With global power demand from data centers expected to more than double by 2030, the MIT Energy Initiative (MITEI) in September launched an effort that brings together MIT researchers and industry experts to explore innovative solutions for powering the data-driven future. At its annual research conference, MITEI announced the Data Center Power Forum, a targeted research effort for MITEI member companies interested in addressing the challenges of data center power demand. The Data Center Power Forum builds on lessons from MITEI’s May 2025 symposium on the energy to power the expansion of artificial intelligence (AI) and focus panels related to data centers at the fall 2024 research conference.
In the United States, data centers consumed 4 percent of the country’s electricity in 2023, with demand expected to increase to 9 percent by 2030, according to the Electric Power Research Institute. Much of the growth in demand is from the increasing use of AI, which is placing an unprecedented strain on the electric grid. This surge in demand presents a serious challenge for the technology and energy sectors, government policymakers, and everyday consumers, who may see their electric bills skyrocket as a result.
“MITEI has long supported research on ways to produce more efficient and cleaner energy and to manage the electric grid. In recent years, MITEI has also funded dozens of research projects relevant to data center energy issues. Building on this history and knowledge base, MITEI’s Data Center Power Forum is convening a specialized community of industry members who have a vital stake in the sustainable growth of AI and the acceleration of solutions for powering data centers and expanding the grid,” says William H. Green, the director of MITEI and the Hoyt C. Hottel Professor of Chemical Engineering.
MITEI’s mission is to advance zero- and low-carbon solutions to expand energy access and mitigate climate change. MITEI works with companies from across the energy innovation chain, including in the infrastructure, automotive, electric power, energy, natural resources, and insurance sectors. MITEI member companies have expressed strong interest in the Data Center Power Forum and are committing to support focused research on a wide range of energy issues associated with data center expansion, Green says.
MITEI’s Data Center Power Forum will provide its member companies with reliable insights into energy supply, grid load operations and management, the built environment, and electricity market design and regulatory policy for data centers. The forum complements MIT’s deep expertise in adjacent topics such as low-power processors, efficient algorithms, task-specific AI, photonic devices, quantum computing, and the societal consequences of data center expansion. As part of the forum, MITEI’s Future Energy Systems Center is funding projects relevant to data center energy in its upcoming proposal cycles. MITEI Research Scientist Deep Deka has been named the program manager for the forum.
“Figuring out how to meet the power demands of data centers is a complicated challenge. Our research is coming at this from multiple directions, from looking at ways to expand transmission capacity within the electrical grid in order to bring power to where it is needed, to ensuring the quality of electrical service for existing users is not diminished when new data centers come online, and to shifting computing tasks to times and places when and where energy is available on the grid," said Deka.
MITEI currently sponsors substantial research related to data center energy topics across several MIT departments. The existing research portfolio includes more than a dozen projects related to data centers, including low- or zero-carbon solutions for energy supply and infrastructure, electrical grid management, and electricity market policy. MIT researchers funded through MITEI’s industry consortium are also designing more energy-efficient power electronics and processors and investigating behind-the-meter low-/no-carbon power plants and energy storage. MITEI-supported experts are studying how to use AI to optimize electrical distribution and the siting of data centers and conducting techno-economic analyses of data center power schemes. MITEI’s consortium projects are also bringing fresh perspectives to data center cooling challenges and considering policy approaches to balance the interests of shareholders.
By drawing together industry stakeholders from across the AI and grid value chain, the Data Center Power Forum enables a richer dialog about solutions to power, grid, and carbon management problems in a noncommercial and collaborative setting.
“The opportunity to meet and to hold discussions on key data center challenges with other forum members from different sectors, as well as with MIT faculty members and research scientists, is a unique benefit of this MITEI-led effort,” Green says.
MITEI addressed the issue of data center power needs with its company members during its fall 2024 Annual Research Conference with a panel session titled, “The extreme challenge of powering data centers in a decarbonized way.” MITEI Director of Research Randall Field led a discussion with representatives from large technology companies Google and Microsoft, known as “hyperscalers,” as well as Madrid-based infrastructure developer Ferrovial S.E. and utility company Exelon Corp. Another conference session addressed the related topic, “Energy storage and grid expansion.” This past spring, MITEI focused its annual Spring Symposium on data centers, hosting faculty members and researchers from MIT and other universities, business leaders, and a representative of the Federal Energy Regulatory Commission for a full day of sessions on the topic, “AI and energy: Peril and promise.”
Particles that enhance mRNA delivery could reduce vaccine dosage and costsUsing 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.”
Charting the future of AI, from safer answers to faster thinkingMIT PhD students who interned with the MIT-IBM Watson AI Lab Summer Program are pushing AI tools to be more flexible, efficient, and grounded in truth.Adoption of new tools and technologies occurs when users largely perceive them as reliable, accessible, and an improvement over the available methods and workflows for the cost. Five PhD students from the inaugural class of the MIT-IBM Watson AI Lab Summer Program are utilizing state-of-the-art resources, alleviating AI pain points, and creating new features and capabilities to promote AI usefulness and deployment — from learning when to trust a model that predicts another’s accuracy to more effectively reasoning over knowledge bases. Together, the efforts from the students and their mentors form a through-line, where practical and technically rigorous research leads to more dependable and valuable models across domains.
Building probes, routers, new attention mechanisms, synthetic datasets, and program-synthesis pipelines, the students’ work spans safety, inference efficiency, multimodal data, and knowledge-grounded reasoning. Their techniques emphasize scaling and integration, with impact always in sight.
Learning to trust, and when
MIT math graduate student Andrey Bryutkin’s research prioritizes the trustworthiness of models. He seeks out internal structures within problems, such as equations governing a system and conservation laws, to understand how to leverage them to produce more dependable and robust solutions. Armed with this and working with the lab, Bryutkin developed a method to peer into the nature of large learning models (LLMs) behaviors. Together with the lab’s Veronika Thost of IBM Research and Marzyeh Ghassemi — associate professor and the Germeshausen Career Development Professor in the MIT Department of Electrical Engineering and Computer Science (EECS) and a member of the Institute of Medical Engineering Sciences and the Laboratory for Information and Decision Systems — Bryutkin explored the “uncertainty of uncertainty” of LLMs.
Classically, tiny feed-forward neural networks two-to-three layers deep, called probes, are trained alongside LLMs and employed to flag untrustworthy answers from the larger model to developers; however, these classifiers can also produce false negatives and only provide point estimates, which don’t offer much information about when the LLM is failing. Investigating safe/unsafe prompts and question-answer tasks, the MIT-IBM team used prompt-label pairs, as well as the hidden states like activation vectors and last tokens from an LLM, to measure gradient scores, sensitivity to prompts, and out-of-distribution data to determine how reliable the probe was and learn areas of data that are difficult to predict. Their method also helps identify potential labeling noise. This is a critical function, as the trustworthiness of AI systems depends entirely on the quality and accuracy of the labeled data they are built upon. More accurate and consistent probes are especially important for domains with critical data in applications like IBM’s Granite Guardian family of models.
Another way to ensure trustworthy responses to queries from an LLM is to augment them with external, trusted knowledge bases to eliminate hallucinations. For structured data, such as social media connections, financial transactions, or corporate databases, knowledge graphs (KG) are natural fits; however, communications between the LLM and KGs often use fixed, multi-agent pipelines that are computationally inefficient and expensive. Addressing this, physics graduate student Jinyeop Song, along with lab researchers Yada Zhu of IBM Research and EECS Associate Professor Julian Shun created a single-agent, multi-turn, reinforcement learning framework that streamlines this process. Here, the group designed an API server hosting Freebase and Wikidata KGs, which consist of general web-based knowledge data, and a LLM agent that issues targeted retrieval actions to fetch pertinent information from the server. Then, through continuous back-and-forth, the agent appends the gathered data from the KGs to the context and responds to the query. Crucially, the system uses reinforcement learning to train itself to deliver answers that strike a balance between accuracy and completeness. The framework pairs an API server with a single reinforcement learning agent to orchestrate data-grounded reasoning with improved accuracy, transparency, efficiency, and transferability.
Spending computation wisely
The timeliness and completeness of a model’s response carry similar weight to the importance of its accuracy. This is especially true for handling long input texts and those where elements, like the subject of a story, evolve over time, so EECS graduate student Songlin Yang is re-engineering what models can handle at each step of inference. Focusing on transformer limitations, like those in LLMs, the lab’s Rameswar Panda of IBM Research and Yoon Kim, the NBX Professor and associate professor in EECS, joined Yang to develop next-generation language model architectures beyond transformers.
Transformers face two key limitations: high computational complexity in long-sequence modeling due to the softmax attention mechanism, and limited expressivity resulting from the weak inductive bias of RoPE (rotary positional encoding). This means that as the input length doubles, the computational cost quadruples. RoPE allows transformers to understand the sequence order of tokens (i.e., words); however, it does not do a good job capturing internal state changes over time, like variable values, and is limited to the sequence lengths seen during training.
To address this, the MIT-IBM team explored theoretically grounded yet hardware-efficient algorithms. As an alternative to softmax attention, they adopted linear attention, reducing the quadratic complexity that limits the feasible sequence length. They also investigated hybrid architectures that combine softmax and linear attention to strike a better balance between computational efficiency and performance.
Increasing expressivity, they replaced RoPE with a dynamic reflective positional encoding based on the Householder transform. This approach enables richer positional interactions for deeper understanding of sequential information, while maintaining fast and efficient computation. The MIT-IBM team’s advancement reduces the need for transformers to break problems into many steps, instead enabling them to handle more complex subproblems with fewer inference tokens.
Visions anew
Visual data contain multitudes that the human brain can quickly parse, internalize, and then imitate. Using vision-language models (VLMs), two graduate students are exploring ways to do this through code.
Over the past two summers and under the advisement of Aude Oliva, MIT director of the MIT-IBM Watson AI Lab and a senior research scientist in the Computer Science and Artificial Intelligence Laboratory; and IBM Research’s Rogerio Feris, Dan Gutfreund, and Leonid Karlinsky (now at Xero), Jovana Kondic of EECS has explored visual document understanding, specifically charts. These contain elements, such as data points, legends, and axes labels, that require optical character recognition and numerical reasoning, which models still struggle with. In order to facilitate the performance on tasks such as these, Kondic’s group set out to create a large, open-source, synthetic chart dataset from code that could be used for training and benchmarking.
With their prototype, ChartGen, the researchers created a pipeline that passes seed chart images through a VLM, which is prompted to read the chart and generate a Python script that was likely used to create the chart in the first place. The LLM component of the framework then iteratively augments the code from many charts to ultimately produce over 200,000 unique pairs of charts and their codes, spanning nearly 30 chart types, as well as supporting data and annotation like descriptions and question-answer pairs about the charts. The team is further expanding their dataset, helping to enable critical multimodal understanding to data visualizations for enterprise applications like financial and scientific reports, blogs, and more.
Instead of charts, EECS graduate student Leonardo Hernandez Cano has his eyes on digital design, specifically visual texture generation for CAD applications and the goal of discovering efficient ways to enable to capabilities in VLMs. Teaming up with the lab groups led by Armando Solar-Lezama, EECS professor and Distinguished Professor of Computing in the MIT Schwarzman College of Computing, and IBM Research’s Nathan Fulton, Hernandez Cano created a program synthesis system that learns to refine code on its own. The system starts with a texture description given by a user in the form of an image. It then generates an initial Python program, which produces visual textures, and iteratively refines the code with the goal of finding a program that produces a texture that matches the target description, learning to search for new programs from the data that the system itself produces. Through these refinements, the novel program can create visualizations with the desired luminosity, color, iridescence, etc., mimicking real materials.
When viewed together, these projects, and the people behind them, are making a cohesive push toward more robust and practical artificial intelligence. By tackling the core challenges of reliability, efficiency, and multimodal reasoning, the work paves the way for AI systems that are not only more powerful, but also more dependable and cost-effective, for real-world enterprise and scientific applications.
Where climate meets communityMIT’s Living Climate Futures Lab takes a human-centered approach to investigating a global challenge.The MIT Living Climate Futures Lab (LCFL) centers the human dimensions of climate change, bringing together expertise from across MIT to address one of the world’s biggest challenges.
The LCFL has three main goals: “addressing how climate change plays out in everyday life, focusing on community-oriented partnerships, and encouraging cross-disciplinary conversations around climate change on campus,” says Chris Walley, the SHASS Dean’s Distinguished Professor of Anthropology and head of MIT’s Anthropology Section. “We think this is a crucial direction for MIT and will make a strong statement about the kind of human-centered, interdisciplinary work needed to tackle this issue.”
Walley is faculty lead of LCFL, working in collaboration with a group of 19 faculty colleagues and researchers. The LCFL began to coalesce in 2022 when MIT faculty and affiliates already working with communities dealing with climate change issues organized a symposium, inviting urban farmers, place-based environmental groups, and others to MIT. Since then, the lab has consolidated the efforts of faculty and affiliates representing disciplines from across the MIT School of Humanities, Arts, and Social Sciences (SHASS) and the Institute.
Amah Edoh, a cultural anthropologist and managing director of LCFL, says the lab’s collaboration with community organizations and development of experiential learning classes aims to bridge the gap that can exist between the classroom and the real world.
“Sometimes we can find ourselves in a bubble where we’re only in conversation with other people from within academia or our own field of practice. There can be a disconnect between what students are learning somewhat abstractly and the ‘real world’ experience of the issues” Edoh says. “By taking up topics from the multidimensional approach that experiential learning makes possible, students learn to take complexity as a given, which can help to foster more critical thinking in them, and inform their future practice in profound ways.”
Edoh points out that the effects of climate change play out in a huge array of areas: health, food security, livelihoods, housing, and governance structures, to name a few.
“The Living Climate Futures Lab supports MIT researchers in developing the long-term collaborations with community partners that are essential to adequately identifying and responding to the challenges that climate change creates in everyday life,” she says.
Manduhai Buyandelger, professor of anthropology and one of the participants in LCFL, developed the class 21A.S01 (Anthro-Engineering: Decarbonization at the Million-Person Scale), which has in turn sparked related classes. The goal is “to merge technological innovation with people-centered environments.” Working closely with residents of Ulaanbaatar, Mongolia, Buyandelger and collaborator Mike Short, the Class of 1941 Professor of Nuclear Science and Engineering, helped develop a molten salt heat bank as a reusable energy source.
“My work with Mike Short on energy and alternative heating in Mongolia helps to cultivate a new generation of creative and socially minded engineers who prioritize people in thinking about technical solutions,” Buyandelger says, adding, “In our course, we collaborate on creating interdisciplinary methods where we fuse anthropological methods with engineering innovations so that we can expand and deepen our approach to mitigate climate change.”
Iselle Barrios ’25, says 21A.S01 was her first anthropology course. She traveled to Mongolia and was able to experience firsthand all the ways in which the air pollution and heating problem was much larger and more complicated than it seemed from MIT’s Cambridge, Massachusetts, campus.
“It was my first exposure to anthropological and STS critiques of science and engineering, as well as international development,” says Barrios, a chemical engineering major. “It fundamentally reshaped the way I see the role of technology and engineers in the broader social context in which they operate. It really helped me learn to think about problems in a more holistic and people-centered way.”
LCFL participant Alvin Harvey, a postdoc in the MIT Media Lab’s Space Enabled Research Group and a citizen of the Navajo Nation, works to incorporate traditional knowledge in engineering and science to “support global stewardship of earth and space ecologies.”
"I envision the Living Climate Futures Lab as a collaborative space that can be an igniter and sustainer of relationships, especially between MIT and those whose have generational and cultural ties to land and space that is being impacted by climate change,” Harvey says. “I think everyone in our lab understands that protecting our climate future is a collective journey."
Kate Brown, the Thomas M. Siebel Distinguished Professor in History of Science, is also a participant in LCFL. Her current interest is urban food sovereignty movements, in which working-class city dwellers used waste to create “the most productive agriculture in recorded human history,” Brown says. While pursuing that work, Brown has developed relationships and worked with urban farmers in Mansfield, Ohio, as well as in Washington and Amsterdam.
Brown and Susan Solomon, the Lee and Geraldine Martin Professor of Environmental Studies and Chemistry, teach a class called STS.055 (Living Dangerously: Environmental Programs from 1900 to Today) that presents the environmental problems and solutions of the 20th century, and how some “solutions” created more problems over time. Brown also plans to teach a class on the history of global food production once she gets access to a small plot of land on campus for a lab site.
“The Living Climate Futures Lab gives us the structure and flexibility to work with communities that are struggling to find solutions to the problems being created by the climate crisis,” says Brown.
Earlier this year, the MIT Human Insight Collaborative (MITHIC) selected the Living Climate Futures Lab as its inaugural Faculty-Driven Initiative (FDI), which comes with a $500,000 seed grant.
MIT Provost Anantha Chandrakasan, co-chair of MITHIC, says the LCFL exemplifies how we can confront the climate crisis by working in true partnership with the communities most affected.
“By combining scientific insight with cultural understanding and lived experience, this initiative brings a deeper dimension to MIT’s climate efforts — one grounded in collaboration, empathy, and real-world impact,” says Chandrakasan.
Agustín Rayo, the Kenan Sahin Dean of SHASS and co-chair of MITHIC, says the LCFL is precisely the type of interdisciplinary collaboration the FDI program was designed to support.
"By bringing together expertise from across MIT, I am confident the Living Climate Futures Lab will make significant contributions in the Institute’s effort to address the climate crisis," says Rayo.
Walley said the seed grant will support a second symposium in 2026 to be co-designed with community groups, a suite of experiential learning classes, workshops, a speaker series, and other programming. Throughout this development phase, the lab will solicit donor support to build it into an ongoing MIT initiative and a leader in the response to climate change.
MIT physicists observe key evidence of unconventional superconductivity in magic-angle grapheneThe 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 worldRuth 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!
MIT researchers invent new human brain model to enable disease research, drug discoveryCultured from induced pluripotent stem cells, “miBrains” integrate all major brain cell types and model brain structures, cellular interactions, activity, and pathological features.A new 3D human brain tissue platform developed by MIT researchers is the first to integrate all major brain cell types, including neurons, glial cells, and the vasculature, into a single culture.
Grown from individual donors’ induced pluripotent stem cells, these models — dubbed Multicellular Integrated Brains (miBrains) — replicate key features and functions of human brain tissue, are readily customizable through gene editing, and can be produced in quantities that support large-scale research.
Although each unit is smaller than a dime, miBrains may be worth a great deal to researchers and drug developers who need more complex living lab models to better understand brain biology and treat diseases.
“The miBrain is the only in vitro system that contains all six major cell types that are present in the human brain,” says Li-Huei Tsai, Picower Professor, director of The Picower Institute for Learning and Memory, and a senior author of the open-access study describing miBrains, published Oct. 17 in the Proceedings of the National Academy of Sciences.
“In their first application, miBrains enabled us to discover how one of the most common genetic markers for Alzheimer’s disease alters cells’ interactions to produce pathology,” she adds.
Tsai’s co-senior authors are Robert Langer, David H. Koch (1962) Institute Professor, and Joel Blanchard, associate professor in the Icahn School of Medicine at Mt. Sinai in New York, and a former Tsai Laboratory postdoc. The study is led by Alice Stanton, former postdoc in the Langer and Tsai labs and now assistant professor at Harvard Medical School and Massachusetts General Hospital, and Adele Bubnys, a former Tsai lab postdoc and current senior scientist at Arbor Biotechnologies.
Benefits from two kinds of models
The more closely a model recapitulates the brain’s complexity, the better suited it is for extrapolating how human biology works and how potential therapies may affect patients. In the brain, neurons interact with each other and with various helper cells, all of which are arranged in a three-dimensional tissue environment that includes blood vessels and other components. All of these interactions are necessary for health, and any of them can contribute to disease.
Simple cultures of just one or a few cell types can be created in quantity relatively easily and quickly, but they cannot tell researchers about the myriad interactions that are essential to understanding health or disease. Animal models embody the brain’s complexity, but can be difficult and expensive to maintain, slow to yield results, and different enough from humans to yield occasionally divergent results.
MiBrains combine advantages from each type of model, retaining much of the accessibility and speed of lab-cultured cell lines while allowing researchers to obtain results that more closely reflect the complex biology of human brain tissue. Moreover, they are derived from individual patients, making them personalized to an individual’s genome. In the model, the six cell types self-assemble into functioning units, including blood vessels, immune defenses, and nerve signal conduction, among other features. Researchers ensured that miBrains also possess a blood-brain-barrier capable of gatekeeping which substances may enter the brain, including most traditional drugs.
“The miBrain is very exciting as a scientific achievement,” says Langer. “Recent trends toward minimizing the use of animal models in drug development could make systems like this one increasingly important tools for discovering and developing new human drug targets.”
Two ideal blends for functional brain models
Designing a model integrating so many cell types presented challenges that required many years to overcome. Among the most crucial was identifying a substrate able to provide physical structure for cells and support their viability. The research team drew inspiration from the environment that surrounds cells in natural tissue, the extracellular matrix (ECM). The miBrain’s hydrogel-based “neuromatrix” mimics the brain’s ECM with a custom blend of polysaccharides, proteoglycans, and basement membrane that provide a scaffold for all the brain’s major cell types while promoting the development of functional neurons.
A second blend would also prove critical: the proportion of cells that would result in functional neurovascular units. The actual ratios of cell types have been a matter of debate for the last several decades, with even the more advanced methodologies providing only rough brushstrokes for guidance, for example 45-75 percent for oligodendroglia of all cells or 19-40 percent for astrocytes.
The researchers developed the six cell types from patient-donated induced pluripotent stem cells, verifying that each cultured cell type closely recreated naturally-occurring brain cells. Then, the team experimentally iterated until they hit on a balance of cell types that resulted in functional, properly structured neurovascular units. This laborious process would turn out to be an advantageous feature of miBrains: because cell types are cultured separately, they can each be genetically edited so that the resulting model is tailored to replicate specific health and disease states.
“Its highly modular design sets the miBrain apart, offering precise control over cellular inputs, genetic backgrounds, and sensors — useful features for applications such as disease modeling and drug testing,” says Stanton.
Alzheimer’s discovery using miBrain
To test miBrain’s capabilities, the researchers embarked on a study of the gene variant APOE4, which is the strongest genetic predictor for the development of Alzheimer’s disease. Although one brain cell type, astrocytes, are known to be a primary producer of the APOE protein, the role that astrocytes carrying the APOE4 variant play in disease pathology is poorly understood.
MiBrains were well-suited to the task for two reasons. First of all, they integrate astrocytes with the brain’s other cell types, so that their natural interactions with other cells can be mimicked. Second, because the platform allowed the team to integrate cell types individually, APOE4 astrocytes could be studied in cultures where all other cell types carried APOE3, a gene variant that does not increase Alzheimer’s risk. This enabled the researchers to isolate the contribution APOE4 astrocytes make to pathology.
In one experiment, the researchers examined APOE4 astrocytes cultured alone, versus ones in APOE4 miBrains. They found that only in the miBrains did the astrocytes express many measures of immune reactivity associated with Alzheimer’s disease, suggesting the multicellular environment contributes to that state.
The researchers also tracked the Alzheimer’s-associated proteins amyloid and phosphorylated tau, and found all-APOE4 miBrains accumulated them, whereas all-APOE3 miBrains did not, as expected. However, in APOE3 miBrains with APOE4 astrocytes, they found that APOE4 miBrains still exhibited amyloid and tau accumulation.
Then the team dug deeper into how APOE4 astrocytes’ interactions with other cell types might lead to their contribution to disease pathology. Prior studies have implicated molecular cross-talk with the brain’s microglia immune cells. Notably, when the researchers cultured APOE4 miBrains without microglia, their production of phosphorylated tau was significantly reduced. When the researchers dosed APOE4 miBrains with culture media from astrocytes and microglia combined, phosphorylated tau increased, whereas when they dosed them with media from cultures of astrocytes or microglia alone, the tau production did not increase. The results therefore provided new evidence that molecular cross-talk between microglia and astrocytes is indeed required for phosphorylated tau pathology.
In the future, the research team plans to add new features to miBrains to more closely model characteristics of working brains, such as leveraging microfluidics to add flow through blood vessels, or single-cell RNA sequencing methods to improve profiling of neurons.
Researchers expect that miBrains could advance research discoveries and treatment modalities for Alzheimer’s disease and beyond.
“Given its sophistication and modularity, there are limitless future directions,” says Stanton. “Among them, we would like to harness it to gain new insights into disease targets, advanced readouts of therapeutic efficacy, and optimization of drug delivery vehicles.”
“I’m most excited by the possibility to create individualized miBrains for different individuals,” adds Tsai. “This promises to pave the way for developing personalized medicine.”
Funding for the study came from the BT Charitable Foundation, Freedom Together Foundation, the Robert A. and Renee E. Belfer Family, Lester A. Gimpelson, Eduardo Eurnekian, Kathleen and Miguel Octavio, David B. Emmes, the Halis Family, the Picower Institute, and an anonymous donor.
MIT study finds targets for a new tuberculosis vaccineUsing these antigens, researchers plan to develop vaccine candidates that they hope would stimulate a strong immune response against the world’s deadliest pathogen.A large-scale screen of tuberculosis proteins has revealed several possible antigens that could be developed as a new vaccine for TB, the world’s deadliest infectious disease.
In the new study, a team of MIT biological engineers was able to identify a handful of immunogenic peptides, out of more than 4,000 bacterial proteins, that appear to stimulate a strong response from a type of T cells responsible for orchestrating immune cells’ response to infection.
There is currently only one vaccine for tuberculosis, known as BCG, which is a weakened version of a bacterium that causes TB in cows. This vaccine is widely administered in some parts of the world, but it poorly protects adults against pulmonary TB. Worldwide, tuberculosis kills more than 1 million people every year.
“There’s still a huge TB burden globally that we’d like to make an impact on,” says Bryan Bryson, an associate professor of biological engineering at MIT and a member of the Ragon Institute of Mass General Brigham, MIT, and Harvard. “What we’ve tried to do in this initial TB vaccine is focus on antigens that we saw frequently in our screen and also appear to stimulate a response in T cells from people with prior TB infection.”
Bryson and Forest White, the Ned C. and Janet C. Rice Professor of Biological Engineering at MIT, and a member of the Koch Institute for Integrative Cancer Research, are the senior authors of the study, which appears today in Science Translational Medicine. Owen Leddy PhD ’25 is the paper’s lead author.
Identifying vaccine targets
Since the BCG vaccine was developed more than 100 years ago, no other TB vaccines have been approved for use. Mycobacterium tuberculosis produces more than 4,000 proteins, which makes it a daunting challenge to pick out proteins that might elicit a strong immune response if used as a vaccine.
In the new study, Bryson and his students set out to narrow the field of candidates by identifying TB proteins presented on the surface of infected human cells. When an immune cell such as a phagocyte is infected with Mycobacterium tuberculosis, some of the bacterial proteins get chopped into fragments called peptides, which are then displayed on the surface of the cell by MHC proteins. These MHC-peptide complexes act as a signal that can activate T cells.
MHCs, or major histocompatibility complexes, come in two types known as class I and class II. Class I MHCs activate killer T cells, while class II MHCs stimulate helper T cells. In human cells, there are three genes that can encode MHC-II proteins, and each of these comes in hundreds of variants. This means that any two people can have a very different repertoire of MHC-II molecules, which present different antigens.
“Instead of looking at all of those 4,000 TB proteins, we wanted to ask which of those proteins from TB actually end up being displayed to the rest of the immune system via MHC,” Bryson says. “If we could just answer that question, then we could design vaccines to match that.”
To try to answer the question, the researchers infected human phagocytes with Mycobacterium tuberculosis. After three days, they extracted MHC-peptide complexes from the cell surfaces, then identified the peptides using mass spectrometry.
Focusing on peptides bound to MHC-II, the researchers found 27 TB peptides, from 13 proteins, that appeared most often in the infected cells. Then, they further tested those peptides by exposing them to T cells donated by people who had previously been infected with TB.
They found that 24 of these peptides did elicit a T cell response in at least some of the samples. None of the proteins from which these peptides came worked for every single donor, but Bryson believes that a vaccine using a combination of these peptides would likely work for most people.
“In a perfect world, if you were trying to design a vaccine, you would pick one protein and that protein would be presented across every donor. It should work for every person,” Bryson says. “However, using our measurements, we’ve not yet found a TB protein that covers every donor we’ve analyzed thus far.”
Enter mRNA vaccines
Among the vaccine candidates that the researchers identified are several peptides from a class of proteins called type 7 secretion systems (T7SSs). Some of these peptides also turned up in an earlier study from Bryson’s lab on MHC-1.
“Type 7 secretion system substrates are a very small sliver of the overall TB proteome, but when you look at MHC class I or MHC class II, it seems as though the cells are preferentially presenting these,” Bryson says.
Two of the best-known of these proteins, EsxA and EsxB, are secreted by bacteria to help them escape from the membranes that phagocytes use to envelop them within the cell. Neither protein can break through the membrane on its own, but when joined together to form a heterodimer, they can poke holes, which also allow other T7SS proteins to escape.
To evaluate whether the proteins they identified could make a good vaccine, the researchers created mRNA vaccines encoding two protein sequences — EsxB and EsxG. The researchers designed several versions of the vaccine, which were targeted to different compartments within the cells.
The researchers then delivered this vaccine into human phagocytes, where they found that vaccines that targeted cell lysosomes — organelles that break down molecules — were the most effective. These vaccines induced 1,000 times more MHC presentation of TB peptides than any of the others.
They later found that the presentation was even higher if they added EsxA to the vaccine, because it allows the formation of the heterodimers that can poke through the lysosomal membrane.
The researchers currently have a mix of eight proteins that they believe could offer protection against TB for most people, but they are continuing to test the combination with blood samples from people around the world. They also hope to run additional studies to explore how much protection this vaccine offers in animal models. Tests in humans are likely several years away.
The research was funded by the MIT Center for Precision Cancer Research at the Koch Institute, the National Institutes of Health, the National Institute of Environmental Health Sciences, and the Frederick National Laboratory for Cancer Research.
Teaching robots to map large environmentsA new approach developed at MIT could help a search-and-rescue robot navigate an unpredictable environment by rapidly generating an accurate map of its surroundings.A robot searching for workers trapped in a partially collapsed mine shaft must rapidly generate a map of the scene and identify its location within that scene as it navigates the treacherous terrain.
Researchers have recently started building powerful machine-learning models to perform this complex task using only images from the robot’s onboard cameras, but even the best models can only process a few images at a time. In a real-world disaster where every second counts, a search-and-rescue robot would need to quickly traverse large areas and process thousands of images to complete its mission.
To overcome this problem, MIT researchers drew on ideas from both recent artificial intelligence vision models and classical computer vision to develop a new system that can process an arbitrary number of images. Their system accurately generates 3D maps of complicated scenes like a crowded office corridor in a matter of seconds.
The AI-driven system incrementally creates and aligns smaller submaps of the scene, which it stitches together to reconstruct a full 3D map while estimating the robot’s position in real-time.
Unlike many other approaches, their technique does not require calibrated cameras or an expert to tune a complex system implementation. The simpler nature of their approach, coupled with the speed and quality of the 3D reconstructions, would make it easier to scale up for real-world applications.
Beyond helping search-and-rescue robots navigate, this method could be used to make extended reality applications for wearable devices like VR headsets or enable industrial robots to quickly find and move goods inside a warehouse.
“For robots to accomplish increasingly complex tasks, they need much more complex map representations of the world around them. But at the same time, we don’t want to make it harder to implement these maps in practice. We’ve shown that it is possible to generate an accurate 3D reconstruction in a matter of seconds with a tool that works out of the box,” says Dominic Maggio, an MIT graduate student and lead author of a paper on this method.
Maggio is joined on the paper by postdoc Hyungtae Lim and senior author Luca Carlone, associate professor in MIT’s Department of Aeronautics and Astronautics (AeroAstro), principal investigator in the Laboratory for Information and Decision Systems (LIDS), and director of the MIT SPARK Laboratory. The research will be presented at the Conference on Neural Information Processing Systems.
Mapping out a solution
For years, researchers have been grappling with an essential element of robotic navigation called simultaneous localization and mapping (SLAM). In SLAM, a robot recreates a map of its environment while orienting itself within the space.
Traditional optimization methods for this task tend to fail in challenging scenes, or they require the robot’s onboard cameras to be calibrated beforehand. To avoid these pitfalls, researchers train machine-learning models to learn this task from data.
While they are simpler to implement, even the best models can only process about 60 camera images at a time, making them infeasible for applications where a robot needs to move quickly through a varied environment while processing thousands of images.
To solve this problem, the MIT researchers designed a system that generates smaller submaps of the scene instead of the entire map. Their method “glues” these submaps together into one overall 3D reconstruction. The model is still only processing a few images at a time, but the system can recreate larger scenes much faster by stitching smaller submaps together.
“This seemed like a very simple solution, but when I first tried it, I was surprised that it didn’t work that well,” Maggio says.
Searching for an explanation, he dug into computer vision research papers from the 1980s and 1990s. Through this analysis, Maggio realized that errors in the way the machine-learning models process images made aligning submaps a more complex problem.
Traditional methods align submaps by applying rotations and translations until they line up. But these new models can introduce some ambiguity into the submaps, which makes them harder to align. For instance, a 3D submap of a one side of a room might have walls that are slightly bent or stretched. Simply rotating and translating these deformed submaps to align them doesn’t work.
“We need to make sure all the submaps are deformed in a consistent way so we can align them well with each other,” Carlone explains.
A more flexible approach
Borrowing ideas from classical computer vision, the researchers developed a more flexible, mathematical technique that can represent all the deformations in these submaps. By applying mathematical transformations to each submap, this more flexible method can align them in a way that addresses the ambiguity.
Based on input images, the system outputs a 3D reconstruction of the scene and estimates of the camera locations, which the robot would use to localize itself in the space.
“Once Dominic had the intuition to bridge these two worlds — learning-based approaches and traditional optimization methods — the implementation was fairly straightforward,” Carlone says. “Coming up with something this effective and simple has potential for a lot of applications.
Their system performed faster with less reconstruction error than other methods, without requiring special cameras or additional tools to process data. The researchers generated close-to-real-time 3D reconstructions of complex scenes like the inside of the MIT Chapel using only short videos captured on a cell phone.
The average error in these 3D reconstructions was less than 5 centimeters.
In the future, the researchers want to make their method more reliable for especially complicated scenes and work toward implementing it on real robots in challenging settings.
“Knowing about traditional geometry pays off. If you understand deeply what is going on in the model, you can get much better results and make things much more scalable,” Carlone says.
This work is supported, in part, by the U.S. National Science Foundation, U.S. Office of Naval Research, and the National Research Foundation of Korea. Carlone, currently on sabbatical as an Amazon Scholar, completed this work before he joined Amazon.
New therapeutic brain implants could defy the need for surgeryMIT researchers created microscopic wireless electronic devices that travel through blood and implant in target brain regions, where they provide electrical stimulation.What if clinicians could place tiny electronic chips in the brain that electrically stimulate a precise target, through a simple injection in the arm? This may someday help treat deadly or debilitating brain diseases, while eliminating surgery-related risks and costs.
MIT researchers have taken a major step toward making this scenario a reality. They developed microscopic, wireless bioelectronics that could travel through the body’s circulatory system and autonomously self-implant in a target region of the brain, where they would provide focused treatment.
In a study on mice, the researchers show that after injection, these miniscule implants can identify and travel to a specific brain region without the need for human guidance. Once there, they can be wirelessly powered to provide electrical stimulation to the precise area. Such stimulation, known as neuromodulation, has shown promise as a way to treat brain tumors and diseases like Alzheimer’s and multiple sclerosis.
Moreover, because the electronic devices are integrated with living, biological cells before being injected, they are not attacked by the body’s immune system and can cross the blood-brain barrier while leaving it intact. This maintains the barrier’s crucial protection of the brain.
The researchers demonstrated the use of this technology, which they call “circulatronics,” to target brain inflammation, a major factor in the progression of many neurological diseases. They show that the implants can provide localized neuromodulation deep inside the brain achieving high precision, to within several microns around the target area.
In addition, the biocompatible implants do not damage surrounding neurons.
While brain implants usually require hundreds of thousands of dollars in medical costs and risky surgical procedures, circulatronics technology holds the potential to make therapeutic brain implants accessible to all by eliminating the need for surgery, says Deblina Sarkar, the AT&T Career Development Associate Professor in the MIT Media Lab and MIT Center for Neurobiological Engineering, head of the Nano-Cybernetic Biotrek Lab, and senior author of a study on the work.
She is joined on the paper by lead author Shubham Yadav, an MIT graduate student; as well as others at MIT, Wellesley College, and Harvard University. The research appears today in Nature Biotechnology.
Hybrid implants
The team has been working on circulatronics for more than six years. The electronic devices, each about one-billionth the length of a grain of rice, are composed of organic semiconducting polymer layers sandwiched between metallic layers to create an electronic heterostructure.
They are fabricated using CMOS-compatible processes in the MIT.nano facilities, and then integrated with living cells to create cell-electronics hybrids. To do this, the researchers lift the devices off the silicon wafer on which they are fabricated, so they are free-floating in a solution.
“The electronics worked perfectly when they were attached to the substrate, but when we originally lifted them off, they didn’t work anymore. Solving that challenge took us more than a year,” Sarkar says.
Key to their operation is the high wireless power conversion efficiency of the tiny electronics. This enables the devices to work deep inside the brain and still harness enough energy for neuromodulation.
The researchers use a chemical reaction to bond the electronic devices to cells. In the new study, they fused the electronics with a type of immune cell called monocytes, which target areas of inflammation in the body. They also applied a fluorescent dye, allowing them to trace the devices as they crossed the intact blood-brain barrier and self-implanted in the target brain region.
While they explored brain inflammation in this study, the researchers hope to use different cell types and engineer the cells to target specific regions of the brain.
“Our cell-electronics hybrid fuses the versatility of electronics with the biological transport and biochemical sensing prowess of living cells,” Sarkar says. “The living cells camouflage the electronics so that they aren’t attacked by the body’s immune system and they can travel seamlessly through the bloodstream. This also enables them to squeeze through the intact blood-brain barrier without the need to invasively open it.”
Over the course of about four years, the team tried many methods to autonomously and noninvasively cross the blood-brain barrier before they perfected this cellular integration technique.
In addition, because the circulatronics devices are so tiny, they offer much higher precision than conventional electrodes. They can self-implant, leading to millions of microscopic stimulation sites that take the exact shape of the target region.
Their small size also enables the biocompatible devices to live alongside neurons without causing harmful effects. Through a series of biocompatibility tests, the researchers found that circulatronics can safely integrate among neurons without impacting the brain processes behind cognition or motion.
After the devices have self-implanted in the target region, a clinician or researcher uses an external transmitter to provide electromagnetic waves, in the form of near-infrared light, that power the technology and enable electrical stimulation of the neurons.
Targeting deadly diseases
The Sarkar lab is currently working on developing their technology to treat multiple diseases including brain cancer, Alzheimer’s disease, and chronic pain.
The tiny size and self-implantation capabilities of circulatronics devices could make them well-suited to treat brain cancers such as glioblastoma that cause tumors at multiple locations, some of which may be too small to identify with imaging techniques. They may also provide new avenues for treating especially deadly cancers like diffuse intrinsic pontine glioma, an aggressive type of tumor found in the brain stem that usually cannot be surgically removed.
“This is a platform technology and may be employed to treat multiple brain diseases and mental illnesses,” Sarkar says. “Also, this technology is not just confined to the brain but could also be extended to other parts of the body in future.”
The researchers hope to move the technology into clinical trials within three years through the recently launched startup Cahira Technologies.
They are also exploring integration of additional nanoelectronic circuits into their devices to enable functionalities including sensing, feedback based on-chip data analysis, and capabilities such as creating synthetic electronic neurons.
“Our tiny electronic devices seamlessly integrate with the neurons and co-live and co-exist with the brain cells creating a unique brain-computer symbiosis. We are working dedicatedly to employ this technology for treating neural diseases, where drugs or standard therapies fail, for alleviating human suffering and envision a future where humans could transcend beyond diseases and biological limitations,” says Sarkar.
What should countries do with their nuclear waste?A new study by MIT researchers analyzes different nuclear waste management strategies, with a focus on the radionuclide iodine-129.One of the highest-risk components of nuclear waste is iodine-129 (I-129), which stays radioactive for millions of years and accumulates in human thyroids when ingested. In the U.S., nuclear waste containing I-129 is scheduled to be disposed of in deep underground repositories, which scientists say will sufficiently isolate it.
Meanwhile, across the globe, France routinely releases low-level radioactive effluents containing iodine-129 and other radionuclides into the ocean. France recycles its spent nuclear fuel, and the reprocessing plant discharges about 153 kilograms of iodine-129 each year, under the French regulatory limit.
Is dilution a good solution? What’s the best way to handle spent nuclear fuel? A new study by MIT researchers and their collaborators at national laboratories quantifies I-129 release under three different scenarios: the U.S. approach of disposing spent fuel directly in deep underground repositories, the French approach of dilution and release, and an approach that uses filters to capture I-129 and disposes of them in shallow underground waste repositories.
The researchers found France’s current practice of reprocessing releases about 90 percent of the waste’s I-129 into the biosphere. They found low levels of I-129 in ocean water around France and the U.K.’s former reprocessing sites, including the English Channel and North Sea. Although the low level of I-129 in the water in Europe is not considered to pose health risks, the U.S. approach of deep underground disposal leads to far less I-129 being released, the researchers found.
The researchers also investigated the effect of environmental regulations and technologies related to I-129 management, to illuminate the tradeoffs associated with different approaches around the world.
“Putting these pieces together to provide a comprehensive view of Iodine-129 is important,” says MIT Assistant Professor Haruko Wainwright, a first author on the paper who holds a joint appointment in the departments of Nuclear Science and Engineering and of Civil and Environmental Engineering. “There are scientists that spend their lives trying to clean up iodine-129 at contaminated sites. These scientists are sometimes shocked to learn some countries are releasing so much iodine-129. This work also provides a life-cycle perspective. We’re not just looking at final disposal and solid waste, but also when and where release is happening. It puts all the pieces together.”
MIT graduate student Kate Whiteaker SM ’24 led many of the analyses with Wainwright. Their co-authors are Hansell Gonzalez-Raymat, Miles Denham, Ian Pegg, Daniel Kaplan, Nikolla Qafoku, David Wilson, Shelly Wilson, and Carol Eddy-Dilek. The study appears today in Nature Sustainability.
Managing waste
Iodine-129 is often a key focus for scientists and engineers as they conduct safety assessments of nuclear waste disposal sites around the world. It has a half-life of 15.7 million years, high environmental mobility, and could potentially cause cancers if ingested. The U.S. sets a strict limit on how much I-129 can be released and how much I-129 can be in drinking water — 5.66 nanograms per liter, the lowest such level of any radionuclides.
“Iodine-129 is very mobile, so it is usually the highest-dose contributor in safety assessments,” Wainwright says.
For the study, the researchers calculated the release of I-129 across three different waste management strategies by combining data from current and former reprocessing sites as well as repository assessment models and simulations.
The authors defined the environmental impact as the release of I-129 into the biosphere that humans could be exposed to, as well as its concentrations in surface water. They measured I-129 release per the total electrical energy generated by a 1-gigawatt power plant over one year, denoted as kg/GWe.y.
Under the U.S. approach of deep underground disposal with barrier systems, assuming the barrier canisters fail at 1,000 years (a conservative estimate), the researchers found 2.14 x 10–8 kg/GWe.y of I-129 would be released between 1,000 and 1 million years from today.
They estimate that 4.51 kg/GWe.y of I-129, or 91 percent of the total, would be released into the biosphere in the scenario where fuel is reprocessed and the effluents are diluted and released. About 3.3 percent of I-129 is captured by gas filters, which are then disposed of in shallow subsurfaces as low-level radioactive waste. A further 5.2 percent remains in the waste stream of the reprocessing plant, which is then disposed of as high-level radioactive waste.
If the waste is recycled with gas filters to directly capture I-129, 0.05 kg/GWe.y of the I-129 is released, while 94 percent is disposed of in the low-level disposal sites. For shallow disposal, some kind of human disruption and intrusion is assumed to occur after government or institutional control expires (typically 100-1,000 years). That results in a potential release of the disposed amount to the environment after the control period.
Overall, the current practice of recycling spent nuclear fuel releases the majority of I-129 into the environment today, while the direct disposal of spent fuel releases around 1/100,000,000 that amount over 1 million years. When the gas filters are used to capture I-129, the majority of I-129 goes to shallow underground repositories, which could be accidentally released through human intrusion down the line.
The researchers also quantified the concentration of I-129 in different surface waters near current and former fuel reprocessing facilities, including the English Channel and the North Sea near reprocessing plants in France and U.K. They also analyzed the U.S. Columbia River downstream of a site in Washington state where material for nuclear weapons was produced during the Cold War, and they studied a similar site in South Carolina. The researchers found far higher concentrations of I-129 within the South Carolina site, where the low-level radioactive effluents were released far from major rivers and hence resulted in less dilution in the environment.
“We wanted to quantify the environmental factors and the impact of dilution, which in this case affected concentrations more than discharge amounts,” Wainwright says. “Someone might take our results to say dilution still works: It’s reducing the contaminant concentration and spreading it over a large area. On the other hand, in the U.S., imperfect disposal has led to locally higher surface water concentrations. This provides a cautionary tale that disposal could concentrate contaminants, and should be carefully designed to protect local communities.”
Fuel cycles and policy
Wainwright doesn’t want her findings to dissuade countries from recycling nuclear fuel. She says countries like Japan plan to use increased filtration to capture I-129 when they reprocess spent fuel. Filters with I-129 can be disposed of as low-level waste under U.S. regulations.
“Since I-129 is an internal carcinogen without strong penetrating radiation, shallow underground disposal would be appropriate in line with other hazardous waste,” Wainwright says. “The history of environmental protection since the 1960s is shifting from waste dumping and release to isolation. But there are still industries that release waste into the air and water. We have seen that they often end up causing issues in our daily life — such as CO2, mercury, PFAS and others — especially when there are many sources or when bioaccumulation happens. The nuclear community has been leading in waste isolation strategies and technologies since the 1950s. These efforts should be further enhanced and accelerated. But at the same time, if someone does not choose nuclear energy because of waste issues, it would encourage other industries with much lower environmental standards.”
The work was supported by MIT’s Climate Fast Forward Faculty Fund and the U.S. Department of Energy.
A new way to understand and predict gene splicingThe KATMAP model, developed by researchers in the Department of Biology, can predict alternative cell splicing, which allows cells to create endless diversity from the same sets of genetic blueprints.Although heart cells and skin cells contain identical instructions for creating proteins encoded in their DNA, they’re able to fill such disparate niches because molecular machinery can cut out and stitch together different segments of those instructions to create endlessly unique combinations.
The ingenuity of using the same genes in different ways is made possible by a process called splicing and is controlled by splicing factors; which splicing factors a cell employs determines what sets of instructions that cell produces, which, in turn, gives rise to proteins that allow cells to fulfill different functions.
In an open-access paper published today in Nature Biotechnology, researchers in the MIT Department of Biology outlined a framework for parsing the complex relationship between sequences and splicing regulation to investigate the regulatory activities of splicing factors, creating models that can be applied to interpret and predict splicing regulation across different cell types, and even different species. Called Knockdown Activity and Target Models from Additive regression Predictions, KATMAP draws on experimental data from disrupting the expression of a splicing factor and information on which sequences the splicing factor interacts with to predict its likely targets.
Aside from the benefits of a better understanding of gene regulation, splicing mutations — either in the gene that is spliced or in the splicing factor itself — can give rise to diseases such as cancer by altering how genes are expressed, leading to the creation or accumulation of faulty or mutated proteins. This information is critical for developing therapeutic treatments for those diseases. The researchers also demonstrated that KATMAP can potentially be used to predict whether synthetic nucleic acids, a promising treatment option for disorders including a subset of muscular atrophy and epilepsy disorders, affect splicing.
Perturbing splicing
In eukaryotic cells, including our own, splicing occurs after DNA is transcribed to produce an RNA copy of a gene, which contains both coding and non-coding regions of RNA. The noncoding intron regions are removed, and the coding exon segments are spliced back together to make a near-final blueprint, which can then be translated into a protein.
According to first author Michael P. McGurk, a postdoc in the lab of MIT Professor Christopher Burge, previous approaches could provide an average picture of regulation, but could not necessarily predict the regulation of splicing factors at particular exons in particular genes.
KATMAP draws on RNA sequencing data generated from perturbation experiments, which alter the expression level of a regulatory factor by either overexpressing it or knocking down its levels. The consequences of overexpression or knockdown are that the genes regulated by the splicing factor should exhibit different levels of splicing after perturbation, which helps the model identify the splicing factor’s targets.
Cells, however, are complex, interconnected systems, where one small change can cause a cascade of effects. KATMAP is also able to distinguish between direct targets from indirect, downstream impacts by incorporating known information about the sequence the splicing factor is likely to interact with, referred to as a binding site or binding motif.
“In our analyses, we identify predicted targets as exons that have binding sites for this particular factor in the regions where this model thinks they need to be to impact regulation,” McGurk says, while non-targets may be affected by perturbation but don’t have the likely appropriate binding sites nearby.
This is especially helpful for splicing factors that aren’t as well-studied.
“One of our goals with KATMAP was to try to make the model general enough that it can learn what it needs to assume for particular factors, like how similar the binding site has to be to the known motif or how regulatory activity changes with the distance of the binding sites from the splice sites,” McGurk says.
Starting simple
Although predictive models can be very powerful at presenting possible hypotheses, many are considered “black boxes,” meaning the rationale that gives rise to their conclusions is unclear. KATMAP, on the other hand, is an interpretable model that enables researchers to quickly generate hypotheses and interpret splicing patterns in terms of regulatory factors while also understanding how the predictions were made.
“I don’t just want to predict things, I want to explain and understand,” McGurk says. “We set up the model to learn from existing information about splicing and binding, which gives us biologically interpretable parameters.”
The researchers did have to make some simplifying assumptions in order to develop the model. KATMAP considers only one splicing factor at a time, although it is possible for splicing factors to work in concert with one another. The RNA target sequence could also be folded in such a way that the factor wouldn’t be able to access a predicted binding site, so the site is present but not utilized.
“When you try to build up complete pictures of complex phenomena, it’s usually best to start simple,” McGurk says. “A model that only considers one splicing factor at a time is a good starting point.”
David McWaters, another postdoc in the Burge Lab and a co-author on the paper, conducted key experiments to test and validate that aspect of the KATMAP model.
Future directions
The Burge lab is collaborating with researchers at Dana-Farber Cancer Institute to apply KATMAP to the question of how splicing factors are altered in disease contexts, as well as with other researchers at MIT as part of an MIT HEALS grant to model splicing factor changes in stress responses. McGurk also hopes to extend the model to incorporate cooperative regulation for splicing factors that work together.
“We’re still in a very exploratory phase, but I would like to be able to apply these models to try to understand splicing regulation in disease or development. In terms of variation of splicing factors, they are related, and we need to understand both,” McGurk says.
Burge, the Uncas (1923) and Helen Whitaker Professor and senior author of the paper, will continue to work on generalizing this approach to build interpretable models for other aspects of gene regulation.
“We now have a tool that can learn the pattern of activity of a splicing factor from types of data that can be readily generated for any factor of interest,” says Burge, who is also an extra-mural member of the Koch Institute for Integrative Cancer Research and an associate member of the Broad Institute of MIT and Harvard. “As we build up more of these models, we’ll be better able to infer which splicing factors have altered activity in a disease state from transcriptomic data, to help understand which splicing factors are driving pathology.”
A new patch could help to heal the heartMIT engineers developed a programmable drug-delivery patch that can promote tissue healing and blood vessel regrowth following a heart attack.MIT engineers have developed a flexible drug-delivery patch that can be placed on the heart after a heart attack to help promote healing and regeneration of cardiac tissue.
The new patch is designed to carry several different drugs that can be released at different times, on a pre-programmed schedule. In a study of rats, the researchers showed that this treatment reduced the amount of damaged heart tissue by 50 percent and significantly improved cardiac function.
If approved for use in humans, this type of patch could help heart attack victims recover more of their cardiac function than is now possible, the researchers say.
“When someone suffers a major heart attack, the damaged cardiac tissue doesn’t regenerate effectively, leading to a permanent loss of heart function. The tissue that was damaged doesn’t recover,” says Ana Jaklenec, a principal investigator at MIT’s Koch Institute for Integrative Cancer Research. “Our goal is to restore that function and help people regain a stronger, more resilient heart after a myocardial infarction.”
Jaklenec and Robert Langer, the David H. Koch Institute Professor at MIT and a member of the Koch Institute, are the senior authors of the new study, which appears today in Cell Biomaterials. Former MIT postdoc Erika Wangis the lead author of the paper.
Programmed drug delivery
After a heart attack, many patients end up having bypass surgery, which improves blood flow to the heart but doesn’t repair the cardiac tissue that was damaged. In the new study, the MIT team wanted to create a patch that could be applied to the heart at the same time that the surgery is performed.
This patch, they hoped, could deliver drugs over an extended time period to promote tissue healing. Many diseases, including heart conditions, require phase-specific treatment, but most systems release drugs all at once. Timed delivery better synchronizes therapy with recovery.
“We wanted to see if it’s possible to deliver a precisely orchestrated therapeutic intervention to help heal the heart, right at the site of damage, while the surgeon is already performing open-heart surgery,” Jaklenec says.
To achieve this, the researchers set out to adapt drug-delivery microparticles they had previously developed, which consist of capsules similar to tiny coffee cups with lids. These capsules are made from a polymer called PLGA and can be sealed with a drug inside.
By changing the molecular weight of the polymers used to form the lids, the researchers can control how quickly they degrade, which enables them to program the particles to release their contents at specific times. For this application, the researchers designed particles that break down during days 1-3, days 7-9, and days 12-14 after implantation.
This allowed them to devise a regimen of three drugs that promote heart healing in different ways. The first set of particles release neuregulin-1, a growth factor that helps to prevent cell death. At the next time point, particles release VEGF, a growth factor that promotes formation of blood vessels surrounding the heart. The last batch of particles releases a small molecule drug called GW788388, which inhibits the formation of scar tissue that can occur following a heart attack.
“When tissue regenerates, it follows a carefully timed series of steps,” Jaklenec says. “Dr. Wang created a system that delivers key components at just the right time, in the sequence that the body naturally uses to heal.”
The researchers embedded rows of these particles into thin sheets of a tough but flexible hydrogel, similar to a contact lens. This hydrogel is made from alginate and PEGDA, two biocompatible polymers that eventually break down in the body. For this study, the researchers created compact, miniature patches only a few millimeters across.
“We encapsulate arrays of these particles in a hydrogel patch, and then we can surgically implant this patch into the heart. In this way, we’re really programming the treatment into this material,” Wang says.
Better heart function
Once they created these patches, the researchers tested them on spheres of heart tissue that included cardiomyocytes generated from induced pluripotent stem cells. These spheres also included endothelial cells and human ventricular cardiac fibroblasts, which are also important components of the heart.
The researchers exposed those spheres to low-oxygen conditions, mimicking the effects of a heart attack, then placed the patches over them. They found that the patches promoted blood vessel growth, helped more cells to survive, and reduced the amount of fibrosis that developed.
In tests in a rat model of heart attack, the researchers also saw significant improvements following treatment with the patch. Compared to no treatment or IV injection of the same drugs, animals treated with the patch showed 33 percent higher survival rates, a 50 percent reduction in the amount of damaged tissue, and significantly increased cardiac output.
The researchers showed that the patches would eventually dissolve over time, becoming a very thin layer over the course of a year without disrupting the heart’s mechanical function.
“This is an important way to combine drug delivery and biomaterials to potentially new treatments for patients,” Langer says.
Of the drugs tested in this study, neuregulin-1 and VEGF have been tested in clinical trials to treat heart conditions, but GW788388 has only been explored in animal models. The researchers now hope to test their patches in additional animal models in hopes of running a clinical trial in the future.
The current version of the patch needs to be implanted surgically, but the researchers are exploring the possibility of incorporating these microparticles into stents that could be inserted into arteries to deliver drugs on a programmed schedule.
Other authors of the paper include Elizabeth Calle, Binbin Ying, Behnaz Eshaghi, Linzixuan Zhang, Xin Yang, Stacey Qiaohui Lin, Jooli Han, Alanna Backx, Yuting Huang, Sevinj Mursalova, Chuhan Joyce Qi, and Yi Liu.
The researchers were supported by the Natural Sciences and Engineering Research Council of Canada and the U.S. National Heart, Lung, and Blood Institute.
Lightning-prediction tool could help protect the planes of the futureThe new approach maps aircraft sections most vulnerable to lightning, including on planes with experimental designs.More than 70 aircraft are struck by lightning every day. If you happen to be flying when a strike occurs, chances are you won’t feel a thing, thanks to lightning protection measures that are embedded in key zones throughout the aircraft.
Lightning protection systems work well, largely because they are designed for planes with a “tube-and-wing” structure, a simple geometry common to most aircraft today. But future airplanes may not look and fly the same way. The aviation industry is exploring new designs, including blended-wing bodies and truss-braced wings, partly to reduce fuel and weight costs. But researchers don’t yet know how these unconventional designs might respond to lightning strikes.
MIT aerospace engineers are hoping to change that with a new physics-based approach that predicts how lightning would sweep across a plane with any design. The tool then generates a zoning map highlighting sections of an aircraft that would require various degrees of lightning protection, given how they are likely to experience a strike.
“People are starting to conceive aircraft that look very different from what we’re used to, and we can’t apply exactly what we know from historical data to these new configurations because they’re just too different,” says Carmen Guerra-Garcia, associate professor of aeronautics and astronautics (AeroAstro) at MIT. “Physics-based methods are universal. They’re agnostic to the type of geometry or vehicle. This is the path forward to be able to do this lightning zoning and protect future aircraft.”
She and her colleagues report their results in a study appearing this week in IEEE Access. The study’s first author is AeroAstro graduate student Nathanael Jenkins. Other co-authors include Louisa Michael and Benjamin Westin of Boeing Research and Technology.
First strike
When lightning strikes, it first attaches to a part of a plane — typically a sharp edge or extremity — and hangs on for up to a second. During this brief flash, the plane continues speeding through the air, causing the lightning current to “sweep” over parts of its surface, potentially changing in intensity and re-attaching at certain points where the intense current flow could damage vulnerable sections of an aircraft.
In previous work, Guerra-Garcia’s group developed a model to predict the parts of a plane where lightning is most likely to first connect. That work, led by graduate student Sam Austin, established a starting point for the team’s new work, which aims to predict how and where the lightning will then sweep over the plane’s surface. The team next converted their lightning sweep predictions into zoning maps to identify vulnerable regions requiring certain levels of protection.
A typical tube-and-wing plane is divided into three main zones, as classified by the aviation industry. Each zone has a clear description of the level of current it must withstand in order to be certified for flight. Parts of a plane that are more likely to be hit by lightning are generally classified as zone 1 and require more protection, which can include embedded metal foil in the skin of the airplane that conducts away a lightning current.
To date, an airplane’s lightning zones have been determined over many years of flight inspections after lightning strikes and fine-tuning of protection measures. Guerra-Garcia and her colleagues looked to develop a zoning approach based on physics, rather than historical flight data. Such a physics-based mapping could be applied to any shape of aircraft, such as unconventional and largely untested designs, to identify regions that really require reinforcement.
“Protecting aircraft from lightning is heavy,” Jenkins says. “Embedding copper mesh or foil throughout an aircraft is an added weight penalty. And if we had the greatest level of protection for every part of the plane’s surface, the plane would weigh far too much. So zoning is about trying to optimize the weight of the system while also having it be as safe as possible.”
In the zone
For their new approach, the team developed a model to predict the pattern of lightning sweep and the corresponding lightning protection zones, for a given airplane geometry. Starting with a specific airplane shape — in their case, a typical tube-and-wing structure — the researchers simulated the fluid dynamics, or how air would flow around a plane, given a certain speed, altitude, and pitch angle. They also incorporated their previous model that predicts the places where lightning is more likely to initially attach.
For each initial attachment point, the team simulated tens of thousands of potential lightning arcs, or angles from which the current strikes the plane. They then ran the model forward to predict how the tens of thousands of potential strikes would follow the air flow across the plane’s surface. These runs produced a statistical representation of where lightning, striking a specific point on a plane, is likely to flow and potentially cause damage. The team converted this statistical representation into a map of zones of varying vulnerability.
They validated the method on a conventional tube-and-wing structure, showing that the zoning maps generated by the physics-based approach were consistent with what the aviation industry has determined over decades of fine-tuning.
“We now have a physics-based tool that provides some metrics like the probability of lightning attachment and dwell time, which is how long an arc will linger at a specific point,” Guerra-Garcia explains. “We convert those physics metrics into zoning maps to show, if I’m in this red region, the lightning arc will stay for a long time, so that region needs to be heavily protected.”
The team is starting to apply the approach to new geometries, such as blended-wing designs and truss-braced structures. The researchers envision that the tool can help designers incorporate safe and efficient lightning-protection systems early on in the design process.
“Lightning is incredible and terrifying at the same time, and I have full confidence in flying on planes at the moment,” Jenkins says. “I want to have that same confidence in 20 years’ time. So, we need a new way to zone aircraft.”
“With physics-based methods like the ones developed with professor Guerra-Garcia’s group we have the opportunity to shape industry standards and as an industry rely on the underlying physics to develop guidelines for aircraft certification through simulation,” says co-author Louisa Michael of Boeing Technology Innovation. Currently, we are engaging with industrial committees to propose these methods to be included in Aerospace Recommended Practices.”
“Zoning unconventional aircraft is not an easy task,” adds co-author Ben Westin of Boeing Technology Innovation. “But these methods will allow us to confidently identify which threat levels each part of the aircraft needs to be protected against and certified for, and they give our design engineers a platform to do their best work to optimize aircraft design.”
Beyond airplanes, Guerra-Garcia is looking at ways to adapt the lightning protection model to other technologies, including wind turbines.
“About 60 percent of blade losses are due to lightning and will become worse as we move offshore because wind turbines will be even bigger and more susceptible to upward lightning,” she says. “They have many of the same challenges of a flowing gas environment. It’s more complex, and we will apply this same sort of methodology to this space.”
This research was funded, in part, by the Boeing Company.
Startup provides a nontechnical gateway to coding on quantum computersCo-founded by Kanav Setia and Jason Necaise ’20, qBraid lets users access the most popular quantum devices and software programs on an intuitive, cloud-based platform.Quantum computers have the potential to model new molecules and weather patterns better than any computer today. They may also one day accelerate artificial intelligence algorithms at a much lower energy footprint. But anyone interested in using quantum computers faces a steep learning curve that starts with getting access to quantum devices and then figuring out one of the many quantum software programs on the market.
Now qBraid, founded by Kanav Setia and Jason Necaise ’20, is providing a gateway to quantum computing with a platform that gives users access to the leading quantum devices and software. Users can log on to qBraid’s cloud-based interface and connect with quantum devices and other computing resources from leading companies like Nvidia, Microsoft, and IBM. In a few clicks, they can start coding or deploy cutting-edge software that works across devices.
“The mission is to take you from not knowing anything about quantum computing to running your first program on these amazing machines in less than 10 minutes,” Setia says. “We’re a one-stop platform that gives access to everything the quantum ecosystem has to offer. Our goal is to enable anyone — whether they’re enterprise customers, academics, or individual users — to build and ultimately deploy applications.”
Since its founding in June of 2020, qBraid has helped more than 20,000 people in more than 120 countries deploy code on quantum devices. That traction is ultimately helping to drive innovation in a nascent industry that’s expected to play a key role in our future.
“This lowers the barrier to entry for a lot of newcomers,” Setia says. “They can be up and running in a few minutes instead of a few weeks. That’s why we’ve gotten so much adoption around the world. We’re one of the most popular platforms for accessing quantum software and hardware.”
A quantum “software sandbox”
Setia met Necaise while the two interned at IBM. At the time, Necaise was an undergraduate at MIT majoring in physics, while Setia was at Dartmouth College. The two enjoyed working together, and Necaise said if Setia ever started a company, he’d be interested in joining.
A few months later, Setia decided to take him up on the offer. At Dartmouth, Setia had taken one of the first applied quantum computing classes, but students spent weeks struggling to install all the necessary software programs before they could even start coding.
“We hadn’t even gotten close to developing any useful algorithms,” Seita said. “The idea for qBraid was, ‘Why don’t we build a software sandbox in the cloud and give people an easy programming setup out of the box?’ Connection with the hardware would already be done.”
The founders received early support from the MIT Sandbox Innovation Fund and took part in the delta v summer startup accelerator run by the Martin Trust Center for MIT Entrepreneurship.
“Both programs provided us with very strong mentorship,” Setia says. “They give you frameworks on what a startup should look like, and they bring in some of the smartest people in the world to mentor you — people you’d never have access to otherwise.”
Necaise left the company in 2021. Setia, meanwhile, continued to find problems with quantum software outside of the classroom.
“This is a massive bottleneck,” Setia says. “I’d worked on several quantum software programs that pushed out updates or changes, and suddenly all hell broke loose on my codebase. I’d spend two to four weeks jostling with these updates that had almost nothing to do with the quantum algorithms I was working on.”
QBraid started as a platform with pre-installed software that let developers start writing code immediately. The company also added support for version-controlled quantum software so developers could build applications on top without worrying about changes. Over time, qBraid added connections to quantum computers and tools that lets quantum programs run across different devices.
“The pitch was you don’t need to manage a bunch of software or a whole bunch of cloud accounts,” Setia says. “We’re a single platform: the quantum cloud.”
QBraid also launched qBook, a learning platform that offers interactive courses in quantum computing.
“If you see a piece of code you like, you just click play and the code runs,” Setia says. “You can run a whole bunch of code, modify it on the fly, and you can understand how it works. It runs on laptops, iPads, and phones. A significant portion of our users are from developing countries, and they’re developing applications from their phones.”
Democratizing quantum computing
Today qBraid’s 20,000 users come from over 400 universities and 100 companies around the world. As qBraid’s user base has grown, the company went from integrating quantum computers onto their platform from the outside to creating a quantum operating system, qBraid-OS, that is currently being used by four leading quantum companies.
“We are productizing these quantum computers,” Setia explains. “Many quantum companies are realizing they want to focus their energy completely on the hardware, with us productizing their infrastructure. We’re like the operating system for quantum computers.”
People are using qBraid to build quantum applications in AI and machine learning, to discover new molecules or develop new drugs, and to develop applications in finance and cybersecurity. With every new use case, Setia says qBraid is democratizing quantum computing to create the quantum workforce that will continue to advance the field.
“[In 2018], an article in The New York Times said there were possibly less than 1,000 people in the world that could be called experts in quantum programming,” Setia says. “A lot of people want to access these cutting-edge machines, but they don’t have the right software backgrounds. They are just getting started and want to play with algorithms. QBraid gives those people an easy programming setup out of the box.”
Helping K-12 schools navigate the complex world of AIMIT’s Teaching Systems Lab, led by Associate Professor Justin Reich, is working to help educators by listening to and sharing their stories.With the rapid advancement of generative artificial intelligence, teachers and school leaders are looking for answers to complicated questions about successfully integrating technology into lessons, while also ensuring students actually learn what they’re trying to teach.
Justin Reich, an associate professor in MIT’s Comparative Media Studies/Writing program, hopes a new guidebook published by the MIT Teaching Systems Lab can support K-12 educators as they determine what AI policies or guidelines to craft.
“Throughout my career, I’ve tried to be a person who researches education and technology and translates findings for people who work in the field,” says Reich. “When tricky things come along I try to jump in and be helpful.”
“A Guide to AI in Schools: Perspectives for the Perplexed,” published this fall, was developed with the support of an expert advisory panel and other researchers. The project includes input from more than 100 students and teachers from around the United States, sharing their experiences teaching and learning with new generative AI tools.
“We’re trying to advocate for an ethos of humility as we examine AI in schools,” Reich says. “We’re sharing some examples from educators about how they’re using AI in interesting ways, some of which might prove sturdy and some of which might prove faulty. And we won’t know which is which for a long time.”
Finding answers to AI and education questions
The guidebook attempts to help K-12 educators, students, school leaders, policymakers, and others collect and share information, experiences, and resources. AI’s arrival has left schools scrambling to respond to multiple challenges, like how to ensure academic integrity and maintain data privacy.
Reich cautions that the guidebook is not meant to be prescriptive or definitive, but something that will help spark thought and discussion.
“Writing a guidebook on generative AI in schools in 2025 is a little bit like writing a guidebook of aviation in 1905,” the guidebook’s authors note. “No one in 2025 can say how best to manage AI in schools.”
Schools are also struggling to measure how student learning loss looks in the age of AI. “How does bypassing productive thinking with AI look in practice?” Reich asks. “If we think teachers provide content and context to support learning and students no longer perform the exercises housing the content and providing the context, that’s a serious problem.”
Reich invites people directly impacted by AI to help develop solutions to the challenges its ubiquity presents. “It’s like observing a conversation in the teacher’s lounge and inviting students, parents, and other people to participate about how teachers think about AI,” he says, “what they are seeing in their classrooms, and what they’ve tried and how it went.”
The guidebook, in Reich’s view, is ultimately a collection of hypotheses expressed in interviews with teachers: well-informed, initial guesses about the paths that schools could follow in the years ahead.
Producing educator resources in a podcast
In addition to the guidebook, the Teaching Systems Lab also recently produced “The Homework Machine,” a seven-part series from the Teachlab podcast that explores how AI is reshaping K-12 education.
Reich produced the podcast in collaboration with journalist Jesse Dukes. Each episode tackles a specific area, asking important questions about challenges related to issues like AI adoption, poetry as a tool for student engagement, post-Covid learning loss, pedagogy, and book bans. The podcast allows Reich to share timely information about education-related updates and collaborate with people interested in helping further the work.
“The academic publishing cycle doesn’t lend itself to helping people with near-term challenges like those AI presents,” Reich says. “Peer review takes a long time, and the research produced isn’t always in a form that’s helpful to educators.” Schools and districts are grappling with AI in real time, bypassing time-tested quality control measures.
The podcast can help reduce the time it takes to share, test, and evaluate AI-related solutions to new challenges, which could prove useful in creating training and resources.
“We hope the podcast will spark thought and discussion, allowing people to draw from others’ experiences,” Reich says.
The podcast was also produced into an hour-long radio special, which was broadcast by public radio stations across the country.
“We’re fumbling around in the dark”
Reich is direct in his assessment of where we are with understanding AI and its impacts on education. “We’re fumbling around in the dark,” he says, recalling past attempts to quickly integrate new tech into classrooms. These failures, Reich suggests, highlight the importance of patience and humility as AI research continues. “AI bypassed normal procurement processes in education; it just showed up on kids’ phones,” he notes.
“We’ve been really wrong about tech in the past,” Reich says. Despite districts’ spending on tools like smartboards, for example, research indicates there’s no evidence that they improve learning or outcomes. In a new article for article for The Conversation, he argues that early teacher guidance in areas like web literacy has produced bad advice that still exists in our educational system. “We taught students and educators not to trust Wikipedia,” he recalls, “and to search for website credibility markers, both of which turned out to be incorrect.” Reich wants to avoid a similar rush to judgment on AI, recommending that we avoid guessing at AI-enabled instructional strategies.
These challenges, coupled with potential and observed student impacts, significantly raise the stakes for schools and students’ families in the AI race. “Education technology always provokes teacher anxiety,” Reich notes, “but the breadth of AI-related concerns is much greater than in other tech-related areas.”
The dawn of the AI age is different from how we’ve previously received tech into our classrooms, Reich says. AI wasn’t adopted like other tech. It simply arrived. It’s now upending educational models and, in some cases, complicating efforts to improve student outcomes.
Reich is quick to point out that there are no clear, definitive answers on effective AI implementation and use in classrooms; those answers don’t currently exist. Each of the resources Reich helped develop invite engagement from the audiences they target, aggregating valuable responses others might find useful.
“We can develop long-term solutions to schools’ AI challenges, but it will take time and work,” he says. “AI isn’t like learning to tie knots; we don’t know what AI is, or is going to be, yet.”
Reich also recommends learning more about AI implementation from a variety of sources. “Decentralized pockets of learning can help us test ideas, search for themes, and collect evidence on what works,” he says. “We need to know if learning is actually better with AI.”
While teachers don’t get to choose regarding AI’s existence, Reich believes it’s important that we solicit their input and involve students and other stakeholders to help develop solutions that improve learning and outcomes.
“Let’s race to answers that are right, not first,” Reich says.
3 Questions: How AI is helping us monitor and support vulnerable ecosystemsMIT PhD student and CSAIL researcher Justin Kay describes his work combining AI and computer vision systems to monitor the ecosystems that support our planet.A recent study from Oregon State University estimated that more than 3,500 animal species are at risk of extinction because of factors including habitat alterations, natural resources being overexploited, and climate change.
To better understand these changes and protect vulnerable wildlife, conservationists like MIT PhD student and Computer Science and Artificial Intelligence Laboratory (CSAIL) researcher Justin Kay are developing computer vision algorithms that carefully monitor animal populations. A member of the lab of MIT Department of Electrical Engineering and Computer Science assistant professor and CSAIL principal investigator Sara Beery, Kay is currently working on tracking salmon in the Pacific Northwest, where they provide crucial nutrients to predators like birds and bears, while managing the population of prey, like bugs.
With all that wildlife data, though, researchers have lots of information to sort through and many AI models to choose from to analyze it all. Kay and his colleagues at CSAIL and the University of Massachusetts Amherst are developing AI methods that make this data-crunching process much more efficient, including a new approach called “consensus-driven active model selection” (or “CODA”) that helps conservationists choose which AI model to use. Their work was named a Highlight Paper at the International Conference on Computer Vision (ICCV) in October.
That research was supported, in part, by the National Science Foundation, Natural Sciences and Engineering Research Council of Canada, and Abdul Latif Jameel Water and Food Systems Lab (J-WAFS). Here, Kay discusses this project, among other conservation efforts.
Q: In your paper, you pose the question of which AI models will perform the best on a particular dataset. With as many as 1.9 million pre-trained models available in the HuggingFace Models repository alone, how does CODA help us address that challenge?
A: Until recently, using AI for data analysis has typically meant training your own model. This requires significant effort to collect and annotate a representative training dataset, as well as iteratively train and validate models. You also need a certain technical skill set to run and modify AI training code. The way people interact with AI is changing, though — in particular, there are now millions of publicly available pre-trained models that can perform a variety of predictive tasks very well. This potentially enables people to use AI to analyze their data without developing their own model, simply by downloading an existing model with the capabilities they need. But this poses a new challenge: Which model, of the millions available, should they use to analyze their data?
Typically, answering this model selection question also requires you to spend a lot of time collecting and annotating a large dataset, albeit for testing models rather than training them. This is especially true for real applications where user needs are specific, data distributions are imbalanced and constantly changing, and model performance may be inconsistent across samples. Our goal with CODA was to substantially reduce this effort. We do this by making the data annotation process “active.” Instead of requiring users to bulk-annotate a large test dataset all at once, in active model selection we make the process interactive, guiding users to annotate the most informative data points in their raw data. This is remarkably effective, often requiring users to annotate as few as 25 examples to identify the best model from their set of candidates.
We’re very excited about CODA offering a new perspective on how to best utilize human effort in the development and deployment of machine-learning (ML) systems. As AI models become more commonplace, our work emphasizes the value of focusing effort on robust evaluation pipelines, rather than solely on training.
Q: You applied the CODA method to classifying wildlife in images. Why did it perform so well, and what role can systems like this have in monitoring ecosystems in the future?
A: One key insight was that when considering a collection of candidate AI models, the consensus of all of their predictions is more informative than any individual model’s predictions. This can be seen as a sort of “wisdom of the crowd:” On average, pooling the votes of all models gives you a decent prior over what the labels of individual data points in your raw dataset should be. Our approach with CODA is based on estimating a “confusion matrix” for each AI model — given the true label for some data point is class X, what is the probability that an individual model predicts class X, Y, or Z? This creates informative dependencies between all of the candidate models, the categories you want to label, and the unlabeled points in your dataset.
Consider an example application where you are a wildlife ecologist who has just collected a dataset containing potentially hundreds of thousands of images from cameras deployed in the wild. You want to know what species are in these images, a time-consuming task that computer vision classifiers can help automate. You are trying to decide which species classification model to run on your data. If you have labeled 50 images of tigers so far, and some model has performed well on those 50 images, you can be pretty confident it will perform well on the remainder of the (currently unlabeled) images of tigers in your raw dataset as well. You also know that when that model predicts some image contains a tiger, it is likely to be correct, and therefore that any model that predicts a different label for that image is more likely to be wrong. You can use all these interdependencies to construct probabilistic estimates of each model’s confusion matrix, as well as a probability distribution over which model has the highest accuracy on the overall dataset. These design choices allow us to make more informed choices over which data points to label and ultimately are the reason why CODA performs model selection much more efficiently than past work.
There are also a lot of exciting possibilities for building on top of our work. We think there may be even better ways of constructing informative priors for model selection based on domain expertise — for instance, if it is already known that one model performs exceptionally well on some subset of classes or poorly on others. There are also opportunities to extend the framework to support more complex machine-learning tasks and more sophisticated probabilistic models of performance. We hope our work can provide inspiration and a starting point for other researchers to keep pushing the state of the art.
Q: You work in the Beerylab, led by Sara Beery, where researchers are combining the pattern-recognition capabilities of machine-learning algorithms with computer vision technology to monitor wildlife. What are some other ways your team is tracking and analyzing the natural world, beyond CODA?
A: The lab is a really exciting place to work, and new projects are emerging all the time. We have ongoing projects monitoring coral reefs with drones, re-identifying individual elephants over time, and fusing multi-modal Earth observation data from satellites and in-situ cameras, just to name a few. Broadly, we look at emerging technologies for biodiversity monitoring and try to understand where the data analysis bottlenecks are, and develop new computer vision and machine-learning approaches that address those problems in a widely applicable way. It’s an exciting way of approaching problems that sort of targets the “meta-questions” underlying particular data challenges we face.
The computer vision algorithms I’ve worked on that count migrating salmon in underwater sonar video are examples of that work. We often deal with shifting data distributions, even as we try to construct the most diverse training datasets we can. We always encounter something new when we deploy a new camera, and this tends to degrade the performance of computer vision algorithms. This is one instance of a general problem in machine learning called domain adaptation, but when we tried to apply existing domain adaptation algorithms to our fisheries data we realized there were serious limitations in how existing algorithms were trained and evaluated. We were able to develop a new domain adaptation framework, published earlier this year in Transactions on Machine Learning Research, that addressed these limitations and led to advancements in fish counting, and even self-driving and spacecraft analysis.
One line of work that I’m particularly excited about is understanding how to better develop and analyze the performance of predictive ML algorithms in the context of what they are actually used for. Usually, the outputs from some computer vision algorithm — say, bounding boxes around animals in images — are not actually the thing that people care about, but rather a means to an end to answer a larger problem — say, what species live here, and how is that changing over time? We have been working on methods to analyze predictive performance in this context and reconsider the ways that we input human expertise into ML systems with this in mind. CODA was one example of this, where we showed that we could actually consider the ML models themselves as fixed and build a statistical framework to understand their performance very efficiently. We have been working recently on similar integrated analyses combining ML predictions with multi-stage prediction pipelines, as well as ecological statistical models.
The natural world is changing at unprecedented rates and scales, and being able to quickly move from scientific hypotheses or management questions to data-driven answers is more important than ever for protecting ecosystems and the communities that depend on them. Advancements in AI can play an important role, but we need to think critically about the ways that we design, train, and evaluate algorithms in the context of these very real challenges.
Turning on an immune pathway in tumors could lead to their destructionMIT researchers show they can use messenger RNA to activate the pathway and trigger the immune system to attack tumors.By stimulating cancer cells to produce a molecule that activates a signaling pathway in nearby immune cells, MIT researchers have found a way to force tumors to trigger their own destruction.
Activating this signaling pathway, known as the cGAS-STING pathway, worked even better when combined with existing immunotherapy drugs known as checkpoint blockade inhibitors, in a study of mice. That dual treatment was successfully able to control tumor growth.
The researchers turned on the cGAS-STING pathway in immune cells using messenger RNA delivered to cancer cells. This approach may avoid the side effects of delivering large doses of a STING activator, and takes advantage of a natural process in the body. This could make it easier to develop a treatment for use in patients, the researchers say.
“Our approach harnesses the tumor’s own machinery to produce immune-stimulating molecules, creating a powerful antitumor response,” says Natalie Artzi, a principal research scientist at MIT’s Institute for Medical Engineering and Science, an associate professor of medicine at Harvard Medical School, a core faculty member at the Wyss Institute for Biologically Inspired Engineering at Harvard, and the senior author of the study.
“By increasing cGAS levels inside cancer cells, we can enhance delivery efficiency — compared to targeting the more scarce immune cells in the tumor microenvironment — and stimulate the natural production of cGAMP, which then activates immune cells locally,” she says. “This strategy not only strengthens antitumor immunity but also reduces the toxicity associated with direct STING agonist delivery, bringing us closer to safer and more effective cancer immunotherapies.”
Alexander Cryer, a visiting scholar at IMES, is the lead author of the paper, which appears this week in the Proceedings of the National Academy of Sciences.
Immune activation
STING (short for stimulator of interferon genes) is a protein that helps to trigger immune responses. When STING is activated, it turns on a pathway that initiates production of type one interferons, which are cytokines that stimulate immune cells.
Many research groups, including Artzi’s, have explored the possibility of artificially stimulating this pathway with molecules called STING agonists, which could help immune cells to recognize and attack tumor cells. This approach has worked well in animal models, but it has had limited success in clinical trials, in part because the required doses can cause harmful side effects.
While working on a project exploring new ways to deliver STING agonists, Cryer became intrigued when he learned from previous work that cancer cells can produce a STING activator known as cGAMP. The cells then secrete cGAMP, which can activate nearby immune cells.
“Part of my philosophy of science is that I really enjoy using endogenous processes that the body already has, and trying to utilize them in a slightly different context. Evolution has done all the hard work. We just need to figure out how push it in a different direction,” Cryer says. “Once I saw that cancer cells produce this molecule, I thought: Maybe there’s a way to take this process and supercharge it.”
Within cells, the production of cGAMP is catalyzed by an enzyme called cGAS. To get tumor cells to activate STING in immune cells, the researchers devised a way to deliver messenger RNA that encodes cGAS. When this enzyme detects double-stranded DNA in the cell body, which can be a sign of either infection or cancer-induced damage, it begins producing cGAMP.
“It just so happens that cancer cells, because they’re dividing so fast and not particularly accurately, tend to have more double-stranded DNA fragments than healthy cells,” Cryer says.
The tumor cells then release cGAMP into tumor microenvironment, where it can be taken up by neighboring immune cells and activate their STING pathway.
Targeting tumors
Using a mouse model of melanoma, the researchers evaluated their new strategy’s potential to kill cancer cells. They injected mRNA encoding cGAS, encapsulated in lipid nanoparticles, into tumors. One group of mice received this treatment alone, while another received a checkpoint blockade inhibitor, and a third received both treatments.
Given on their own, cGAS and the checkpoint inhibitor each significantly slowed tumor growth. However, the best results were seen in the mice that received both treatments. In that group, tumors were completely eradicated in 30 percent of the mice, while none of the tumors were fully eliminated in the groups that received just one treatment.
An analysis of the immune response showed that the mRNA treatment stimulated production of interferon as well as many other immune signaling molecules. A variety of immune cells, including macrophages and dendritic cells, were activated. These cells help to stimulate T cells, which can then destroy cancer cells.
The researchers were able to elicit these responses with just a small dose of cancer-cell-produced cGAMP, which could help to overcome one of the potential obstacles to using cGAMP on its own as therapy: Large doses are required to stimulate an immune response, and these doses can lead to widespread inflammation, tissue damage, and autoimmune reactions. When injected on its own, cGAMP tends to spread through the body and is rapidly cleared from the tumor, while in this study, the mRNA nanoparticles and cGAMP remained at the tumor site.
“The side effects of this class of molecule can be pretty severe, and one of the potential advantages of our approach is that you’re able to potentially subvert some toxicity that you might see if you’re giving the free molecules,” Cryer says.
The researchers now hope to work on adapting the delivery system so that it could be given as a systemic injection, rather than injecting it into the tumor. They also plan to test the mRNA therapy in combination with chemotherapy drugs or radiotherapy that damage DNA, which could make the therapy even more effective because there could be even more double-stranded DNA available to help activate the synthesis of cGAMP.
A faster problem-solving tool that guarantees feasibilityThe FSNet system, developed at MIT, could help power grid operators rapidly find feasible solutions for optimizing the flow of electricity.Managing a power grid is like trying to solve an enormous puzzle.
Grid operators must ensure the proper amount of power is flowing to the right areas at the exact time when it is needed, and they must do this in a way that minimizes costs without overloading physical infrastructure. Even more, they must solve this complicated problem repeatedly, as rapidly as possible, to meet constantly changing demand.
To help crack this consistent conundrum, MIT researchers developed a problem-solving tool that finds the optimal solution much faster than traditional approaches while ensuring the solution doesn’t violate any of the system’s constraints. In a power grid, constraints could be things like generator and line capacity.
This new tool incorporates a feasibility-seeking step into a powerful machine-learning model trained to solve the problem. The feasibility-seeking step uses the model’s prediction as a starting point, iteratively refining the solution until it finds the best achievable answer.
The MIT system can unravel complex problems several times faster than traditional solvers, while providing strong guarantees of success. For some extremely complex problems, it could find better solutions than tried-and-true tools. The technique also outperformed pure machine learning approaches, which are fast but can’t always find feasible solutions.
In addition to helping schedule power production in an electric grid, this new tool could be applied to many types of complicated problems, such as designing new products, managing investment portfolios, or planning production to meet consumer demand.
“Solving these especially thorny problems well requires us to combine tools from machine learning, optimization, and electrical engineering to develop methods that hit the right tradeoffs in terms of providing value to the domain, while also meeting its requirements. You have to look at the needs of the application and design methods in a way that actually fulfills those needs,” says Priya Donti, the Silverman Family Career Development Professor in the Department of Electrical Engineering and Computer Science (EECS) and a principal investigator at the Laboratory for Information and Decision Systems (LIDS).
Donti, senior author of an open-access paper on this new tool, called FSNet, is joined by lead author Hoang Nguyen, an EECS graduate student. The paper will be presented at the Conference on Neural Information Processing Systems.
Combining approaches
Ensuring optimal power flow in an electric grid is an extremely hard problem that is becoming more difficult for operators to solve quickly.
“As we try to integrate more renewables into the grid, operators must deal with the fact that the amount of power generation is going to vary moment to moment. At the same time, there are many more distributed devices to coordinate,” Donti explains.
Grid operators often rely on traditional solvers, which provide mathematical guarantees that the optimal solution doesn’t violate any problem constraints. But these tools can take hours or even days to arrive at that solution if the problem is especially convoluted.
On the other hand, deep-learning models can solve even very hard problems in a fraction of the time, but the solution might ignore some important constraints. For a power grid operator, this could result in issues like unsafe voltage levels or even grid outages.
“Machine-learning models struggle to satisfy all the constraints due to the many errors that occur during the training process,” Nguyen explains.
For FSNet, the researchers combined the best of both approaches into a two-step problem-solving framework.
Focusing on feasibility
In the first step, a neural network predicts a solution to the optimization problem. Very loosely inspired by neurons in the human brain, neural networks are deep learning models that excel at recognizing patterns in data.
Next, a traditional solver that has been incorporated into FSNet performs a feasibility-seeking step. This optimization algorithm iteratively refines the initial prediction while ensuring the solution does not violate any constraints.
Because the feasibility-seeking step is based on a mathematical model of the problem, it can guarantee the solution is deployable.
“This step is very important. In FSNet, we can have the rigorous guarantees that we need in practice,” Hoang says.
The researchers designed FSNet to address both main types of constraints (equality and inequality) at the same time. This makes it easier to use than other approaches that may require customizing the neural network or solving for each type of constraint separately.
“Here, you can just plug and play with different optimization solvers,” Donti says.
By thinking differently about how the neural network solves complex optimization problems, the researchers were able to unlock a new technique that works better, she adds.
They compared FSNet to traditional solvers and pure machine-learning approaches on a range of challenging problems, including power grid optimization. Their system cut solving times by orders of magnitude compared to the baseline approaches, while respecting all problem constraints.
FSNet also found better solutions to some of the trickiest problems.
“While this was surprising to us, it does make sense. Our neural network can figure out by itself some additional structure in the data that the original optimization solver was not designed to exploit,” Donti explains.
In the future, the researchers want to make FSNet less memory-intensive, incorporate more efficient optimization algorithms, and scale it up to tackle more realistic problems.
“Finding solutions to challenging optimization problems that are feasible is paramount to finding ones that are close to optimal. Especially for physical systems like power grids, close to optimal means nothing without feasibility. This work provides an important step toward ensuring that deep-learning models can produce predictions that satisfy constraints, with explicit guarantees on constraint enforcement,” says Kyri Baker, an associate professor at the University of Colorado Boulder, who was not involved with this work.
"A persistent challenge for machine learning-based optimization is feasibility. This work elegantly couples end-to-end learning with an unrolled feasibility-seeking procedure that minimizes equality and inequality violations. The results are very promising and I look forward to see where this research will head," adds Ferdinando Fioretto, an assistant professor at the University of Virginia, who was not involved with this work.
Study: Good management of aid projects reduces local violenceWorld Bank data show how the organization of programs influences political conflict — indicating a path to better aid delivery.Good management of aid projects in developing countries reduces violence in those areas — but poorly managed projects increase the chances of local violence, according to a new study by an MIT economist.
The research, examining World Bank projects in Africa, illuminates a major question surrounding international aid. Observers have long wondered if aid projects, by bringing new resources into developing countries, lead to conflict over those goods as an unintended consequence. Previously, some scholars have identified an increase in violence attached to aid, while others have found a decrease.
The new study shows those prior results are not necessarily wrong, but not entirely right, either. Instead, aid oversight matters. World Bank programs earning the highest evaluation scores for their implementation reduce the likelihood of conflict by up to 12 percent, compared to the worst-managed programs.
“I find that the management quality of these projects has a really strong effect on whether that project leads to conflict or not,” says MIT economist Jacob Moscona, who conducted the research. “Well-managed aid projects can actually reduce conflict, and poorly managed projects increase conflict, relative to no project. So, the way aid programs are organized is very important.”
The findings also suggest aid projects can work well almost anywhere. At times, observers have suggested the political conditions in some countries prevent aid from being effective. But the new study finds otherwise.
“There are ways these programs can have their positive effects without the negative consequences,” Moscona says. “And it’s not the result of what politics looks like on the receiving end; it’s about the organization itself.”
Moscona’s paper detailing the study, “The Management of Aid and Conflict in Africa,” is published in the November issue of the American Economic Journal: Economic Policy. Moscona, the paper’s sole author, is the 3M Career Development Assistant Professor in MIT’s Department of Economics.
Decisions on the ground
To conduct the study, Moscona examined World Bank data from the 1997-2014 time period, using the information compiled by AidData, a nonprofit group that also studies World Bank programs. Importantly, the World Bank conducts extensive evaluations of its projects and includes the identities of project leaders as part of those reviews.
“There are a lot of decisions on the ground made by managers of aid, and aid organizations themselves, that can have a huge impact on whether or not aid leads to conflict, and how aid resources are used and whether they are misappropriated or captured and get into the wrong hands,” Moscona says.
For instance, diligent daily checks about food distribution programs can and have substantially reduced the amount of food that is stolen or “leaks” out of the program. Other projects have created innovative ways of tagging small devices to ensure those objects are used by program participants, reducing appropriation by others.
Moscona combined the World Bank data with statistics from the Armed Conflict Location and Event Data Project (ACLED), a nonprofit that monitors political violence. That enabled him to evaluate how the quality of aid project implementation — and even the quality of the project leadership — influenced local outcomes.
For instance, by looking at the ratings of World Bank project leaders, Moscona found that shifting from a project leader at the 25th percentile, in terms of how frequently projects are linked with conflict, to one at the 75th percentile, increases the chances of local conflict by 15 percent.
“The magnitudes are pretty large, in terms of the probability that a conflict starts in the vicinity of a project,” Moscona observes.
Moscona’s research identified several other aspects of the interaction between aid and conflict that hold up over the region and time period. The establishment of aid programs does not seem to lead to long-term strategic activity by non-government forces, such as land acquisition or the establishment of rebel bases. The effects are also larger in areas that have had recent political violence. And armed conflict is greater when the resources at stake can be expropriated — such as food or medical devices.
“It matters most if you have more divertable resources, like food and medical devices that can be captured, as opposed to infrastructure projects,” Moscona says.
Reconciling the previous results
Moscona also found a clear trend in the data about the timing of violence in relation to aid. Government and other armed groups do not engage in much armed conflict when aid programs are being established; it is the appearance of desired goods themselves that sets off violent activity.
“You don’t see much conflict when the projects are getting off the ground,” Moscona says.” You really see the conflict start when the money is coming in or when the resources start to flow. Which is consistent with the idea of the relevant mechanism being about aid resources and their misappropriation, rather than groups trying to deligitimize a project.”
All told, Moscona’s study finds a logical mechanism explaining the varying results other scholars have found with regard to aid and conflict. If aid programs are not equally well-administered, it stands to reason that their outcomes will not be identical, either.
“There wasn’t much work trying to make those two sets of results speak to each other,” says Moscona. “I see it less as overturning existing results than providing a way to reconcile different results and experiences.”
Moscona’s findings may also speak to the value of aid in general — and provide actionable ideas for institutions such as the World Bank. If better management makes such a difference, then the potential effectiveness of aid programs may increase.
“One goal is to change the conversation about aid,” Moscona says. The data, he suggests, shows that the public discourse about aid can be “less defeatist about the potential negative consequences of aid, and the idea that it’s out of the control of the people who administer it.”
New nanoparticles stimulate the immune system to attack ovarian tumorsTargeted particles carrying the cytokine IL-12 can jump-start T cells, allowing them to clear tumors while avoiding side effects.Cancer immunotherapy, which uses drugs that stimulate the body’s immune cells to attack tumors, is a promising approach to treating many types of cancer. However, it doesn’t work well for some tumors, including ovarian cancer.
To elicit a better response, MIT researchers have designed new nanoparticles that can deliver an immune-stimulating molecule called IL-12 directly to ovarian tumors. When given along with immunotherapy drugs called checkpoint inhibitors, IL-12 helps the immune system launch an attack on cancer cells.
Studying a mouse model of ovarian cancer, the researchers showed that this combination treatment could eliminate metastatic tumors in more than 80 percent of the mice. When the mice were later injected with more cancer cells, to simulate tumor recurrence, their immune cells remembered the tumor proteins and cleared them again.
“What’s really exciting is that we’re able to deliver IL-12 directly in the tumor space. And because of the way that this nanomaterial is designed to allow IL-12 to be borne on the surfaces of the cancer cells, we have essentially tricked the cancer into stimulating immune cells to arm themselves against that cancer,” says Paula Hammond, an MIT Institute Professor, MIT’s vice provost for faculty, and a member of the Koch Institute for Integrative Cancer Research.
Hammond and Darrell Irvine, a professor of immunology and microbiology at the Scripps Research Institute, are the senior authors of the new study, which appears today in Nature Materials. Ivan Pires PhD ’24, now a postdoc at Brigham and Women’s Hospital, is the lead author of the paper.
“Hitting the gas”
Most tumors express and secrete proteins that suppress immune cells, creating a microenvironment in which the immune response is weakened. One of the main players that can kill tumor cells are T cells, but they get sidelined or blocked by the cancer cells and are unable to attack the tumor. Checkpoint inhibitors are an FDA-approved treatment designed to take those brakes off the immune system by removing the immune-suppressing proteins so that T cells can mount an attack on tumor cells
For some cancers, including some types of melanoma and lung cancer, removing the brakes is enough to provoke the immune system into attacking cancer cells. However, ovarian tumors have many ways to suppress the immune system, so checkpoint inhibitors alone usually aren’t enough to launch an immune response.
“The problem with ovarian cancer is no one is hitting the gas. So, even if you take off the brakes, nothing happens,” Pires says.
IL-12 offers one way to “hit the gas,” by supercharging T cells and other immune cells. However, the large doses of IL-12 required to get a strong response can produce side effects due to generalized inflammation, such as flu-like symptoms (fever, fatigue, GI issues, headaches, and fatigue), as well as more severe complications such as liver toxicity and cytokine release syndrome — which can be so severe they may even lead to death.
In a 2022 study, Hammond’s lab developed nanoparticles that could deliver IL-12 directly to tumor cells, which allows larger doses to be given while avoiding the side effects seen when the drug is injected. However, these particles tended to release their payload all at once after reaching the tumor, which hindered their ability to generate a strong T cell response.
In the new study, the researchers modified the particles so that IL-12 would be released more gradually, over about a week. They achieved this by using a different chemical linker to attach IL-12 to the particles.
“With our current technology, we optimize that chemistry such that there’s a more controlled release rate, and that allowed us to have better efficacy,” Pires says.
The particles consist of tiny, fatty droplets known as liposomes, with IL-12 molecules tethered to the surface. For this study, the researchers used a linker called maleimide to attach IL-12 to the liposomes. This linker is more stable than the one they used in the previous generation of particles, which was susceptible to being cleaved by proteins in the body, leading to premature release.
To make sure that the particles get to the right place, the researchers coat them with a layer of a polymer called poly-L-glutamate (PLE), which helps them directly target ovarian tumor cells. Once they reach the tumors, the particles bind to the cancer cell surfaces, where they gradually release their payload and activate nearby T cells.
Disappearing tumors
In tests in mice, the researchers showed that the IL-12-carrying particles could effectively recruit and stimulate T cells that attack tumors. The cancer models used for these studies are metastatic, so tumors developed not only in the ovaries but throughout the peritoneal cavity, which includes the surface of the intestines, liver, pancreas, and other organs. Tumors could even be seen in the lung tissues.
First, the researchers tested the IL-12 nanoparticles on their own, and they showed that this treatment eliminated tumors in about 30 percent of the mice. They also found a significant increase in the number of T cells that accumulated in the tumor environment.
Then, the researchers gave the particles to mice along with checkpoint inhibitors. More than 80 percent of the mice that received this dual treatment were cured. This happened even when the researchers used models of ovarian cancer that are highly resistant to immunotherapy or to the chemotherapy drugs usually used for ovarian cancer.
Patients with ovarian cancer are usually treated with surgery followed by chemotherapy. While this may be initially effective, cancer cells that remain after surgery are often able to grow into new tumors. Establishing an immune memory of the tumor proteins could help to prevent that kind of recurrence.
In this study, when the researchers injected tumor cells into the cured mice five months after the initial treatment, the immune system was still able to recognize and kill the cells.
“We don’t see the cancer cells being able to develop again in that same mouse, meaning that we do have an immune memory developed in those animals,” Pires says.
The researchers are now working with MIT’s Deshpande Center for Technological Innovation to spin out a company that they hope could further develop the nanoparticle technology. In a study published earlier this year, Hammond’s lab reported a new manufacturing approach that should enable large-scale production of this type of nanoparticle.
The research was funded by the National Institutes of Health, the Marble Center for Nanomedicine, the Deshpande Center for Technological Innovation, the Ragon Institute of MGH, MIT, and Harvard, and the Koch Institute Support (core) Grant from the National Cancer Institute.
Using classic physical phenomena to solve new problemsMarco Graffiedi, a doctoral student in nuclear science and engineering, is researching quenching processes to help cool nuclear cores, and NASA craft the next generation of space vehicles.Quenching, a powerful heat transfer mechanism, is remarkably effective at transporting heat away. But in extreme environments, like nuclear power plants and aboard spaceships, a lot rides on the efficiency and speed of the process.
It’s why Marco Graffiedi, a fifth-year doctoral student at MIT’s Department of Nuclear Science and Engineering (NSE), is researching the phenomenon to help develop the next generation of spaceships and nuclear plants.
Growing up in small-town Italy
Graffiedi’s parents encouraged a sense of exploration, giving him responsibilities for family projects even at a young age. When they restored a countryside cabin in a small town near Palazzolo, in the hills between Florence and Bologna, the then-14-year-old Marco got a project of his own. He had to ensure the animals on the property had enough accessible water without overfilling the storage tank. Marco designed and built a passive hydraulic system that effectively solved the problem and is still functional today.
His proclivity for science continued in high school in Lugo, where Graffiedi enjoyed recreating classical physics phenomena, through experiments. Incidentally, the high school is named after Gregorio Ricci-Curbastro, a mathematician who laid the foundation for the theory of relativity — history that is not lost on Graffiedi. After high school, Graffiedi attended the International Physics Olympiad in Bangkok, a formative event that cemented his love for physics.
A gradual shift toward engineering
A passion for physics and basic sciences notwithstanding, Graffiedi wondered if he’d be a better fit for engineering, where he could use the study of physics, chemistry, and math as tools to build something.
Following that path, he completed a bachelor’s and master’s in mechanical engineering — because an undergraduate degree in Italy takes only three years, pretty much everyone does a master’s, Graffiedi laughs — at the Università di Pisa and the Scuola Superiore Sant’Anna (School of Engineering). The Sant’Anna is a highly selective institution that most students attend to complement their university studies.
Graffiedi’s university studies gradually moved him toward the field of environmental engineering. He researched concentrated solar power in order to reduce the cost of solar power by studying the associated thermal cycle and trying to improve solar power collection. While the project was not very successful, it reinforced Graffiedi’s impression of the necessity of alternative energies. Still firmly planted in energy studies, Graffiedi worked on fracture mechanics for his master’s thesis, in collaboration with (what was then) GE Oil and Gas, researching how to improve the effectiveness of centrifugal compressors. And a summer internship at Fermilab had Graffiedi working on the thermal characterization of superconductive coatings.
With his studies behind him, Graffiedi was still unsure about this professional path. Through the Edison Program from GE Oil and Gas, where he worked shortly after graduation, Graffiedi got to test drive many fields — from mechanical and thermal engineering to exploring gas turbines and combustion. He eventually became a test engineer, coordinating a team of engineers to test a new upgrade to the company’s gas turbines. “I set up the test bench, understanding how to instrument the machine, collect data, and run the test,” Graffiedi remembers, “there was a lot you need to think about, from a little turbine blade with sensors on it to the location of safety exits on the test bench.”
The move toward nuclear engineering
As fun as the test engineering job was, Graffiedi started to crave more technical knowledge and wanted to pivot to science. As part of his exploration, he came across nuclear energy and, understanding it to be the future, decided to lean on his engineering background to apply to MIT NSE.
He found a fit in Professor Matteo Bucci’s group and decided to explore boiling and quenching. The move from science to engineering, and back to science, was now complete.
NASA, the primary sponsor of the research, is interested in preventing boiling of cryogenic fuels, because boiling leads to loss of fuel and the resulting vapor will need to be vented to avoid overpressurizing a fuel tank.
Graffiedi’s primary focus is on quenching, which will play an important role in refueling in space — and in the cooling of nuclear cores. When a cryogen is used to cool down a surface, it undergoes what is known as the Leidenfrost effect, which means it first forms a thin vapor film that acts as an insulator and prevents further cooling. To facilitate rapid cooling, it’s important to accelerate the collapse of the vapor film. Graffiedi is exploring the mechanics of the quenching process on a microscopic level, studies that are important for land and space applications.
Boiling can be used for yet another modern application: to improve the efficiency of cooling systems for data centers. The growth of data centers and electric transportation systems needs effective heat transfer mechanisms to avoid overheating. Immersion cooling using dielectric fluids — fluids that do not conduct electricity — is one way to do so. These fluids remove heat from a surface by leaning on the principle of boiling. For effective boiling, the fluid must overcome the Leidenfrost effect and break the vapor film that forms. The fluid must also have high critical heat flux (CHF), which is the maximum value of the heat flux at which boiling can effectively be used to transfer heat from a heated surface to a liquid. Because dielectric fluids have lower CHF than water, Graffiedi is exploring solutions to enhance these limits. In particular, he is investigating how high electric fields can be used to enhance CHF and even to use boiling as a way to cool electronic components in the absence of gravity. He published this research in Applied Thermal Engineering in June.
Beyond boiling
Graffiedi’s love of science and engineering shows in his commitment to teaching as well. He has been a teaching assistant for four classes at NSE, winning awards for his contributions. His many additional achievements include winning the Manson Benedict Award presented to an NSE graduate student for excellence in academic performance and professional promise in nuclear science and engineering, and a service award for his role as past president of the MIT Division of the American Nuclear Society.
Boston has a fervent Italian community, Graffiedi says, and he enjoys being a part of it. Fittingly, the MIT Italian club is called MITaly. When he’s not at work or otherwise engaged, Graffiedi loves Latin dancing, something he makes time for at least a couple of times a week. While he has his favorite Italian restaurants in the city, Graffiedi is grateful for another set of skills his parents gave him when was just 11: making perfect pizza and pasta.
Q&A: How MITHIC is fostering a culture of collaboration at MITA presidential initiative, the MIT Human Insight Collaborative is supporting new interdisciplinary initiatives and projects across the Institute.The MIT Human Insight Collaborative (MITHIC) is a presidential initiative with a mission of elevating human-centered research and teaching and connecting scholars in the humanities, arts, and social sciences with colleagues across the Institute.
Since its launch in 2024, MITHIC has funded 31 projects led by teaching and research staff representing 22 different units across MIT. The collaborative is holding its annual event on Nov. 17.
In this Q&A, Keeril Makan, associate dean in the MIT School of Humanities, Arts, and Social Sciences, and Maria Yang, interim dean of the MIT School of Engineering, discuss the value of MITHIC and the ways it’s accelerating new research and collaborations across the Institute. Makan is the Michael (1949) Sonja Koerner Music Composition Professor and faculty lead for MITHIC. Yang is the William E. Leonhard (1940) Professor in the Department of Mechanical Engineering and co-chair of MITHIC’s SHASS+ Connectivity Fund.
Q: You each come from different areas of MIT. Looking at MITHIC from your respective roles, why is this initiative so important for the Institute?
Makan: The world is counting on MIT to develop solutions to some of the world’s greatest challenges, such as artificial intelligence, poverty, and health care. These are all issues that arise from human activity, a thread that runs through much of the research we’re focused on in SHASS. Through MITHIC, we’re embedding human-centered thinking and connecting the Institute’s top scholars in the work needed to find innovative ways of addressing these problems.
Yang: MITHIC is very important to MIT, and I think of this from the point of view as an engineer, which is my background. Engineers often think about the technology first, which is absolutely important. But for that technology to have real impact, you have to think about the human insights that make that technology relevant and can be deployed in the world. So really having a deep understanding of that is core to MITHIC and MIT’s engineering enterprise.
Q: How does MITHIC fit into MIT’s broader mission?
Makan: MITHIC highlights how the work we do in the School of Humanities, Arts, and Social Sciences is aligned with MIT’s mission, which is to address the world’s great problems. But MITHIC has also connected all of MIT in this endeavor. We have faculty from all five schools and the MIT Schwarzman College of Computing involved in evaluating MITHIC project proposals. Each of them represent a different point of view and are engaging with these projects that originate in SHASS, but actually cut across many different fields. Seeing their perspectives on these projects has been inspiring.
Yang: I think of MIT’s main mission as using technology and many other things to make impact in the world, especially social impact. The kind of interdisciplinary work that MITHIC catalyzes really enables all of that work to happen in a new and profound way. The SHASS+ Connectivity Fund, which connects SHASS faculty and researchers with colleagues outside of SHASS, has resulted in collaborations that were not possible before. One example is a project being led by professors Mark Rau, who has a shared appointment between Music and Electrical Engineering and Computer Science, and Antoine Allanore in Materials Science and Engineering. The two of them are looking at how they can take ancient unplayable instruments and recreate them using new technologies for scanning and fabrication. They’re also working with the Museum of Fine Arts, so it’s a whole new type of collaboration that exemplifies MITHIC.
Q: What has been the community response to MITHIC in its first year?
Makan: It’s been very strong. We found a lot of pent-up demand, both from faculty in SHASS and faculty in the sciences and engineering. Either there were preexisting collaborations that they could take to the next level through MITHIC, or there was the opportunity to meet someone new and talk to someone about a problem and how they could collaborate. MITHIC also hosted a series of Meeting of the Minds events, which are a chance to have faculty and members of the community get to know one another on a certain topic. This community building has been exciting, and led to an overwhelming number of applications last year. There has also been significant student involvement, with several projects bringing on UROPs [Undergraduate Research Opportunities Program projects] and PhD students to help with their research. MITHIC gives a real morale boost and a lot of hope that there is a focus upon building collaborations at MIT and on not forgetting that the world needs humanists, artists, and social scientists.
Yang: One faculty member told me the SHASS+ Connectivity Fund has given them hope for the kind of research that we do because of the cross collaboration. There’s a lot of excitement and enthusiasm for this type of work.
Q: The SHASS+ Connectivity Fund is designed to support interdisciplinary collaborations at MIT. What’s an example of a SHASS+ project that’s worked particularly well?
Makan: One exciting collaboration is between professors Jörn Dunkel in Mathematics and In Song Kim in Political science. In Song is someone who has done a lot of work on studying lobbying and its effect upon the legislative process. He met Jörn, I believe, at one of MIT’s daycare centers, so it’s a relationship that started in a very informal fashion. But they found they actually had ways of looking at math and quantitative analysis that could complement one another. Their work is creating a new subfield and taking the research in a direction that would not be possible without this funding.
Yang: One of the SHASS+ projects that I think is really interesting is between professors Marzyeh Ghassemi in Electrical Engineering and Computer Science and Esther Duflo in Economics. The two of them are looking at how they can use AI to help health diagnostics in low-resource global settings, where there isn’t a lot of equipment or technology to do basic health diagnostics. They can use handheld, low-cost equipment to do things like predict if someone is going to have a heart attack. And they are not only developing the diagnostic tool, but evaluating the fairness of the algorithm. The project is an excellent example of using a MITHIC grant to make impact in the world.
Q: What has been MITHIC’s impact in terms of elevating research and teaching within SHASS?
Makan: In addition to the SHASS+ Connectivity Fund, there are two other possibilities to help support both SHASS research as well as educational initiatives: the Humanities Cultivation Fund and the SHASS Education Innovation Fund. And both of these are providing funding in excess of what we normally see within SHASS. It both recognizes the importance of the work of our faculty and it also gives them the means to actually take ideas to a much further place.
One of the projects that MITHIC is helping to support is the Compass Initiative. Compass was started by Lily Tsai, one of our professors in Political Science, along with other faculty in SHASS to create essentially an introductory class to the different methodologies within SHASS. So we have philosophers, music historians, etc., all teaching together, all addressing how we interact with one another, what it means to be a good citizen, what it means to be socially aware and civically engaged. This is a class that is very timely for MIT and for the world. And we were able to give it robust funding so they can take this and develop it even further.
MITHIC has also been able to take local initiatives in SHASS and elevate them. There has been a group of anthropologists, historians, and urban planners that have been working together on a project called the Living Climate Futures Lab. This is a group interested in working with frontline communities around climate change and sustainability. They work to build trust with local communities and start to work with them on thinking about how climate change affects them and what solutions might look like. This is a powerful and uniquely SHASS approach to climate change, and through MITHIC, we’re able to take this seed effort, robustly fund it, and help connect it to the larger climate project at MIT.
Q: What excites you most about the future of MITHIC at MIT?
Yang: We have a lot of MIT efforts that are trying to break people out of their disciplinary silos, and MITHIC really is a big push on that front. It’s a presidential initiative, so it’s high on the priority list of what people are thinking about. We’ve already done our first round, and the second round is going to be even more exciting, so it’s only going to gain in force. In SHASS+, we’re actually having two calls for proposals this academic year instead of just one. I feel like there’s still so much possibility to bring together interdisciplinary research across the Institute.
Makan: I’m excited about how MITHIC is changing the culture of MIT. MIT thinks of itself in terms of engineering, science, and technology, and this is an opportunity to think about those STEM fields within the context of human activity and humanistic thinking. Having this shift at MIT in how we approach solving problems bodes well for the world, and it places SHASS as this connective tissue at the Institute. It connects the schools and it can also connect the other initiatives, such as manufacturing and health and life sciences. There’s an opportunity for MITHIC to seed all these other initiatives with the work that goes on in SHASS.
Battery-powered appliances make it easy to switch from gas to electricFounded by Sam Calisch SM ’14, PhD ’19, Copper offers electric kitchen ranges that plug into standard wall outlets, with no electrical upgrades required.As batteries have gotten cheaper and more powerful, they have enabled the electrification of everything from vehicles to lawn equipment, power tools, and scooters. But electrifying homes has been a slower process. That’s because switching from gas appliances often requires ripping out drywall, running new wires, and upgrading the electrical box.
Now the startup Copper, founded by Sam Calisch SM ’14, PhD ’19, has developed a battery-equipped kitchen range that can plug into a standard 120-volt wall outlet. The induction range features a lithium iron phosphate battery that charges when energy is cheapest and cleanest, then delivers power when you’re ready to cook.
“We’re making ‘going electric’ like an appliance swap instead of a construction project,” says Calisch. “If you have a gas stove today, there is almost certainly an outlet within reach because the stove has an oven light, clock, or electric igniters. That’s big if you’re in a single-family home, but in apartments it’s an existential factor. Rewiring a 100-unit apartment building is such an expensive proposition that basically no one’s doing it.”
Copper has shipped about 1,000 of its battery-powered ranges to date, often to developers and owners of large apartment complexes. The company also has an agreement with the New York City Housing Authority for at least 10,000 units.
Once installed, the ranges can contribute to a distributed, cleaner, and more resilient energy network. In fact, Copper recently piloted a program in California to offer cheap, clean power to the grid from its home batteries when it would otherwise need to fire up a gas-powered plant to meet spiking electricity demand.
“After these appliances are installed, they become a grid asset,” Calisch says. “We can manage the fleet of batteries to help provide firm power and help grids deliver more clean electricity. We use that revenue, in turn, to further drive down the cost of electrification.”
Finding a mission
Calisch has been working on climate technologies his entire career. It all started at the clean technology incubator Otherlab that was founded by Saul Griffith SM ’01, PhD ’04.
“That’s where I caught the bug for technology and product development for climate impact,” Calisch says. “But I realized I needed to up my game, so I went to grad school in [MIT Professor] Neil Gershenfeld’s lab, the Center for Bits and Atoms. I got to dabble in software engineering, mechanical engineering, electrical engineering, mathematical modeling, all with the lens of building and iterating quickly.”
Calisch stayed at MIT for his PhD, where he worked on approaches in manufacturing that used fewer materials and less energy. After finishing his PhD in 2019, Calisch helped start a nonprofit called Rewiring America focused on advocating for electrification. Through that work, he collaborated with U.S. Senate offices on the Inflation Reduction Act.
The cost of lithium ion batteries has decreased by about 97 percent since their commercial debut in 1991. As more products have gone electric, the manufacturing process for everything from phones to drones, robots, and electric vehicles has converged around an electric tech stack of batteries, electric motors, power electronics, and chips. The countries that master the electric tech stack will be at a distinct manufacturing advantage.
Calisch started Copper to boost the supply chain for batteries while contributing to the electrification movement.
“Appliances can help deploy batteries, and batteries help deploy appliances,” Calisch says. “Appliances can also drive down the installed cost of batteries.”
The company is starting with the kitchen range because its peak power draw is among the highest in the home. Flattening that peak brings big benefits. Ranges are also meaningful: It’s where people gather around and cook each night. People take pride in their kitchen ranges more than, say, a water heater.
Copper’s 30-inch induction range heats up more quickly and reaches more precise temperatures than its gas counterpart. Installing it is as easy as swapping a fridge or dishwasher. Thanks to its 5-kilowatt-hour battery, the range even works when the power goes out.
“Batteries have become 10 times cheaper and are now both affordable and create tangible improvements in quality of life,” Calisch says. “It’s a new notion of climate impact that isn’t about turning down thermostats and suffering for the planet, it’s about adopting new technologies that are better.”
Scaling impact
Calisch says there’s no way for the U.S. to maintain resilient energy systems in the future without a lot of batteries. Because of power transmission and regulatory limitations, those batteries can’t all be located out on the grid.
“We see an analog to the internet,” Calisch says. “In order to deliver millions of times more information across the internet, we didn’t add millions of times more wires. We added local storage and caching across the network. That’s what increased throughput. We’re doing the same thing for the electric grid.”
This summer, Copper raised $28 million to scale its production to meet growing demand for its battery equipped appliances. Copper is also working to license its technology to other appliance manufacturers to help speed the electric transition.
“These electric technologies have the potential to improve people’s lives and, as a byproduct, take us off of fossil fuels,” Calisch says. “We’re in the business of identifying points of friction for that transition. We are not an appliance company; we’re an energy company.”
Looking back, Calisch credits MIT with equipping him with the knowledge needed to run a technical business.
“My time at MIT gave me hands-on experience with a variety of engineering systems,” Calisch. “I can talk to our embedded engineering team or electrical engineering team or mechanical engineering team and understand what they’re saying. That’s been enormously useful for running a company.”
He adds: “I also developed an expansive view of infrastructure at MIT, which has been instrumental in launching Copper and thinking about the electrical grid not just as wires on the street, but all of the loads in our buildings. It’s about making homes not just consumers of electricity, but participants in this broader network.”
Study reveals the role of geography in the opioid crisisThe findings point to state policies involving the presence of “pill mills” as influences on addiction over time.The U.S. opioid crisis has varied in severity across the country, leading to extended debate about how and why it has spread.
Now, a study co-authored by MIT economists sheds new light on these dynamics, examining the role that geography has played in the crisis. The results show how state-level policies inadvertently contributed to the rise of opioid addiction, and how addiction itself is a central driver of the long-term problem.
The research analyzes data about people who moved within the U.S., as a way of addressing a leading question about the crisis: How much of the problem is attributable to local factors, and to what extent do people have individual characteristics making them prone to opioid problems?
“We find a very large role for place-based factors, but that doesn’t mean there aren’t person-based factors as well,” says MIT economist Amy Finkelstein, co-author of a new paper detailing the study’s findings. “As is usual, it’s rare to find an extreme answer, either one or the other.”
In scrutinizing the role of geography, the scholars developed new insights about the spread of the crisis in relation to the dynamics of addiction. The study concludes that laws restricting pain clinics, or “pill mills,” where opioids were often prescribed, reduced risky opioid use by 5 percent over the 2006-2019 study period. Due to the path of addiction, enacting those laws near the onset of the crisis, in the 1990s, could have reduced risky use by 30 percent over that same time.
“What we do find is that pill mill laws really matter,” says MIT PhD student Dean Li, a co-author of the paper. “The striking thing is that they mattered a lot, and a lot of the effect was through transitions into opioid addiction.”
The paper, “What Drives Risky Prescription Opioid Use: Evidence from Migration,” appears in the Quarterly Journal of Economics. The authors are Finkelstein, who is the John and Jennie S. MacDonald Professor of Economics; Matthew Gentzkow, a professor of economics at Stanford University; and Li, a PhD student in MIT’s Department of Economics.
The opioid crisis, as the scholars note in the paper, is one of the biggest U.S. health problems in recent memory. As of 2017, there were more than twice as many U.S. deaths from opioids as from homicide. There were also at least 10 times as many opioid deaths compared to the number of deaths from cocaine during the 1980s-era crack epidemic in the U.S.
Many accounts and analyses of the crisis have converged on the increase in medically prescribed opioids starting in the 1990s as a crucial part of the problem; this was in turn a function of aggressive marketing by pharmaceutical companies, among other things. But explanations of the crisis beyond that have tended to fracture. Some analyses emphasize the personal characteristics of those who fall into opioid use, such as a past history of substance use, mental health conditions, age, and more. Other analyses focus on place-based factors, including the propensity of area medical providers to prescribe opioids.
To conduct the study, the scholars examined data on prescription opioid use from adults in the Social Security Disability Insurance program from 2006 to 2019, covering about 3 million cases in all. They defined “risky” use as an average daily morphine-equivalent dose of more than 120 milligrams, which has been shown to increase drug dependence.
By studying people who move, the scholars were developing a kind of natural experiment — Finkelstein has also used this same method to examine questions about disparities in health care costs and longevity across the U.S. In this case, in focusing on the opioid consumption patterns of the same people as they lived in different places, the scholars can disentangle the extent to which place-based and personal factors drive usage.
Overall, the study found a somewhat greater role for place-based factors than for personal characteristics in accounting for the drivers of risky opioid use. To see the magnitude of place-based effects, consider someone moving to a state with a 3.5 percentage point higher rate of risky use — akin to moving from the state with the 10th lowest rate of risky use to the state with the 10th highest rate. On average, that person’s probability of risky opioid use would increase by a full percentage point in the first year, then by 0.3 percentage points in each subsequent year.
Some of the study’s key findings involve the precise mechanisms at work beneath these top-line numbers.
In the research, the scholars examine what they call the “addiction channel,” in which opioid users fall into addiction, and the “availability channel,” in which the already-addicted find ways to sustain their use. Over the 2006-2019 period, they find, people falling into addiction through new prescriptions had an impact on overall opioid uptake that was 2.5 times as large as that of existing users getting continued access to prescribed opiods.
When people who are not already risky users of opioids move to places with higher rates of risky opioid use, Finkelstein observes, “One thing you can see very clearly in the data is that in the addiction channel, there’s no immediate change in behavior, but gradually as they’re in this new place you see an increase in risky opioid use.”
She adds: “This is consistent with a model where people move to a new place, have a back problem or car accident and go to a hospital, and if the doctor is more likely to prescribe opioids, there’s more of a risk they’re going to become addicted.”
By contrast, Finkelstein says, “If we look at people who are already risky users of opioids and they move to a new place with higher rates of risky opioid use, you see there’s an immediate increase in their opioid use, which suggests it’s just more available. And then you also see the gradual increase indicating more addiction.”
By looking at state-level policies, the researchers found this trend to be particularly pronounced in over a dozen states that lagged in enacting restrictions on pain clinics, or “pill mills,” where providers had more latitude to prescribe opioids.
In this way the research does not just evaluate the impact of place versus personal characteristics; it quantifies the problem of addiction as an additional dimension of the issue. While many analyses have sought to explain why people first use opioids, the current study reinforces the importance of preventing the onset of addiction, especially because addicted users may later seek out nonprescription opioids, exacerbating the problem even further.
“The persistence of addiction is a huge problem,” Li says. “Even after the role of prescription opioids has subsided, the opioid crisis persists. And we think this is related to the persistence of addiction. Once you have this set in, it’s so much harder to change, compared to stopping the onset of addiction in the first place.”
Research support was provided by the National Institute on Aging, the Social Security Administration, and the Stanford Institute for Economic Policy Research.
Injectable antenna could safely power deep-tissue medical implantsThe technology would allow battery-free, minimally invasive, scalable bioelectronic implants such as pacemakers, neuromodulators, and body process monitors.Researchers from the MIT Media Lab have developed an antenna — about the size of a fine grain of sand — that can be injected into the body to wirelessly power deep-tissue medical implants, such as pacemakers in cardiac patients and neuromodulators in people suffering from epilepsy or Parkinson’s disease.
“This is the next major step in miniaturizing deep-tissue implants,” says Baju Joy, a PhD student in the Media Lab’s Nano-Cybernetic Biotrek research group. “It enables battery-free implants that can be placed with a needle, instead of major surgery.”
A paper detailing this work was published in the October issue of IEEE Transactions on Antennas and Propagation. Joy is joined on the paper by lead author Yubin Cai, PhD student at the Media Lab; Benoît X. E. Desbiolles and Viktor Schell, former MIT postdocs; Shubham Yadav, an MIT PhD student in media arts and sciences; David C. Bono, an instructor in the MIT Department of Materials Science and Engineering; and senior author Deblina Sarkar, the AT&T Career Development Associate Professor at the Media Lab and head of the Nano-Cybernetic Biotrek group.
Deep-tissue implants are currently powered either with a several-centimeters-long battery that is surgically implanted in the body, requiring periodic replacement, or with a surgically placed magnetic coil, also of a centimeter-scale size, that can harvest power wirelessly. The coil method functions only at high frequencies, which can cause tissue heating, limiting how much power can be safely delivered to the implant when miniaturized to sub-millimeter sizes.
“After that limit, you start damaging the cells,” says Joy.
As is stated in the team’s IEEE Transactions on Antennas and Propagation paper, “developing an antenna at ultra-small dimensions (less then 500 micrometers) which can operate efficiently in the low-frequency band is challenging.”
The 200-micrometer antenna — developed through research led by Sarkar — operates at low frequencies (109 kHz) thanks to a novel technology in which a magnetostrictive film, which deforms when a magnetic field is applied, is laminated with a piezoelectric film, which converts deformation to electric charge. When an alternating magnetic field is applied, magnetic domains within the magnetostrictive film contort it in the same way that a piece of fabric interwoven with pieces of metal would contort if subjected to a strong magnet. The mechanical strain in the magnetostrictive layer causes the piezoelectric layer to generate electric charges across electrodes placed above and below.
“We are leveraging this mechanical vibration to convert the magnetic field to an electric field,” Joy says.
Sarkar says the newly developed antenna delivers four to five orders of magnitude more power than implantable antennas of similar size that rely on metallic coils and operate in the GHz frequency range.
“Our technology has the potential to introduce a new avenue for minimally invasive bioelectric devices that can operate wirelessly deep within the human body,” she says.
The magnetic field that activates the antenna is provided by a device similar to a rechargeable wireless cell phone charger, and is small enough to be applied to the skin as a stick-on patch or slipped into a pocket close to the skin surface.
Because the antenna is fabricated with the same technology as a microchip, it can be easily integrated with already-existing microelectronics.
“These electronics and electrodes can be easily made to be much smaller than the antenna itself, and they would be integrated with the antenna during nanofabrication,” Joy says, adding that the researchers’ work leverages 50 years of research and development applied to making transistors and other electronics smaller and smaller. “The other components can be tiny, and the entire system can be placed with a needle injection.”
Manufacture of the antennas could be easily scaled up, the researchers say, and multiple antennas and implants could be injected to treat large areas of the body.
Another possible application of this antenna, in addition to pacemaking and neuromodulation, is glucose sensing in the body. Circuits with an optical sensor for detecting glucose already exist, but the process would benefit greatly with a wireless power supply that can be non-invasively integrated inside of the body.
“That’s just one example,” Joy says. “We can leverage all these other techniques that are also developed using the same fabrication methods, and then just integrate them easily to the antenna.”
Burning things to make thingsSili Deng, the Doherty Chair in Ocean Utilization and associate professor of mechanical engineering at MIT, is driving research into sustainable and efficient combustion technologies.Around 80 percent of global energy production today comes from the combustion of fossil fuels. Combustion, or the process of converting stored chemical energy into thermal energy through burning, is vital for a variety of common activities including electricity generation, transportation, and domestic uses like heating and cooking — but it also yields a host of environmental consequences, contributing to air pollution and greenhouse gas emissions.
Sili Deng, the Doherty Chair in Ocean Utilization and associate professor of mechanical engineering at MIT, is leading research to drive the transition from the heavy dependence on fossil fuels to renewable energy with storage.
“I was first introduced to flame synthesis in my junior year in college,” Deng says. “I realized you can actually burn things to make things, [and] that was really fascinating.”
Deng says she ultimately picked combustion as a focus of her work because she likes the intellectual challenge the concept offers. “In combustion you have chemistry, and you have fluid mechanics. Each subject is very rich in science. This also has very strong engineering implications and applications.”
Deng’s research group targets three areas: building up fundamental knowledge on combustion processes and emissions; developing alternative fuels and metal combustion to replace fossil fuels; and synthesizing flame-based materials for catalysis and energy storage, which can bring down the cost of manufacturing battery materials.
One focus of the team has been on low-cost, low-emission manufacturing of cathode materials for lithium-ion batteries. Lithium-ion batteries play an increasingly critical role in transportation electrification (e.g., batteries for electric vehicles) and grid energy storage for electricity that is generated from renewable energy sources like wind and solar. Deng’s team has developed a technology they call flame-assisted spray pyrolysis, or FASP, which can help reduce the high manufacturing costs associated with cathode materials.
FASP is based on flame synthesis, a technology that dates back nearly 3,000 years. In ancient China, this was the primary way black ink materials were made. “[People burned] vegetables or woods, such that afterwards they can collect the solidified smoke,” Deng explains. “For our battery applications, we can try to fit in the same formula, but of course with new tweaks.”
The team is also interested in developing alternative fuels, including looking at the use of metals like aluminum to power rockets. “We’re interested in utilizing aluminum as a fuel for civil applications,” Deng says, because aluminum is abundant in the earth, cheap, and it’s available globally. “What we are trying to do is to understand [aluminum combustion] and be able to tailor its ignition and propagation properties.”
Among other accolades, Deng is a 2025 recipient of the Hiroshi Tsuji Early Career Researcher Award from the Combustion Institute, an award that recognizes excellence in fundamental or applied combustion science research.
Study: Identifying kids who need help learning to read isn’t as easy as A, B, CWhile most states mandate screenings to guide early interventions for children struggling with reading, many teachers feel underprepared to administer and interpret them.In most states, schools are required to screen students as they enter kindergarten — a process that is meant to identify students who may need extra help learning to read. However, a new study by MIT researchers suggests that these screenings may not be working as intended in all schools.
The researchers’ survey of about 250 teachers found that many felt they did not receive adequate training to perform the tests, and about half reported that they were not confident that children who need extra instruction in reading end up receiving it.
When performed successfully, these screens can be essential tools to make sure children get the extra help they need to learn to read. However, the new findings suggest that many school districts may need to tweak how they implement the screenings and analyze the results, the researchers say.
“This result demonstrates the need to have a systematic approach for how the basic science on how children learn to read is translated into educational opportunity,” says John Gabrieli, the Grover Hermann Professor of Health Sciences and Technology, a professor of brain and cognitive sciences, and a member of MIT’s McGovern Institute for Brain Research.
Gabrieli is the senior author of the new open-access study, which appears today in Annals of Dyslexia. Ola Ozernov-Palchik, an MIT research scientist who is also a research assistant professor at Boston University Wheelock College of Education and Human Development, is the lead author of the study.
Boosting literacy
Over the past 20 years, national reading proficiency scores in the United States have trended up, but only slightly. In 2022, 33 percent of fourth-graders achieved reading proficiency, compared to 29 percent in 1992, according to the National Assessment of Educational Progress reading report card. (The highest level achieved in the past 20 years was 37 percent, in 2017.)
In hopes of boosting those rates, most states have passed laws requiring students to be screened for potential reading struggles early in elementary school. In most cases, the screenings are required two or three times per year, in kindergarten, first grade, and second grade.
These tests are designed to identify students who have difficulty with skills such as identifying letters and the sounds they make, blending sounds to make words, and recognizing words that rhyme. Students with low scores in these measures can then be offered extra interventions designed to help them catch up.
“The indicators of future reading disability or dyslexia are present as early as within the first few months of kindergarten,” Ozernov-Palchik says. “And there’s also an overwhelming body of evidence showing that interventions are most effective in the earliest grades.”
In the new study, the researchers wanted to evaluate how effectively these screenings are being implemented in schools. With help from the National Center for Improving Literacy, they posted on social media sites seeking classroom teachers and reading specialists who are responsible for administering literacy screening tests.
The survey respondents came from 39 states and represented public and private schools, located in urban, suburban, and rural areas. The researchers asked those teachers dozens of questions about their experience with the literacy screenings, including questions about their training, the testing process itself, and the results of the screenings.
One of the significant challenges reported by the respondents was a lack of training. About 75 percent reported that they received fewer than three hours of training on how to perform the screens, and 44 percent received no training at all or less than an hour of training.
“Under ideal conditions, there is an expert who trains the educators, they provide practice opportunities, they provide feedback, and they observe the educators administer the assessment,” Ozernov-Palchik says. “None of this was done in many of the cases.”
Instead, many educators reported that they spent their own time figuring out how to give the evaluations, sometimes working with colleagues. And, new hires who arrived at a school after the initial training was given were often left on their own to figure it out.
Another major challenge was suboptimal conditions for administering the tests. About 80 percent of teachers reported interruptions during the screenings, and 40 percent had to do the screens in noisy locations such as a school hallway. More than half of the teachers also reported technical difficulties in administering the tests, and that rate was higher among teachers who worked at schools with a higher percentage of students from low socioeconomic (SES) backgrounds.
Teachers also reported difficulties when it came to evaluating students categorized as English language learners (ELL). Many teachers relayed that they hadn’t been trained on how to distinguish students who were having trouble reading from those who struggled on the tests because they didn’t speak English well.
“The study reveals that there’s a lot of difficulty understanding how to handle English language learners in the context of screening,” Ozernov-Palchik says. “Overall, those kids tend to be either over-identified or under-identified as needing help, but they’re not getting the support that they need.”
Unrealized potential
Most concerning, the researchers say, is that in many schools, the results of the screening tests are not being used to get students the extra help that they need. Only 44 percent of the teachers surveyed said that their schools had a formal process for creating intervention plans for students after the screening was performed.
“Even though most educators said they believe that screening is important to do, they’re not feeling that it has the potential to drive change the way that it’s currently implemented,” Ozernov-Palchik says.
In the study, the researchers recommended several steps that state legislatures or individual school districts can take to make the screening process run more smoothly and successfully.
“Implementation is the key here,” Ozernov-Palchik says. “Teachers need more support and professional development. There needs to be systematic support as they administer the screening. They need to have designated spaces for screening, and explicit instruction in how to handle children who are English language learners.”
The researchers also recommend that school districts train an individual to take charge of interpreting the screening results and analyzing the data, to make sure that the screenings are leading to improved success in reading.
In addition to advocating for those changes, the researchers are also working on a technology platform that uses artificial intelligence to provide more individualized instruction in reading, which could help students receive help in the areas where they struggle the most.
The research was funded by Schmidt Futures, the Chan Zuckerberg Initiative for the Reach Every Reader project, and the Halis Family Foundation.
This is your brain without sleep New research shows attention lapses due to sleep deprivation coincide with a flushing of fluid from the brain — a process that normally occurs during sleep.Nearly everyone has experienced it: After a night of poor sleep, you don’t feel as alert as you should. Your brain might seem foggy, and your mind drifts off when you should be paying attention.
A new study from MIT reveals what happens inside the brain as these momentary failures of attention occur. The scientists found that during these lapses, a wave of cerebrospinal fluid (CSF) flows out of the brain — a process that typically occurs during sleep and helps to wash away waste products that have built up during the day. This flushing is believed to be necessary for maintaining a healthy, normally functioning brain.
When a person is sleep-deprived, it appears that their body attempts to catch up on this cleansing process by initiating pulses of CSF flow. However, this comes at a cost of dramatically impaired attention.
“If you don’t sleep, the CSF waves start to intrude into wakefulness where normally you wouldn’t see them. However, they come with an attentional tradeoff, where attention fails during the moments that you have this wave of fluid flow,” says Laura Lewis, the Athinoula A. Martinos Associate Professor of Electrical Engineering and Computer Science, a member of MIT’s Institute for Medical Engineering and Science and the Research Laboratory of Electronics, and an associate member of the Picower Institute for Learning and Memory.
Lewis is the senior author of the study, which appears today in Nature Neuroscience. MIT visiting graduate student Zinong Yang is the lead author of the paper.
Flushing the brain
Although sleep is a critical biological process, it’s not known exactly why it is so important. It appears to be essential for maintaining alertness, and it has been well-documented that sleep deprivation leads to impairments of attention and other cognitive functions.
During sleep, the cerebrospinal fluid that cushions the brain helps to remove waste that has built up during the day. In a 2019 study, Lewis and colleagues showed that CSF flow during sleep follows a rhythmic pattern in and out of the brain, and that these flows are linked to changes in brain waves during sleep.
That finding led Lewis to wonder what might happen to CSF flow after sleep deprivation. To explore that question, she and her colleagues recruited 26 volunteers who were tested twice — once following a night of sleep deprivation in the lab, and once when they were well-rested.
In the morning, the researchers monitored several different measures of brain and body function as the participants performed a task that is commonly used to evaluate the effects of sleep deprivation.
During the task, each participant wore an electroencephalogram (EEG) cap that could record brain waves while they were also in a functional magnetic resonance imaging (fMRI) scanner. The researchers used a modified version of fMRI that allowed them to measure not only blood oxygenation in the brain, but also the flow of CSF in and out of the brain. They also measured each subject’s heart rate, breathing rate, and pupil diameter.
The participants performed two attentional tasks while in the fMRI scanner, one visual and one auditory. For the visual task, they had to look at a screen that had a fixed cross. At random intervals, the cross would turn into a square, and the participants were told to press a button whenever they saw this happen. For the auditory task, they would hear a beep instead of seeing a visual transformation.
Sleep-deprived participants performed much worse than well-rested participants on these tasks, as expected. Their response times were slower, and for some of the stimuli, the participants never registered the change at all.
During these momentary lapses of attention, the researchers identified several physiological changes that occurred at the same time. Most significantly, they found a flux of CSF out of the brain just as those lapses occurred. After each lapse, CSF flowed back into the brain.
“The results are suggesting that at the moment that attention fails, this fluid is actually being expelled outward away from the brain. And when attention recovers, it’s drawn back in,” Lewis says.
The researchers hypothesize that when the brain is sleep-deprived, it begins to compensate for the loss of the cleansing that normally occurs during sleep, even though these pulses of CSF flow come with the cost of attention loss.
“One way to think about those events is because your brain is so in need of sleep, it tries its best to enter into a sleep-like state to restore some cognitive functions,” Yang says. “Your brain’s fluid system is trying to restore function by pushing the brain to iterate between high-attention and high-flow states.”
A unified circuit
The researchers also found several other physiological events linked to attentional lapses, including decreases in breathing and heart rate, along with constriction of the pupils. They found that pupil constriction began about 12 seconds before CSF flowed out of the brain, and pupils dilated again after the attentional lapse.
“What’s interesting is it seems like this isn’t just a phenomenon in the brain, it’s also a body-wide event. It suggests that there’s a tight coordination of these systems, where when your attention fails, you might feel it perceptually and psychologically, but it’s also reflecting an event that’s happening throughout the brain and body,” Lewis says.
This close linkage between disparate events may indicate that there is a single circuit that controls both attention and bodily functions such as fluid flow, heart rate, and arousal, according to the researchers.
“These results suggest to us that there’s a unified circuit that’s governing both what we think of as very high-level functions of the brain — our attention, our ability to perceive and respond to the world — and then also really basic fundamental physiological processes like fluid dynamics of the brain, brain-wide blood flow, and blood vessel constriction,” Lewis says.
In this study, the researchers did not explore what circuit might be controlling this switching, but one good candidate, they say, is the noradrenergic system. Recent research has shown that this system, which regulates many cognitive and bodily functions through the neurotransmitter norepinephrine, oscillates during normal sleep.
The research was funded by the National Institutes of Health, a National Defense Science and Engineering Graduate Research Fellowship, a NAWA Fellowship, a McKnight Scholar Award, a Sloan Fellowship, a Pew Biomedical Scholar Award, a One Mind Rising Star Award, and the Simons Collaboration on Plasticity in the Aging Brain.
New method could improve manufacturing of gene-therapy drugsSelective crystallization can greatly improve the purity, selectivity, and active yield of viral vector-based gene therapy drugs, MIT study finds.Some of the most expensive drugs currently in use are gene therapies to treat specific diseases, and their high cost limits their availability for those who need them. Part of the reason for the cost is that the manufacturing process yields as much as 90 percent non-active material, and separating out these useless parts is slow, leads to significant losses, and is not well adapted to large-scale production. Separation accounts for almost 70 percent of the total gene therapy manufacturing cost. But now, researchers at MIT’s Department of Chemical Engineering and Center for Biomedical Innovation have found a way to greatly improve that separation process.
The findings are described in the journal ACS Nano, in a paper by MIT Research Scientist Vivekananda Bal, Edward R. Gilliland Professor Richard Braatz, and five others.
“Since 2017, there have been around 10,000 clinical trials of gene therapy drugs,” Bal says. Of those, about 60 percent are based on adeno-associated virus, which is used as a carrier for the modified gene or genes. These viruses consist of a sort of shell structure, known as capsids, that protects the genetic material within, but the production systems used to manufacture these drugs tend to produce large quantities of empty capsids with no genetic material inside.
These empty capsids, which can make up anywhere from half to 90 percent of the yield, are useless therapeutically, and in fact can be counterproductive because they can add to any immune reaction in the patient without providing any benefit. They must be removed prior to the formulation as a part of the manufacturing process. The existing purification processes are not scalable and involve multiple stages, have long processing times, and incur high product losses and high cost.
Separating full from empty capsids is complicated by the fact that in almost every way, they appear nearly identical. “They both have similar structure, the same protein sequences,” Bal says. “They also have similar molecular weight, and similar density.” Given the similarity, it’s extremely challenging to separate them. “How do you come up with a method?”
Most systems presently use a method based on chromatography, in which the mixture passes through a column of absorbent material, and slight differences in the properties can cause them to pass through at different rates, so that they can be separated out. Because the differences are so slight, the process requires multiple rounds of processing, in addition to filtration steps, adding to the time and cost. The method is also inefficient, wasting up to 30 or 40 percent of the product, Bal says. And the resulting product is still only about two-thirds pure, with a third of inactive material remaining.
There is another purification method that is widely used in the small molecule pharmaceutical industry, which uses a preferential crystallization process instead of chromatography, but this method had not been tried for protein purification — specifically, capsid-based drugs — before. Bal decided to try it, since with this method “its operating time is low and the product loss is also very low, and the purity achieved is very, very high because of the high selectivity,” he says. The method separates out empty from full capsids in the solution, as well as separating out cell debris and other useless material, all in one step, without requiring the significant pre-processing and post-processing steps needed by the other methods.
“The time required for purification using the crystallization method is around four hours, compared to that required for the chromatography method, which is about 37 to 40 hours,” he says. “So basically, it is about 10 times more effective in terms of operating time.” This novel method will reduce the cost of gene therapy drugs by five to 10 times, he says.
The method relies on a very slight difference in the electrical potential of the full versus empty capsids. DNA molecules have a slight negative charge, whereas the surface of the capsids has a positive charge. “Because of that, the overall charge density distribution of the full capsids will be different from that of the empty capsids,” he says. That difference leads to a difference in the crystallization rates, which can be used to create conditions that favor the crystallization of the full capsids while leaving the empty ones behind.
Tests proved the effectiveness of the method, which can be easily adapted to large-scale pharmaceutical manufacturing processes, he says. The team has applied for a patent through MIT’s Technology Licensing Office, and is already in discussions with a number of pharmaceutical companies about beginning trials of the system, which could lead to the system becoming commercialized within a couple of years, Bal says.
“They’re basically collaborating,” he says of the companies. “They’re transferring their samples for a trial with our method,” and ultimately the process will either be licensed to a company, or form the basis of a new startup company, he says.
In addition to Bal and Braatz, the research team also included Jacqueline Wolfrum, Paul Barone, Stacy Springs, Anthony Sinskey, and Robert Kotin, all of MIT’s Center for Biomedical Innovation. The work was supported by the Massachusetts Life Sciences Center, Sanofi S.A., Sartorius AG, Artemis Life Sciences, and the U.S. Food and Drug Administration.
The joy of life (sciences)Mary Gallagher’s deeply rooted MIT experience and love of all life supports growth at the MIT Department of Biology.For almost 30 years, Mary Gallagher has supported award-winning faculty members and their labs in the same way she tends the soil beneath her garden. In both, she pairs diligence and experience with a delight in the way that interconnected ecosystems contribute to the growth of a plant, or an idea, seeded in the right place.
Gallagher, a senior administrative assistant in the Department of Biology, has spent much of her career at MIT. Her mastery in navigating the myriad tasks required by administrators, and her ability to build connections, have supported and elevated everyone she interacts with, at the Institute and beyond.
Oh, the people you’ll know
Gallagher didn’t start her career at MIT. Her first role following graduation from the University of Vermont in the early 1980s was at a nearby community arts center, where she worked alongside a man who would become a household name in American politics.
“This guy had just been elected mayor, shockingly, of Burlington, Vermont, by under 100 votes, unseating the incumbent. He went in and created this arts council and youth office,” Gallagher recalls.
That political newcomer was none other than a young Bernie Sanders, now the longest-serving independent senator in U.S. congressional history.
Gallagher arrived at MIT in 1996, becoming an administrative assistant (aka “lab admin”) in what was then called the MIT Energy Laboratory. Shortly after her arrival, Cecil and Ida Green Professor of Physics and Engineering Systems Ernest Moniz transformed the laboratory into the MIT Energy Initiative (MITEI).
Gallagher quickly learned how versatile the work of an administrator can be. As MITEI rapidly grew, she interacted with people across campus and its vast array of disciplines at the Institute, including mechanical engineering, political science, and economics.
“Admin jobs at MIT are really crazy because of the depth of work that we’re willing to do to support the institution. I was hired to do secretarial work, and next thing I know, I was traveling all the time, and planning a five-day, 5,000-person event down in D.C.,” Gallagher says. “I developed crazy computer and event-planner skills.”
Although such tasks may seem daunting to some, Gallagher has been thrilled with the opportunities she’s had to meet so many people and develop so many new skills. As a lab admin in MITEI for 18 years, she mastered navigating MIT administration, lab finances, and technical support. When Moniz left MITEI to lead the U.S. Department of Energy under President Obama, she moved to the Department of Biology at MIT.
Mutual thriving
Over the years, Gallagher has fostered the growth of students and colleagues at MIT, and vice versa.
Friend and former colleague Samantha Farrell recalls her first days at MITEI as a rather nervous and very "green" temp, when Gallagher offered an excellent cappuccino from Gallagher’s new Nespresso coffee machine.
“I treasure her friendship and knowledge,” Farrell says. “She taught me everything I needed to know about being an admin and working in research.”
Gallagher’s experience has also set faculty across the Institute up for success.
According to one principal investigator she currently supports, Novartis Professor of Biology Leonard Guarente, Gallagher is “extremely impactful and, in short, an ideal administrative assistant."
Similarly, professor of biology Daniel Lew is grateful that her extensive MIT experience was available as he moved his lab to the Institute in recent years. “Mary was invaluable in setting up and running the lab, teaching at MIT, and organizing meetings and workshops,” Lew says. “She is a font of knowledge about MIT.”
A willingness to share knowledge, resources, and sometimes a cappuccino, is just as critical as a willingness to learn, especially at a teaching institution like MIT. So it goes without saying that the students at MIT have left their mark on Gallagher in turn — including teaching her how to format a digital table of contents on her very first day at MIT.
“Working with undergrads and grad students is my favorite part of MIT. Their generosity leaves me breathless,” says Gallagher. “No matter how busy they are, they’re always willing to help another person.”
Campus community
Gallagher cites the decline in community following the Covid-19 pandemic shutdown as one of her most significant challenges.
Prior to Covid, Gallagher says, “MIT had this great sense of community. Everyone had projects, volunteered, and engaged. The campus was buzzing, it was a hoot!”
She nurtured that community, from active participation in the MIT Women’s League to organizing an award-winning relaunch of Artist Behind the Desk. This subgroup of the MIT Working Group for Support Staff Issues hosted lunchtime recitals and visual art shows to bring together staff artists around campus, for which the group received a 2005 MIT Excellence Award for Creating Connections.
Moreover, Gallagher is an integral part of the smaller communities within the labs she supports.
Professor of biology and American Cancer Society Professor Graham Walker, yet another Department of Biology faculty member Gallagher supports, says, “Mary’s personal warmth and constant smile has lit up my lab for many years, and we are all grateful to have her as such a good colleague and friend.”
She strives to restore the sense of community that the campus used to have, but recognizes that striving for bygone days is futile.
“You can never go back in time and make the future what it was in the past,” she says. “You have to reimagine how we can make ourselves special in a new way.”
Spreading her roots
Gallagher’s life has been inextricably shaped by the Institute, and MIT, in turn, would not be what it is if not for Gallagher’s willingness to share her wisdom on the complexities of administration alongside the “joie de vivre” of her garden’s butterflies.
She recently bought a home in rural New Hampshire, trading the buzzing crowds of campus for the buzzing of local honeybees. Her work ethic is reflected in her ongoing commitment to curiosity, through reading about native plant life and documenting pollinating insects as they wander about her flowers.
Just as she can admire each bug and flower for the role it plays in the larger system, Gallagher has participated in and contributed to a culture of appreciating the role of every individual within the whole.
“At MIT’s core, they believe that everybody brings something to the table,” she says. “I wouldn’t be who I am if I didn’t work at MIT and meet all these people.”
Studying war in the new nuclear ageMIT political scientist Caitlin Talmadge scrutinizes military postures and international dynamics to understand the risks of escalation.Nuclear security can be a daunting topic: The consequences seem unimaginable, but the threat is real. Some scholars, though, thrive on the close study of the world’s most dangerous weapons. That includes Caitlin Talmadge PhD ’11, an MIT faculty member who is part of the Institute’s standout group of nuclear security specialists.
Talmadge, who joined the MIT faculty in 2023, has become a prominent scholar in security studies, conducting meticulous research about militaries’ on-the-ground capabilities and how they are influenced by political circumstances.
Earlier in her career, Talmadge studied the military capabilities of armies run by dictatorships. For much of the last decade, though, she has focused on specific issues of nuclear security: When can conventional wars raise risks of nuclear use? In what circumstances will countries ratchet up nuclear threats?
“A scenario that’s interested me a lot is one where the conduct of a conventional war actually raises specific nuclear escalation risks,” Talmadge says, noting that military operations may put pressure on an adversary’s nuclear capabilities. “There are many other instabilities in the world. But I’ve gotten pretty interested in what it means that the U.S., unlike in the Cold War when there was more of a bipolar competition, now faces multiple nuclear-armed adversaries.”
MIT is a natural intellectual home for Talmadge, who is the Raphael Dorman and Helen Starbuck Associate Professor in MIT’s Department of Political Science. She is also part of MIT’s Security Studies Program, long the home of several of the Institute’s nuclear experts, and a core member of the recently launched MIT Center for Nuclear Security Policy, which supports scholarship as well as engagement with nuclear security officials.
“I think dialogue for practitioners and scholars is important for both sides,” says Talmadge, who served on the Defense Policy Board, a panel of outside experts that directly advises senior Pentagon leaders, during the Biden administration. “It’s important for me to do scholarship that speaks to real-world problems. And part of what we do at MIT is train future practitioners. We also sometimes brief current practitioners, meet with them, and get a perspective on the very difficult problems they encounter. That interaction is mutually beneficial.”
Why coup-proofing hurts armies
From a young age, Talmadge was interested in global events, especially military operations, while growing up in a family that supported her curiosity about the world.
“I was fortunate to have parents that encouraged those interests,” Talmadge says. “Education was a really big value in our family. I had great teachers as well.”
Talmadge earned her BA degree at Harvard University, where her interests in international relations and military operations expanded.
“I didn’t even know the term security studies before I went to college,” she says. “But I did, in college, get very interested in studying the problems that had been left by the Soviet nuclear legacy.”
Talmadge then worked at a think tank before deciding to attend graduate school. She had not been fully set on academia, as opposed to, say, working in Washington policy circles. But while earning her PhD at the Institute, she recalls, “it turned out that I really liked research, and I really liked teaching. And I loved being at MIT.”
Talmadge is quick to credit MIT’s security studies faculty for their intellectual guidance, citing the encouragement of a slew of faculty, including Barry Posen (her dissertation advisor), Taylor Fravel, Roger Peterson, Cindy Williams, Owen Cote, and Harvey Sapolsky. Her dissertation examined the combat power of armies run by authoritarians.
That research became her 2015 book, “The Dictator’s Army: Battlefield Effectiveness in Authoritarian Regimes,” published by Cornell University Press. In it she examines how, for one thing, using a military for domestic “coup-proofing” limits its utility against external forces. In the Iran-Iraq war of the 1980s, to cite one example, Iraq’s military improved in the later years of the war, after coup-proofing measures were dropped, whereas Iran’s army performed worse over time as it became more preoccupied with domestic opposition.
“We tend to think of militaries as being designed for external conventional wars, but autocrats use the military for regime-protection tasks, and the more you optimize your military for doing that, sometimes it’s harder to aggregate combat power against an external adversary,” Talmadge says.
In the time since that book was published, even more examples have become evident in the world.
“It may be why the Russian invasion of Ukraine did so poorly in 2022,” she adds. “When you’re a personalist dictator and divide the military so it can’t be strong enough to overthrow you, and direct the intelligence apparatus internally instead of at Ukraine, it affects what your military can achieve. It was not the only factor in 2022, but I think the authoritarian character of Russia’s civil-military relations has played a role in Russia’s rather surprising underperformance in that war.”
On to nuclear escalation
After earning her PhD from MIT, Talmadge joined the faculty of George Washington University, where she taught from 2011 to 2018; she then served on the faculty at Georgetown University, before returning to MIT. And for the last decade, she has continued to study conventional military operations while also exploring the relationship between those operations and nuclear risk.
One issue is that conventional military strikes that might degrade an opponent’s nuclear capabilities. Talmadge is examining why states adopt military postures that threaten adversaries in this way in a book that’s in progress; her co-author is Brendan Rittenhouse Green PhD ’11, a political scientist at the University of Cincinnati.
The book focuses on why the U.S. has at times adopted military postures that increase nuclear pressure on opponents. Historically these escalatory postures have been viewed as unintentional, the result of aggressive military planning.
“In this book we make a different argument, which is that often these escalatory risks are hardwired into force posture deliberately and knowingly by civilian [government leaders] who at times have strategic rationales,” Talmadge says. “If you’re my opponent and I want to deter you from starting a war, it might be helpful to convince you that if you start that war, you’re eventually going to be backed into a nuclear corner.”
This logic may explain why many countries adopt force postures that seem dangerous, and it may offer clues as to how future wars involving the U.S., Russia, China, North Korea, India, or Pakistan could unfold. It also suggests that reining in nuclear escalation risk requires more attention to civilian decisions, not just military behavior.
While being in the middle of research, book-writing, teaching, and engaging with others in the field, Talmadge is certain she has landed in an ideal academic home, especially with MIT’s work in her field being bolstered by the Stanton Foundation gift to establish the Center for Nuclear Security Policy.
“We’re so grateful for the support of the Stanton Foundation,” Talmadge says. “It’s incredibly invigorating to be in a place with so much talent and just constantly learning from the people around you. It’s really amazing, and I do not take it for granted.”
She adds: “It is a little surreal at times to be here because I’m going into the same rooms where I have memories as myself as a grad student, but now I’m the professor. I have a little bit of nostalgia. But one of my primary reasons for coming to MIT, besides the great faculty colleagues, was the students, including the chance to work with the PhD students in the Security Studies Program, and I have not been disappointed. It doesn’t feel like work. It’s a joy to try to have a positive influence helping them become scholars.”
Astronomical data collection of Taurus Molecular Cloud-1 reveals over 100 different moleculesThe discovery will help researchers understand how chemicals form and change before stars and planets are born.MIT researchers recently studied a region of space called the Taurus Molecular Cloud-1 (TMC-1) and discovered more than 100 different molecules floating in the gas there — more than in any other known interstellar cloud. They used powerful radio telescopes capable of detecting very faint signals across a wide range of wavelengths in the electromagnetic spectrum.
With over 1,400 observing hours on the Green Bank Telescope (GBT) — the world’s largest fully steerable radio telescope, located in West Virginia — researchers in the group of Brett McGuire collected the astronomical data needed to search for molecules in deep space and have made the full dataset publicly available. From these observations, published in The Astrophysical Journal Supplement Series (ApJS), the team censused 102 molecules in TMC-1, a cold interstellar cloud where sunlike stars are born. Most of these molecules are hydrocarbons (made only of carbon and hydrogen) and nitrogen-rich compounds, in contrast to the oxygen-rich molecules found around forming stars. Notably, they also detected 10 aromatic molecules (ring-shaped carbon structures), which make up a small but significant fraction of the carbon in the cloud.
“This project represents the single largest amount of telescope time for a molecular line survey that has been reduced and publicly released to date, enabling the community to pursue discoveries such as biologically relevant organic matter,” said Ci Xue, a postdoc in the McGuire Group and the project’s principal researcher. “This molecular census offers a new benchmark for the initial chemical conditions for the formation of stars and planets.”
To handle the immense dataset, the researchers built an automated system to organize and analyze the results. Using advanced statistical methods, they determined the amounts of each molecule present, including variations containing slightly different atoms (such as carbon-13 or deuterium).
“The data we’re releasing here are the culmination of more than 1,400 hours of observational time on the GBT, one of the NSF’s premier radio telescopes,” says McGuire, the Class of 1943 Career Development Associate Professor of Chemistry. “In 2021, these data led to the discovery of individual PAH molecules in space for the first time, answering a three-decade-old mystery dating back to the 1980s. In the following years, many more and larger PAHs have been discovered in these data, showing that there is indeed a vast and varied reservoir of this reactive organic carbon present at the earliest stages of star and planet formation. There is still so much more science, and so many new molecular discoveries, to be made with these data, but our team feels strongly that datasets like this should be opened to the scientific community, which is why we’re releasing the fully calibrated, reduced, science-ready product freely for anyone to use.”
Overall, this study provides the single largest publicly released molecular line survey to date, enabling the scientific community to pursue discoveries such as biologically relevant molecules. This molecular census offers a new benchmark for understanding the chemical conditions that exist before stars and planets form.
MIT students stretch minds and bodiesExercise is Medicine class integrates physical activity and academics.We’ve known since ancient times that physical activity can prevent and treat a broad range of mental and physical illnesses. But today, exercise is not a central focus of modern health-care systems. Why? This is the motivating question behind MIT’s class STS.041/PE&W.0537 (Exercise is Medicine: From Ancient Civilizations to Modern Healthcare Systems) — a collaboration between the MIT Program in Science, Technology, and Society (STS) and the Department of Athletics, Physical Education, and Recreation (DAPER).
Going beyond the MIT tradition of hands-on learning, Exercise is Medicine (EIM) offers full-body experiential education, combining readings, lectures, and physical activity at the Zesiger Center and on MIT’s playing fields. Students investigate topics including barriers to exercise, loneliness as a public health issue, and social determinants of health through partner acrobatics, broomball, and sailing. During midterm week, they reflect on the mental health impact of activities, including meditation and pickleball. They also learn about the principles of traditional Chinese medicine through Qigong.
Co-taught by professors Jennifer Light and Carrie Moore, in addition to other DAPER instructors, EIM was first offered in spring 2024 for 20 undergraduates. Students from every major are invited to enroll — the next offering filled quickly, doubling in size to 40 students, with a long waitlist.
Exercise is Medicine is one of three courses Light and Moore offer as part of the MIT Project on Embodied Education, launched in 2022. Professor Light was eager to create an academic class where students spent at least 50 percent of their learning time out of their seats doing a physical activity that reinforced the academic objectives she was presenting.
“I was developing a new research project on the ancient wisdom and modern science of movement and learning, and was looking to develop courses that put this method into practice. Through Anthony Grant, athletic director and head of the DAPER, I connected with Carrie. We are having so much fun collaborating; one course quickly became two, and now three,” says Light.
History of medicine and health systems courses have long been a staple of the STS program. In EIM, students visit with MIT Chief Health Officer Cecelia Stuopis, who offers insight into the place of exercise in health care throughout the history of the Institute. Discussions also include the economic factors that may impact ideas and innovations from STEM fields.
The partnership with DAPER helps students deepen their understanding of the readings and lectures and, Light hopes, sets them up to find ways to integrate movement into their lives after the semester’s end. Moore adds, “This course allows students to reflect on the impact of movement on their cognition — experiencing increases in motivation, mood, focus, and community, as well as improved retention of content by engaging more parts of the brain.”
“DAPER instructors have an amazing ability to make so many physical activities accessible at the beginner level, and students come away from the course appreciating new activities they can do while on campus or as they move into the real world,” says Light.
Nathan Kim, a senior in Course 15 (Management), says, “When I think of my MIT education, I mostly think about problem sets and studying for exams. Learning is initially thought of as a cognitive output and performance. Even in project-based classes, there’s little attention to the body’s role in comprehension. However, this course broke that mold. Instead of treating the body as separate from the mind, it treated it as an essential partner in learning.”
“I love that this class stretches students’ minds and bodies at the same time. They get to learn serious academic content, try all sorts of new physical activities, and do so in a context that aims to make what they’re learning personally relevant to the remainder of their time in college and life beyond. The idea that their bodies aren’t just there to transport their heads around campus — but can be resources for academic learning — is a revelation to pretty much everyone in the class,” says Light.
Emily Zhou, a senior in computer science and engineering, adds, “After reading about the role of team sports in reducing loneliness and improving mental health, I didn’t expect the connection to feel so immediate. But the moment I was slipping and falling on the ice [while playing broomball] with my teammates, some of whom I had never met before, it clicked for me. As we coordinated strategies and cheered together every time we made a goal, I gained a deeper understanding of the reading, and why collective physical activity builds meaningful connections. I could genuinely feel how community forms differently when I’m trusting people with my physical body.”
“It’s a unique and enriching experience for the students to have experiential learning be a component of the class. Not only does it create shared memories of something special that we hope they will have for a lifetime, but it’s also a lot of fun. It frees their minds from to-do lists and other tasks and it gives them extra energy throughout the day. Their brains may be tired at the end of the day, but not their bodies,” says Moore.
The class also fulfills MIT’s General Institute Requirements. Students who successfully complete the class earn HASS credit and two Physical Education and Wellness points.
Earlier this year, Light and Moore presented findings from their ongoing class collaborations at the National Association for Kinesiology in Higher Education conference. The pair showcased how they connected the academic side of MIT with the activity side of campus, with the hopes of inspiring others to follow in a similar direction. They’re also working to help other MIT instructors bridge the two sides of Massachusetts Avenue.
“Professor Light and I have created a synergy of what education could be,” says Moore. “The model created works at MIT and is received well by our students, so we want to help faculty reshape the way they teach to enrich learning and the student experience. We hope that when our students become leaders in their careers, they will share the lessons they learned in our classes with their colleagues. If they do so, then we’ve done our job.”
Professor Ioannis Yannas, pioneer of regenerative medicine who invented artificial skin for the treatment of severe burns, dies at 90A beloved member of the Department of Mechanical Engineering for nearly 60 years, Yannas helped save the lives of thousands of burn victims through his research and innovation.Professor Ioannis V. Yannas SM ’59, a physical chemist and engineer known for the invention of artificial skin for the treatment of severe burns, and a longtime member of the MIT faculty, died on Oct. 19 at the age of 90.
“Professor Yannas was a beloved and distinguished colleague, teacher, and mentor. The impact of his inventions, and his legacy on the field of bioengineering was immense,” says John Hart, the Class of 1922 Professor and head of the Department of Mechanical Engineering.
Yannas, known to friends and colleagues as Yanni, held appointments in the MIT Department of Mechanical Engineering and the Harvard-MIT Program in Health Sciences and Technology. His principal research interest throughout his career was the process of induced organ regeneration used to replace organs that are either severely injured or terminally diseased. His work also advanced the clinical use of collagen tubes to treat peripheral nerve injuries.
In 1969, when Yannas approached the late John Burke of Massachusetts General Hospital to collaborate, Burke took him on a tour of a children’s burn unit. “There was a great deal of human misery that was confronting me, and I felt I had to do something about it,” said Yannas in later interviews. In 1981, the pair announced their success: an amalgam of a silicone outer sheet over a scaffolding of molecular material drawn from cow tendon and shark cartilage. Offering protection from infection and dehydration, the scaffolding enabled healthy skin cells to grow. Their discovery would be transformative for the treatment of burn victims.
Their artificial skin, patented and now manufactured as Integra, is still widely used on patients with severe and extensive burns, and for other applications including some types of plastic surgery and the treatment of chronic skin wounds commonly suffered by people with diabetes. The groundbreaking advance, which was later recognized as the first example of organ regeneration in adults, had previously been considered impossible.
“Yanni’s boldness in attacking a wide array of medical problems, including spinal cord transection, in his investigations of applications of collagen-based implants, inspired others, including myself, to work toward solutions to devastating conditions such as blindness, stroke, and spinal cord injury,” says Myron Spector, professor emeritus of orthopedic surgery (biomaterials) at Massachusetts General Brigham and Harvard Medical School, and an affiliate of the Harvard-MIT Program in Health Sciences and Technology. Yannas and Spector created several MIT courses together, including 2.79 (Biomaterial-Tissue Interactions).
“As we were talking about the content [for 2.79], Yanni proposed that we codify the cell behavior underlying the tissue response to implants,” explains Spector. “Within a short time, we laid out the plan for ‘unit cell processes’ to offer students a code to decipher the often inconceivably complex cellular processes that not only underlie the tissue response to implants, but that can guide the selection of the tools necessary to engineer medical devices and reveal their targets for treatment. This was all Yanni, taking a fundamental concept, the control volume used in chemical engineering to analyze systems, and applying it to cellular processes in the human body. I since use UCPs myself all the time.”
As a colleague serving as a collaborator in teaching and in research, Spector says Yannas was eager to help and to learn, bold in his thinking, smart in his choices, able to keep his eye on the goal, respectful of students as well as faculty and other colleagues, and selfless. “These are just the traits that we teach our students to look for when seeking the collaborators who are so necessary in science and engineering.”
Yannas was born on April 14, 1935, in Athens, Greece, where he completed his high school education at Athens College. He received a BA in chemistry at Harvard College in 1957, followed by an MS in chemical engineering from MIT in 1959. After a period of industrial research on polymers at W. R. Grace & Co., in Cambridge, Massachusetts, he attended Princeton University, where he completed an MS degree in 1965 and a PhD in 1966, both in physical chemistry. Yannas joined the MIT faculty immediately thereafter and remained at the Institute for the next 59 years until his passing.
For his discoveries in organ regeneration, Yannas was elected member of the National Academy of Medicine (1987), the National Inventors Hall of Fame (2015), and the National Academy of Engineering (2017). He was also elected Fellow of the American Institute of Medical and Biomedical Engineering.
Further, he was the recipient of many prestigious awards including the Society for Biomaterials Founders Award (1982) and the Society’s Clemson Award for Applied Science and Engineering (1992). He was an author of numerous journal articles, and the sole author of the influential book, “Tissue and Organ Regeneration in Adults.”
Yannas’ work, and 2015 induction into the National Inventors Hall of Fame, was the subject of “Hope Regenerated,” a video produced by the MIT Department of Mechanical Engineering. The film chronicles the development of Integra, which was initially characterized as a “failed experiment” but became a life-saving discovery that launched a new field of regenerative medicine.
“My father's relationship with MIT was deeply meaningful to him,” says Tania Yannas Kluzak. “He regarded MIT as the ideal partner in his life's work — pioneering lifesaving research in organ regeneration.”
Yannas was predeceased by his brother, Pavlos. He is survived by his two children, Tania Kluzak and her husband Gordon, and Alexi Yannas and his wife Maria; his grandchildren — Alexandra, Marina, Sophia, Philippos, and Nefeli; his sister, Elizabeth Sitinas; and many loving relatives and friends. A celebration of life will be announced at a later date.
With a new molecule-based method, physicists peer inside an atom’s nucleus An alternative to massive particle colliders, the approach could reveal insights into the universe’s starting ingredients.Physicists at MIT have developed a new way to probe inside an atom’s nucleus, using the atom’s own electrons as “messengers” within a molecule.
In a study appearing today in the journal Science, the physicists precisely measured the energy of electrons whizzing around a radium atom that had been paired with a fluoride atom to make a molecule of radium monofluoride. They used the environments within molecules as a sort of microscopic particle collider, which contained the radium atom’s electrons and encouraged them to briefly penetrate the atom’s nucleus.
Typically, experiments to probe the inside of atomic nuclei involve massive, kilometers-long facilities that accelerate beams of electrons to speeds fast enough to collide with and break apart nuclei. The team’s new molecule-based method offers a table-top alternative to directly probe the inside of an atom’s nucleus.
Within molecules of radium monofluoride, the team measured the energies of a radium atom’s electrons as they pinged around inside the molecule. They discerned a slight energy shift and determined that electrons must have briefly penetrated the radium atom’s nucleus and interacted with its contents. As the electrons winged back out, they retained this energy shift, providing a nuclear “message” that could be analyzed to sense the internal structure of the atom’s nucleus.
The team’s method offers a new way to measure the nuclear “magnetic distribution.” In a nucleus, each proton and neutron acts like a small magnet, and they align differently depending on how the nucleus’ protons and neutrons are spread out. The team plans to apply their method to precisely map this property of the radium nucleus for the first time. What they find could help to answer one of the biggest mysteries in cosmology: Why do we see much more matter than antimatter in the universe?
“Our results lay the groundwork for subsequent studies aiming to measure violations of fundamental symmetries at the nuclear level,” says study co-author Ronald Fernando Garcia Ruiz, who is the Thomas A. Franck Associate Professor of Physics at MIT. “This could provide answers to some of the most pressing questions in modern physics.”
The study’s MIT co-authors include Shane Wilkins, Silviu-Marian Udrescu, and Alex Brinson, along with collaborators from multiple institutions including the Collinear Resonance Ionization Spectroscopy Experiment (CRIS) at CERN in Switzerland, where the experiments were performed.
Molecular trap
According to scientists’ best understanding, there must have been almost equal amounts of matter and antimatter when the universe first came into existence. However, the overwhelming majority of what scientists can measure and observe in the universe is made from matter, whose building blocks are the protons and neutrons within atomic nuclei.
This observation is in stark contrast to what our best theory of nature, the Standard Model, predicts, and it is thought that additional sources of fundamental symmetry violation are required to explain the almost complete absence of antimatter in our universe. Such violations could be seen within the nuclei of certain atoms such as radium.
Unlike most atomic nuclei, which are spherical in shape, the radium atom’s nucleus has a more asymmetrical configuration, similar to a pear. Scientists predict that this pear shape could significantly enhance their ability to sense the violation of fundamental symmetries, to the extent that they may be potentially observable.
“The radium nucleus is predicted to be an amplifier of this symmetry breaking, because its nucleus is asymmetric in charge and mass, which is quite unusual,” says Garcia Ruiz, whose group has focused on developing methods to probe radium nuclei for signs of fundamental symmetry violation.
Peering inside the nucleus of a radium atom to investigate fundamental symmetries is an incredibly tricky exercise.
“Radium is naturally radioactive, with a short lifetime and we can currently only produce radium monofluoride molecules in tiny quantities,” says study lead author Shane Wilkins, a former postdoc at MIT. “We therefore need incredibly sensitive techniques to be able measure them.”
The team realized that by placing a radium atom in a molecule, they could contain and amplify the behavior of its electrons.
“When you put this radioactive atom inside of a molecule, the internal electric field that its electrons experience is orders of magnitude larger compared to the fields we can produce and apply in a lab,” explains Silviu-Marian Udrescu PhD ’24, a study co-author. “In a way, the molecule acts like a giant particle collider and gives us a better chance to probe the radium’s nucleus.”
Energy shift
In their new study, the team first paired radium atoms with fluoride atoms to create molecules of radium monofluoride. They found that in this molecule, the radium atom’s electrons were effectively squeezed, increasing the chance for electrons to interact with and briefly penetrate the radium nucleus.
The team then trapped and cooled the molecules and sent them through a system of vacuum chambers, into which they also sent lasers, which interacted with the molecules. In this way the researchers were able to precisely measure the energies of electrons inside each molecule.
When they tallied the energies, they found that the electrons appeared to have a slightly different energy compared to what physicists expect if they did not penetrate the nucleus. Although this energy shift was small — just a millionth of the energy of the laser photon used to excite the molecules — it gave unambiguous evidence of the molecules’ electrons interacting with the protons and neutrons inside the radium nucleus.
“There are many experiments measuring interactions between nuclei and electrons outside the nucleus, and we know what those interactions look like,” Wilkins explains. “When we went to measure these electron energies very precisely, it didn’t quite add up to what we expected assuming they interacted only outside of the nucleus. That told us the difference must be due to electron interactions inside the nucleus.”
“We now have proof that we can sample inside the nucleus,” Garcia Ruiz says. “It’s like being able to measure a battery’s electric field. People can measure its field outside, but to measure inside the battery is far more challenging. And that’s what we can do now.”
Going forward, the team plans to apply the new technique to map the distribution of forces inside the nucleus. Their experiments have so far involved radium nuclei that sit in random orientations inside each molecule at high temperature. Garcia Ruiz and his collaborators would like to be able to cool these molecules and control the orientations of their pear-shaped nuclei such that they can precisely map their contents and hunt for the violation of fundamental symmetries.
“Radium-containing molecules are predicted to be exceptionally sensitive systems in which to search for violations of the fundamental symmetries of nature,” Garcia Ruiz says. “We now have a way to carry out that search.”
This research was supported, in part, by the U.S. Department of Energy.
At MIT, a day of hands-on, kid-friendly learningOrganized by the MIT Museum, the 2025 Cambridge Science Carnival included activities with air cannons, sea bots, and electron microscopes.Back and better than ever, the Cambridge Science Carnival, an annual free family-friendly science extravaganza, was held on Sunday, Sept. 21, at the Kendall/MIT Open Space.
Founded by the MIT Museum in 2007, and organized with the support of MIT and the City of Cambridge, the 2025 event drew approximately 20,000 attendees and featured more than 140 activities, demonstrations, and installations tied to the topics of science, technology, engineering, arts, and mathematics (STEAM).
Among the carnival’s wide variety of activities was the popular robot petting zoo, an annual showcase involving more than a dozen companies and local robotics clubs, including FIRST Tech Challenge and FIRST Robotics Competition. Participants were invited to engage with a range of different robots, from building with LEGOs and erector sets to piloting underwater robots to learning about the science of automation.
“Every exhibit and every moment of discovery today reinforces why Cambridge remains a global leader in STEAM,” Cambridge Mayor Denise Simmons said in her remarks at the event. “The creativity, ingenuity, and joy on display here today are a powerful reminder that science isn’t just for labs and lecture halls — it’s for everyone.”
Other activities included an appearance from the popular kid-friendly podcast “Tumble Science,” with co-host Marshall Escamilla testing fans’ knowledge of different STEAM topics drawn from “Tumble Science.” Clark University’s smoke-ring air cannons were a particular hit with the under-7-year-old set, while “Cycle To Science” showed off a gravity-defying bicycle wheel that, while spinning, was suspended on one side by a simple piece of string. Attendees also enjoyed live music, food trucks, and activities exploring everything from pipette art to the chemistry of glass.
At the robot petting zoo, FIRST Robotics volunteer mentor Dominique Regli reflected on the event as someone who was herself first inspired by similar festivals more than a decade earlier.
“Seeing kids of all ages interact with the robots made me think back to when I was a seventh grader, and how getting to see some of these robots for the first time was truly life-changing for me,” said Regli, who has been involved with FIRST Robotics since 2018 and is now an MIT computer science PhD student and affiliate of the Computer Science and Artificial Intelligence Laboratory (CSAIL). “These types of events are so important to expose students to what's possible.”
Throughout its history, a key aspect of the carnival has been MIT’s close collaboration with the City of Cambridge, which ran several activities. Cambridge Public School teachers led and the Public Works Department hosted a “Trash or Treasure” activity, which helped teach kids about recycling and composting. The carnival is a major contribution to the Institute’s objective of connecting the MIT ecosystem with Cambridge residents and local communities.
“Cambridge is one of the world’s leading science cities, with more Nobel laureates per capita than any other city on the planet,” says Michael John Gorman, director of the MIT Museum. “The Cambridge Science Carnival is a beloved day in the Cambridge calendar which brings science out of the labs and onto the streets.”
With a focus on engaging families and kids ranging from kindergarten to the eighth grade, one important outcome this year was to give undergraduate and graduate students the opportunity to showcase their work and hone their skills in clearly communicating science concepts to the public. There were over 50 activities led by MIT students, as well as participants from other local schools such as Boston College and Boston, Clark, Harvard, Northeastern, and Tufts universities.
Typically organized as part of the annual Cambridge Science Festival, this year the Cambridge Science Carnival returned as a standalone event while the larger festival undergoes a strategic transition for its relaunch in 2026. The MIT Museum offered free admission during the carnival and is always free to Cambridge residents, as well as active military, EBT cardholders, members of the Massachusetts Teachers Association, and MIT ID holders.
“For MIT researchers, discovery often happens in a lab or a classroom, but the truth is, the spark of discovery can happen anywhere,” said Alfred Ironside, MIT vice president for communications, in remarks at the event. “That’s really what today is about: feeding curiosity, encouraging questions, and showing that science is not locked away behind closed doors. It’s for everyone.”
Startup’s tablets deliver cancer drugs more evenly over timeAn MIT team’s technology could allow cancer drugs to be delivered more steadily into the bloodstream, to improve effectiveness and reduce side effects.Pills are by far the most convenient form of cancer treatment, but most oral cancer drugs quickly dissolve in the stomach, delivering a burst of chemicals into the bloodstream all at once. That can cause side effects. It also may limit the drug’s effectiveness because its concentration in the blood may become too low after the initial burst.
Now, the startup Enzian Pharmaceutics, founded by Aron Blaesi PhD ’14 and former principal research scientist Nannaji Saka ScD ’74, is developing an oral tablet that delivers drugs into the gastric fluid and the blood steadily over time. The company’s tablets use tiny 3D-printed fibers that turn into a gel-like substance when exposed to water. The tablets have been shown to stay in the stomach of animals for up to a day, slowly degrading while releasing the drug in controlled quantities.
The company is currently validating its tablets’ ability to stay in place in a small number of healthy human volunteers. In about a year, it plans to begin testing the technology’s ability to improve the effectiveness and safety of cancer drugs in patients.
“A lot of orally delivered cancer drugs could benefit from this,” says Blaesi, who incorporated the company in 2016. “Right now, soon after someone has taken a cancer drug, its concentration in the blood can be up to 50 times greater than when they are supposed to take the next pill. During the peak, the drug goes into the heart, it goes into the liver, the brain, and it can cause a lot of problems, while at the end of the dosing interval the concentration in the blood may be too low. By taking out that peak and increasing the time the drug is released, we could improve the effectiveness of treatments and mitigate certain side effects.”
In search of innovation
When Blaesi came to MIT, he knew he wanted his mechanical engineering PhD work to form the basis of a company. Early on, as part of the Novartis-MIT Center for Continuous Manufacturing, he worked on manufacturing pills with an injection molding machine that melted and solidified the material, in contrast to the traditional process of compacting powder. He noticed injection molding made the pills far less porous.
“If you put a typical pill into a fluid or into the stomach, the fluid percolates the pores and quickly dissolves it,” Blaesi explains. “That’s not the case when you have an injection molded product. That’s when Dr. Saka, who I met almost daily to discuss my research with, and I started to realize that microstructure is very important.”
The researchers began exploring how different tablet microstructures changed the rate at which drugs are released. For more precision, they moved from injection molding to 3D printing.
Using MIT machine shops, Blaesi built a 3D printer and produced tightly wound microstructures that could carry the drugs. He focused on fibrous structures with space between the fibers, because they would allow gastrointestinal fluid to percolate the pill and dissolve rapidly. He tested the structures in both his Cambridge, Massachusetts, apartment and at MIT’s shared facilities.
Blaesi then experimented with different carrier materials, finding that the higher the molecular weight, the longer it took the pill to dissolve because the material would absorb water and expand before degrading.
“Initially I thought, ‘Oh no, the drug isn’t being dissolved fast enough anymore,’” Blaesi recalls. “Then we thought, ‘Everything has its place.’ This could stay in the stomach for longer because of the expansion. Then it could release the drug over time. We realized this wouldn’t just improve manufacturing, it would improve the product.”
In 2019, Blaesi and Saka published the first paper on their expandable fibrous tablets for prolonged drug delivery. It received a mixed reception.
“Some reviewers said, ‘Research on similar gastroretentive dosage forms has been done for 40 years and no one’s really succeeded,’” Blaesi recalls. “People said, ‘It will never work. Do experiments in animals and then we’ll talk.’”
Blaesi moved back to Switzerland during the Covid-19 pandemic and ran his animal experiments there.
“The reviewers were right: What we had didn’t work,” Blaesi says. “But we adjusted the design and showed we could make the pill stay in the stomach for longer.”
Inside Enzian’s final tablet design, tiny fibers are arranged in a grid. When water flows into the spaces between the fibers, they expand to form a strong gel-like substance that slowly erodes in the stomach, steadily releasing the drug. In animal studies, Enzian’s team showed its technology allowed tablets to remain in the stomach for 12 to 24 hours before being safely excreted.
The team soon found cancer drugs would be a good fit for their technology.
“A lot of cancer drugs are only soluble in acidic solutions, so they can only be absorbed while the drug is in the stomach,” Blaesi explains. “But on an empty stomach, the drug may be in the stomach for just 30 or 40 minutes at present. For a full stomach, it’s a few hours. And because you have a short time to deliver the drug, you need to release a high dose immediately. That shoots up the blood concentration, and if you dose every 12 hours, the concentration is going down during the other 10 hours.”
From the lab to patients
In upcoming human trials, Enzian plans to use its tablets to deliver a drug for prostate cancer that Blaesi says is currently dosed at several hundred milligrams a day. He hopes to get down to about a tenth of that with a better therapeutic effect.
Enzian also believes its technology could improve treatments for blood, skin, and breast cancers.
“This could really be used to improve treatment for a variety of cancers,” Blaesi says. “We believe this is a more efficient and effective way to deliver drugs.”
Maximizing effectiveness and minimizing side effects is also important in clinical trials, where a new drug’s superiority over existing treatments must be shown, and a single adverse event can end its development.
The upcoming move into patients is the culmination of more than a decade of work for Blaesi, who is confident Enzian can deliver on its promise of improving treatments.
“The opportunity is enormous,” Blaesi says. “So many oral cancer drugs have this delivery problem. We still have to do the efficacy and safety studies on patients, but we expect this to be a game changer.”
Five with MIT ties elected to National Academy of Medicine for 2025Professors Facundo Batista and Dina Katabi, along with three additional MIT alumni, are honored for their outstanding professional achievement and commitment to service.On Oct. 20 during its annual meeting, the National Academy of Medicine announced the election of 100 new members, including MIT faculty members Dina Katabi and Facundo Batista, along with three additional MIT alumni.
Election to the National Academy of Medicine (NAM) is considered one of the highest honors in the fields of health and medicine, recognizing individuals who have demonstrated outstanding professional achievement and commitment to service.
Facundo Batista is the associate director and scientific director of the Ragon Institute of MGH, MIT and Harvard, as well as the first Phillip T. and Susan M. Ragon Professor in the MIT Department of Biology. The National Academy of Medicine recognized Batista for “his work unraveling the biology of antibody-producing B cells to better understand how our body’s immune systems responds to infectious disease.” More recently, Facundo’s research has advanced preclinical vaccine and therapeutic development for globally important diseases including HIV, malaria, and influenza.
Batista earned a PhD from the International School of Advanced Studies and established his lab in 2002 as a member of the Francis Crick Institute (formerly the London Research Institute), simultaneously holding a professorship at Imperial College London. In 2016, he joined the Ragon Institute to pursue a new research program applying his expertise in B cells and antibody responses to vaccine development, and preclinical vaccinology for diseases including SARS-CoV-2 and HIV. Batista is an elected fellow or member of the U.K. Academy of Medical Sciences, the American Academy of Microbiology, the Academia de Ciencias de América Latina, and the European Molecular Biology Organization, and he is chief editor of The EMBO Journal.
Dina Katabi SM ’99, PhD ’03 is the Thuan (1990) and Nicole Pham Professor in the Department of Electrical Engineering and Computer Science at MIT. Her research spans digital health, wireless sensing, mobile computing, machine learning, and computer vision. Katabi’s contributions include efficient communication protocols for the internet, advanced contactless biosensors, and novel AI models that interpret physiological signals. The NAM recognized Katabi for “pioneering digital health technology that enables non-invasive, off-body remote health monitoring via AI and wireless signals, and for developing digital biomarkers for Parkinson’s progression and detection. She has translated this technology to advance objective, sensitive measures of disease trajectory and treatment response in clinical trials.”
Katabi is director of the MIT Center for Wireless Networks and Mobile Computing. She is also a member of the Computer Science and Artificial Intelligence Laboratory (CSAIL), where she leads the Networks at MIT Research Group. Katabi received a bachelor’s degree from the University of Damascus and MS and PhD degrees in computer science from MIT. She is a MacArthur Fellow; a member of the American Academy of Arts and Sciences, National Academy of Sciences, and National Academy of Engineering; and a recipient of the ACM Computing Prize.
Additional MIT alumni who were elected to the NAM for 2025 are:
Established originally as the Institute of Medicine in 1970 by the National Academy of Sciences, the National Academy of Medicine addresses critical issues in health, science, medicine, and related policy, and inspires positive actions across sectors.
“I am deeply honored to welcome these extraordinary health and medicine leaders and researchers into the National Academy of Medicine,” says NAM President Victor J. Dzau. “Their demonstrated excellence in tackling public health challenges, leading major discoveries, improving health care, advancing health policy, and addressing health equity will critically strengthen our collective ability to tackle the most pressing health challenges of our time.”
A “seating chart” for atoms helps locate their positions in materialsThe DIGIT imaging tool could enable the design of quantum devices and shed light on atomic-scale processes in cells and tissues.If you think of a single atom as a grain of sand, then a wavelength of visible light — which is a thousand times larger than the atom’s width — is comparable to an ocean wave. The light wave can dwarf an atom, missing it entirely as it passes by. This gulf in size has long made it impossible for scientists to see and resolve individual atoms using optical microscopes alone.
Only recently have scientists found ways to break this “diffraction limit,” to see features that are smaller than the wavelength of light. With new techniques known as super-resolution microscopy, scientists can see down to the scale of a single molecule.
And yet, individual atoms have still been too small for optical microscopes — which are much simpler and less expensive than super-resolution techniques — to distinguish, until now.
In an open-access paper appearing today in Nature Communications, MIT scientists present a new computational method that enables optical microscopes to resolve individual atoms and zero in on their exact locations in a crystal structure.
The team’s new “discrete grid imaging technique,” or DIGIT, is a computational imaging approach that scientists can apply to optical data to calculate the most probable location of individual atoms based on a very important clue: the material’s known atomic configuration. As long as scientists have an idea of what a material’s physical atomic layout should be, they can use this layout as a sort of map to determine where specific atoms or features must be located.
“It’s like you know there’s a seating chart,” says lead author Yuqin “Sophia” Duan, a graduate student in MIT’s Department of Electrical Engineering and Computer Science (EECS). “Previous methods could tell you what section an atom is in. But now we can take this seating chart as prior knowledge, and can pinpoint exactly which seat the atom is in.”
With DIGIT, the team can now pinpoint individual atoms with a resolution of 0.178 angstroms. (One angstrom is one-tenth of a nanometer, which is less than half the width of a single atom). The technique enables optical microscopes to localize atomic-scale features in any material that has a known atomic pattern, such as crystalline materials or certain proteins with repeating molecular chains.
The team says the method could help guide the design of quantum devices, which often require placing individual atoms precisely within a crystal. Beyond quantum technologies, DIGIT can also provide new insights into how defects and impurities shape the behavior of advanced materials — from semiconductors to superconductors.
Duan’s co-authors at MIT are Qiushi Gu, Hanfeng Wang, Yong Hu, Kevin Chen, Matthew Trusheim, and EECS Professor Dirk Englund.
Grid support
Scientists can image features smaller than a nanometer, and sometimes as small as a single atom, but not with optical microscopes. In these cases, they use transmission or scanning electron microscopes, which send high-energy beams of electrons into a sample to generate an image based on the pattern in which the electrons scatter. These electron-based methods produce highly detailed, near-atomic-scale images, but they require imaging in a vacuum and at high energies, and only work in ultrathin, synthetic, or solid-state materials. Electron-based imaging methods are too harsh for more delicate living specimens.
In contrast, optical microscopes work at lower energies, in ambient conditions, and are safe to apply to biological samples. But they cannot discern features past the diffraction limit. Essentially, a microscope is unable to see features that are smaller than half the wavelength of visible light (about 200 to 300 nanometers) that a microscope sends in to probe a sample. Atoms, then, have long eluded optical microscopes.
In 2014, however, the Nobel Prize in Chemistry was awarded to developers of a technique to overcome the diffraction limit. Super-resolution microscopy works by shining laser light on a sample at a specific frequency that is known to resonate with a feature of interest, such as a certain molecule. When that molecule resonates, it effectively announces its presence in the material. With this optical manipulation, scientists can visualize features as small as 10 nanometers, on the scale of a single molecule.
Duan and Englund looked to resolve even smaller features by combining super-resolution techniques with statistical analysis and knowledge of materials that has often been overlooked.
“One thing that gets ignored in imaging optical systems is the physical configuration of your system,” Duan says. “For example, if you want to visualize defects in a diamond system, these defects can only be at certain positions, since they have to follow the grid of the atomic diamond structure. In proteins, there are some structures that grow in an organized grid, and their location must be somewhere along that physical grid.”
The researchers suspected that if they had a reasonably accurate map of a material’s atomic structure (imagine the ball-and-stick models of molecules in a chemistry classroom), they might use such maps as a template and try out many different orientations and rotation angles to find the closest match to whatever features are initially visualized using super-resolution microscopy.
“No one has ever done this before, to include the physical constraints or system information into the resolution technique,” Duan says.
Blurriness, collapsed
To test their idea, the researchers worked with a sample of diamond — a crystal whose microstructure is well-understood and resembles an organized grid, or lattice, of repeating carbon atoms. The researchers blindly knocked out some carbon atoms in the lattice and replaced them with silicon atoms using facilities at MIT.nano. Their goal was to identify and determine the precise locations of the errant silicon atoms.
To do so, they first used established techniques of super-resolution microscopy to probe the diamond sample, using lasers set to specific wavelengths at frequencies known to resonate with the silicon atoms but not the carbon atoms. With this technique, researchers produced images that depicted the silicon atoms, but only as a uniform blur.
The team then applied DIGIT to further resolve the picture. Knowing that diamond in general has a grid-like configuration of carbon atoms, the researchers took this configuration as a map, or seating chart of sorts, and assumed that any silicon atoms that took the place of a carbon atom must sit within the grid, which has a known spacing between atoms.
“Because the silicon atoms are substituting carbon atoms in the lattice, that means they must obey some integer multiple of the atomic spacing of the crystal lattice, separating any two silicon atoms,” Englund says. “That prior knowledge makes the localization different than if you add a purely amorphous material.”
The researchers essentially simulated many possibilities of orientations and rotation angles of the diamond lattice, superimposed on the blurry image of atoms that the super-resolution microscopy technique produced.
“The trick is that, in certain materials, atoms aren’t spread out randomly — they sit on a grid inside a crystal,” Duan explains. “We used that prior knowledge to sharpen the microscope’s picture. Once we factored in that ‘atomic grid,’ the blurriness collapsed, and we could pinpoint exact positions.”
In the end, they found the technique could pinpoint the location of individual silicon atoms within the diamond lattice, with a precision of 0.178 angstroms — the sharpest resolution of any optical-based imaging technique. The team has made the DIGIT code available on GitHub for anyone to apply to their optical measurements, provided their sample of interest has a well-understood atomic structure. Then, they hope that scientists will start to see much finer and detailed features and processes using light.
“It’s a big step — it takes optical microscopes into the realm of atomic scale, something people thought only electron microscopes or X-rays could do,” Duan says. “That opens up a whole new way of studying materials and biology.”
Charts can be social artifacts that communicate more than just data Researchers find that design elements of data visualizations influence viewers’ assumptions about the source of the information and its trustworthiness.The degree to which someone trusts the information depicted in a chart can depend on their assumptions about who made the data visualization, according to a pair of studies by MIT researchers.
For instance, if someone infers that a graph about a controversial topic like gun violence was produced by an organization they feel is in opposition with their beliefs or political views, they may discredit the information or dismiss the visualization all together.
The researchers found that even the clearest visualizations often communicate more than the data they explicitly depict, and can elicit strong judgments from viewers about the social contexts, identities, and characteristics of those who made the chart.
Readers make these assessments about the social context of a visualization primarily from its design features, like the color palette or the way information is arranged, rather than the underlying data. Often, these inferences are unintended by the designers.
Qualitative and quantitative studies revealed that these social inferences aren’t restricted to certain subgroups, nor are they caused by limited data literacy.
The researchers consolidate their findings into a framework that scientists and communicators can use to think critically about how design choices might affect these social assumptions. Ultimately, they hope this work leads to better strategies for scientific communication.
“If you are scrolling through social media and you see a chart, and you immediately dismiss it as something an influencer has produced just to get attention, that shapes your entire experience with the chart before you even dig into the data. We’ve shown in these papers that visualizations do more than just communicate the data they are depicting — they also communicate other social signals,” says Arvind Satyanarayan, an associate professor in the MIT Department of Electrical Engineering and Computer Science (EECS) and member of the Computer Science and Artificial Intelligence Laboratory (CSAIL) and co-senior author of this research.
He is joined on the paper by co-lead authors Amy Rae Fox, a former CSAIL postdoc, and Michelle Morgenstern, a current postdoc in MIT’s anthropology program; and co-senior author Graham M. Jones, professor of anthropology. Two related papers on this research will be presented at the IEEE Visualization Conference.
Charts as social artifacts
During the height of the Covid-19 pandemic, social media was awash in charts from organizations like the World Health Organization and Centers for Disease Control and Prevention, which were designed to convey information about the spread of disease.
The MIT researchers studied how these visualizations were being used to discuss the pandemic. They found that some citizen scientists were using the underlying data to make visualizations of their own, challenging the findings of mainstream science.
“This was an unexpected discovery as, previously, citizen scientists were typically aligned with mainstream scientists. It took us a few years to figure out how to study this phenomenon more deeply,” Satyanarayan says.
Most research into data visualization studies how charts communicate data. Instead, the researchers wanted to explore visualizations from a social and linguistic perspective to assess the information they convey beyond the data.
Linguistic anthropologists have found that, while language allows people to communicate ideas, it also holds social meaning beyond the words people use. For instance, an accent or dialect can indicate that someone is part of a particular community.
By “pointing” to certain social meanings, identities, and characteristics, language serves what is known as a socio-indexical function.
“We wanted to see if things in the visual language of data communication might point to certain institutions, or the kinds of people in those institutions, that carry a meaning that could be unintended by the makers of the visualization,” Jones says.
To do this, the researchers conducted an initial, qualitative study of users on the social media platform Tumblr. During one-on-one interviews, the researchers showed users a variety of real visualizations from online sources, as well as modified visualizations where they removed the textual information, like titles and axes labels.
Stripping out the textual information was particularly important, since it mimics the way people often interact with online visualizations.
“Our engagement with social media is a few quick seconds. People aren’t taking the time to read the title of a chart or look at the data very carefully,” Satyanarayan says.
The interviews revealed that users made detailed inferences about the people or organizations who created the visualizations based on what they called “vibes,” design elements, like colors or the use of certain graphics. These inferences in turn impacted their trust in the data.
For instance, after seeing a chart with the flags of Georgia and Texas and a graph with two lines in red and black, but no text, one user said, “This kind of looks like something a Texas Republican (legislator) would put on Twitter or on their website, or as part of a campaign presentation.”
A quantitative approach
Building on this initial work, the researchers used the same methodology in three quantitative studies involving surveys sent to larger groups of people from a variety of backgrounds.
They found the same phenomenon: People make inferences about the social context of a visualization based on its design, which can lead to misunderstandings about, and mistrust in, the data it depicts.
For instance, users felt some visualizations were so neatly arranged they believed them to be advertisements, and therefore not trustworthy. In another example, one user dismissed a chart by a Pulitzer-prize winning designer because they felt the hand-drawn graphical style indicated it was made by “some female Instagram influencer who is just trying to look for attention.”
“If that is the first reaction someone has to a chart, it is going to massively impact the degree to which they trust it,” Satyanarayan says.
Moreover, when the researchers reintroduced text in the visualizations from which it had been removed, users still made these social inferences.
Typically, in data visualization, the solution to such a problem would be to create clearer charts or educate people about data literacy. But this research points to a completely different kind of data literacy, Jones says.
“It is not erroneous for people to be drawing these inferences. It requires a lot of cultural knowledge about where visualizations come from, how they are made, and how they circulate. Drawing these inferences is a feature, not a bug, of the way we use signs,” he says.
From these results, they created a classification framework to organize the social inferences users made and the design elements that contributed to them. They hope the typology serves as a tool designers can use to develop more effective visualizations, as well as a starting point for additional studies.
Moving forward, the researchers want to continue exploring the role of data visualizations as social artifacts, perhaps by drilling down on each design feature they identified in the typology. They also want to expand the scope of their study to include visualizations in research papers and scientific journals.
“Part of the value of this work is a methodological contribution to render a set of phenomena amenable to experimental study. But this work is also important because it showcases an interdisciplinary cross-pollination that is powerful and unique to MIT,” Jones says.
This work was supported, in part, by MIT METEOR and PFPFEE fellowships, an Amar G. Bose Fellowship, an Alfred P. Sloan Fellowship, and the National Science Foundation.
The student becomes the teacherTitus Roesler was ready to drop his class in signal processing. Now, he hopes to become an expert in the field.Coming from a small high school in rural South Dakota that didn’t offer advanced placement (AP) classes, Titus Roesler ’25 didn’t have the easiest start at MIT. But when his efforts to catch up academically to his peers led to a job as a teaching assistant, it changed everything.
Roesler, who graduated last spring with a bachelor’s degree in electrical engineering and is now working on a master’s, has built a reputation for himself as a student-teacher at MIT. Since discovering his affinity for teaching and mentoring, he’s been a teaching assistant for four different classes and designed two seminars from scratch.
Through teaching, Roesler has not only helped other students, but also improved his own grasp of complex subjects. That includes signal processing, which involves manipulating signals, such as radio waves, to make them more useful for applications like wireless communications. He has become fascinated by the topic and hopes to continue working in the field.
Roesler lights up when talking about teaching, but he didn’t always think it was in the cards.
“I don't know that anyone who knew me pre-MIT would believe that I do things like give recitations to crowded rooms, because I think everyone thought, ‘Titus is that quiet kid, he never talked at all.’”
Learning through teaching
Growing up in Marion, South Dakota, a town with a population around 800, Roesler didn’t have MIT on his radar, but he knew he liked math. His high school capstone project involved helping his classmates on the math section of the ACT, and he tutored a few of his classmates. His teacher let him teach trigonometry one day, and he toured local colleges with the plan of becoming a high school math teacher.
But that changed after he self-studied calculus through MIT’s OpenCourseWare offerings and set his sights on the Institute.
Roesler worked overtime during his first year at MIT to catch up with what his peers had learned back in high school. On his first physics exam, he answered only one question correctly — a multiple-choice question he had guessed on. But MIT’s Experimental Study Group (ESG) kept him afloat during his first year, and it quickly led to more opportunities.
When, in the spring of his first year, his multivariable calculus instructor asked him to stay after class one day, Roesler was sure he was in trouble. She actually wanted to see if he could TA for her next year.
“I was flattered because there was still a month left in the class. Plenty of time for me to fail,” Roesler jokes.
He loved the job. During a Friday night office hour session, he stayed for extra hours to help a student whom he saw a lot of himself in — someone who was also from a rural background and had also entered MIT without a strong mathematics background. He went on to become the student’s tutor. The position gave him the opportunity to be the teacher he’d always wanted to have.
As a TA, “I wasn't coming at things from the perspective of ‘Everyone already knows A, B, C’ before I explained. I would always try to start from the ground up and give my perspective on it,” Roesler says.
From his mentorship and teaching work, he received the Undergraduate Teaching Award from the Department of Electrical Engineering and Computer Science and the Outstanding Associate Advisor Award from the Office of the First Year. After joining ESG during his first year, Roesler stayed on as an associate advisor in the learning community for the next three years. His work earned him the Fiekowsky Award for Excellence in Teaching and the Fiekowsky Award for Community Service.
The right blend
Signal processing, the focus of his graduate work, “is where calculus, geometry, linear algebra, probability, statistics, algorithms, and numerical analysis all come into play on practical problems of real-world interest,” Roesler says. “For me, it’s the right blend of theory and application.”
Due to the field’s wide scope, Roesler notices potential applications for signal processing everywhere, and how different fields intersect within the discipline. “Everything comes together in just the right way,” he says.
He is especially interested in signal-processing problems such as source separation, which aims to recover a set of source signals from a set of mixed signals. During his senior year, he spent two semesters on a project where he wrote a Python program to separate harmonies in Bach chorales.
For his master’s degree, following a summer research internship at MIT Lincoln Laboratory, Roesler has stayed at the laboratory, this time venturing into high-frequency radio communications. He’s currently working on a research project that applies the theory of compressed sensing (which states that, under certain conditions, it is possible to reconstruct signals from very few measurements) to communications.
What fascinates Roesler are “something-from-nothing” problems.
“The kind of problems I’m interested in are underdetermined, inverse problems,” he says. For example, imagine trying to reconstruct a full image from only a handful of pixels. While on the surface this seems impossible, researchers have recovered quality images by applying the techniques of compressed sensing.
Running and serving
Roesler has also spent extensive time running, a sport he’s loved since fifth grade. In 2023, he raced a marathon in 2 hours and 46 minutes and went on to run the Boston Marathon in both 2024 and 2025. To prepare, he spent a lot of time reading up on the psychology of running, which he says was the first time he used the scientific method. Now, he just runs for fun and uses it as a way to focus and collect this thoughts.
He has also served on the executive team of the Undergraduate Mathematics Association, as a resident peer mentor at Baker House, and a tutor for two classes. At the PKG Center, he’s been a program lead and counselor for its pre-orientation program.
Roesler still gets excited about seeing the impact of his teaching. At the end of one semester teaching a tutorial, he took his class on a picnic. They surprised him with a card and a bag of goodies.
Recalling the moment, he says: “I thought, How does it get better? It was wonderful.”
Neural activity helps circuit connections mature into optimal signal transmittersScientists identified how circuit connections in fruit flies tune to the right size and degree of signal transmission capability. Understanding this could lead to a way to tweak abnormal signal transmission in certain disorders.Nervous system functions, from motion to perception to cognition, depend on the active zones of neural circuit connections, or “synapses,” sending out the right amount of their chemical signals at the right times. By tracking how synaptic active zones form and mature in fruit flies, researchers at The Picower Institute for Learning and Memory at MIT have revealed a fundamental model for how neural activity during development builds properly working connections.
Understanding how that happens is important, not only for advancing fundamental knowledge about how nervous systems develop, but also because many disorders such as epilepsy, autism, or intellectual disability can arise from aberrations of synaptic transmission, says senior author Troy Littleton, the Menicon Professor in The Picower Institute and MIT’s Department of Biology. The new findings, funded in part by a 2021 grant from the National Institutes of Health, provide insights into how active zones develop the ability to send neurotransmitters across synapses to their circuit targets. It’s not instant or predestined, the study shows. It can take days to fully mature, and that is regulated by neural activity.
If scientists can fully understand the process, Littleton says, then they can develop molecular strategies to intervene to tweak synaptic transmission when it’s happening too much or too little in disease.
“We’d like to have the levers to push to make synapses stronger or weaker, that’s for sure,” Littleton says. “And so knowing the full range of levers we can tug on to potentially change output would be exciting.”
Littleton Lab research scientist Yuliya Akbergenova led the study published Oct. 14 in the Journal of Neuroscience.
How newborn synapses grow up
In the study, the researchers examined neurons that send the neurotransmitter glutamate across synapses to control muscles in the fly larvae. To study how the active zones in the animals matured, the scientists needed to keep track of their age. That hasn’t been possible before, but Akbergenova overcame the barrier by cleverly engineering the fluorescent protein mMaple, which changes its glow from green to red when zapped with 15 seconds of ultraviolet light, into a component of the glutamate receptors on the receiving side of the synapse. Then, whenever she wanted, she could shine light and all the synapses already formed before that time would glow red, and any new ones that formed subsequently would glow green.
With the ability to track each active zone’s birthday, the authors could then document how active zones developed their ability to increase output over the course of days after birth. The researchers actually watched as synapses were built over many hours by tagging each of eight kinds of proteins that make up an active zone. At first, the active zones couldn’t transmit anything. Then, as some essential early proteins accumulated, they could send out glutamate spontaneously, but not if evoked by electrical stimulation of their host neuron (simulating how that neuron might be signaled naturally in a circuit). Only after several more proteins arrived did active zones possess the mature structure for calcium ions to trigger the fusion of glutamate vesicles to the cell membrane for evoked release across the synapse.
Activity matters
Of course, construction does not go on forever. At some point, the fly larva stops building one synapse and then builds new ones further down the line as the neuronal axon expands to keep up with growing muscles. The researchers wondered whether neural activity had a role in driving that process of finishing up one active zone and moving on to build the next.
To find out, they employed two different interventions to block active zones from being able to release glutamate, thereby preventing synaptic activity. Notably, one of the methods they chose was blocking the action of a protein called Synaptotagmin 1. That’s important because mutations that disrupt the protein in humans are associated with severe intellectual disability and autism. Moreover, the researchers tailored the activity-blocking interventions to just one neuron in each larva because blocking activity in all their neurons would have proved lethal.
In neurons where the researchers blocked activity, they observed two consequences: the neurons stopped building new active zones and instead kept making existing active zones larger and larger. It was as if the neuron could tell the active zone wasn’t releasing glutamate and tried to make it work by giving it more protein material to work with. That effort came at the expense of starting construction on new active zones.
“I think that what it’s trying to do is compensate for the loss of activity,” Littleton says.
Testing indicated that the enlarged active zones the neurons built in hopes of restarting activity were functional (or would have been if the researchers weren’t artificially blocking them). This suggested that the way the neuron sensed that glutamate wasn’t being released was therefore likely to be a feedback signal from the muscle side of the synapse. To test that, the scientists knocked out a glutamate receptor component in the muscle, and when they did, they found that the neurons no longer made their active zones larger.
Littleton says the lab is already looking into the new questions the discoveries raise. In particular: What are the molecular pathways that initiate synapse formation in the first place, and what are the signals that tell an active zone it has finished growing? Finding those answers will bring researchers closer to understanding how to intervene when synaptic active zones aren’t developing properly.
In addition to Littleton and Akbergenova, the paper’s other authors are Jessica Matthias and Sofya Makeyeva.
In addition to the National Institutes of Health, The Freedom Together Foundation provided funding for the study.
MIT Maritime Consortium releases “Nuclear Ship Safety Handbook”First-of-its-kind handbook serves as a guide for design safety for civilian nuclear ships.Commercial shipping accounts for 3 percent of all greenhouse gas emissions globally. As the sector sets climate goals and chases a carbon-free future, nuclear power — long used as a source for military vessels — presents an enticing solution. To date, however, there has been no clear, unified public document available to guide design safety for certain components of civilian nuclear ships. A new “Nuclear Ship Safety Handbook” by the MIT Maritime Consortium aims to change that and set the standard for safe maritime nuclear propulsion.
“This handbook is a critical tool in efforts to support the adoption of nuclear in the maritime industry,” explains Themis Sapsis, the William I. Koch Professor of Mechanical Engineering at MIT, director of the MIT Center for Ocean Engineering, and co-director of the MIT Maritime Consortium. “The goal is to provide a strong basis for initial safety on key areas that require nuclear and maritime regulatory research and development in the coming years to prepare for nuclear propulsion in the maritime industry.”
Using research data and standards, combined with operational experiences during civilian maritime nuclear operations, the handbook provides unique insights into potential issues and resolutions in the design efficacy of maritime nuclear operations, a topic of growing importance on the national and international stage.
“Right now, the nuclear-maritime policies that exist are outdated and often tied only to specific technologies, like pressurized water reactors,” says Jose Izurieta, a graduate student in the Department of Mechanical Engineering (MechE) Naval Construction and Engineering (2N) Program, and one of the handbook authors. “With the recent U.K.-U.S. Technology Prosperity Deal now including civil maritime nuclear applications, I hope the handbook can serve as a foundation for creating a clear, modern regulatory framework for nuclear-powered commercial ships.”
The recent memorandum of understanding signed by the U.S. and U.K calls for the exploration of “novel applications of advanced nuclear energy, including civil maritime applications,” and for the parties to play “a leading role informing the establishment of international standards, potential establishment of a maritime shipping corridor between the Participants’ territories, and strengthening energy resilience for the Participants’ defense facilities.”
“The U.S.-U.K. nuclear shipping corridor offers a great opportunity to collaborate with legislators on establishing the critical framework that will enable the United States to invest on nuclear-powered merchant vessels — an achievement that will reestablish America in the shipbuilding space,” says Fotini Christia, the Ford International Professor of the Social Sciences, director of the Institute for Data, Systems, and Society (IDSS), and co-director of the MIT Maritime Consortium.
“With over 30 nations now building or planning their first reactors, nuclear energy’s global acceptance is unprecedented — and that momentum is key to aligning safety rules across borders for nuclear-powered ships and the respective ports,” says Koroush Shirvan, the Atlantic Richfield Career Development Professor in Energy Studies at MIT and director of the Reactor Technology Course for Utility Executives.
The handbook, which is divided into chapters in areas involving the overlapping nuclear and maritime safety design decisions that will be encountered by engineers, is careful to balance technical and practical guidance with policy considerations.
Commander Christopher MacLean, MIT associate professor of the practice in mechanical engineering, naval construction, and engineering, says the handbook will significantly benefit the entire maritime community, specifically naval architects and marine engineers, by providing standardized guidelines for design and operation specific to nuclear powered commercial vessels.
“This will assist in enhancing safety protocols, improve risk assessments, and ensure consistent compliance with international regulations,” MacLean says. “This will also help foster collaboration amongst engineers and regulators. Overall, this will further strengthen the reliability, sustainability, and public trust in nuclear-powered maritime systems.”
Anthony Valiaveedu, the handbook’s lead author, and co-author Nat Edmonds, are both students in the MIT Master’s Program in Technology and Policy (TPP) within the IDSS. The pair are also co-authors of a paper published in Science Policy Review earlier this year that offered structured advice on the development of nuclear regulatory policies.
“It is important for safety and technology to go hand-in-hand,” Valiaveedu explains. “What we have done is provide a risk-informed process to begin these discussions for engineers and policymakers.”
“Ultimately, I hope this framework can be used to build strong bilateral agreements between nations that will allow nuclear propulsion to thrive,” says fellow co-author Izurieta.
Impact on industry
“Maritime designers needed a source of information to improve their ability to understand and design the reactor primary components, and development of the 'Nuclear Ship Safety Handbook' was a good step to bridge this knowledge gap,” says Christopher J. Wiernicki, American Bureau of Shipping (ABS) chair and CEO. “For this reason, it is an important document for the industry.”
The ABS, which is the American classification society for the maritime industry, develops criteria and provides safety certification for all ocean-going vessels. ABS is among the founding members of the MIT Maritime Consortium. Capital Clean Energy Carriers Corp., HD Korea Shipbuilding and Offshore Engineering, and Delos Navigation Ltd. are also consortium founding members. Innovation members are Foresight-Group, Navios Maritime Partners L.P., Singapore Maritime Institute, and Dorian LPG.
“As we consider a net-zero framework for the shipping industry, nuclear propulsion represents a potential solution. Careful investigation remains the priority, with safety and regulatory standards at the forefront,” says Jerry Kalogiratos, CEO of Capital Clean Energy Carriers Corp. “As first movers, we are exploring all options. This handbook lays the technical foundation for the development of nuclear-powered commercial vessels.”
Sangmin Park, senior vice president at HD Korea Shipbuilding and Offshore Engineering, says “The 'Nuclear Ship Safety Handbook' marks a groundbreaking milestone that bridges shipbuilding excellence and nuclear safety. It drives global collaboration between industry and academia, and paves the way for the safe advancement of the nuclear maritime era.”
Maritime at MIT
MIT has been a leading center of ship research and design for over a century, with work at the Institute today representing significant advancements in fluid mechanics and hydrodynamics, acoustics, offshore mechanics, marine robotics and sensors, and ocean sensing and forecasting. Maritime Consortium projects, including the handbook, reflect national priorities aimed at revitalizing the U.S. shipbuilding and commercial maritime industries.
The MIT Maritime Consortium, which launched in 2024, brings together MIT and maritime industry leaders to explore data-powered strategies to reduce harmful emissions, optimize vessel operations, and support economic priorities.
“One of our most important efforts is the development of technologies, policies, and regulations to make nuclear propulsion for commercial ships a reality,” says Sapsis. “Over the last year, we have put together an interdisciplinary team with faculty and students from across the Institute. One of the outcomes of this effort is this very detailed document providing detailed guidance on how such effort should be implemented safely.”
Handbook contributors come from multiple disciplines and MIT departments, labs, and research centers, including the Center for Ocean Engineering, IDSS, MechE’s Course 2N Program, the MIT Technology and Policy Program, and the Department of Nuclear Science and Engineering.
MIT faculty members and research advisors on the project include Sapsis; Christia; Shirvan; MacLean; Jacopo Buongiorno, the Battelle Energy Alliance Professor in Nuclear Science and Engineering, director, Center for Advanced Nuclear Energy Systems, and director of science and technology for the Nuclear Reactor Laboratory; and Captain Andrew Gillespy, professor of the practice and director of the Naval Construction and Engineering (2N) Program.
“Proving the viability of nuclear propulsion for civilian ships will entail getting the technologies, the economics and the regulations right,” says Buongiorno. “This handbook is a meaningful initial contribution to the development of a sound regulatory framework.”
“We were lucky to have a team of students and knowledgeable professors from so many fields,” says Edmonds. “Before even beginning the outline of the handbook, we did significant archival and history research to understand the existing regulations and overarching story of nuclear ships. Some of the most relevant documents we found were written before 1975, and many of them were stored in the bellows of the NS Savannah.”
The NS Savannah, which was built in the late 1950s as a demonstration project for the potential peacetime uses of nuclear energy, was the first nuclear-powered merchant ship. The Savannah was first launched on July 21, 1959, two years after the first nuclear-powered civilian vessel, the Soviet ice-breaker Lenin, and was retired in 1971.
Historical context for this project is important, because the reactor technologies envisioned for maritime propulsion today are quite different from the traditional pressurized water reactors used by the U.S. Navy. These new reactors are being developed not just in the maritime context, but also to power ports and data centers on land; they all use low-enriched uranium and are passively cooled. For the maritime industry, Sapsis says, “the technology is there, it’s safe, and it’s ready.”
“The Nuclear Ship Safety Handbook” is publicly available on the MIT Maritime Consortium website and from the MIT Libraries.
In a surprising discovery, scientists find tiny loops in the genomes of dividing cellsEnabled by a new high-resolution mapping technique, the findings overturn a long-held belief that the genome loses its 3D structure when cells divide.Before cells can divide, they first need to replicate all of their chromosomes, so that each of the daughter cells can receive a full set of genetic material. Until now, scientists had believed that as division occurs, the genome loses the distinctive 3D internal structure that it typically forms.
Once division is complete, it was thought, the genome gradually regains that complex, globular structure, which plays an essential role in controlling which genes are turned on in a given cell.
However, a new study from MIT shows that in fact, this picture is not fully accurate. Using a higher-resolution genome mapping technique, the research team discovered that small 3D loops connecting regulatory elements and genes persist in the genome during cell division, or mitosis.
“This study really helps to clarify how we should think about mitosis. In the past, mitosis was thought of as a blank slate, with no transcription and no structure related to gene activity. And we now know that that’s not quite the case,” says Anders Sejr Hansen, an associate professor of biological engineering at MIT. “What we see is that there’s always structure. It never goes away.”
The researchers also discovered that these regulatory loops appear to strengthen when chromosomes become more compact in preparation for cell division. This compaction brings genetic regulatory elements closer together and encourages them to stick together. This may help cells “remember” interactions present in one cell cycle and carry it to the next one.
“The findings help to bridge the structure of the genome to its function in managing how genes are turned on and off, which has been an outstanding challenge in the field for decades,” says Viraat Goel PhD ’25, the lead author of the study.
Hansen and Edward Banigan, a research scientist in MIT’s Institute for Medical Engineering and Science, are the senior authors of the paper, which appears today in Nature Structural and Molecular Biology. Leonid Mirny, a professor in MIT’s Institute for Medical Engineering and Science and the Department of Physics, and Gerd Blobel, a professor at the Perelman School of Medicine at the University of Pennsylvania, are also authors of the study.
A surprising finding
Over the past 20 years, scientists have discovered that inside the cell nucleus, DNA organizes itself into 3D loops. While many loops enable interactions between genes and regulatory regions that may be millions of base pairs away from each other, others are formed during cell division to compact chromosomes. Much of the mapping of these 3D structures has been done using a technique called Hi-C, originally developed by a team that included MIT researchers and was led by Job Dekker at the University of Massachusetts Chan Medical School. To perform Hi-C, researchers use enzymes to chop the genome into many small pieces and biochemically link pieces that are near each other in 3D space within the cell’s nucleus. They then determine the identities of the interacting pieces by sequencing them.
However, that technique doesn’t have high enough resolution to pick out all specific interactions between genes and regulatory elements such as enhancers. Enhancers are short sequences of DNA that can help to activate the transcription of a gene by binding to the gene’s promoter — the site where transcription begins.
In 2023, Hansen and others developed a new technique that allows them to analyze 3D genome structures with 100 to 1,000 times greater resolution than was previously possible. This technique, known as Region-Capture Micro-C (RC-MC), uses a different enzyme that cuts the genome into small fragments of similar size. It also focuses on a smaller segment of the genome, allowing for high-resolution 3-D mapping of a targeted genome region.
Using this technique, the researchers were able to identify a new kind of genome structure that hadn’t been seen before, which they called “microcompartments.” These are tiny highly connected loops that form when enhancers and promoters located near each other stick together.
In that paper, experiments revealed that these loops were not formed by the same mechanisms that form other genome structures, but the researchers were unable to determine exactly how they do form. In hopes of answering that question, the team set out to study cells as they undergo cell division. During mitosis, chromosomes become much more compact, so that they can be duplicated, sorted, and divvied up between two daughter cells. As this happens, larger genome structures called A/B compartments and topologically associating domains (TADs) disappear completely.
The researchers believed that the microcompartments they had discovered would also disappear during mitosis. By tracking cells through the entire cell division process, they hoped to learn how the microcompartments appear after mitosis is completed.
“During mitosis, it has been thought that almost all gene transcription is shut off. And before our paper, it was also thought that all 3D structure related to gene regulation was lost and replaced by compaction. It’s a complete reset every cell cycle,” Hansen says.
However, to their surprise, the researchers found that microcompartments could still be seen during mitosis, and in fact they become more prominent as the cell goes through cell division.
“We went into this study thinking, well, the one thing we know for sure is that there’s no regulatory structure in mitosis, and then we accidentally found structure in mitosis,” Hansen says.
Using their technique, the researchers also confirmed that larger structures such as A/B compartments and TADs do disappear during mitosis, as had been seen before.
“This study leverages the unprecedented genomic resolution of the RC-MC assay to reveal new and surprising aspects of mitotic chromatin organization, which we have overlooked in the past using traditional 3C-based assays. The authors reveal that, contrary to the well-described dramatic loss of TADs and compartmentalization during mitosis, fine-scale “microcompartments” — nested interactions between active regulatory elements — are maintained or even transiently strengthened,” says Effie Apostolou, an associate professor of molecular biology in medicine at Weill Cornell Medicine, who was not involved in the study.
A spike in transcription
The findings may offer an explanation for a spike in gene transcription that usually occurs near the end of mitosis, the researchers say. Since the 1960s, it had been thought that transcription ceased completely during mitosis, but in 2016 and 2017, a few studies showed that cells undergo a brief spike of transcription, which is quickly suppressed until the cell finishes dividing.
In their new study, the MIT team found that during mitosis, microcompartments are more likely to be found near the genes that spike during cell division. They also discovered that these loops appear to form as a result of the genome compaction that occurs during mitosis. This compaction brings enhancers and promoters closer together, allowing them to stick together to form microcompartments.
Once formed, the loops that constitute microcompartments may activate gene transcription somewhat by accident, which is then shut off by the cell. When the cell finishes dividing, entering a state known as G1, many of these small loops become weaker or disappear.
“It almost seems like this transcriptional spiking in mitosis is an undesirable accident that arises from generating a uniquely favorable environment for microcompartments to form during mitosis,” Hansen says. “Then, the cell quickly prunes and filters many of those loops out when it enters G1.”
Because chromosome compaction can also be influenced by a cell’s size and shape, the researchers are now exploring how variations in those features affect the structure of the genome and in turn, gene regulation.
“We are thinking about some natural biological settings where cells change shape and size, and whether we can perhaps explain some 3D genome changes that previously lack an explanation,” Hansen says. “Another key question is how does the cell then pick what are the microcompartments to keep and what are the microcompartments to remove when you enter G1, to ensure fidelity of gene expression?”
The research was funded in part by the National Institutes of Health, a National Science Foundation CAREER Award, the Gene Regulation Observatory of the Broad Institute, a Pew-Steward Scholar Award for Cancer Research, the Mathers Foundation, the MIT Westaway Fund, the Bridge Project of the Koch Institute and Dana-Farber/Harvard Cancer Center, and the Koch Institute Support (core) Grant from the National Cancer Institute.
Method teaches generative AI models to locate personalized objectsAfter being trained with this technique, vision-language models can better identify a unique item in a new scene.Say a person takes their French Bulldog, Bowser, to the dog park. Identifying Bowser as he plays among the other canines is easy for the dog-owner to do while onsite.
But if someone wants to use a generative AI model like GPT-5 to monitor their pet while they are at work, the model could fail at this basic task. Vision-language models like GPT-5 often excel at recognizing general objects, like a dog, but they perform poorly at locating personalized objects, like Bowser the French Bulldog.
To address this shortcoming, researchers from MIT, the MIT-IBM Watson AI Lab, the Weizmann Institute of Science, and elsewhere have introduced a new training method that teaches vision-language models to localize personalized objects in a scene.
Their method uses carefully prepared video-tracking data in which the same object is tracked across multiple frames. They designed the dataset so the model must focus on contextual clues to identify the personalized object, rather than relying on knowledge it previously memorized.
When given a few example images showing a personalized object, like someone’s pet, the retrained model is better able to identify the location of that same pet in a new image.
Models retrained with their method outperformed state-of-the-art systems at this task. Importantly, their technique leaves the rest of the model’s general abilities intact.
This new approach could help future AI systems track specific objects across time, like a child’s backpack, or localize objects of interest, such as a species of animal in ecological monitoring. It could also aid in the development of AI-driven assistive technologies that help visually impaired users find certain items in a room.
“Ultimately, we want these models to be able to learn from context, just like humans do. If a model can do this well, rather than retraining it for each new task, we could just provide a few examples and it would infer how to perform the task from that context. This is a very powerful ability,” says Jehanzeb Mirza, an MIT postdoc and senior author of a paper on this technique.
Mirza is joined on the paper by co-lead authors Sivan Doveh, a postdoc at Stanford University who was a graduate student at Weizmann Institute of Science when this research was conducted; and Nimrod Shabtay, a researcher at IBM Research; James Glass, a senior research scientist and the head of the Spoken Language Systems Group in the MIT Computer Science and Artificial Intelligence Laboratory (CSAIL); and others. The work will be presented at the International Conference on Computer Vision.
An unexpected shortcoming
Researchers have found that large language models (LLMs) can excel at learning from context. If they feed an LLM a few examples of a task, like addition problems, it can learn to answer new addition problems based on the context that has been provided.
A vision-language model (VLM) is essentially an LLM with a visual component connected to it, so the MIT researchers thought it would inherit the LLM’s in-context learning capabilities. But this is not the case.
“The research community has not been able to find a black-and-white answer to this particular problem yet. The bottleneck could arise from the fact that some visual information is lost in the process of merging the two components together, but we just don’t know,” Mirza says.
The researchers set out to improve VLMs abilities to do in-context localization, which involves finding a specific object in a new image. They focused on the data used to retrain existing VLMs for a new task, a process called fine-tuning.
Typical fine-tuning data are gathered from random sources and depict collections of everyday objects. One image might contain cars parked on a street, while another includes a bouquet of flowers.
“There is no real coherence in these data, so the model never learns to recognize the same object in multiple images,” he says.
To fix this problem, the researchers developed a new dataset by curating samples from existing video-tracking data. These data are video clips showing the same object moving through a scene, like a tiger walking across a grassland.
They cut frames from these videos and structured the dataset so each input would consist of multiple images showing the same object in different contexts, with example questions and answers about its location.
“By using multiple images of the same object in different contexts, we encourage the model to consistently localize that object of interest by focusing on the context,” Mirza explains.
Forcing the focus
But the researchers found that VLMs tend to cheat. Instead of answering based on context clues, they will identify the object using knowledge gained during pretraining.
For instance, since the model already learned that an image of a tiger and the label “tiger” are correlated, it could identify the tiger crossing the grassland based on this pretrained knowledge, instead of inferring from context.
To solve this problem, the researchers used pseudo-names rather than actual object category names in the dataset. In this case, they changed the name of the tiger to “Charlie.”
“It took us a while to figure out how to prevent the model from cheating. But we changed the game for the model. The model does not know that ‘Charlie’ can be a tiger, so it is forced to look at the context,” he says.
The researchers also faced challenges in finding the best way to prepare the data. If the frames are too close together, the background would not change enough to provide data diversity.
In the end, finetuning VLMs with this new dataset improved accuracy at personalized localization by about 12 percent on average. When they included the dataset with pseudo-names, the performance gains reached 21 percent.
As model size increases, their technique leads to greater performance gains.
In the future, the researchers want to study possible reasons VLMs don’t inherit in-context learning capabilities from their base LLMs. In addition, they plan to explore additional mechanisms to improve the performance of a VLM without the need to retrain it with new data.
“This work reframes few-shot personalized object localization — adapting on the fly to the same object across new scenes — as an instruction-tuning problem and uses video-tracking sequences to teach VLMs to localize based on visual context rather than class priors. It also introduces the first benchmark for this setting with solid gains across open and proprietary VLMs. Given the immense significance of quick, instance-specific grounding — often without finetuning — for users of real-world workflows (such as robotics, augmented reality assistants, creative tools, etc.), the practical, data-centric recipe offered by this work can help enhance the widespread adoption of vision-language foundation models,” says Saurav Jha, a postdoc at the Mila-Quebec Artificial Intelligence Institute, who was not involved with this work.
Additional co-authors are Wei Lin, a research associate at Johannes Kepler University; Eli Schwartz, a research scientist at IBM Research; Hilde Kuehne, professor of computer science at Tuebingen AI Center and an affiliated professor at the MIT-IBM Watson AI Lab; Raja Giryes, an associate professor at Tel Aviv University; Rogerio Feris, a principal scientist and manager at the MIT-IBM Watson AI Lab; Leonid Karlinsky, a principal research scientist at IBM Research; Assaf Arbelle, a senior research scientist at IBM Research; and Shimon Ullman, the Samy and Ruth Cohn Professor of Computer Science at the Weizmann Institute of Science.
This research was funded, in part, by the MIT-IBM Watson AI Lab.
Darcy McRose and Mehtaab Sawhney ’20, PhD ’24 named 2025 Packard Fellows for Science and EngineeringMcRose, an environmental microbiologist, is recognized for researching the ecological roles of antibiotics in shaping ecosystems, agriculture, and health.The David and Lucile Packard Foundation has announced that two MIT affiliates have been named 2025 Packard Fellows for Science and Engineering. Darcy McRose, the Thomas D. and Virginia W. Cabot Career Development Professor in the MIT Department of Civil and Environmental Engineering, has been honored, along with Mehtaab Sawhney ’20, PhD ’24, a graduate of the Department of Mathematics who is now at Columbia University.
The honorees are among 20 junior faculty named among the nation’s most innovative early-career scientists and engineers. Each Packard Fellow receives an unrestricted research grant of $875,000 over five years to support their pursuit of pioneering research and bold new ideas.
“I’m incredibly grateful and honored to be awarded a Packard Fellowship,” says McRose. “It will allow us to continue our work exploring how small molecules control microbial communities in soils and on plant roots, with much-appreciated flexibility to follow our imagination wherever it leads us.”
McRose and her lab study secondary metabolites — small organic molecules that microbes and plants release into soils. Often known as antibiotics, these compounds do far more than fight infections; they can help unlock soil nutrients, shape microbial communities around plant roots, and influence soil fertility.
“Antibiotics made by soil microorganisms are widely used in medicine, but we know surprisingly little about what they do in nature,” explains McRose. “Just as healthy microbiomes support human health, plant microbiomes support plant health, and secondary metabolites can help to regulate the microbial community, suppressing pathogens and promoting beneficial microbes.”
Her lab integrates techniques from genetics, chemistry, and geosciences to investigate how these molecules shape interactions between microbes and plants in soil — one of Earth’s most complex and least-understood environments. By using secondary metabolites as experimental tools, McRose aims to uncover the molecular mechanisms that govern processes like soil fertility and nutrient cycling that are foundational to sustainable agriculture and ecosystem health.
Studying antibiotics in the environments where they evolved could also yield new strategies for combating soil-borne pathogens and improving crop resilience. “Soil is a true scientific frontier,” McRose says. “Studying these environments has the potential to reveal fascinating, fundamental insights into microbial life — many of which we can’t even imagine yet.”
A native of California, McRose earned her bachelor’s and master’s degrees from Stanford University, followed by a PhD in geosciences from Princeton University. Her graduate thesis focused on how bacteria acquire trace metals from the environment. Her postdoctoral research on secondary metabolites at Caltech was supported by multiple fellowships, including the Simons Foundation Marine Microbial Ecology Postdoctoral Fellowship, the L’Oréal USA For Women in Science Fellowship, and a Division Fellowship from Biology and Biological Engineering at Caltech.
McRose joined the MIT faculty in 2022. In 2025, she was named a Sloan Foundation Research Fellow in Earth System Science and awarded the Maseeh Excellence in Teaching Award.
Past Packard Fellows have gone on to earn the highest honors, including Nobel Prizes in chemistry and physics, the Fields Medal, Alan T. Waterman Awards, Breakthrough Prizes, Kavli Prizes, and elections to the National Academies of Science, Engineering, and Medicine. Each year, the foundation reviews 100 nominations for consideration from 50 invited institutions. The Packard Fellowships Advisory Panel, a group of 12 internationally recognized scientists and engineers, evaluates the nominations and recommends 20 fellows for approval by the Packard Foundation Board of Trustees.
MIT engineers solve the sticky-cell problem in bioreactors and other industriesTheir system uses electrochemically generated bubbles to detach cells from surfaces, which could accelerate the growth of carbon-absorbing algae and lifesaving cell therapies.To help mitigate climate change, companies are using bioreactors to grow algae and other microorganisms that are hundreds of times more efficient at absorbing CO2 than trees. Meanwhile, in the pharmaceutical industry, cell culture is used to manufacture biologic drugs and other advanced treatments, including lifesaving gene and cell therapies.
Both processes are hampered by cells’ tendency to stick to surfaces, which leads to a huge amount of waste and downtime for cleaning. A similar problem slows down biofuel production, interferes with biosensors and implants, and makes the food and beverage industry less efficient.
Now, MIT researchers have developed an approach for detaching cells from surfaces on demand, using electrochemically generated bubbles. In an open-access paper published in Science Advances, the researchers demonstrated their approach in a lab prototype and showed it could work across a range of cells and surfaces without harming the cells.
“We wanted to develop a technology that could be high-throughput and plug-and-play, and that would allow cells to attach and detach on demand to improve the workflow in these industrial processes,” says Professor Kripa Varanasi, senior author of the study. “This is a fundamental issue with cells, and we’ve solved it with a process that can scale. It lends itself to many different applications.”
Joining Varanasi on the study are co-first authors Bert Vandereydt, a PhD student in mechanical engineering, and former postdoc Baptiste Blanc.
Solving a sticky problem
The researchers began with a mission.
“We’ve been working on figuring out how we can efficiently capture CO2 across different sources and convert it into valuable products for various end markets,” Varanasi says. “That’s where this photobioreactor and cell detachment comes into the picture.”
Photobioreactors are used to grow carbon-absorbing algae cells by creating tightly controlled environments involving water and sunlight. They feature long, winding tubes with clear surfaces to let in the light algae need to grow. When algae stick to those surfaces, they block out the light, requiring cleaning.
“You have to shut down and clean up the entire reactor as frequently as every two weeks,” Varanasi says. “It’s a huge operational challenge.”
The researchers realized other industries have similar problem due to many cells’ natural adhesion, or stickiness. Each industry has its own solution for cell adhesion depending on how important it is that the cells survive. Some people scrape the surfaces clean, while others use special coatings that are toxic to cells.
In the pharmaceutical and biotech industries, cell detachment is typically carried out using enzymes. However, this method poses several challenges — it can damage cell membranes, is time-consuming, and requires large amounts of consumables, resulting in millions of liters of biowaste.
To create a better solution, the researchers began by studying other efforts to clear surfaces with bubbles, which mainly involved spraying bubbles onto surfaces and had been largely ineffective.
“We realized we needed the bubbles to form on the surfaces where we don’t want these cells to stick, so when the bubbles detach it creates a local fluid flow that creates shear stress at the interface and removes the cells,” Varanasi explains.
Electric currents generate bubbles by splitting water into hydrogen and oxygen. But previous attempts at using electricity to detach cells were hampered because the cell culture mediums contain sodium chloride, which turns into bleach when combined with an electric current. The bleach damages the cells, making it impractical for many applications.
“The culprit is the anode — that’s where the sodium chloride turns to bleach,” Vandereydt explained. “We figured if we could separate that electrode from the rest of the system, we could prevent bleach from being generated.”
To make a better system, the researchers built a 3-square-inch glass surface and deposited a gold electrode on top of it. The layer of gold is so thin it doesn’t block out light. To keep the other electrode separate, the researchers integrated a special membrane that only allows protons to pass through. The set up allowed the researchers to send a current through without generating bleach.
To test their setup, they allowed algae cells from a concentrated solution to stick to the surfaces. When they applied a voltage, the bubbles separated the cells from the surfaces without harming them.
The researchers also studied the interaction between the bubbles and cells, finding the higher the current density, the more bubbles were created and the more algae was removed. They developed a model for understanding how much current would be needed to remove algae in different settings and matched it with results from experiments involving algae as well as cells from ovarian cancer and bones.
“Mammalian cells are orders of magnitude more sensitive than algae cells, but even with those cells, we were able to detach them with no impact to the viability of the cell,” Vandereydt says.
Getting to scale
The researchers say their system could represent a breakthrough in applications where bleach or other chemicals would harm cells. That includes pharmaceutical and food production.
“If we can keep these systems running without fouling and other problems, then we can make them much more economical,” Varanasi says.
For cell culture plates used in the pharmaceutical industry, the team envisions their system comprising an electrode that could be robotically moved from one culture plate to the next, to detach cells as they’re grown. It could also be coiled around algae harvesting systems.
“This has general applicability because it doesn’t rely on any specific biological or chemical treatments, but on a physical force that is system-agnostic,” Varanasi says. “It’s also highly scalable to a lot of different processes, including particle removal.”
Varanasi cautions there is much work to be done to scale up the system. But he hopes it can one day make algae and other cell harvesting more efficient.
“The burning problem of our time is to somehow capture CO2 in a way that’s economically feasible,” Varanasi says. “These photobioreactors could be used for that, but we have to overcome the cell adhesion problem.”
The work was supported, in part, by Eni S.p.A through the MIT Energy Initiative, the Belgian American Educational Foundation Fellowship, and the Maria Zambrano Fellowship.
Why some quantum materials stall while others scaleIn a new study, MIT researchers evaluated quantum materials’ potential for scalable commercial success — and identified promising candidates.People tend to think of quantum materials — whose properties arise from quantum mechanical effects — as exotic curiosities. But some quantum materials have become a ubiquitous part of our computer hard drives, TV screens, and medical devices. Still, the vast majority of quantum materials never accomplish much outside of the lab.
What makes certain quantum materials commercial successes and others commercially irrelevant? If researchers knew, they could direct their efforts toward more promising materials — a big deal since they may spend years studying a single material.
Now, MIT researchers have developed a system for evaluating the scale-up potential of quantum materials. Their framework combines a material’s quantum behavior with its cost, supply chain resilience, environmental footprint, and other factors. The researchers used their framework to evaluate over 16,000 materials, finding that the materials with the highest quantum fluctuation in the centers of their electrons also tend to be more expensive and environmentally damaging. The researchers also identified a set of materials that achieve a balance between quantum functionality and sustainability for further study.
The team hopes their approach will help guide the development of more commercially viable quantum materials that could be used for next generation microelectronics, energy harvesting applications, medical diagnostics, and more.
“People studying quantum materials are very focused on their properties and quantum mechanics,” says Mingda Li, associate professor of nuclear science and engineering and the senior author of the work. “For some reason, they have a natural resistance during fundamental materials research to thinking about the costs and other factors. Some told me they think those factors are too ‘soft’ or not related to science. But I think within 10 years, people will routinely be thinking about cost and environmental impact at every stage of development.”
The paper appears in Materials Today. Joining Li on the paper are co-first authors and PhD students Artittaya Boonkird, Mouyang Cheng, and Abhijatmedhi Chotrattanapituk, along with PhD students Denisse Cordova Carrizales and Ryotaro Okabe; former graduate research assistants Thanh Nguyen and Nathan Drucker; postdoc Manasi Mandal; Instructor Ellan Spero of the Department of Materials Science and Engineering (DMSE); Professor Christine Ortiz of the Department of DMSE; Professor Liang Fu of the Department of Physics; Professor Tomas Palacios of the Department of Electrical Engineering and Computer Science (EECS); Associate Professor Farnaz Niroui of EECS; Assistant Professor Jingjie Yeo of Cornell University; and PhD student Vsevolod Belosevich and Assostant Professor Qiong Ma of Boston College.
Materials with impact
Cheng and Boonkird say that materials science researchers often gravitate toward quantum materials with the most exotic quantum properties rather than the ones most likely to be used in products that change the world.
“Researchers don’t always think about the costs or environmental impacts of the materials they study,” Cheng says. “But those factors can make them impossible to do anything with.”
Li and his collaborators wanted to help researchers focus on quantum materials with more potential to be adopted by industry. For this study, they developed methods for evaluating factors like the materials’ price and environmental impact using their elements and common practices for mining and processing those elements. At the same time, they quantified the materials’ level of “quantumness” using an AI model created by the same group last year, based on a concept proposed by MIT professor of physics Liang Fu, termed quantum weight.
“For a long time, it’s been unclear how to quantify the quantumness of a material,” Fu says. “Quantum weight is very useful for this purpose. Basically, the higher the quantum weight of a material, the more quantum it is.”
The researchers focused on a class of quantum materials with exotic electronic properties known as topological materials, eventually assigning over 16,000 materials scores on environmental impact, price, import resilience, and more.
For the first time, the researchers found a strong correlation between the material’s quantum weight and how expensive and environmentally damaging it is.
“That’s useful information because the industry really wants something very low-cost,” Spero says. “We know what we should be looking for: high quantum weight, low-cost materials. Very few materials being developed meet that criteria, and that likely explains why they don’t scale to industry.”
The researchers identified 200 environmentally sustainable materials and further refined the list down to 31 material candidates that achieved an optimal balance of quantum functionality and high-potential impact.
The researchers also found that several widely studied materials exhibit high environmental impact scores, indicating they will be hard to scale sustainably. “Considering the scalability of manufacturing and environmental availability and impact is critical to ensuring practical adoption of these materials in emerging technologies,” says Niroui.
Guiding research
Many of the topological materials evaluated in the paper have never been synthesized, which limited the accuracy of the study’s environmental and cost predictions. But the authors say the researchers are already working with companies to study some of the promising materials identified in the paper.
“We talked with people at semiconductor companies that said some of these materials were really interesting to them, and our chemist collaborators also identified some materials they find really interesting through this work,” Palacios says. “Now we want to experimentally study these cheaper topological materials to understand their performance better.”
“Solar cells have an efficiency limit of 34 percent, but many topological materials have a theoretical limit of 89 percent. Plus, you can harvest energy across all electromagnetic bands, including our body heat,” Fu says. “If we could reach those limits, you could easily charge your cell phone using body heat. These are performances that have been demonstrated in labs, but could never scale up. That’s the kind of thing we’re trying to push forward."
This work was supported, in part, by the National Science Foundation and the U.S. Department of Energy.