the International Space Station, which will decommission in 2030. Wang’s creative prototyping tools, such as a modular electromechanical construction kit, are used worldwide through the Fab Foundation, a network of 2,500+ community digital fabrication labs.

An avid cook, Wang created with friends Mince, a pop-up restaurant that serves fine-dining meals to MIT students. Through MIT Global Teaching Labs, Wang taught STEM courses in Kazakhstan and Germany, and she taught digital fabrication and 3D printing workshops across the U.S. as a teacher and cyclist with MIT Spokes. 

MIT Haystack scientists study recent geospace storms and resulting light shows

Solar maximum occurred within the past year — good news for aurora watchers, as the most active period for displays at New England latitudes occurs in the three years following solar maximum.

The northern lights, or aurora borealis, one of nature's most spectacular visual shows, can be elusive. Conventional wisdom says that to see them, we need to travel to northern Canada or Alaska. However, in the past two years, New Englanders have been seeing these colorful atmospheric displays on a few occasions — including this week — from the comfort of their backyards, as auroras have been visible in central and southern New England and beyond. These unusual auroral events have been driven by increased space weather activity, a phenomenon studied by a team of MIT Haystack Observatory scientists.

Auroral events are generated when particles in space are energized by complicated processes in the near-Earth environment, following which they interact with gases high up in the atmosphere. Space weather events such as coronal mass ejections, in which large amounts of material are ejected from our sun, along with geomagnetic storms, greatly increase energy input into those space regions near Earth. These inputs then trigger other processes that cause an increase in energetic particles entering our atmosphere. 

The result is variable colorful lights when the newly energized particles crash into atoms and molecules high above Earth's surface. Recent significant geomagnetic storm events have triggered these auroral displays at latitudes lower than normal — including sightings across New England and other locations across North America.

New England has been enjoying more of these spectacular light shows, such as this week's displays and those during the intense geomagnetic solar storms in May and October 2024, because of increased space weather activity.

Research has determined that auroral displays occur when selected atoms and molecules high in the upper atmosphere are excited by incoming charged particles, which are boosted in energy by intense solar activity. The most common auroral display colors are pink/red and green, with colors varying according to the altitude at which these reactions occur. Red auroras come from lower-energy particles exciting neutral oxygen and cause emissions at altitudes above 150 miles. Green auroras come from higher-energy particles exciting neutral oxygen and cause emissions at altitudes below 150 miles. Rare purple and blue aurora come from excited molecular nitrogen ions and occur during the most intense events.

Scientists measure the magnitude of geomagnetic activity driving auroras in several different ways. One of these uses sensitive magnetic field-measuring equipment at stations around the planet to obtain a geomagnetic storm measurement known as Kp, on a scale from 1 (least activity) to 9 (greatest activity), in three-hour intervals. Higher Kp values indicate the possibility — not a guarantee — of greater auroral sightings as the location of auroral displays move to lower latitudes. Typically, when the Kp index reaches a range of 6 or higher, this indicates that aurora viewings are more likely outside the usual northern ranges. The geomagnetic storm events of this week reached a Kp value of 9, indicating very strong activity in the sun–Earth system.

At MIT Haystack Observatory in Westford, Massachusetts, geospace and atmospheric physics scientists study the atmosphere and its aurora year-round by combining observations from many different instruments. These include ground-based sensors — including large upper-atmosphere radars that bounce signals off particles in the ionosphere — as well as data from space satellites. These tools provide key information, such as density, temperature, and velocity, on conditions and disturbances in the upper atmosphere: basic information that helps researchers at MIT and elsewhere understand the weather in space. 

Haystack geospace research is primarily funded through science funding by U.S. federal agencies such as the National Science Foundation (NSF) and NASA. This work is crucial for our increasingly spacefaring civilization, which requires continual expansion of our understanding of how space weather affects life on Earth, including vital navigation systems such as GPS, worldwide communication infrastructure, and the safety of our power grids. Research in this area is especially important in modern times, as humans increasingly use low Earth orbit for commercial satellite constellations and other systems, and as civilization further progresses into space.

Studies of the variations in our atmosphere and its charged component, known as the ionosphere, have revealed the strong influence of the sun. Beyond the normal white light that we experience each day, the sun also emits many other wavelengths of light, from infrared to extreme ultraviolet. Of particular interest are the extreme ultraviolet portions of solar output, which have enough energy to ionize atoms in the upper atmosphere. Unlike its white light component, the sun's output at these very short wavelengths has many different short- and long-term variations, but the most well known is the approximately 11-year solar cycle, in which the sun goes from minimum to maximum output. 

Scientists have determined that the most recent peak in activity, known as solar maximum, occurred within the past 12 months. This is good news for auroral watchers, as the most active period for severe geomagnetic storms that drive auroral displays at New England latitudes occurs during the three-year period following solar maximum.

Despite intensive research to date, we still have a great deal more to learn about space weather and its effects on the near-Earth environment. MIT Haystack Observatory continues to advance knowledge in this area. 

Larisa Goncharenko, lead geospace scientist and assistant director at Haystack, states, "In general, understanding space weather well enough to forecast it is considerably more challenging than even normal weather forecasting near the ground, due to the vast distances involved in space weather forces. Another important factor comes from the combined variation of Earth's neutral atmosphere, affected by gravity and pressure, and from the charged particle portion of the atmosphere, created by solar radiation and additionally influenced by the geometry of our planet's magnetic field. The complex interplay between these elements provides rich complexity and a sustained, truly exciting scientific opportunity to improve our understanding of basic physics in this vital part of our home in the solar system, for the benefit of civilization."

For up-to-date space weather forecasts and predictions of possible aurora events, visit SpaceWeather.com or NOAA's Aurora Viewline site.

MIT startup aims to expand America’s lithium production

Lithios, founded by Mo Alkhadra PhD ’22 and Professor Martin Bazant, is scaling up an electrochemical lithium extraction technology to secure supply chains of the critical metal.

China dominates the global supply of lithium. The country processes about 65 percent of the battery material and has begun on-again, off-again export restrictions of lithium-based products critical to the economy.

Fortunately, the U.S. has significant lithium reserves, most notably in the form of massive underground brines across south Arkansas and east Texas. But recovering that lithium through conventional techniques would be an energy-intensive and environmentally damaging proposition — if it were profitable at all.

Now, the startup Lithios, founded by Mo Alkhadra PhD ’22 and Martin Z. Bazant, the Chevron Chair Professor of Chemical Engineering, is commercializing a new process of lithium recovery it calls Advanced Lithium Extraction. The company uses electricity to drive a reaction with electrode materials that capture lithium from salty brine water, leaving behind other impurities.

Lithios says its process is more selective and efficient than other direct lithium-extraction techniques being developed. It also represents a far cleaner and less energy-intensive alternative to mining and the solar evaporative ponds that are used to extract lithium from underground brines in the high deserts of South America.

Lithios has been continuously running a pilot system extracting lithium from brine waters from around the world since June. It also recently shipped an early version of its system to a commercial partner scaling up operations in Arkansas.

With the core technology of its modular systems largely validated, next year Lithios plans to begin operating a larger version capable of producing 10 to 100 tons of lithium carbonate per year. From there, the company plans to build a commercial facility that will be able to produce 25,000 tons of lithium carbonate each year. That would represent a massive increase in the total lithium production of the U.S., which is currently limited to less than 5,000 tons per year.

“There’s been a big push recently, and especially in the last year, to secure domestic supplies of lithium and break away from the Chinese chokehold on the critical mineral supply chain,” Alkhadra says. “We have an abundance of lithium deposits at our disposal in the U.S., but we lack the tools to turn those resources into value.”

Adapting a technology

Bazant realized the need for new approaches to mining lithium while working with battery companies through his lab in MIT’s Department of Chemical Engineering. His group has studied battery materials and electrochemical separation for decades.

As part of his PhD in Bazant’s lab, Alkhadra studied electrochemical processes for separation of dissolved metals, with a focus on removing lead from drinking water and treating industrial wastewater. As Alkhadra got closer to graduation, he and Bazant looked at the most promising commercial applications for his work.

It was 2021, and lithium prices were in the midst of a historic spike driven by the metal’s importance in batteries.

Today, lithium comes primarily from mining or through a slow evaporative process that uses miles of surface ponds to refine and recover lithium from wastewater. Both are energy-intensive and damaging to the environment. They are also dominated by Chinese companies and supply chains.

“A lot of hard rock mining is done in Australia, but most of the rock is shipped as a concentrate to China for refining because they’re the ones who have the technology,” Bazant explains.

Other direct lithium-extraction methods use chemicals and filters, but the founders say those methods struggle to be profitable with U.S. lithium reserves, which have low concentrations of lithium and high levels of impurities.

“Those methods work when you have a good grade of lithium brine, but they become increasingly uneconomical as you get lower-quality resources, which is exactly what the industry is going through right now,” Alkhadra says. “The evaporative process has a huge footprint — we’re talking about the size of Manhattan island for a single project. Conveniently, recovering minerals from those low concentrations was the essence of my PhD work at MIT. We simply had to adapt the technology to the new use case.”

While conducting early talks with potential customers, Alkhadra received guidance from MIT’s Venture Mentoring Service, the MIT Sandbox Innovation Fund, and the Massachusetts Clean Energy Center. Lithios officially formed when he completed his PhD in 2022 and received the Activate Fellowship. Lithios grew at The Engine, an MIT startup incubator, before moving to their pilot and manufacturing facility in Medford, Massachusetts, in 2024.

Today, Lithios uses an undisclosed electrode material that attaches to lithium when exposed to precise voltages.

“Think of a big battery with water flowing into the system,” Alkhadra explains. “When the brine comes into contact with our electrodes, it selectively pulls lithium while rejecting all the other contaminants. When the lithium has been loaded onto our capture materials, we can simply change the direction of the electrical current to release the lithium back into a clean water stream. It’s similar to charging and discharging a battery.”

Bazant says the company’s lithium-absorbing materials are an ideal fit for this application.

“One of the main challenges of using battery electrodes to extract lithium is how to complete the system,” Bazant says. “We have a great lithium-extraction material that is very stable in water and has wonderful performance. We also learned how to formulate both electrodes with controlled ion transport and mixing to make the process much more efficient and low cost.”

Growing in the ‘MIT spirit’

The U.S. Geological Survey last year showed the underground Smackover Formation contains between 5 and 19 million tons of lithium in southwest Arkansas alone.

“If you just estimate how much lithium is in that region based on today’s prices, it’s about $2 trillion worth of lithium that can’t be accessed,” Bazant says. “If you could extract these resources efficiently, it would make a huge impact.”

Earlier this year, Lithios shipped its pilot system to a commercial partner in Arkansas to further validate its approach in the region. Lithios also plans to deploy several additional pilot and demonstration projects with other major partners in the oil and gas and mining industries in the coming years.

“After this field deployment, Lithios will quickly scale toward a commercial demonstration plant that will be operational by 2027, with the intent to scale to a kiloton-per-year commercial facility before the end of the decade,” Alkhadra says.

Although Lithios is currently focused on lithium, Bazant says the company’s approach could also be adopted to materials such as rare earth elements and transition metals further down the line.

“We’re developing a unique technology that could make the U.S. the center of the world for critical minerals separation, and we couldn’t have done this anywhere else,” Bazant says. “MIT was the perfect environment, mainly because of the people. There are so many fantastic scientists and businesspeople in the MIT ecosystem who are very technically savvy and ready to jump into a project like this. Our first employees were all MIT people, and they really brought the MIT spirit to our company.”

How drones are altering contemporary warfare

A new book by scholar and military officer Erik Lin-Greenberg examines the evolving dynamics of military and state action centered around drones.

In recent months, Russia has frequently flown drones into NATO territory, where NATO countries typically try to shoot them down. By contrast, when three Russian fighter jets made an incursion into Estonian airspace in September, they were intercepted and no attempt was made to shoot them down — although the incident did make headlines and led to a Russian diplomat being expelled from Estonia.

Those incidents follow a global pattern of recent years. Drone operations, to this point, seem to provoke different responses compared to other kinds of military action, especially the use of piloted warplanes. Drone warfare is expanding but not necessarily provoking major military responses, either by the countries being attacked or by the aggressor countries that have drones shot down.

“There was a conventional wisdom that drones were a slippery slope that would enable leaders to use force in all kinds of situations, with a massively destabilizing effect,” says MIT political scientist Erik Lin-Greenberg. “People thought if drones were used all over the place, this would lead to more escalation. But in many cases where drones are being used, we don’t see that escalation.”

On the other hand, drones have made military action more pervasive. It is at least possible that in the future, drone-oriented combat will be both more common and more self-contained.

“There is a revolutionary effect of these systems, in that countries are essentially increasing the range of situations in which leaders are willing to deploy military force,” Lin-Greenberg says. To this point, though, he adds, “these confrontations are not necessarily escalating.”

Now Lin-Greenberg examines these dynamics in a new book, “The Remote Revolution: Drones and Modern Statecraft,” published by Cornell University Press. Lin-Greenberg is an associate professor in MIT’s Department of Political Science.

Lin-Greenberg brings a distinctive professional background to the subject of drone warfare. Before returning to graduate school, he served as a U.S. Air Force officer; today he commands a U.S. Air Force reserve squadron. His thinking is informed by his experiences as both a scholar and practitioner.

“The Remote Revolution” also has a distinctive methodology that draws on multiple ways of studying the topic. In writing the book, Lin-Greenberg conducted experiments based on war games played by national security professionals; conducted surveys of expert and public thinking about drones; developed in-depth case studies from history; and dug into archives broadly to fully understand the history of drone use, which in fact goes back several decades.

The book’s focus is drone use during the 2000s, as the technology has become more readily available; today about 100 countries have access to military drones. Many have used them during tensions and skirmishes with other countries.

“Where I argue this is actually revolutionary is during periods of crises, which fall below the threshold of war, in that these new technologies take human operators out of harm’s way and enable states to do things they wouldn’t otherwise do,” Lin-Greenberg says.

Indeed, a key point is that drones lower the costs of military action for countries — and not just financial costs, but human and political costs, too. Incidents and problems that might plague leaders if they involved military personnel, forcing major responses, seem to lessen when drones are involved.

“Because these systems don’t have a human on board, they’re inherently cheaper and different in the minds of decision-makers,” Lin-Greenberg says. “That means they’re willing to use these systems during disputes, and if other states are shooting them down, the side sending them is less likely to retaliate, because they’re losing a machine but not a man or woman on board.”

In this sense, the uses of drones “create new rungs on the escalation ladder,” as Lin-Greenberg writes in the book. Drone incidents don’t necessarily lead to wider military action, and may not even lead to the same kinds of international relations issues as incidents involving piloted aircraft.

Consider a counterfactual that Lin-Greenberg raises in the book. One of the most notorious episodes of Cold War tension between the U.S. and U.S.S.R. occurred in 1960, when U.S. pilot Gary Powers was shot down and captured in the Soviet Union, leading to a diplomatic standoff and a canceled summit between U.S. President Dwight Eisenhower and Soviet leader Nikita Khrushchev.

“Had that been a drone, it’s very likely the summit would have continued,” Lin-Greenberg says. “No one would have said anything. The Soviet Union would have been embarrassed to admit their airspace was violated and the U.S. would have just [publicly] ignored what was going on, because there would not have been anyone sitting in a prison. There are a lot of exercises where you can ask how history could have been different.”

None of this is to say that drones present straightforward solutions to international relations problems. They may present the appearance of low-cost military engagement, but as Lin-Greenberg underlines in the book, the effects are more complicated.

“To be clear, the remote revolution does not suggest that drones prevent war,” Lin-Greenberg writes. Indeed, one of the problems they raise, he emphasizes, is the “moral hazard” that arises from leaders viewing drones as less costly, which can lead to even more military confrontations.

Moreover, the trends in drone warfare so far yield predictions for the future that are “probabilistic rather than deterministic,” as Lin-Greenberg writes. Perhaps some political or military leaders will start to use drones to attack new targets that will inevitably generate major responses and quickly escalate into broad wars. Current trends do not guarantee future outcomes.

“There are a lot of unanswered questions in this area,” Lin-Greenberg says. “So much is changing. What does it look like when more drones are more autonomous? I still hope this book lays a foundation for future dicussions, even as drones are used in different ways.”

Other scholars have praised “The Remote Revolution.” Joshua Kertzer, a professor of international studies and government at Harvard University, has hailed Lin-Greenberg’s “rich expertise, methodological rigor, and creative insight,” while Michael Horowitz, a political scientist and professor of international relations at the University of Pennsylvania, has called it “an incredible book about the impact of drones on the international security environment.”

For his part, Lin-Greenberg says, “My hope is the book will be read by academics and practitioners and people who choose to focus on parts of it they’re interested in. I tried to write the book in way that’s approachable.”

Publication of the book was supported by funding from MIT’s Security Studies Program. 

New lightweight polymer film can prevent corrosion

Because it’s nearly impermeable to gases, the polymer coating developed by MIT engineers could be used to protect solar panels, machinery, infrastructure, and more.

MIT researchers have developed a lightweight polymer film that is nearly impenetrable to gas molecules, raising the possibility that it could be used as a protective coating to prevent solar cells and other infrastructure from corrosion, and to slow the aging of packaged food and medicines.

The polymer, which can be applied as a film mere nanometers thick, completely repels nitrogen and other gases, as far as can be detected by laboratory equipment, the researchers found. That degree of impermeability has never been seen before in any polymer, and rivals the impermeability of molecularly-thin crystalline materials such as graphene.

“Our polymer is quite unusual. It’s obviously produced from a solution-phase polymerization reaction, but the product behaves like graphene, which is gas-impermeable because it’s a perfect crystal. However, when you examine this material, one would never confuse it with a perfect crystal,” says Michael Strano, the Carbon P. Dubbs Professor of Chemical Engineering at MIT.

The polymer film, which the researchers describe today in Nature, is made using a process that can be scaled up to large quantities and applied to surfaces much more easily than graphene.

Strano and Scott Bunch, an associate professor of mechanical engineering at Boston University, are the senior authors of the new study. The paper’s lead authors are Cody Ritt, a former MIT postdoc who is now an assistant professor at the University of Colorado at Boulder; Michelle Quien, an MIT graduate student; and Zitang Wei, an MIT research scientist.

Bubbles that don’t collapse

Strano’s lab first reported the novel material — a two-dimensional polymer called a 2D polyaramid that self-assembles into molecular sheets using hydrogen bonds — in 2022. To create such 2D polymer sheets, which had never been done before, the researchers used a building block called melamine, which contains a ring of carbon and nitrogen atoms. Under the right conditions, these monomers can expand in two dimensions, forming nanometer-sized disks. These disks stack on top of each other, held together by hydrogen bonds between the layers, which make the structure very stable and strong.

That polymer, which the researchers call 2DPA-1, is stronger than steel but has only one-sixth the density of steel.

In their 2022 study, the researchers focused on testing the material’s strength, but they also did some preliminary studies of its gas permeability. For those studies, they created “bubbles” out of the films and filled them with gas. With most polymers, such as plastics, gas that is trapped inside will seep out through the material, causing the bubble to deflate quickly.

However, the researchers found that bubbles made of 2DPA-1 did not collapse — in fact, bubbles that they made in 2021 are still inflated. “I was quite surprised initially,” Ritt says. “The behavior of the bubbles didn’t follow what you’d expect for a typical, permeable polymer. This required us to rethink how to properly study and understand molecular transport across this new material.”  

“We set up a series of careful experiments to first prove that the material is molecularly impermeable to nitrogen,” Strano says. “It could be considered tedious work. We had to make micro-bubbles of the polymer and fill them with a pure gas like nitrogen, and then wait. We had to repeatedly check over an exceedingly long period of time that they weren’t collapsed, in order to report the record impermeability value.”

Traditional polymers allow gases through because they consist of a tangle of spaghetti-like molecules that are loosely joined together. This leaves tiny gaps between the strands. Gas molecules can seep through these gaps, which is why polymers always have at least some degree of gas permeability.

However, the new 2D polymer is essentially impermeable because of the way that the layers of disks stick to each other.

“The fact that they can pack flat means there’s no volume between the two-dimensional disks, and that’s unusual. With other polymers, there’s still space between the one-dimensional chains, so most polymer films allow at least a little bit of gas to get through,” Strano says.

George Schatz, a professor of chemistry and chemical and biological engineering at Northwestern University, described the results as “remarkable.”

“Normally polymers are reasonably permeable to gases, but the polyaramids reported in this paper are orders of magnitude less permeable to most gases under conditions with industrial relevance,” says Schatz, who was not involved in the study.

A protective coating

In addition to nitrogen, the researchers also exposed the polymer to helium, argon, oxygen, methane, and sulfur hexafluoride. They found that 2DPA-1’s permeability to those gases was at least 1/10,000 that of any other existing polymer. That makes it nearly as impermeable as graphene, which is completely impermeable to gases because of its defect-free crystalline structure.

Scientists have been working on developing graphene coatings as a barrier to prevent corrosion in solar cells and other devices. However, scaling up the creation of graphene films is difficult, in large part because they can’t be simply painted onto surfaces.

“We can only make crystal graphene in very small patches,” Strano says. “A little patch of graphene is molecularly impermeable, but it doesn’t scale. People have tried to paint it on, but graphene does not stick to itself but slides when sheared. Graphene sheets moving past each other are considered almost frictionless.”

On the other hand, the 2DPA-1 polymer sticks easily because of the strong hydrogen bonds between the layered disks. In this paper, the researchers showed that a layer just 60 nanometers thick could extend the lifetime of a perovskite crystal by weeks. Perovskites are materials that hold promise as cheap and lightweight solar cells, but they tend to break down much faster than the silicon solar panels that are now widely used.

A 60-nanometer coating extended the perovskite’s lifetime to about three weeks, but a thicker coating would offer longer protection, the researchers say. The films could also be applied to a variety of other structures.

“Using an impermeable coating such as this one, you could protect infrastructure such as bridges, buildings, rail lines — basically anything outside exposed to the elements. Automotive vehicles, aircraft and ocean vessels could also benefit. Anything that needs to be sheltered from corrosion. The shelf life of food and medications can also be extended using such materials,” Strano says.

The other application demonstrated in this paper is a nanoscale resonator — essentially a tiny drum that vibrates at a particular frequency. Larger resonators, with sizes around 1 millimeter or less, are found in cell phones, where they allow the phone to pick up the frequency bands it uses to transmit and receive signals.

“In this paper, we made the first polymer 2D resonator, which you can do with our material because it’s impermeable and quite strong, like graphene,” Strano says. “Right now, the resonators in your phone and other communications devices are large, but there’s an effort to shrink them using nanotechnology. To make them less than a micron in size would be revolutionary. Cell phones and other devices could be smaller and reduce the power expenditures needed for signal processing.”

Resonators can also be used as sensors to detect very tiny molecules, including gas molecules. 

The research was funded, in part, by the Center for Enhanced Nanofluidic Transport-Phase 2, an Energy Frontier Research Center funded by the U.S. Department of Energy Office of Science, as well as the National Science Foundation.

This research was carried out, in part, using MIT.nano’s facilities.

Teaching large language models how to absorb new knowledge

With a new method developed at MIT, an LLM behaves more like a student, writing notes that it studies to memorize new information.

In an MIT classroom, a professor lectures while students diligently write down notes they will reread later to study and internalize key information ahead of an exam.

Humans know how to learn new information, but large language models can’t do this in the same way. Once a fully trained LLM has been deployed, its “brain” is static and can’t permanently adapt itself to new knowledge.

This means that if a user tells an LLM something important today, it won’t remember that information the next time this person starts a new conversation with the chatbot.

Now, a new approach developed by MIT researchers enables LLMs to update themselves in a way that permanently internalizes new information. Just like a student, the LLM generates its own study sheets from a user’s input, which it uses to memorize the information by updating its inner workings.

The model generates multiple self-edits to learn from one input, then applies each one to see which improves its performance the most. This trial-and-error process teaches the model the best way to train itself.

The researchers found this approach improved the accuracy of LLMs at question-answering and pattern-recognition tasks, and it enabled a small model to outperform much larger LLMs.

While there are still limitations that must be overcome, the technique could someday help artificial intelligence agents consistently adapt to new tasks and achieve changing goals in evolving environments.   

“Just like humans, complex AI systems can’t remain static for their entire lifetimes. These LLMs are not deployed in static environments. They are constantly facing new inputs from users. We want to make a model that is a bit more human-like — one that can keep improving itself,” says Jyothish Pari, an MIT graduate student and co-lead author of a paper on this technique.

He is joined on the paper by co-lead author Adam Zweiger, an MIT undergraduate; graduate students Han Guo and Ekin Akyürek; and senior authors Yoon Kim, 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), and Pulkit Agrawal, an associate professor in EECS and member of CSAIL. The research will be presented at the Conference on Neural Information Processing Systems.

Teaching the model to learn

LLMs are neural network models that have billions of parameters, called weights, that contain the model’s knowledge and process inputs to make predictions. During training, the model adapts these weights to learn new information contained in its training data.

But once it is deployed, the weights are static and can’t be permanently updated anymore.

However, LLMs are very good at a process called in-context learning, in which a trained model learns a new task by seeing a few examples. These examples guide the model’s responses, but the knowledge disappears before the next conversation.

The MIT researchers wanted to leverage a model’s powerful in-context learning capabilities to teach it how to permanently update its weights when it encounters new knowledge.

The framework they developed, called SEAL for “self-adapting LLMs,” enables an LLM to generate new synthetic data based on an input, and then determine the best way to adapt itself and learn from that synthetic data. Each piece of synthetic data is a self-edit the model can apply.

In the case of language, the LLM creates synthetic data by rewriting the information, and its implications, in an input passage. This is similar to how students make study sheets by rewriting and summarizing original lecture content.

The LLM does this multiple times, then quizzes itself on each self-edit to see which led to the biggest boost in performance on a downstream task like question answering. It uses a trial-and-error method known as reinforcement learning, where it receives a reward for the greatest performance boost.

Then the model memorizes the best study sheet by updating its weights to internalize the information in that self-edit.

“Our hope is that the model will learn to make the best kind of study sheet — one that is the right length and has the proper diversity of information — such that updating the model based on it leads to a better model,” Zweiger explains.

Choosing the best method

Their framework also allows the model to choose the way it wants to learn the information. For instance, the model can select the synthetic data it wants to use, the rate at which it learns, and how many iterations it wants to train on.

In this case, not only does the model generate its own training data, but it also configures the optimization that applies that self-edit to its weights.

“As humans, we know how we learn best. We want to grant that same ability to large language models. By providing the model with the ability to control how it digests this information, it can figure out the best way to parse all the data that are coming in,” Pari says.

SEAL outperformed several baseline methods across a range of tasks, including learning a new skill from a few examples and incorporating knowledge from a text passage. On question answering, SEAL improved model accuracy by nearly 15 percent and on some skill-learning tasks, it boosted the success rate by more than 50 percent.

But one limitation of this approach is a problem called catastrophic forgetting: As the model repeatedly adapts to new information, its performance on earlier tasks slowly declines.

The researchers plan to mitigate catastrophic forgetting in future work. They also want to apply this technique in a multi-agent setting where several LLMs train each other.

“One of the key barriers to LLMs that can do meaningful scientific research is their inability to update themselves based on their interactions with new information. Though fully deployed self-adapting models are still far off, we hope systems able to learn this way could eventually overcome this and help advance science,” Zweiger says.

This work is supported, in part, by the U.S. Army Research Office, the U.S. Air Force AI Accelerator, the Stevens Fund for MIT UROP, and the MIT-IBM Watson AI Lab. 

Understanding the nuances of human-like intelligence

Associate Professor Phillip Isola studies the ways in which intelligent machines “think,” in an effort to safely integrate AI into human society.

What can we learn about human intelligence by studying how machines “think?” Can we better understand ourselves if we better understand the artificial intelligence systems that are becoming a more significant part of our everyday lives?

These questions may be deeply philosophical, but for Phillip Isola, finding the answers is as much about computation as it is about cogitation.

Isola, the newly tenured associate professor in the Department of Electrical Engineering and Computer Science (EECS), studies the fundamental mechanisms involved in human-like intelligence from a computational perspective.

While understanding intelligence is the overarching goal, his work focuses mainly on computer vision and machine learning. Isola is particularly interested in exploring how intelligence emerges in AI models, how these models learn to represent the world around them, and what their “brains” share with the brains of their human creators.

“I see all the different kinds of intelligence as having a lot of commonalities, and I’d like to understand those commonalities. What is it that all animals, humans, and AIs have in common?” says Isola, who is also a member of the Computer Science and Artificial Intelligence Laboratory (CSAIL).

To Isola, a better scientific understanding of the intelligence that AI agents possess will help the world integrate them safely and effectively into society, maximizing their potential to benefit humanity.

Asking questions

Isola began pondering scientific questions at a young age.

While growing up in San Francisco, he and his father frequently went hiking along the northern California coastline or camping around Point Reyes and in the hills of Marin County.

He was fascinated by geological processes and often wondered what made the natural world work. In school, Isola was driven by an insatiable curiosity, and while he gravitated toward technical subjects like math and science, there was no limit to what he wanted to learn.

Not entirely sure what to study as an undergraduate at Yale University, Isola dabbled until he came upon cognitive sciences.

“My earlier interest had been with nature — how the world works. But then I realized that the brain was even more interesting, and more complex than even the formation of the planets. Now, I wanted to know what makes us tick,” he says.

As a first-year student, he started working in the lab of his cognitive sciences professor and soon-to-be mentor, Brian Scholl, a member of the Yale Department of Psychology. He remained in that lab throughout his time as an undergraduate.

After spending a gap year working with some childhood friends at an indie video game company, Isola was ready to dive back into the complex world of the human brain. He enrolled in the graduate program in brain and cognitive sciences at MIT.

“Grad school was where I felt like I finally found my place. I had a lot of great experiences at Yale and in other phases of my life, but when I got to MIT, I realized this was the work I really loved and these are the people who think similarly to me,” he says.

Isola credits his PhD advisor, Ted Adelson, the John and Dorothy Wilson Professor of Vision Science, as a major influence on his future path. He was inspired by Adelson’s focus on understanding fundamental principles, rather than only chasing new engineering benchmarks, which are formalized tests used to measure the performance of a system.

A computational perspective

At MIT, Isola’s research drifted toward computer science and artificial intelligence.

“I still loved all those questions from cognitive sciences, but I felt I could make more progress on some of those questions if I came at it from a purely computational perspective,” he says.

His thesis was focused on perceptual grouping, which involves the mechanisms people and machines use to organize discrete parts of an image as a single, coherent object.

If machines can learn perceptual groupings on their own, that could enable AI systems to recognize objects without human intervention. This type of self-supervised learning has applications in areas such autonomous vehicles, medical imaging, robotics, and automatic language translation.

After graduating from MIT, Isola completed a postdoc at the University of California at Berkeley so he could broaden his perspectives by working in a lab solely focused on computer science.

“That experience helped my work become a lot more impactful because I learned to balance understanding fundamental, abstract principles of intelligence with the pursuit of some more concrete benchmarks,” Isola recalls.

At Berkeley, he developed image-to-image translation frameworks, an early form of generative AI model that could turn a sketch into a photographic image, for instance, or turn a black-and-white photo into a color one.

He entered the academic job market and accepted a faculty position at MIT, but Isola deferred for a year to work at a then-small startup called OpenAI.

“It was a nonprofit, and I liked the idealistic mission at that time. They were really good at reinforcement learning, and I thought that seemed like an important topic to learn more about,” he says.

He enjoyed working in a lab with so much scientific freedom, but after a year Isola was ready to return to MIT and start his own research group.

Studying human-like intelligence

Running a research lab instantly appealed to him.

“I really love the early stage of an idea. I feel like I am a sort of startup incubator where I am constantly able to do new things and learn new things,” he says.

Building on his interest in cognitive sciences and desire to understand the human brain, his group studies the fundamental computations involved in the human-like intelligence that emerges in machines.

One primary focus is representation learning, or the ability of humans and machines to represent and perceive the sensory world around them.

In recent work, he and his collaborators observed that the many varied types of machine-learning models, from LLMs to computer vision models to audio models, seem to represent the world in similar ways.

These models are designed to do vastly different tasks, but there are many similarities in their architectures. And as they get bigger and are trained on more data, their internal structures become more alike.

This led Isola and his team to introduce the Platonic Representation Hypothesis (drawing its name from the Greek philosopher Plato) which says that the representations all these models learn are converging toward a shared, underlying representation of reality.

“Language, images, sound — all of these are different shadows on the wall from which you can infer that there is some kind of underlying physical process — some kind of causal reality — out there. If you train models on all these different types of data, they should converge on that world model in the end,” Isola says.

A related area his team studies is self-supervised learning. This involves the ways in which AI models learn to group related pixels in an image or words in a sentence without having labeled examples to learn from.

Because data are expensive and labels are limited, using only labeled data to train models could hold back the capabilities of AI systems. With self-supervised learning, the goal is to develop models that can come up with an accurate internal representation of the world on their own.

“If you can come up with a good representation of the world, that should make subsequent problem solving easier,” he explains.

The focus of Isola’s research is more about finding something new and surprising than about building complex systems that can outdo the latest machine-learning benchmarks.

While this approach has yielded much success in uncovering innovative techniques and architectures, it means the work sometimes lacks a concrete end goal, which can lead to challenges.

For instance, keeping a team aligned and the funding flowing can be difficult when the lab is focused on searching for unexpected results, he says.

“In a sense, we are always working in the dark. It is high-risk and high-reward work. Every once in while, we find some kernel of truth that is new and surprising,” he says.

In addition to pursuing knowledge, Isola is passionate about imparting knowledge to the next generation of scientists and engineers. Among his favorite courses to teach is 6.7960 (Deep Learning), which he and several other MIT faculty members launched four years ago.

The class has seen exponential growth, from 30 students in its initial offering to more than 700 this fall.

And while the popularity of AI means there is no shortage of interested students, the speed at which the field moves can make it difficult to separate the hype from truly significant advances.

“I tell the students they have to take everything we say in the class with a grain of salt. Maybe in a few years we’ll tell them something different. We are really on the edge of knowledge with this course,” he says.

But Isola also emphasizes to students that, for all the hype surrounding the latest AI models, intelligent machines are far simpler than most people suspect.

“Human ingenuity, creativity, and emotions — many people believe these can never be modeled. That might turn out to be true, but I think intelligence is fairly simple once we understand it,” he says.

Even though his current work focuses on deep-learning models, Isola is still fascinated by the complexity of the human brain and continues to collaborate with researchers who study cognitive sciences.

All the while, he has remained captivated by the beauty of the natural world that inspired his first interest in science.

Although he has less time for hobbies these days, Isola enjoys hiking and backpacking in the mountains or on Cape Cod, skiing and kayaking, or finding scenic places to spend time when he travels for scientific conferences.

And while he looks forward to exploring new questions in his lab at MIT, Isola can’t help but contemplate how the role of intelligent machines might change the course of his work.

He believes that artificial general intelligence (AGI), or the point where machines can learn and apply their knowledge as well as humans can, is not that far off.

“I don’t think AIs will just do everything for us and we’ll go and enjoy life at the beach. I think there is going to be this coexistence between smart machines and humans who still have a lot of agency and control. Now, I’m thinking about the interesting questions and applications once that happens. How can I help the world in this post-AGI future? I don’t have any answers yet, but it’s on my mind,” he says.

Particles that enhance mRNA delivery could reduce vaccine dosage and costs

Using these nanoparticles to deliver a flu vaccine, researchers observed an effective immune response at a much lower dose.

A new delivery particle developed at MIT could make mRNA vaccines more effective and potentially lower the cost per vaccine dose.

In studies in mice, the researchers showed that an mRNA influenza vaccine delivered with their new lipid nanoparticle could generate the same immune response as mRNA delivered by nanoparticles made with FDA-approved materials, but at around 1/100 the dose.

“One of the challenges with mRNA vaccines is the cost,” says Daniel Anderson, a professor in MIT’s Department of Chemical Engineering and a member of MIT’s Koch Institute for Integrative Cancer Research and Institute for Medical Engineering and Science (IMES). “When you think about the cost of making a vaccine that could be distributed widely, it can really add up. Our goal has been to try to make nanoparticles that can give you a safe and effective vaccine response but at a much lower dose.”

While the researchers used their particles to deliver a flu vaccine, they could also be used for vaccines for Covid-19 and other infectious diseases, they say.

Anderson is the senior author of the study, which appears today in Nature Nanotechnology. The lead authors of the paper are Arnab Rudra, a visiting scientist at the Koch Institute; Akash Gupta, a Koch Institute research scientist; and Kaelan Reed, an MIT graduate student.

Efficient delivery

To protect mRNA vaccines from breaking down in the body after injection, they are packaged inside a lipid nanoparticle, or LNP. These fatty spheres help mRNA get into cells so that it can be translated into a fragment of a protein from a pathogen such as influenza or SARS-CoV-2.

In the new study, the MIT team sought to develop particles that can induce an effective immune response, but at a lower dose than the particles now used to deliver Covid-19 mRNA vaccines. That could not only reduce the costs per vaccine dose, but may also help to lessen the potential side effects, the researchers say.

LNPs typically consist of five elements: an ionizable lipid, cholesterol, a helper phospholipid, a polyethylene glycol lipid, and mRNA. In this study, the researchers focused on the ionizable lipid, which plays a key role in vaccine strength.

Based on their knowledge of chemical structures that might improve delivery efficiency, the researchers designed a library of new ionizable lipids. These contained cyclic structures, which can help enhance mRNA delivery, as well as chemical groups called esters, which the researchers believed could also help improve biodegradability.

The researchers then created and screened many combinations of these particle structures in mice to see which could most effectively deliver the gene for luciferase, a bioluminescent protein. Then, they took their top-performing particle and created a library of new variants, which they tested in another round of screening.

From these screens, the top LNP that emerged is one that the researchers called AMG1541. One key feature of these new LNPs is that they are more effective in dealing with a major barrier for delivery particles, known as endosomal escape. After LNPs enter cells, they are isolated in cellular compartments called endosomes, which they need to break out of to deliver their mRNA. The new particles did this more effectively than existing LNPs.

Another advantage of the new LNPs is that the ester groups in the tails make the particles degradable once they have delivered their cargo. This means they can be cleared from the body quickly, which the researchers believe could reduce side effects from the vaccine.

More powerful vaccines

To demonstrate the potential applications of the AMG1541 LNP, the researchers used it to deliver an mRNA influenza vaccine in mice. They compared this vaccine’s effectiveness to a flu vaccine made with a lipid called SM-102, which is FDA-approved and was used by Moderna in its Covid-19 vaccine.

Mice vaccinated with the new particles generated the same antibody response as mice vaccinated with the SM-102 particle, but only 1/100 of the dose was needed to generate that response, the researchers found.

“It’s almost a hundredfold lower dose, but you generate the same amount of antibodies, so that can significantly lower the dose. If it translates to humans, it should significantly lower the cost as well,” Rudra says.

Further experiments revealed that the new LNPs are better able to deliver their cargo to a critical type of immune cells called antigen-presenting cells. These cells chop up foreign antigens and display them on their surfaces, which signals other immune cells such as B and T cells to become activated against that antigen.

The new LNPs are also more likely to accumulate in the lymph nodes, where they encounter many more immune cells.

Using these particles to deliver mRNA flu vaccines could allow vaccine developers to better match the strains of flu that circulate each winter, the researchers say. “With traditional flu vaccines, they have to start being manufactured almost a year ahead of time,” Reed says. “With mRNA, you can start producing it much later in the season and get a more accurate guess of what the circulating strains are going to be, and it may help improve the efficacy of flu vaccines.”

The particles could also be adapted for vaccines for Covid-19, HIV, or any other infectious disease, the researchers say.

“We have found that they work much better than anything that has been reported so far. That’s why, for any intramuscular vaccines, we think that our LNP platforms could be used to develop vaccines for a number of diseases,” Gupta says.

The research was funded by Sanofi, the National Institutes of Health, the Marble Center for Cancer Nanomedicine, and the Koch Institute Support (core) Grant from the National Cancer Institute.

Giving buildings an “MRI” to make them more energy-efficient and resilient

Founded by a team from MIT, Lamarr.AI uses drones, thermal imaging, and AI to help property owners make targeted investments in their buildings.

Older buildings let thousands of dollars-worth of energy go to waste each year through leaky roofs, old windows, and insufficient insulation. But even as building owners face mounting pressure to comply with stricter energy codes, making smart decisions about how to invest in efficiency is a major challenge.

Lamarr.AI, born in part from MIT research, is making the process of finding ways to improve the energy efficiency of buildings as easy as clicking a button. When customers order a building review, it triggers a coordinated symphony of drones, thermal and visible-range cameras, and artificial intelligence designed to identify problems and quantify the impact of potential upgrades. Lamarr.AI’s technology also assesses structural conditions, creates detailed 3D models of buildings, and recommends retrofits. The solution is already being used by leading organizations across facilities management as well as by architecture, engineering, and construction firms.

“We identify the root cause of the anomalies we find,” says CEO and co-founder Tarek Rakha PhD ’15. “Our platform doesn’t just say, ‘This is a hot spot and this is a cold spot.’ It specifies ‘This is infiltration or exfiltration. This is missing insulation. This is water intrusion.’ The detected anomalies are also mapped to a 3D model of the building, and there are deeper analytics, such as the cost of each retrofit and the return on investment.”

To date, the company estimates its platform has helped clients across health care, higher education, and multifamily housing avoid over $3 million in unnecessary construction and retrofit costs by recommending targeted interventions over costly full-system replacements, while improving energy performance and extending asset life. For building owners managing portfolios worth hundreds of millions of dollars, Lamarr.AI’s approach represents a fundamental shift from reactive maintenance to strategic asset management.

The founders, who also include MIT Professor John Fernández and Research Scientist Norhan Bayomi SM ’17, PhD ’21, are thrilled to see their technology accelerating the transition to more energy-efficient and higher-performing buildings.

“Reducing carbon emissions in buildings gets you the greatest return on investment in terms of climate interventions, but what has been needed are the technologies and tools to help the real estate and construction sectors make the right decisions in a timely and economical way,” Fernández says.

Automating building scans

Bayomi and Rakha completed their PhDs in the MIT Department of Architecture’s Building Technology Program. For her thesis, Bayomi developed technology to detect features of building exteriors and classify thermal anomalies through scans of buildings, with a specific focus on the impact of heat waves on low-income communities. Bayomi and her collaborators eventually deployed the system to detect air leaks as part of a partnership with a community in New York City.

After graduating MIT, Rakha became an assistant professor at Syracuse University. In 2015, together with fellow Syracuse University Professor Senem Velipasalar, he began developing his concept for drone-based building analytics — an idea that later received support through a grant from New York State’s Department of Economic Development. In 2019, Bayomi and Fernández joined the project, and the team received a $1.8 million research award from the U.S. Department of Energy.

“The technology is like giving a building an MRI using drones, infrared imaging, visible light imaging, and proprietary AI that we developed through computer vision technology, along with large language models for report generation,” Rakha explains.

“When we started the research, we saw firsthand how vulnerable communities were suffering from inefficient buildings, but couldn’t afford comprehensive diagnostics,” Bayomi says. “We knew that if we could automate this process and reduce costs while improving accuracy, we’d unlock a massive market. Now we’re seeing demand from everyone, from municipal buildings to major institutional portfolios.”

Lamarr.AI was officially founded in 2021 to commercialize the technology, and the founders wasted no time tapping into MIT’s entrepreneurial ecosystem. First, they received a small seed grant from the MIT Sandbox Innovation Fund. In 2022, they won the MITdesignX prize and were semifinalists in the MIT $100K Entrepreneurship Competition. The founders named the company after Hedy Lamarr, the famous actress and inventor of a patented technology that became the basis for many modern secure communications.

Current methods for detecting air leaks in buildings utilize fan pressurizers or smoke. Contractors or building engineers may also spot-check buildings with handheld infrared cameras to manually identify temperature differences across individual walls, windows, and ductwork.

Lamarr.AI’s system can perform building inspections far more quickly. Building managers can order the company’s scans online and select when they’d like the drone to fly. Lamarr.AI partners with drone companies worldwide to fly off-the-shelf drones around buildings, providing them with flight plans and specifications for success. Images are then uploaded onto Lamarr.AI’s platform for automated analysis.

“As an example, a survey of a 180,000-square-foot building like the MIT Schwarzman College of Computing, which we scanned, produces around 2,000 images,” Fernández says. “For someone to go through those manually would take a couple of weeks. Our models autonomously analyze those images in a few seconds.”

After the analysis, Lamarr.AI’s platform generates a report that includes the suspected root cause of every weak point found, an estimated cost to correct that problem, and its estimated return on investment using advanced building energy simulations.

“We knew if we were able to quickly, inexpensively, and accurately survey the thermal envelope of buildings and understand their performance, we would be addressing a huge need in the real estate, building construction, and built environment sectors,” Fernández explains. “Thermal anomalies are a huge cause of unwanted heat loss, and more than 45 percent of construction defects are tied to envelope failures.”

The ability to operate at scale is especially attractive to building owners and operators, who often manage large portfolios of buildings across multiple campuses.

“We see Lamarr.AI becoming the premier solution for building portfolio diagnostics and prognosis across the globe, where every building can be equipped not just for the climate crisis, but also to minimize energy losses and be more efficient, safer, and sustainable,” Rakha says.

Building science for everyone

Lamarr.AI has worked with building operators across the U.S. as well as in Canada, the United Kingdom, and the United Arab Emirates.

In June, Lamarr.AI partnered with the City of Detroit, with support from Newlab and Michigan Central, to inspect three municipal buildings to identify areas for improvement. Across two of the buildings, the system identified more than 460 problems like insulation gaps and water leaks. The findings were presented in a report that also utilized energy simulations to demonstrate that upgrades, such as window replacements and targeted weatherization, could reduce HVAC energy use by up to 22 percent.

The entire process took a few days. The founders note that it was the first building inspection drone flight to utilize an off-site operator, an approach that further enhances the scalability of their platform. It also helps further reduce costs, which could make building scans available to a broader swath of people around the world.

“We’re democratizing access to very high-value building science expertise that previously cost tens of thousands per audit,” Bayomi says. “Our platform makes advanced diagnostics affordable enough for routine use, not just one-time assessments. The bigger vision is automated, regular building health monitoring that keeps facilities teams informed in real-time, enabling proactive decisions rather than reactive crisis management. When building intelligence becomes continuous and accessible, operators can optimize performance systematically rather than waiting for problems to emerge.”

MIT physicists observe key evidence of unconventional superconductivity in magic-angle graphene

The findings could open a route to new forms of higher-temperature superconductors.

Superconductors are like the express trains in a metro system. Any electricity that “boards” a superconducting material can zip through it without stopping and losing energy along the way. As such, superconductors are extremely energy efficient, and are used today to power a variety of applications, from MRI machines to particle accelerators.

But these “conventional” superconductors are somewhat limited in terms of uses because they must be brought down to ultra-low temperatures using elaborate cooling systems to keep them in their superconducting state. If superconductors could work at higher, room-like temperatures, they would enable a new world of technologies, from zero-energy-loss power cables and electricity grids to practical quantum computing systems. And so scientists at MIT and elsewhere are studying “unconventional” superconductors — materials that exhibit superconductivity in ways that are different from, and potentially more promising than, today’s superconductors.

In a promising breakthrough, MIT physicists have today reported their observation of new key evidence of unconventional superconductivity in “magic-angle” twisted tri-layer graphene (MATTG) — a material that is made by stacking three atomically-thin sheets of graphene at a specific angle, or twist, that then allows exotic properties to emerge.

MATTG has shown indirect hints of unconventional superconductivity and other strange electronic behavior in the past. The new discovery, reported in the journal Science, offers the most direct confirmation yet that the material exhibits unconventional superconductivity.

In particular, the team was able to measure MATTG’s superconducting gap — a property that describes how resilient a material’s superconducting state is at given temperatures. They found that MATTG’s superconducting gap looks very different from that of the typical superconductor, meaning that the mechanism by which the material becomes superconductive must also be different, and unconventional.

“There are many different mechanisms that can lead to superconductivity in materials,” says study co-lead author Shuwen Sun, a graduate student in MIT’s Department of Physics. “The superconducting gap gives us a clue to what kind of mechanism can lead to things like room-temperature superconductors that will eventually benefit human society.”

The researchers made their discovery using a new experimental platform that allows them to essentially “watch” the superconducting gap, as the superconductivity emerges in two-dimensional materials, in real-time. They plan to apply the platform to further probe MATTG, and to map the superconducting gap in other 2D materials — an effort that could reveal promising candidates for future technologies.

“Understanding one unconventional superconductor very well may trigger our understanding of the rest,” says Pablo Jarillo-Herrero, the Cecil and Ida Green Professor of Physics at MIT and a member of the Research Laboratory of Electronics. “This understanding may guide the design of superconductors that work at room temperature, for example, which is sort of the Holy Grail of the entire field.”

The study’s other co-lead author is Jeong Min Park PhD ’24; Kenji Watanabe and Takashi Taniguchi of the National Institute for Materials Science in Japan are also co-authors.

The ties that bind

Graphene is a material that comprises a single layer of carbon atoms that are linked in a hexagonal pattern resembling chicken wire. A sheet of graphene can be isolated by carefully exfoliating an atom-thin flake from a block of graphite (the same stuff of pencil lead). In the 2010s, theorists predicted that if two graphene layers were stacked at a very special angle, the resulting structure should be capable of exotic electronic behavior.

In 2018, Jarillo-Herrero and his colleagues became the first to produce magic-angle graphene in experiments, and to observe some of its extraordinary properties. That discovery sprouted an entire new field known as “twistronics,” and the study of atomically thin, precisely twisted materials. Jarillo-Herrero’s group has since studied other configurations of magic-angle graphene with two, three, and more layers, as well as stacked and twisted structures of other two-dimensional materials. Their work, along with other groups, have revealed some signatures of unconventional superconductivity in some structures.

Superconductivity is a state that a material can exhibit under certain conditions (usually at very low temperatures). When a material is a superconductor, any electrons that pass through can pair up, rather than repelling and scattering away. When they couple up in what is known as “Cooper pairs,” the electrons can glide through a material without friction, instead of knocking against each other and flying away as lost energy. This pairing up of electrons is what enables superconductivity, though the way in which they are bound can vary.

“In conventional superconductors, the electrons in these pairs are very far away from each other, and weakly bound,” says Park. “But in magic-angle graphene, we could already see signatures that these pairs are very tightly bound, almost like a molecule. There were hints that there is something very different about this material.”

Tunneling through

In their new study, Jarillo-Herrero and his colleagues aimed to directly observe and confirm unconventional superconductivity in a magic-angle graphene structure. To do so, they would have to measure the material’s superconducting gap.

“When a material becomes superconducting, electrons move together as pairs rather than individually, and there’s an energy ‘gap’ that reflects how they’re bound,” Park explains. “The shape and symmetry of that gap tells us the underlying nature of the superconductivity.”

Scientists have measured the superconducting gap in materials using specialized techniques, such as tunneling spectroscopy. The technique takes advantage of a quantum mechanical property known as “tunneling.” At the quantum scale, an electron behaves not just as a particle, but also as a wave; as such, its wave-like properties enable an electron to travel, or “tunnel,” through a material, as if it could move through walls.

Such tunneling spectroscopy measurements can give an idea of how easy it is for an electron to tunnel into a material, and in some sense, how tightly packed and bound the electrons in the material are. When performed in a superconducting state, it can reflect the properties of the superconducting gap. However, tunneling spectroscopy alone cannot always tell whether the material is, in fact, in a superconducting state. Directly linking a tunneling signal to a genuine superconducting gap is both essential and experimentally challenging.

In their new work, Park and her colleagues developed an experimental platform that combines electron tunneling with electrical transport — a technique that is used to gauge a material’s superconductivity, by sending current through and continuously measuring its electrical resistance (zero resistance signals that a material is in a superconducting state).

The team applied the new platform to measure the superconducting gap in MATTG. By combining tunneling and transport measurements in the same device, they could unambiguously identify the superconducting tunneling gap, one that appeared only when the material exhibited zero electrical resistance, which is the hallmark of superconductivity. They then tracked how this gap evolved under varying temperature and magnetic fields. Remarkably, the gap displayed a distinct V-shaped profile, which was clearly different from the flat and uniform shape of conventional superconductors.

This V shape reflects a certain unconventional mechanism by which electrons in MATTG pair up to superconduct. Exactly what that mechanism is remains unknown. But the fact that the shape of the superconducting gap in MATTG stands out from that of the typical superconductor provides key evidence that the material is an unconventional superconductor.

In conventional superconductors, electrons pair up through vibrations of the surrounding atomic lattice, which effectively jostle the particles together. But Park suspects that a different mechanism could be at work in MATTG.

“In this magic-angle graphene system, there are theories explaining that the pairing likely arises from strong electronic interactions rather than lattice vibrations,” she posits. “That means electrons themselves help each other pair up, forming a superconducting state with special symmetry.”

Going forward, the team will test other two-dimensional twisted structures and materials using the new experimental platform.

“This allows us to both identify and study the underlying electronic structures of superconductivity and other quantum phases as they happen, within the same sample,” Park says. “This direct view can reveal how electrons pair and compete with other states, paving the way to design and control new superconductors and quantum materials that could one day power more efficient technologies or quantum computers.”

This research was supported, in part, by the U.S. Army Research Office, the U.S. Air Force Office of Scientific Research, the MIT/MTL Samsung Semiconductor Research Fund, the Sagol WIS-MIT Bridge Program, the National Science Foundation, the Gordon and Betty Moore Foundation, and the Ramon Areces Foundation.

Q&A: How folk ballads explain the world

Ruth Perry’s new book profiles Anna Gordon, a Scotswoman who preserved and transmitted precious popular ballads, and with them national traditions.

Traditional folk ballads are one of our most enduring forms of cultural expression. They can also be lost to society, forgotten over time. That’s why, in the mid-1700s, when a Scottish woman named Anna Gordon was found to know three dozen ancient ballads, collectors tried to document all of these songs — a volume of work that became a kind of sensation in its time, a celebrated piece of cultural heritage.

That story is told in MIT Professor Emerita Ruth Perry’s latest book, “The Ballad World of Anna Gordon, Mrs. Brown of Falkland,” published this year by Oxford University Press. In it, Perry details what we know about the ways folk ballads were created and transmitted; how Anna Gordon came to know so many; the social and political climate in which they existed; and why these songs meant so much in Scotland and elsewhere in the Atlantic world. Indeed, Scottish immigrants brought their music to the U.S., among other places.

MIT News sat down with Perry, who is MIT’s Ann Fetter Friedlaender Professor of Humanities, Emerita, to talk about the book.

Q: This is fascinating topic with a lot of threads woven together. To you, what is the book about?

A: It’s really three books. It’s a book about Anna Gordon and her family, a very interesting middle-class family living in Aberdeen in the middle of the 18th century. And it’s a book about balladry and what a ballad is — a story told in song, and ballads are the oldest known poetry in English. Some of them are gorgeous. Third, it’s a book about the relationship between Scotland and England, the effects of the Jacobite uprising in 1745, social attitudes, how people lived, what they ate, education — it’s very much about 18th century Scotland.

Q: Okay, who was Anna Gordon, and what was her family milieu?

A: Anna’s father, Thomas Gordon, was a professor at King’s College, now the University of Aberdeen. He was a professor of humanity, which in those days meant Greek and Latin, and was well-connected to the intellectual community of the Scottish Enlightenment. A friend of his, an Edinburgh writer, lawyer, and judge, William Tytler, who heard cases all over the country and always stayed with Thomas Gordon and his family when he came to Aberdeen, was intensely interested in Scottish traditional music. He found out that Anna Gordon had learned all these ballads as a child, from her mother and aunt and some servants. Tytler asked if she would write them down, both tunes and words.

That was the earliest manuscript of ballads ever collected from a named person in Scotland. Once it was in existence, all kinds of people wanted to see it; it got spread throughout the country. In my book, I detail much of the excitement over this manuscript.

The thing about Anna’s ballads is: It’s not just that there are more of them, and more complete versions that are fuller, with more verses. They’re more beautiful. The language is more archaic, and there are marvelous touches. It is thought, and I agree, that Anna Gordon was an oral poet. As she remembered ballads and reproduced them, she improved on them. She had a great memory for the best bits and would improve other parts.

Q: How did it come about that at this time, a woman such as Anna Gordon would be the keeper and creator of cultural knowledge?

A: Women were more literate in Scotland than elsewhere. The Scottish Parliament passed an act in 1695 requiring every parish in the Church of Scotland to have not only a minister, but a teacher. Scotland was the most literate country in Europe in the 18th century. And those parish schoolmasters taught local kids. The parents did have to pay a few pennies for their classes, and, true, more parents paid for sons than for daughters. But there were daughters who took classes. And there were no opportunities like this in England at the time. Education was better for women in Scotland. So was their legal position, under common law in Scotland. When the Act of Union was formed in 1707, Scotland retained its own legal system, which had more extensive rights for women than in England.

Q: I know it’s complex, but generally, why was this?

A: Scotland was a much more democratic country, culture, and society than England, period. When Elizabeth I died in 1603, the person who inherited the throne was the King of Scotland James VI, who went to England with his court — which included the Scottish aristocracy. So, the Scottish aristocracy ended up in London. I’m sure they went back to their hunting lodges for the hunting season, but they didn’t live there [in Scotland] and they didn’t set the tone of the country. It was democratized because all that was left were a lot of lawyers and ministers and teachers.

Q: What is distinctive about the ballads in this corpus of songs Anna Gordon knew and documented?

A: A common word about ballads is that there’s a high body count, and they’re all about people dying and killing each other. But that is not true of Anna Gordon’s ballads. They’re about younger women triumphing in the world, often against older women, which is interesting, and even more often against fathers. The ballads are about family discord, inheritance, love, fidelity, lack of fidelity, betrayal. There are ballads about fighting and bloodshed, but not so many. They’re about the human condition. And they have interesting qualities because they’re oral poetry, composed and remembered and changed and transmitted from mouth to ear and not written down. There are repetitions and parallelisms, and other hallmarks of oral poetry. The sort of thing you learned when you read Homer.

Q: So is this a form of culture generated in opposition to those controlling society? Or at least, one that’s popular regardless of what some elites thought?

A: It is in Scotland, because of the enmity between Scotland and England. We’re talking about the period of Great Britain when England is trying to gobble up Scotland and some Scottish folks don’t want that. They want to retain their Scottishness. And the ballad was a Scottish tradition that was not influenced by England. That’s one reason balladry was so important in 18th-century Scotland. Everybody was into balladry partly because it was a unique part of Scottish culture.

Q: To that point, it seems like an unexpected convergence, for the time, to see a more middle-class woman like Anna Gordon transmitting ballads that had often been created and sung by people of all classes.

A: Yes. At first I thought I was just working on a biography of Anna Gordon. But it’s fascinating how the culture was transmitted, how intellectually rich that society was, how much there is to examine in Scottish culture and society of the 18th century. Today people may watch “Outlander,” but they still wouldn’t know anything about this!