Transistors, the building blocks of modern electronics, are typically made of silicon. Because it’s a semiconductor, this material can control the flow of electricity in a circuit. But silicon has fundamental physical limits that restrict how compact and energy-efficient a transistor can be.

MIT researchers have now replaced silicon with a magnetic semiconductor, creating a magnetic transistor that could enable smaller, faster, and more energy-efficient circuits. The material’s magnetism strongly influences its electronic behavior, leading to more efficient control of the flow of electricity. 

The team used a novel magnetic material and an optimization process that reduces the material’s defects, which boosts the transistor’s performance.

The material’s unique magnetic properties also allow for transistors with built-in memory, which would simplify circuit design and unlock new applications for high-performance electronics.

“People have known about magnets for thousands of years, but there are very limited ways to incorporate magnetism into electronics. We have shown a new way to efficiently utilize magnetism that opens up a lot of possibilities for future applications and research,” says Chung-Tao Chou, an MIT graduate student in the departments of Electrical Engineering and Computer Science (EECS) and Physics, and co-lead author of a paper on this advance.

Chou is joined on the paper by co-lead author Eugene Park, a graduate student in the Department of Materials Science and Engineering (DMSE); Julian Klein, a DMSE research scientist; Josep Ingla-Aynes, a postdoc in the MIT Plasma Science and Fusion Center; Jagadeesh S. Moodera, a senior research scientist in the Department of Physics; and senior authors Frances Ross, TDK Professor in DMSE; and Luqiao Liu, an associate professor in EECS, and a member of the Research Laboratory of Electronics; as well as others at the University of Chemistry and Technology in Prague. The paper appears today in Physical Review Letters.

Overcoming the limits

In an electronic device, silicon semiconductor transistors act like tiny light switches that turn a circuit on and off, or amplify weak signals in a communication system. They do this using a small input voltage.

But a fundamental physical limit of silicon semiconductors prevents a transistor from operating below a certain voltage, which hinders its energy efficiency.

To make more efficient electronics, researchers have spent decades working toward magnetic transistors that utilize electron spin to control the flow of electricity. Electron spin is a fundamental property that enables electrons to behave like tiny magnets.

So far, scientists have mostly been limited to using certain magnetic materials. These lack the favorable electronic properties of semiconductors, constraining device performance.

“In this work, we combine magnetism and semiconductor physics to realize useful spintronic devices,” Liu says.

The researchers replace the silicon in the surface layer of a transistor with chromium sulfur bromide, a two-dimensional material that acts as a magnetic semiconductor.

Due to the material’s structure, researchers can switch between two magnetic states very cleanly. This makes it ideal for use in a transistor that smoothly switches between “on” and “off.”

“One of the biggest challenges we faced was finding the right material. We tried many other materials that didn’t work,” Chou says.

They discovered that changing these magnetic states modifies the material’s electronic properties, enabling low-energy operation. And unlike many other 2D materials, chromium sulfur bromide remains stable in air.

To make a transistor, the researchers pattern electrodes onto a silicon substrate, then carefully align and transfer the 2D material on top. They use tape to pick up a tiny piece of material, only a few tens of nanometers thick, and place it onto the substrate.

“A lot of researchers will use solvents or glue to do the transfer, but transistors require a very clean surface. We eliminate all those risks by simplifying this step,” Chou says.

Leveraging magnetism

This lack of contamination enables their device to outperform existing magnetic transistors. Most others can only create a weak magnetic effect, changing the flow of current by a few percent or less. Their new transistor can switch or amplify the electric current by a factor of 10.

They use an external magnetic field to change the magnetic state of the material, switching the transistor using significantly less energy than would usually be required.

The material also allows them to control the magnetic states with electric current. This is important because engineers cannot apply magnetic fields to individual transistors in an electronic device. They need to control each one electrically.

The material’s magnetic properties could also enable transistors with built-in memory, simplifying the design of logic or memory circuits.

A typical memory device has a magnetic cell to store information and a transistor to read it out. Their method can combine both into one magnetic transistor.

“Now, not only are transistors turning on and off, they are also remembering information. And because we can switch the transistor with greater magnitude, the signal is much stronger so we can read out the information faster, and in a much more reliable way,” Liu says.

Building on this demonstration, the researchers plan to further study the use of electrical current to control the device. They are also working to make their method scalable so they can fabricate arrays of transistors.

This research was supported, in part, by the Semiconductor Research Corporation, the U.S. Defense Advanced Research Projects Agency (DARPA), the U.S. National Science Foundation (NSF), the U.S. Department of Energy, the U.S. Army Research Office, and the Czech Ministry of Education, Youth, and Sports. The work was partially carried out at the MIT.nano facilities.

The blobfish, once considered the ugliest animal in the world, has since had quite the redemption arc. Years after it was first discovered, scientists realized that the deep-sea creature appeared so unnervingly blobby only because it went through an extreme change in pressure when it was brought up to the surface. In its natural environment, 4,000 feet underwater, the fish looks perfectly handsome.

Structural biologists, whose goal is to deduce a molecule’s structure and function within a cell, face the risk of making a similar mistake. If biomolecular complexes are extracted from the cell, better-quality images can be obtained, but the molecules may not look natural. On the other hand, studying molecules without disrupting their environment at all is technically challenging, like filming deep underwater.

A new method, called purification-free ribosome imaging from subcellular mixtures (cryoPRISM), offers an appealing compromise. Developed by graduate students Mira May and Gabriela López-Pérez in the Davis lab in the MIT Department of Biology and recently published in PNAS, the technique allows biologists to visualize molecular complexes without taking them too far out of their natural context.

CryoPRISM captures molecular structures in cells that have just been broken open. This comes as close to preserving the natural interactions between molecules as possible, short of the extremely resource-intensive in-cell structural imaging, according to associate professor of biology Joey Davis, the faculty lead of the study.

“We think that the cryoPRISM method is a sweet spot where we preserve much of the native cellular contacts, but still have the resolution that lets us actually see molecular details,” Davis says. “Even in the extremely well-trodden system of translation in E. coli, which people have worked on for over 50 years, we are still finding new states that had just escaped people’s attention.”

A negative control that was not so negative

The development of cryoPRISM, as many discoveries in science, resulted from an unexpected observation that Mira May, the co-first author of the study, made while working on a different project.

Like all living organisms, bacteria rely on a process called translation to manufacture the proteins that carry out essential functions within the cell, from copying DNA to digesting nutrients. A key machine involved in translation is the ribosome — a biomolecular complex that assembles proteins based on instructions encoded by another molecule called mRNA. To regulate its activity, cells employ additional proteins that can change the shape of the ribosome, thus guiding its function.

May sought to identify new players in ribosomal regulation using cryoEM, by rapidly freezing lots of purified molecules and collecting thousands of 2D images to reconstruct their 3D structures. May was trying to pull ribosomes out of cells to visualize them together with their regulators. For her experiments, she designed a negative control containing unpurified bacterial lysate — a mixture of everything spilled from burst cells.

May expected to get noisy, low-quality images from this sample. To her surprise, instead, she saw intact ribosomes together with their natural interacting partners.

In just a few days, this technique experimentally validated data that would have taken months to acquire using other approaches. 

“As I found more and more ribosomal states, this project became a method, not just a one-off finding,” May recalls.

Discovering new biology in a saturated field

Once May and her colleagues were confident that cryoPRISM could detect known ribosomal states, they began searching for ones that had previously escaped detection. 

“It’s not just that we can recapitulate things that have been previously observed, but we can actually also discover novel ribosomal biology,” May says. 

One of the novel states May identified has important implications for our understanding of the evolution of translation regulation.

During active translation, bacterial ribosomes are accompanied by a group of helper proteins called elongation factors. These factors bring in the materials for protein synthesis, like tRNAs and amino acids.

When cells encounter unfavorable conditions, such as colder temperatures, they reduce translation, which means that many ribosomes are out of work. These idle, hibernating ribosomes stop decoding mRNA, and the interface where they usually interact with helper molecules gets blocked by a hibernation factor called RaiA. This protein helps idle ribosomes avoid reactivation, like a sleeping mask that prevents a person from being woken up by light.

May observed the idle ribosomal state in her data, which on its own did not surprise her – this state had been described before. What surprised her was that some inactive ribosomes were interacting not only with RaiA, but also with an elongation factor called EF-G, which in bacteria was previously believed to only interact with active ribosomes.

A similar phenomenon has been seen before in more complex organisms, but observing it in a microbe suggests that its evolutionary origin may be older than previously thought. 

“It fits an emerging model in the field, that elongation factors might bind to hibernating ribosomes to protect both the ribosome and themselves from degradation during periods of stress,” May explains. “Think of it like short-term storage.” 

An unstressed cell might quickly eliminate unneeded inactive ribosomes, but because any stressor that puts ribosomes to sleep could be temporary, the cell may prefer to hold off on destroying them. That way, the ribosomes can be quickly reactivated if conditions improve.

The future of cryoPRISM

May has already teamed up with other MIT researchers to use cryoPRISM to visualize ribosomes in cells that are notoriously difficult to work with, including pathogenic organisms, which can be challenging to culture at the scale required for particle purification, and red blood cells isolated from patients, which cannot be cultured at all.

Besides its immediate application for translation research, cryoPRISM is a stepping stone toward the broader goal of structural biology: studying biomolecules in their natural environment.

To truly learn about deep-sea fish, scientists need to look at them in the deep sea; and to learn about cellular machines, scientists need to look at them in cells. According to Davis, cryoPRISM perfectly fits into the “theme of structural biology moving closer and closer to cellular context.”

Could a three-hour workshop on an advanced materials analysis technique turn someone into a detective — or perhaps an art restorer?

At MIT’s Center for Bits and Atoms (CBA) in late January, about a dozen students explored that possibility during an Independent Activities Period (IAP) workshop on Raman spectroscopy, a technique that uses laser light to “fingerprint” materials. The session even featured a robotic dog equipped with sensing equipment, demonstrating how chemical analysis can be done remotely.

The workshop, led by MIT postdoc Lamyaa Almehmadi in collaboration with the CBA, introduced participants to a powerful technique now used by law enforcement and first responders to identify narcotics and explosives, by gemologists to authenticate precious stones, and pharmaceutical companies to verify raw materials and ensure product quality. CBA graduate researcher Jiaming Liu co-hosted, delivering lectures, demonstrating Raman equipment, and contributing to the curriculum and hands-on demonstrations.

“It can open up new possibilities for innovation across many fields,” said Almehmadi, an analytical chemist in the Department of Materials Science and Engineering (DMSE). After attendees learned the fundamentals, she encouraged them to think creatively about new applications: “My hope is to inspire all of you to think about doing something with Raman spectroscopy that no one has done before.”

Fingerprinting materials

Participants brought items to class to analyze using handheld devices, which fire laser light and measure how it bounces back. The resulting pattern behaves like a molecular fingerprint, identifying the materials in the item — whether it’s a paper clip, a piece of tree bark, or a mixing bowl.

Workshop attendee Sarah Ciriello, an administrative assistant at DMSE who brought a stone she found at the beach, was taken aback by the results. The Raman device suggested a 39 percent probability that the sample contained concrete-like material, with the remaining readings matching synthetic compounds — blurring the line between natural and manufactured materials.

“It’s man-made — I was surprised,” Ciriello said.

Developed in 1928 by Indian scientist C.V. Raman, who later won the Nobel Prize in Physics, Raman spectroscopy was groundbreaking because it used visible light to probe materials without destroying them, a major advantage over other techniques at the time, such as chromatography or mass spectrometry. But for decades, the Raman signal — the light scattered back from a sample — was weak, and the instruments were big and bulky, limiting its practical use.

Advances in lasers, computing power, and miniaturized optics have transformed Raman spectroscopy into a portable tool. Today’s handheld devices can instantly compare a sample’s molecular fingerprint against vast digital libraries, allowing users to identify thousands of materials in seconds. Because it doesn’t destroy the sample, Raman is especially useful in fields that require preserving materials — such as law enforcement, where evidence must remain intact, and art restoration.

Almehmadi’s own research focuses on advancing Raman spectroscopy by developing highly sensitive, semiconductor-based sensors that make portable chemical analysis possible, with applications ranging from medical diagnostics to forensic and environmental monitoring.

“Raman can be used to analyze any material,” Almehmadi says. “That’s why I decided to introduce it to students from diverse backgrounds.”

IAP classes are open to students and staff across MIT, and the Raman workshop reflected that range — from administrative staff to graduate and undergraduate students and postdocs in departments and labs including DMSE, the Department of Mechanical Engineering, the Media Lab, and the Broad Institute.

Walking the robot dog

A crowd-pleasing element in the workshop was the integration of a robot dog that belongs to the MIT Computer Science and Artificial Intelligence Laboratory (CSAIL). The demonstration highlighted how Raman technology can be used in dangerous environments, such as crime scenes or toxic industrial sites.

The handheld device was secured to the robot using tape, and Almehmadi showed how she could navigate the dog to a plastic bag filled with a white powder — baking soda.

But in a real-world scenario, “How can we know if it is baking soda or not?” she says. “So we just shined the light, and then the instrument told us what it was.”

Participants used a Wi-Fi app on their phones to view the results and a small remote controller to operate the robotic dog themselves.

“I loved the robot dog,” Ciriello says. “I was able to control it a bit, but it was challenging because the gauge was really sensitive.”

Michael Kitcher, a postdoc in DMSE, also praises the robot demonstration.

“Given that we just duct taped the device onto the dog — it was cool to see it actually worked,” he says.

Looking ahead

Kitcher, who researches magnetic materials for electronic applications, joined the workshop to learn more about Raman spectroscopy, which he had read about but never used. He was impressed by its versatility — in addition to the beach stone and baking soda, the device identified materials in a contact lens, cosmetics, and even a diamond.

Although it struggled to analyze a piece of chocolate he brought — other signals from the chocolate interfered — Kitcher sees strong potential for his own research. One area he’s interested in is unconventional magnetic materials, such as altermagnets, with unusual magnetic behavior that researchers hope to better understand and control for more energy-efficient electronics.

“Over the last couple of years, people have been trying to get a better sense of why these materials behave the way they do — how we can control this unconventional magnetic order,” he says. Raman spectroscopy can probe the vibrations of atoms in a material, helping researchers detect patterns in the crystal structure that underlie unusual magnetic behaviors. By understanding these vibrations, scientists could unlock material design rules that enable ultra-fast, low-energy computing.

Hands-on workshops like this — that inspire innovative future applications — Almehmadi says, are at the heart of an MIT education.

“I’ve always learned best by doing,” she says. “Lectures and reading are important, but real understanding comes from hands-on experience.”

Weekends at MIT are often a time for students to catch up on sleep or finish p-sets, lab work, and other school assignments. But for more than two decades, through a student-driven initiative supported by the Division of Student Life (DSL), students have been able to find welcoming activities designed to build community on Friday and Saturday nights through Weekends@MIT. All events are open to both graduate and undergraduate students.

At the heart of Weekends@MIT is a leadership team within the Wellbeing Ambassadors program. Ten leadership team members plan and host a variety of events from 9 to 11 p.m. in the MIT Wellbeing Lab, transforming the space into a hub for connection and creativity. While DSL staff provide advising, logistical support, and funding, event ideas come from students. Club members are committed to facilitating student social activities, all while increasing health awareness.

Student-led activities

Student ownership is intentional, says Robyn Priest, an assistant dean in the Division of Student Life. “All the ideas for activities come from the students. Leaders brainstorm themes, vote on their favorite concepts, and spearhead events in small teams. The only criterion is that it be substance-free. The students involved are dedicated, and the time commitment can be significant, so they are paid. But our students consistently step up, motivated by the opportunity to create experiences for their peers.”

Past events have included craft nights with boba tea, yoga, trivia competitions, bracelet-making workshops, waffle nights with customizable toppings, and even Spooky Skate, a Halloween costume ice-skating event hosted by the club in the Z Center.

Priest notes that just this past fall semester, more than 2,000 students attended the Friday night events, with many programs designed as drop-in experiences so students can participate around their busy schedules.

“I joined Weekends@MIT because I really liked the idea of helping organize activities on campus that promoted well-being for students and provided them with chill events that they can attend to build community and feel good on Friday nights,” says junior Emily Crespin Guerra.

Senior Ruting Hung adds, “I wanted to become more involved in promoting wellness on campus. Since then, I've found that it has also served as a way for me to recharge after a long week.”

Expanding Saturday events

Saturdays bring additional variety through collaborations with student clubs and groups. Organizations can apply for funding — typically several hundred dollars — to host events between 9 and 11 p.m. that are open to all students.

Undergraduate and graduate organizations, cultural groups, and hobby-based clubs have all contributed to programming. The partnerships also introduce new audiences to the Wellbeing Lab, helping the space become a familiar and welcoming destination across campus communities.

Connecting the campus through communication

Another key component of Weekends@MIT is a weekly newsletter distributed to thousands of students. The newsletter highlights upcoming programs in the Wellbeing Lab, along with other campus events that align with the initiative’s goals of connection and community without alcohol.

First-year student Vivian Dinh notes, “I love how the events provide a fun escape from the stress of classes and problem sets. The Wellbeing Lab is such a nice facility on campus for students to relax and enjoy themselves.”

A long tradition, evolving for the future

The current initiative builds on a long history of student-led weekend programming that began more than 20 years ago. Over time, the effort has evolved — from early safety campaigns to today’s comprehensive model focused on well-being, belonging, and social connection — but the core idea remains the same: students creating healthy spaces for other students.

Looking ahead, Weekends@MIT aims to continue expanding collaborations and exploring new ways to bring communities together on weekends. Additional events for this semester include: pupusas; blitz chess tournament with the Chess Club; craft night; movies and waffles; mocktails and latte art; a Bob Ross paint night, and much more.

Who benefits from artificial intelligence? This basic question, which has been especially salient during the AI surge of the last few years, was front and center at a conference at MIT on Wednesday, as speakers and audience members grappled with the many dimensions of AI’s impact.

In one of the conferences’s keynote talks, journalist Karen Hao ’15 called for an altered trajectory of AI development, including a move away from the massive scale-up of data use, data centers, and models being used to develop tools under the rubric of “artificial general intelligence.”

“This scale is unnecessary,” said Hao, who has become a prominent voice in AI discussions. “You do not need this scale of AI and compute to realize the benefits.” Indeed, she added, “If we really want AI to be broadly beneficial, we urgently need to shift away from this approach.”

Hao is a former staff member at The Wall Street Journal and MIT Technology Review, and author of the 2025 book, “Empire of AI.” She has reported extensively on the growth of the AI industry.

In her remarks, Hao outlined the astonishing size of datasets now being used by the biggest AI firms to develop large language models. She also emphasized some of the tradeoffs in this scale-up, such as the massive energy consumption and emissions of hyper-scale data centers, which also consume large amounts of water. Drawing on her own reporting, Hao also noted the human toll from the input work that global gig-economy employees do, inputting data manually for the hyper-scale models.

By contrast, Hao offered, an alternate path for AI might exist in the example of AlphaFold, the Nobel Prize-winning tool used to identify protein structures. This represents the concept of the “small, task-specific AI model tackling a well-scoped problem that lends itself to the computational strengths of AI,” Hao said.

She added: “It’s trained on highly curated data sets that only have to do with the problem at hand: protein folding and amino acid sequences. … There’s no need for fast supercomputing because the datasets are small, the model is small, and it’s still unlocking enormous benefit.”

In a second keynote address, scholar Paola Ricaurte underscored the desirability of purpose-driven AI approaches, outlining a number of conceptual keys to evaluating the usefulness of AI.

“There is no sense in having technologies that are not going to respond to the communities that are going to use them,” said Ricaurte.

She is a professor at Tecnologico de Monterrey in Mexico and a faculty associate at Harvard University’s Berkman Klein Center for Internet and Society. Ricaurte has also served on expert committees such as the Global Partnership for AI, UNESCO’s AI Ethics Experts Without Borders, and the Women for Ethical AI project.

The event was hosted by the MIT Program in Women’s and Gender Studies. Manduhai Buyandelger, the program’s director and a professor of anthropology, provided introductory remarks.

Titled “Gender, Empire, and AI: Symposium and Design Workshop,” the event was held in the conference space at the MIT Schwartzman College of Computing, with over 300 people in attendance for the keynote talks. There was also a segment of the event devoted to discussion groups, and an afternoon session on design, in a half-dozen different subject areas.

In her talk, Hao decried the often-vague nature of AI discourse, suggesting it impedes a more thoughtful discussion about the industry’s direction.

“Part of the challenge in talking about AI is the complete lack of specificity in the term ‘artificial intelligence,’” Hao said. “It’s like the word ‘transportation.’ You could be referring to anything from a bicycle to a rocket.” As a result, she said, “when we talk about accessing its benefits, we actually have to be very specific. Which AI technologies are we talking about, and which ones do we want more of?”

In her view, the smaller-sized tools — more akin to the bicycle, by analogy — are more useful on an everyday basis. As another example, Hao mentioned the project Climate Change AI, focused on tools that can help improve the energy efficiency of buildings, track emissions, optimize supply chains, forecast extreme weather, and more.

“This is the vision of AI that we should be building towards,” Hao said.

In conclusion, Hao encouraged audience members to be active participants in AI-related discourse and projects, saying the trajectory of the technology was not yet fixed, and that public interventions matter.

Citing the writer Rebecca Solnit, Hao suggested to the audience that “Hope locates itself in the premise that we don’t know what will happen, and that in the spaciousness of uncertainty is room to act.” She also noted, “Each and every one of you has an active role to play in shaping technology development.”

Ricaurte, similarly, encouraged attendees to be proactive participants in AI matters, noting that technologies will work best when the pressing everyday needs of all citizens are addressed.

“We have the responsibility to make hope possible,” Ricaurte said.

MIT Picower Professor Li-Huei Tsai, who has led The Picower Institute for Learning and Memory since 2009, will step down from the role of director at the end of the academic year in May. Her decision frees her to focus exclusively on her academic work, including her continued leadership of MIT’s Aging Brain Initiative and the Alana Down Syndrome Center. Meanwhile, the search for the Picower Institute’s next director has begun.

“During her exceptional 16-year tenure in the role of director, Li-Huei has led substantial growth at the Picower Institute,” says Nergis Mavalvala, dean of the MIT School of Science and the Curtis and Kathleen Marble professor of astrophysics. “She has markedly expanded the faculty — eight of the current 16 labs joined Picower under her directorship — through successful recruitment of highly talented neuroscientists. She has done this, and more, all while leading one of our most productive and influential labs, working on a quintessentially grand challenge in human health: combating Alzheimer’s disease.”

To conduct the search for a new Picower Institute director, Mavalvala has appointed a committee led by Sherman Fairchild Professor Matthew Wilson, associate director of the institute. Serving with Wilson are Picower Professor and former institute director Mark Bear, Menicon Professor Troy Littleton, Assistant Professor Sara Prescott, and Professor Fan Wang. They will identify and interview candidates, producing a report to Mavalvala later this spring.

Growing an institute

Tsai, a professor in MIT’s Department of Brain and Cognitive Sciences and a member of The Broad Institute of MIT and Harvard, says she is grateful to have had the opportunity to build the Picower Institute into a preeminent center for neuroscience research.

“I’m immensely proud of what our institute represents: world-renowned neuroscience research that is creative, rigorous, novel, and impactful,” Tsai says. “Our labs produce innovations, discoveries, and often translational strategies that have broken new ground and pushed science, medicine, and technology forward. We also provide excellent training that has enabled us to launch the careers of many of the field’s new and future leaders. It has been a tremendous honor to be able to build on the incredible foundation and inspiration provided by my predecessors Susumu Tonegawa and Mark Bear to enable the institute’s growth and success.”

Founded by Tonegawa as the Center for Learning and Memory in 1994, and then renamed The Picower Institute for Learning and Memory after a transformative gift by Barbara and Jeffry Picower in 2002, the institute now comprises about 400 scientists, students, and staff across 16 labs in MIT’s buildings 46 and 68.

But when Tsai became director in July 2009, just three years after coming to MIT from Harvard Medical School, the Picower Institute was a smaller enterprise of 11 labs, and a community closer to 200 members. Over the ensuing years, Tsai worked closely with the Picowers’ foundation, formerly the JPB Foundation and now the Freedom Together Foundation, to develop several strategic initiatives to accelerate growth and enhance research productivity. These have included programs specifically designed to support junior faculty, to catalyze more applications for more private grant funding, and to sustain fellowships for more than 18 postdocs and graduate students. Working with the foundation, she has also expanded the scope of research support provided by the Picower Institute Innovation Fund begun under Bear.

Eager to galvanize colleagues across MIT in fighting neurodegenerative diseases and neurological disorders affecting cognition, Tsai also built and launched two campus-wide initiatives: The Aging Brain Initiative, founded in 2015 and sustained by a broad coalition of donors, and the Alana Down Syndrome Center, established in 2019 with a gift from The Alana Foundation.

Research focus

As the Picower Institute has grown, Tsai’s research has, too. In work spanning molecular, cellular, circuit, and network scales in the brain, Tsai has led numerous highly cited discoveries about the neurobiology of Alzheimer’s disease and has translated several of these insights into specific therapeutic strategies, including one now undergoing a national phase III clinical trial. In all, she has published more than 230 peer-reviewed neuroscience studies, generated numerous patents, and helped launch several startups. She has been named a fellow of the National Academy of Medicine, the American Academy of Arts and Sciences, and the National Academy of Inventors, and received awards including the Society for Neuroscience Mika Salpeter Lifetime Achievement Award and the Hans Wigzell Prize.

Tsai’s earliest discoveries identified key roles in neurodegeneration for the enzyme CDK5. She has pioneered understanding of how epigenetic changes in brain cells affect Alzheimer’s pathology and memory. Her work has also highlighted a critical role for DNA double-strand breaks in disease.

In more recent work, Tsai’s lab has conducted several studies using innovative human stem-cell-based cultures to advance understanding of how the biggest genetic risk factor for Alzheimer’s (a gene variant called APOE4) contributes to pathology, and how some existing medications and supplements might help. In collaboration with MIT professor of computer science Manolis Kellis, she has also published several sweeping atlases documenting how gene expression and epigenetics differ in Alzheimer’s disease. These studies have provided the field with troves of new data and have yielded new insights into what makes the brain vulnerable to disease, and what helps some people remain resilient.

Tsai has also led a collaboration with professors Emery N. Brown and Edward S. Boyden that’s discovered a potential noninvasive, device-based treatment for Alzheimer’s and possibly other neurological disorders. Called “Gamma Entrainment Using Sensory Stimuli” (GENUS), the technology stimulates the senses (vision, hearing, or touch) to increase the power and synchrony of 40Hz frequency “gamma” waves in the brain. Numerous studies, involving either lab animals or human volunteers by her group and others, have shown that the approach can preserve brain volume and learning and memory and reduce signs of Alzheimer’s pathology. Via an MIT spinoff company, the technology has now advanced to pivotal clinical trial enrolling hundreds of people around the country.

“After 16 years leading the Picower Institute, I’m now eager to sharpen my focus on advancing human health through the work in my lab, the Aging Brain Initiative, and the Alana Center,” Tsai says.

The following is a joint announcement from the MIT School of Architecture and Planning, MIT Schwarzman College of Computing, Hasso Plattner Institute, and Hasso Plattner Foundation.

The MIT Morningside Academy for Design (MAD), MIT Schwarzman College of Computing, Hasso Plattner Institute (HPI), and Hasso Plattner Foundation celebrated the launch of the MIT and HPI AI and Creativity Hub (MHACH) at a signing ceremony this week. This 10-year initiative aims to deepen ties between computing and design as advances in artificial intelligence are reshaping how ideas are conceived and shared.

Funded by the Hasso Plattner Foundation, MIT and HPI will work together to foster collaborative interdisciplinary research and support a portfolio of educational programs, fellowships, and faculty engagement focused on AI and creativity, expanding scholarly inquiry into AI applications across disciplines, industries, and societal challenges. The collaboration begins with an inaugural two-day workshop March 19-20 at MIT, bringing together faculty, students, and researchers to set early priorities.

“As we hear from our faculty, as the Information Age gives way to an era of imagination, we expect a new emphasis on human creativity,” reflects MIT President Sally Kornbluth. “Through this collaboration, MIT and HPI are creating a shared space where students and faculty will come together across disciplines to explore new ideas, experiment with emerging tools, and invent new frontiers at the intersection of human creativity and AI.”

“The best minds need the right environment to do their most creative work,” says Rouven Westphal, from the Hasso Plattner Foundation. “When HPI and MIT come together across disciplines and borders, they create exactly that. The Hasso Plattner Foundation is committed to supporting this collaboration for the long term, building on Hasso Plattner’s vision of uniting technological excellence with human-centered design and creativity.”

Deepening collaboration at the intersection of technology, creativity, and societal impact

Building on the success of the Hasso Plattner Institute-MIT Research Program on Designing for Sustainability, established in 2022 between MIT MAD and HPI, the new MHACH hub represents a commitment to deepen collaboration at the intersection of technology, creativity, and societal impact.

“MIT and HPI share a common commitment to turning scientific excellence into real-world impact. Through this collaboration, we will create an environment where students and researchers from both sides of the Atlantic can work together, experiment across disciplines, and learn from one another — at a time when artificial intelligence is set to profoundly shape our lives. We are convinced that this collaboration will generate ideas with impact far beyond both institutions and inspire international cooperation and innovation,” says Professor Tobias Friedrich, dean and managing director of the Hasso Plattner Institute.

“HPI and MIT exist at the nexus of technology and creativity. Expanding this dynamic relationship will generate new paths for the infusion of AI, design, and creativity, enabling students, faculty, and researchers to dream and discover novel solutions, moving more quickly than ever from idea to implementation. MAD was established to connect thinkers across and beyond the Institute, and this new era of collaboration with HPI advances that mission on a global scale,” comments Hashim Sarkis, dean of the MIT School of Architecture and Planning and the Elizabeth and James Killian (1926) Professor.

Academic leadership from MIT and HPI will jointly shape the hub’s research and teaching agenda. Based in Potsdam, Germany, HPI is a center of excellence for digital engineering advancing research, education, and societal transfer in IT systems engineering, data engineering, cybersecurity, entrepreneurship, and digital health. Through its globally recognized HPI d-school and pioneering work in design thinking methodology, HPI brings a distinctive perspective on human-centered innovation to the collaboration, alongside a strong record in AI and data science research and technology transfer.

Expanding research and education on AI and creativity

The efforts of this multifaceted initiative are intended to foster a dynamic academic community spanning MIT and HPI, anchored by Hasso Plattner–named professorships and graduate fellowships whose recipients will be actively engaged in the hub. The long-term framework is designed to provide continuity for faculty appointments, doctoral training, and cross-campus research.

The agreement also includes the development of classes and educational programs in areas of shared AI focus, along with expanded experiential opportunities through AI-focused workshops, hackathons, and summer exchanges. A steering committee composed of representatives from the MIT School of Architecture and Planning, MIT Schwarzman College of Computing, and Hasso Plattner Institute will facilitate the shared governance of MHACH.

“Creativity has always been about extending human capability. At its core, this collaboration asks what it truly means to create something new. The question isn’t whether AI diminishes creativity, but how new forms of intelligence can deepen and enrich that process. Our goal is to explore that intersection with rigor and build a cross-disciplinary scholarly and research community that shapes how AI supports the creation of new ideas and knowledge,” says Dan Huttenlocher, dean of the MIT Schwarzman College of Computing and the Henry Ellis Warren (1894) Professor of Electrical Engineering and Computer Science.

This collaboration is made possible by the Hasso Plattner Foundation’s long-term philanthropic commitment to institutions that connect technological innovation with design thinking and education. The Hasso Plattner Foundation has played a central role in establishing and supporting institutions such as the Hasso Plattner Institute and international design thinking programs that bridge disciplines and geographies.

Growing up in the village of Kewa — located between Santa Fe and Albuquerque in New Mexico — William Pacheco, a member of the Santo Domingo Pueblo, learned the value of his language, its history, and the traditions it carries.

“We speak Keres, a language isolate found in seven villages and communities in central New Mexico,” he says. “It’s an endangered language with fewer than 10,000 speakers.” The Pueblos’ conception of ‘language,’ according to Pacheco, evokes the idea that speaking “comes from deep within.”

Pacheco is a graduate student in the MIT Indigenous Languages Initiative, a special master’s program in linguistics for members of communities whose languages are threatened. The two-year program provides its graduates with the linguistic knowledge to help them keep their communities’ languages alive. The initiative also offers expanded opportunities for students and faculty to become involved in Indigenous and endangered languages, working with both native speaker linguists in the master’s program and outside groups, ideas that appealed to him.

“There’s some complexity to our language that defies traditional instruction,” says Pacheco, who will complete his studies this spring. “I want to develop the linguistic tools I need to improve my understanding of its construction and how best to teach and preserve it.” Pacheco is keenly aware of cultural differences in how language transmission occurs. Language, he believes, evolves over time and is best learned experientially; the Western model of language learning prioritizes immediacy and test-taking.

A variety of factors complicate efforts to preserve and potentially teach Keres. Each of the villages where it’s spoken has its own distinct dialect. These dialects are mutually intelligible to various degrees based on where they’re being spoken. Additionally, the last three decades have seen a significant increase in English usage by young Pueblos, which further endangers Keres’ existence.

Furthermore, Keres isn’t a written language. For centuries, the Pueblo have relied on daily use within their homes and communities to maintain its vitality. “The community doesn’t want it written,” Pacheco says. 

Contact with the wider world has previously imperiled Indigenous ideas, an outcome Pacheco wants to avoid. “We believe [Keres] is a form of intellectual property, a tradition and artifact that is best served by empowering our people to preserve it,” he says.

From the Southwest to MIT

While he’s now passionate about linguistics, languages weren’t Pacheco’s first choice when considering an educational path. “I always admired [MIT alumnus and Nobel laureate] Richard Feynman,” he recalls. “I wanted to study physics.”

After earning an undergraduate degree from the University of New Mexico, Pacheco, who’d been working as a K-12 educator, began efforts to preserve Keres, increasing the language’s vitality and preserving its usefulness for, and value to, future generations. He sought permission and certification from the tribe to teach the language at the Santa Fe Indian School, an off-reservation boarding school. He soon discovered that a traditional Western approach to language learning wouldn’t suffice.

“Students weren’t taking the course to be scholars of the language; they wanted to learn it to build community and create opportunities to connect with elders,” Pacheco says. It was students’ advocacy, he notes, that led to the Keres learning initiative. While designing the course, however, he found gaps in his knowledge that led him to consider graduate study. 

“There are fascinating idiosyncrasies in Keres, including, for example, verb morphology — the ways in which verbs and verb sounds change,” he notes. “I wasn’t sure about how to teach them.” He sought to improve his understanding and ability by earning a master’s degree in learning design, innovation, and technology from Harvard University. While completing his studies there, he had another burst of inspiration.

“I thought a background in linguistics would prove useful,” he says. “An advisor told me about the Indigenous Languages Initiative at MIT and recommended I apply.” Pacheco knew of Professor Emeritus Noam Chomsky’s pioneering work in generative linguistics at the Institute and sought to learn more about the field’s potential to help him become a better, more effective educator and linguist. 

Upon arriving at MIT in 2024, Pacheco found himself embraced by faculty and students alike. “[MIT linguists] Adam Albright and Norvin Richards have been wonderfully supportive mentors, offering enthusiasm and expertise” he says. “I’ve benefited from MIT’s approach to linguistics and its use of scientific inquiry as a tool to explore language.” Engaging with other students working to preserve languages at risk of extinction continues to drive his work.

“MIT continually encourages us to use its resources, to collaborate, and to help one another find solutions to our unique challenges,” he says. “Networking, gathering good ideas, and having access to professors and students from a variety of disciplines is incredibly valuable.” 

MIT’s scholars, Pacheco says, are experienced with Indigenous language learning, education, and pedagogy.

Developing an organized approach to Keres research and instruction

While gratified that his work created opportunities for him to preserve and teach Keres, Pacheco marvels at his path to the Institute and its impact on his life. “It was my language, not my interest in physics, which led me to Harvard and MIT,” he says. “How did I end up at these places?”

An advantage of language and linguistics education at MIT is the rigor with which it explores language acquisition modeling and allows for alternatives to established systems. Pacheco is after new ideas for Keres language learning and education, working to develop an effective course based on generative linguistics that both preserves the Pueblos’ approach to community and offers an educational model students are likely to embrace. He’s already had opportunities to test novel theories and practices as an educator back home. 

“I was teaching students to use Keres as a programming tool,” he says. “We modeled a robot as a member of the community navigating a maze, and students would have to teach it to accept commands in Keres.” 

Pacheco also wants to explore community-centered language issues. He wants to standardize the development and education of community linguists, creating a cohort of scholars trained to use the tools he designs that are deeply invested in Keres’ preservation and instruction.

“We want to drive inquiries into Keres and how it’s taught,” he says, “while also centering Indigenous knowledge systems and expanding access to linguistics study for Indigenous scholars.”

Pacheco believes there’s value in exposing scholars and communities to the cultural and ideological exchanges he’s enjoyed between the sciences, humanities, Indigenous ideas, and experiences. “Indigenous scholars exist at MIT,” he says. “We’re here, and the Institute’s support helps preserve languages like Keres as important communal and cultural artifacts.” 

Pacheco is grateful for the opportunities his research at MIT have afforded him. While his education as a linguist and scholar continues, Pacheco’s community, culture, and support for Keres language learning remain top priorities.

“I want to amplify the impact in tribal language policy and Indigenous-centered education,” he says. “Language, its study, and its transmission is both science and art.”

Researchers have developed a new method for monitoring iron flux — the movement and rate at which cells take in, store, use and release iron — in stem cells known as mesenchymal stromal cells (MSCs). The system can provide insights within a minute about a cell’s ability to grow cartilage tissue for cartilage repair. 

The breakthrough offers a promising pathway toward more consistent and efficient manufacturing of high‑quality MSCs for regenerative therapies to treat joint diseases such as osteoarthritis, chronic joint degeneration conditions, and cartilage injuries.

The work was led by researchers from the Critical Analytics for Manufacturing Personalized-Medicine (CAMP) group within the Singapore-MIT Alliance for Research and Technology (SMART), and was supported by the SMART Antimicrobial Resistance (AMR) research group, in collaboration with MIT and the National University of Singapore (NUS).

A paper describing the work, “Cellular iron flux measurement by micromagnetic resonance relaxometry as a critical quality attribute of mesenchymal stromal cells,” was published in February in the journal Stem Cells Translational Medicine.

Regenerative therapies hold significant promise for patients with the potential to repair damaged tissues rather than simply manage symptoms. However, one of the biggest challenges in bringing these therapies to patients lies in the unpredictable quality of the MSC’s chondrogenic potential — a cell’s ability to develop and form cartilage tissue — during the in vitro manufacturing process.

Even when grown under controlled laboratory conditions, MSCs are prone to losing some of their potential and ability to form cartilage tissue, leading to inconsistent cartilage repair outcomes due to the varying quality of MSC batches. Existing tests that evaluate the quality of MSCs’ cartilage‑forming potential are destructive in nature, which causes irreversible damage to the cells being tested and renders them unusable for further therapeutic or manufacturing purposes.

In addition, the tests require a prolonged — up to 21-day — period for cells to grow. This slows decision‑making, extends production timelines, and can hinder the timely translation of MSC-based therapies into clinical use and delay treatment for patients. As MSCs can lose chondrogenic potential during this process, early assessment is essential for manufacturers to determine whether a batch should be continued or discontinued. Hence, there is a need for a reliable and rapid method to predict MSCs’ chondrogenic potential during the cell manufacturing process.

The new developement represents a rapid, non-destructive method to monitor iron flux in MSCs by measuring iron changes in spent media — residual components in the culture medium after cell growth. Using an inexpensive benchtop micromagnetic resonance relaxometry (µMRR) device, the approach enables real‑time monitoring of cellular iron changes without damaging the cells. The inexpensive µMRR device can be easily integrated into existing laboratories and manufacturing workflows, enabling routine, real‑time quality monitoring without significant infrastructure or cost barriers.

Iron homeostasis is a critical process that maintains normal levels of iron for cell function, maintaining the balance between providing sufficient iron for essential processes, while preventing toxic accumulation. The study found that iron homeostasis is highly correlated with the MSC’s chondrogenic potential, where significant iron uptake and accumulation will reduce the cell’s ability to form cartilage. The researchers also found that supplementing the cell growth process with ascorbic acid (AA) helps regulate iron homeostasis by limiting iron flux, thereby improving the MSC’s chondrogenic potential.

Using this novel method, spent media are collected as samples and treated with AA. The µMRR device is then used to track and provide real-time insights into small iron concentration changes within the spent media. These iron concentration changes reflect how MSCs take up and release iron and can provide an early indicator of whether a batch is likely to succeed in forming good cartilage.

These findings allow manufacturers to not only monitor MSCs quality for cartilage repair in real-time, but also to assess when, and to what extent, interventions such as AA supplementation are likely to be beneficial - supporting efficient manufacturing of more effective and consistent MSC‑based therapies.

“One of the key challenges in cartilage regeneration is the inability to reliably predict whether MSCs will retain their chondrogenic potential during manufacturing. Our study addresses this by introducing a rapid, non-destructive method to monitor iron flux dynamics as a novel critical quality attribute (CQA) of MSCs' chondrogenic capacity. This approach enables early identification of suboptimal cell batches during culture, enhancing quality control efficiency, reducing manufacturing costs, and accelerating clinical translation,” says Yanmeng Yang, CAMP postdoc and first author of the paper.

“Our research sheds light on a fundamental biological process that, until now, has been extremely difficult to measure. By monitoring iron flux in real-time without destroying the cells, we can gain actionable insights into a cell batch’s chondrogenic potential, which allows for early decision-making during the manufacturing process. The findings support µMRR‑based iron monitoring as an effective quality control strategy for MSC-based therapy manufacturing, paving the way for more consistent and clinically viable regenerative medicine for cartilage regeneration,” says MIT Professor Jongyoon Han, co-head CAMP PI, AMP PI, and corresponding author of the paper.

This method represents a promising step toward improving manufacturing consistency and functional characterisation of MSC-based cellular products. Beyond advancing cell therapy manufacturing, it contributes to the scientific industry studying iron biology by providing real-time iron flux measurements that were previously unavailable. The research also advances clinical translation of high-quality cell therapies for cartilage regeneration, bringing these closer to patients with joint degeneration conditions and cartilage injuries.

Building on these findings, the researchers plan to carry out future preclinical and clinical studies to expand this approach beyond quality control in manufacturing, with the aim of establishing µMRR as a validated method for the clinical translation of MSC-based therapies in patients for cartilage repair.

The research, conducted at SMART, was supported by the National Research Foundation Singapore under its Campus for Research Excellence and Technological Enterprise (CREATE) program.

MIT researchers have spent more than a decade studying techniques that enable robots to find and manipulate hidden objects by “seeing” through obstacles. Their methods utilize surface-penetrating wireless signals that reflect off concealed items.

Now, the researchers are leveraging generative artificial intelligence models to overcome a longstanding bottleneck that limited the precision of prior approaches. The result is a new method that produces more accurate shape reconstructions, which could improve a robot’s ability to reliably grasp and manipulate objects that are blocked from view.

This new technique builds a partial reconstruction of a hidden object from reflected wireless signals and fills in the missing parts of its shape using a specially trained generative AI model.

The researchers also introduced an expanded system that uses generative AI to accurately reconstruct an entire room, including all the furniture. The system utilizes wireless signals sent from one stationary radar, which reflect off humans moving in the space.  

This overcomes one key challenge of many existing methods, which require a wireless sensor to be mounted on a mobile robot to scan the environment. And unlike some popular camera-based techniques, their method preserves the privacy of people in the environment.

These innovations could enable warehouse robots to verify packed items before shipping, eliminating waste from product returns. They could also allow smart home robots to understand someone’s location in a room, improving the safety and efficiency of human-robot interaction.

“What we’ve done now is develop generative AI models that help us understand wireless reflections. This opens up a lot of interesting new applications, but technically it is also a qualitative leap in capabilities, from being able to fill in gaps we were not able to see before to being able to interpret reflections and reconstruct entire scenes,” says Fadel Adib, associate professor in the Department of Electrical Engineering and Computer Science, director of the Signal Kinetics group in the MIT Media Lab, and senior author of two papers on these techniques. “We are using AI to finally unlock wireless vision.”

Adib is joined on the first paper by lead author and research assistant Laura Dodds; as well as research assistants Maisy Lam, Waleed Akbar, and Yibo Cheng; and on the second paper by lead author and former postdoc Kaichen Zhou; Dodds; and research assistant Sayed Saad Afzal. Both papers will be presented at the IEEE Conference on Computer Vision and Pattern Recognition.

Surmounting specularity

The Adib Group previously demonstrated the use of millimeter wave (mmWave) signals to create accurate reconstructions of 3D objects that are hidden from view, like a lost wallet buried under a pile.

These waves, which are the same type of signals used in Wi-Fi, can pass through common obstructions like drywall, plastic, and cardboard, and reflect off hidden objects.

But mmWaves usually reflect in a specular manner, which means a wave reflects in a single direction after striking a surface. So large portions of the surface will reflect signals away from the mmWave sensor, making those areas effectively invisible.

“When we want to reconstruct an object, we are only able to see the top surface and we can’t see any of the bottom or sides,” Dodds explains.

The researchers previously used principles from physics to interpret reflected signals, but this limits the accuracy of the reconstructed 3D shape.

In the new papers, they overcame that limitation by using a generative AI model to fill in parts that are missing from a partial reconstruction.

“But the challenge then becomes: How do you train these models to fill in these gaps?” Adib says.

Usually, researchers use extremely large datasets to train a generative AI model, which is one reason models like Claude and Llama exhibit such impressive performance. But no mmWave datasets are large enough for training.

Instead, the researchers adapted the images in large computer vision datasets to mimic the properties in mmWave reflections.

“We were simulating the property of specularity and the noise we get from these reflections so we can apply existing datasets to our domain. It would have taken years for us to collect enough new data to do this,” Lam says.

The researchers embed the physics of mmWave reflections directly into these adapted data, creating a synthetic dataset they use to teach a generative AI model to perform plausible shape reconstructions.

The complete system, called Wave-Former, proposes a set of potential object surfaces based on mmWave reflections, feeds them to the generative AI model to complete the shape, and then refines the surfaces until it achieves a full reconstruction.

Wave-Former was able to generate faithful reconstructions of about 70 everyday objects, such as cans, boxes, utensils, and fruit, boosting accuracy by nearly 20 percent over state-of-the-art baselines. The objects were hidden behind or under cardboard, wood, drywall, plastic, and fabric.

Seeing “ghosts”

The team used this same approach to build an expanded system that fully reconstructs entire indoor scenes by leveraging mmWave reflections off humans moving in a room.

Human motion generates multipath reflections. Some mmWaves reflect off the human, then reflect again off a wall or object, and then arrive back at the sensor, Dodds explains.

These secondary reflections create so-called “ghost signals,” which are reflected copies of the original signal that change location as a human moves. These ghost signals are usually discarded as noise, but they also hold information about the layout of the room.

“By analyzing how these reflections change over time, we can start to get a coarse understanding of the environment around us. But trying to directly interpret these signals is going to be limited in accuracy and resolution.” Dodds says.

They used a similar training method to teach a generative AI model to interpret those coarse scene reconstructions and understand the behavior of multipath mmWave reflections. This model fills in the gaps, refining the initial reconstruction until it completes the scene.

They tested their scene reconstruction system, called RISE, using more than 100 human trajectories captured by a single mmWave radar. On average, RISE generated reconstructions that were about twice as precise than existing techniques.

In the future, the researchers want to improve the granularity and detail in their reconstructions. They also want to build large foundation models for wireless signals, like the foundation models GPT, Claude, and Gemini for language and vision, which could open new applications.

This work is supported, in part, by the National Science Foundation (NSF), the MIT Media Lab, and Amazon.

Large language models (LLMs) can generate credible but inaccurate responses, so researchers have developed uncertainty quantification methods to check the reliability of predictions. One popular method involves submitting the same prompt multiple times to see if the model generates the same answer.

But this method measures self-confidence, and even the most impressive LLM might be confidently wrong. Overconfidence can mislead users about the accuracy of a prediction, which might result in devastating consequences in high-stakes settings like health care or finance.   

To address this shortcoming, MIT researchers introduced a new method for measuring a different type of uncertainty that more reliably identifies confident but incorrect LLM responses.

Their method involves comparing a target model’s response to responses from a group of similar LLMs. They found that measuring cross-model disagreement more accurately captures this type of uncertainty than traditional approaches.

They combined their approach with a measure of LLM self-consistency to create a total uncertainty metric, and evaluated it on 10 realistic tasks, such as question-answering and math reasoning. This total uncertainty metric consistently outperformed other measures and was better at identifying unreliable predictions.

“Self-consistency is being used in a lot of different approaches for uncertainty quantification, but if your estimate of uncertainty only relies on a single model’s outcome, it is not necessarily trustable. We went back to the beginning to understand the limitations of current approaches and used those as a starting point to design a complementary method that can empirically improve the results,” says Kimia Hamidieh, an electrical engineering and computer science (EECS) graduate student at MIT and lead author of a paper on this technique.

She is joined on the paper by Veronika Thost, a research scientist at the MIT-IBM Watson AI Lab; Walter Gerych, a former MIT postdoc who is now an assistant professor at Worcester Polytechnic Institute; Mikhail Yurochkin, a staff research scientist at the MIT-IBM Watson AI Lab; and senior author Marzyeh Ghassemi, an associate professor in EECS and a member of the Institute of Medical Engineering Sciences and the Laboratory for Information and Decision Systems.

Understanding overconfidence

Many popular methods for uncertainty quantification involve asking a model for a confidence score or testing the consistency of its responses to the same prompt. These methods estimate aleatoric uncertainty, or how internally confident a model is in its own prediction.

However, LLMs can be confident when they are completely wrong. Research has shown that epistemic uncertainty, or uncertainty about whether one is using the right model, can be a better way to assess true uncertainty when a model is overconfident.

The MIT researchers estimate epistemic uncertainty by measuring disagreement across a similar group of LLMs.    

“If I ask ChatGPT the same question multiple times and it gives me the same answer over and over again, that doesn’t mean the answer is necessarily correct. If I switch to Claude or Gemini and ask them the same question, and I get a different answer, that is going to give me a sense of the epistemic uncertainty,” Hamidieh explains.

Epistemic uncertainty attempts to capture how far a target model diverges from the ideal model for that task. But since it is impossible to build an ideal model, researchers use surrogates or approximations that often rely on faulty assumptions.

To improve uncertainty quantification, the MIT researchers needed a more accurate way to estimate epistemic uncertainty.

An ensemble approach

The method they developed involves measuring the divergence between the target model and a small ensemble of models with similar size and architecture. They found that comparing semantic similarity, or how closely the meanings of the responses match, could provide a better estimate of epistemic uncertainty.

To achieve the most accurate estimate, the researchers needed a set of LLMs that covered diverse responses, weren’t too similar to the target model, and were weighted based on credibility.

“We found that the easiest way to satisfy all these properties is to take models that are trained by different companies. We tried many different approaches that were more complex, but this very simple approach ended up working best,” Hamidieh says.

Once they had developed this method for estimating epistemic uncertainty, they combined it with a standard approach that measures aleatoric uncertainty. This total uncertainty metric (TU) offered the most accurate reflection of whether a model’s confidence level is trustworthy.

“Uncertainty depends on the uncertainty of the given prompt as well as how close our model is to the optimal model. This is why summing up these two uncertainty metrics is going to give us the best estimate,” Hamidieh says.

TU could more effectively identify situations where an LLM is hallucinating, since epistemic uncertainty can flag confidently wrong outputs that aleatoric uncertainty might miss. It could also enable researchers to reinforce an LLM’s confidently correct answers during training, which may improve performance.

They tested TU using multiple LLMs on 10 common tasks, such as question-answering, summarization, translation, and math reasoning. Their method more effectively identified unreliable predictions than either measure on its own.

Measuring total uncertainty often required fewer queries than calculating aleatoric uncertainty, which could reduce computational costs and save energy.

Their experiments also revealed that epistemic uncertainty is most effective on tasks with a unique correct answer, like factual question-answering, but may underperform on more open-ended tasks.

In the future, the researchers could adapt their technique to improve its performance on open-ended queries. They may also build on this work by exploring other forms of aleatoric uncertainty.

This work is funded, in part, by the MIT-IBM Watson AI Lab.

A mosquito finds its target with the help of certain cues in its environment, such as a person’s silhouette and the carbon dioxide they exhale.

Now researchers at MIT and Georgia Tech have found that these visual and chemical cues help determine the insects’ flight paths. The team has developed the first three-dimensional model of mosquito flight, based on experiments with mosquitoes flying in the presence of different sensory cues.

Their model, reported today in the journal Science Advances, identifies three flight patterns that mosquitoes exhibit in response to sensory stimuli.

When they can only see a potential target, mosquitoes take a “fly-by” approach, quickly diving in toward the target, then flying back out if they do not detect any other host-confirming cues.

When they can’t see a target but can smell a chemical cue such as carbon dioxide, mosquitoes will do “double-takes,” slowing down and flitting back and forth to keep close to the source.

Interestingly, when mosquitoes receive both visual and chemical cues, such as seeing a silhouette and smelling carbon dioxide, they switch to an “orbiting” pattern, flying around a target at a steady speed as they prepare to land, much like a shark circling its prey.

The researchers say the new model can be used to predict how mosquitoes will fly in response to other cues, such as heat, humidity, and certain odors. Such predictions could help to design more effective traps and mosquito control strategies.

“Our work suggests that mosquito traps need specifically calibrated, multisensory lures to keep mosquitoes engaged long enough to be captured,” says study author Jörn Dunkel, MathWorks Professor of Mathematics at MIT. “We hope this establishes a new paradigm for studying pest behavior by using 3D tracking and data-driven modeling to decode their movement and solve major public health challenges.”

The study’s MIT co-authors are Chenyi Fei, a postdoc in MIT’s Department of Mathematics, and Alexander Cohen PhD ’26, a recent MIT chemical engineering PhD student advised by Dunkel and Professor Martin Bazant, along with Christopher Zuo, Soohwan Kim, and David L. Hu ’01, PhD ’06 of Georgia Tech, and Ring Carde of the University of California at Riverside.

Flight by numbers

Mosquitoes are considered to be the most dangerous animals in the world, given their collective impact on human health. The blood-sucking insects transmit malaria, dengue fever, West Nile virus, and other deadly diseases that together cause over 770,000 deaths each year.

Of the 3,500 known species of mosquitoes, around 100 have evolved to specifically target humans, including Aedes aegypti, a species that uses a variety of cues to seek out human hosts. Scientists have studied how certain cues attract mosquitoes, mainly by setting up experiments in wind tunnels, where they can waft cues such as carbon dioxide and study how mosquitoes respond. Such experiments have mainly recorded data such as where and when the insects land. The researchers say no study has explored how mosquitoes fly as they hunt for a host.

“The big question was: How do mosquitoes find a human target?” says Fei. “There were previous experimental studies on what kind of cues might be important. But nothing has been especially quantitative.”

At MIT, Dunkel’s group develops mathematical models to describe and predict the behavior of complex living systems, such as how worms untangle, how starfish embryos develop and swim, and how microbes evolve their community structure over time.

Dunkel looked to apply similar quantitative techniques to predict flight patterns of mosquitoes after giving a talk at Georgia Tech. David Hu, a former MIT graduate student who is now a professor of mechanical engineering at Georgia Tech, proposed a collaboration; Hu’s lab was carrying out experiments with mosquitoes at a facility at the Centers of Disease Control and Prevention in Atlanta, where they were studying the insects’ behavior in response to sensory cues. Could Dunkel’s group use the collected data to identify significant flight behavior that could ultimately help scientists control mosquito populations?

“One of the original motivations was designing better traps for mosquitoes,” says Cohen. “Figuring out how they fly around a human gives insights on how we can avoid them.”

Taking cues

For their new study, Hu and his colleagues at Georgia Tech carried out experiments with 50 to 100 mosquitoes of the Aedes aegypti species. The insects flew around inside a long, white, slightly angled rectangular room as cameras around the room captured detailed three-dimensional trajectories of each mosquito as it flew around. In the center of the room, they placed an object to represent a certain visual or chemical cue.

In some trials, they placed a black Styrofoam sphere on a stand to represent a simple visual cue. (Mosquitoes would be able to see the black sphere against the room’s white background). In other trials, they set up a white sphere with a tube running through to pump out carbon dioxide at rates similar to what humans breathe out. These trials represented the presence of a chemical cue, but not a visual cue.

The researchers also studied the mosquitoes’ response to both visual and chemical cues, using a black sphere that emitted carbon dioxide. Finally, they observed how mosquitoes behaved around a human volunteer who wore protective clothing that was black on one side and white on the other.

Across 20 experiments, the team generated more than 53 million data points and over 477,220 mosquito flight paths. Hu shared the data with Dunkel, whose group used the measurements to develop a model for mosquito flight behavior.

“We are proposing a very broad range of dynamical equations, and when you start out, the equation to predict a mosquito’s flight path is very complicated, with a lot of terms, including the relative importance of a visual versus a chemical cue,” Dunkel explains. “Then through iteration against data, we reduce the complexity of that equation until we get the simplest model that still agrees with the data.”

In the end, the group whittled down a simple model that accurately predicts how a mosquito will fly, given the presence of a visual cue, a chemical cue, or both. The flight paths in response to one or the other cue are markedly different. And interestingly, when both cues are present, the researchers noted that the resulting path is not “additive.” In other words, a mosquito does not simply combine the paths that it would separately take when it can both see and smell a target. Instead, the insects take a distinct path, circling, rather than diving or darting around their target.

“Our work suggests that mosquito traps need specifically calibrated ‘multisensory’ lures to keep mosquitoes engaged long enough to be captured,” Dunkel says.

“Obviously there are additional cues that humans emit, like odor, heat, and humidity,” Cohen notes. “For the species we study, visual and carbon dioxide cues are the most important. But we can apply this model to study different species and how they respond to other sensory cues.”

The researchers have developed an interactive app that incorporates the new mosquito flight model. Users can experiment with different objects and set parameters such as the number of mosquitoes around the object and the type of sensory cue that is present. The model then visualizes how the mosquitoes would fly in response.

“The original hope was to have a quantitative model that can simulate mosquito behavior around various trap designs,” Cohen says. “Now that we have a model, we can start to design more intelligent traps.”

This work was supported, in part, by the National Science Foundation, Schmidt Sciences, LLC, the NDSEG Fellowship Program, and the MIT MathWorks Professorship Fund. 

MIT senior Srihitha Dasari never imagined she would be speaking in front of the United Nations about health care, technology, and the power of co-designing public health interventions in collaboration with impacted communities. 

But when she stepped up to the podium to speak about digital well-being and community-centered health care design, she carried with her more than research findings. She brought several years of experiential learning in public health environments, ranging from visiting exam rooms of New England’s largest safety net hospital to collaborating with nurses in rural Argentina and working on maternal health in India and Nepal. 

Dasari arrived at MIT intending to major in brain and cognitive sciences and follow a pre-med track. Like many aspiring physicians, she pictured her MIT years filled with lab work, shadowing doctors, and preparing for medical school. Instead, during her first Independent Activities Period (IAP), she enrolled in the PKG Center for Social Impact’s IAP Health Program and began to broaden her understanding of practicing medicine. 

“What was really incredible about IAP Health,” says Dasari, is that “I did it so early in not only my academic career, but just in the beginning of when I was actually formulating a lot of my career aspirations, [and] it really immersed me into what public health looks like.”

Through IAP Health, Dasari worked as an intern at the Boston Medical Center Autism Program. There, she provided in-clinic support to children with autism and their families, helping guide them through appointments and collaborating with physicians to adapt exam techniques to meet patients’ needs.

“When you think about how medicine is delivered, it can feel very systematic — like there are boxes you have to check,” she says. “But working in that clinic showed me … you can modify the experience to truly care for the whole person.”

The program exposed her not only to clinical care, but to the broader forces that shape health outcomes. “I didn’t envision myself doing public health when I entered college,” Dasari says. “But looking back, public health is the through line of everything I’ve done.”

She remained at Boston Medical Center as an intern for over a year with continued support and funding from the PKG Center’s Federal Work-Study and Social Impact Internship programs. The sustained engagement deepened her understanding of how health-care systems can either reinforce or reduce disparities — a systems-level perspective that carried into her global work.

During her second-year IAP, Dasari received a PKG Fellowship to develop an electronic health record system for a maternal ward in a rural hospital in Argentina. The project grew out of a relationship she developed through the student group MIT Global Health Alliance, which supports co-designing public health interventions with impacted communities.  

Dasari’s collaboration with the hospital evolved into a social enterprise that she co-founded: PuntoSalud, an AI-powered chatbot designed to bridge health information gaps in rural Argentina. Dasari and her co-founders received a $5,000 award and seed funding to prototype and develop PuntoSalud through the PKG IDEAS Social Innovation Incubator, MIT’s only entrepreneurship program focused solely on social impact. 

Speaking at the United Nations underscored a lesson she absorbed throughout her varied experience: Meaningful health innovation begins with relationships.

“I’ve been able to meet people from so many different facets of the health-care pipeline that I didn’t envision myself meeting,” Dasari says.

The mindset she developed through PKG programming has informed her experience beyond the center. Through MIT D-Lab, Dasari conducted maternal and neonatal health needs assessments in rural Nepal, interviewing community members to better understand gaps in care. The findings informed efforts to retrofit birthing centers with improved heating systems in cold climates. Later, supported by the MIT International Science and Technology Initiatives, she traveled to India to interview health-care providers about strategies to reduce non-medical cesarean section rates, with the goal of developing policy recommendations for other health systems.

“I came in thinking I would practice medicine one-on-one,” Dasari says. “Now I want to increase my impact in the health care field. I see that as clinical medicine intersected with public health, relieving health disparities for a wider population.”

As Dasari prepares to leave MIT for a year in clinical research, she does so with a systems lens on science and health care, and a commitment to social impact. 

“The path I’ve taken in health care as an undergrad student has given me both a sense of purpose and fulfillment as I prepare to leave MIT,” she says. “It’s shown me that meaningful impact can begin long before medical school, and that I want to carry forward the values these experiences instilled in me.”

For Dasari, experiential learning didn’t redirect her ambitions, but enhanced them. 

“I feel like the PKG Center … it’s not changing your goals,” she says. “It’s shaping them into their fullest potential.”

One of the symptoms of schizophrenia is difficulty incorporating new information about the world. This can lead people with schizophrenia to struggle with making decisions and, eventually, to lose touch with reality.

MIT neuroscientists have now identified a gene mutation that appears to give rise to this type of difficulty. In a study of mice, the researchers found that the mutated gene impairs the function of a brain circuit that is responsible for updating beliefs based on new input.

This mutation, in a gene called grin2a, was originally identified in a large-scale screen of patients with schizophrenia. The new study suggests that drugs targeting this brain circuit could help with some of the cognitive impairments seen in people with schizophrenia.

“If this circuit doesn’t work well, you cannot quickly integrate information,” says Guoping Feng, the James W. and Patricia T. Poitras Professor in Brain and Cognitive Sciences at MIT, a member of the Broad Institute of Harvard and MIT, and the associate director of the McGovern Institute for Brain Research at MIT. “We are quite confident this circuit is one of the mechanisms that contributes to the cognitive impairment that is a major part of the pathology of schizophrenia.”

Feng and Michael Halassa, a professor of psychiatry and neuroscience and director of translational research at Tufts University School of Medicine, are the senior authors of the new study, which appears today in Nature Neuroscience. Tingting Zhou, a research scientist at the McGovern Institute, and Yi-Yun Ho, a former MIT postdoc, are the lead authors of the paper.

Adapting to new information

Schizophrenia is known to have a strong genetic component. For the general population, the risk of developing the disease is about 1 percent, but that goes up to 10 percent for those who have a parent or sibling with the disease, and 50 percent for people who have an identical twin with the disease.

Researchers at the Stanley Center for Psychiatric Research at the Broad Institute have identified more than 100 gene variants linked to schizophrenia, using genome-wide association studies. However, many of those variants are located in non-coding regions of the genome, making it difficult to figure out how they might influence development of the disease.

More recently, researchers at the Stanley Center used a different strategy, known as whole-exome sequencing, to reveal gene mutations linked to schizophrenia. This technique sequences only the protein-coding regions of the genome, so it can reveal mutations that are located in known genes.

Using this approach on about 25,000 sequences from people with schizophrenia and 100,000 sequences from control subjects, the researchers identified 10 genes in which mutations significantly increase the risk of developing schizophrenia.

In the new Nature Neuroscience study, Feng and his students created a mouse model with a mutation in one of those genes, grin2a. This gene encodes a protein that forms part of the NMDA receptor — a receptor that is activated by the neurotransmitter glutamate and is often found on the surface of neurons.

Zhou then investigated whether these mice displayed any of the characteristic behaviors seen in people with schizophrenia. These individuals show many complex symptoms, including psychoses such as hallucinations and delusions (loss of contact with reality). Those are difficult to study in mice, but it is possible to study related symptoms such as difficulty in interpreting new sensory input.

Over the past two decades, schizophrenia researchers have hypothesized that psychosis may stem from an impaired ability to update beliefs based on new information.

“Our brain can form a prior belief of reality, and when sensory input comes into the brain, a neurotypical brain can use this new input to update the prior belief. This allows us to generate a new belief that’s close to what the reality is,” Zhou says. “What happens in schizophrenia patients is that they weigh too heavily on the prior belief. They don’t use as much current input to update what they believed before, so the new belief is detached from reality.”

To study this, Zhou designed an experiment that required mice to choose between two levers to press to earn a food reward. One lever was low-reward — mice had to push it six times to get one drop of milk. A high-reward lever dispensed three drops per push.

At the beginning of the study, all of the mice learned to prefer the high-reward lever. However, as the experiment went on, the number of presses required to dispense the higher reward gradually went up, while there were no changes to the low-reward lever.

As the effort required went up, healthy mice start to switch back and forth between the two levers. Once they had to press the high-reward lever around 18 times for three drops of milk, making the effort per drop about the same for each lever, they eventually switched permanently to the low-reward lever. However, mice with a mutation in grin2a showed a different behavior pattern. They spent more time switching back and forth between the two levers, and they made the switch to the low-reward side much later.

“We find that neurotypical animals make adaptive decisions in this changing environment,” Zhou says. “They can switch from the high-reward side to the low-reward side around the equal value point, while for the animals with the mutation, the switch happens much later. Their adaptive decision-making is much slower compared to the wild-type animals.”

An impaired circuit

Using functional ultrasound imaging and electrical recordings, the researchers found that the brain region affected most by the grin2a mutation was the mediodorsal thalamus. This part of the brain connects with the prefrontal cortex to form a thalamocortical circuit that is responsible for regulating cognitive functions such as executive control and decision-making.

The researchers found that neuronal activity in the mediodorsal thalamus appears to keep track of the changes in value of the two reward options. Additionally, the mice showed different patterns of neural activity depending on which state they were — either an exploratory state or committed to one side.

The researchers also showed that they could use optogenetics to reverse the behavioral symptoms of the mice with mutated grin2a. They engineered the neurons of the mediodorsal thalamus so that they could be activated by light, and when these neurons were activated, the mice began behaving similarly to mice without the grin2a mutation.

While only a very small percentage of schizophrenia patients have mutations in the grin2a gene, it’s possible that this circuit dysfunction is a converging mechanism of cognitive impairment for a subset of schizophrenia patients with different causes.

Targeting this circuit could offer a way to overcome some of the cognitive impairments seen in people with schizophrenia, the researchers say. To do that, they are now working on identifying targets within the circuit that could be potentially druggable.

The research was funded by the National Institutes of Mental Health, the Poitras Center for Psychiatric Disorders Research at MIT, the Yang Tan Collective at MIT, the K. Lisa Yang and Hock E. Tan Center for Molecular Therapeutics at MIT, the Stelling Family Research Fund at MIT, the Stanley Center for Psychiatric Research, and the Brain and Behavior Research Foundation.

Thermal batteries can efficiently store energy as heat. But building them requires a carefully designed system with materials that can withstand cycles of extremely high temperatures, without succumbing to problems like corrosion, thermal expansion, and structural fatigue.

Many thermal battery systems move high-temperature gas or molten salt around through metal pipes. Fourth Power, founded by MIT Professor Asegun Henry, is turning these materials inside out, using molten metal to transport the heat, which is stored in carbon bricks.

“The idea was, instead of making the system from metal, let’s move liquid metals,” says Henry SM ’06, PhD ’09.

Henry’s approach earned him a Guinness World Record for the hottest liquid pump back in 2017 — important because when you double the absolute temperature of a material, to the point where it glows white-hot, the amount of light it emits doesn’t just double, it increases 16 times (or to the fourth power).

The company is harvesting all that light with thermophotovoltaic cells, which work like solar cells to convert light into electricity. Henry and his collaborators broke another record when they demonstrated a thermophotovoltaic cell that could convert light to electricity with an efficiency above 40 percent.

Fourth Power is working to use those record-breaking innovations to provide energy for power grids, power producers, and technology companies building power-hungry infrastructure like data centers. Henry says the batteries can provide anywhere from 10 to over 100 hours of electricity at a storage cost that is significantly cheaper than lithium-ion batteries at grid scale. The company is currently cycling each section of its system through relevant operating temperatures — which are nearly half as hot as the sun — and plans to have a fully integrated demonstration unit operating later this year.

“Explaining why our system is such a huge improvement over everything else centers around power density,” explains Henry, who serves as Fourth Power’s chief technologist. “We realized if you push the temperature higher, you will transfer heat at a higher rate and shrink the system. Then everything gets cheaper. That’s why we pursue such high temperatures at Fourth Power. We operate our thermal battery between 1,900 and 2,400 degrees Celsius, which allows us to save a tremendous amount on the balance of system costs.”

A career in heat

Henry earned his master’s and PhD degrees from MIT before working in faculty positions at Georgia Tech and MIT. As a professor at both schools, his research has focused on thermal transport, storage, renewable energy, and other technologies that could lead to improvements in sustainability and decarbonization. Today, he is the George N. Hatsopoulos Professor in Thermodynamics in MIT’s Department of Mechanical Engineering.

Heat transfer systems are usually made out of metals like iron and nickel. Generally, the higher temperature you want to reach, the more expensive the metal. Henry noticed ceramics can get much hotter than metals, but they’re not used nearly as often. He started asking why.

“The answer is often pretty straightforward: You can’t weld ceramics,” Henry says. “Ceramics aren’t ductile. They generally fail in a catastrophically brittle way, and that’s not how we like large systems to behave. But I couldn’t find many problems beyond that.”

After receiving funding from the Department of Energy and the MIT Energy Initiative, Henry spent years developing a pump made from ceramics and graphite (which is similar to a ceramic). In 2017, his pump set the record for the highest recorded operating temperature for a liquid pump, at 1,200 Celsius. The pump used white-hot liquid tin as a fuel. He chose tin because it doesn’t react with carbon, eliminating corrosion. It also has a relatively low melting point and high boiling point, which keeps it liquid in a large temperature range.

The challenge then became designing the system.

“Typically, a mechanical engineer would come up with a design and say, ‘Give me the best materials to do this,’” Henry says. “We flipped the problem, so we were saying, ‘We know what materials will work, now we need to figure out how to make a system out of it.’”

In 2023, Henry met Arvin Ganesan, who had previously led global energy work at Apple. At first, Ganesan wasn’t interested in joining a startup — he had two young kids and wanted to prioritize his family — but he was intrigued by the potential of the technology. At their first meeting, the two connected over shared values and fatherhood, as Henry surprised Ganesan by bringing his own young children.

“I had a sense this technology had the promise to tackle the twin crises of affordability and climate change at the same time,” says Ganesan, who is now Fourth Power’s CEO. “As energy demand becomes more pronounced, we either need to deploy harder and deeper tech, which is also important, or improve existing tech. Fourth Power is trying to simplify the physics and thermodynamic principles to deliver an approach that has been very well-studied for a very long time.”

The system Fourth Power designed takes in excess electricity from sources like the grid and uses it to heat a series of 6-foot-long, 20-inch thick graphite bricks until they reach about 2,400 Celsius. At that point the system is considered fully charged.

When the customer wants the electricity back, the bricks are used to heat up liquid tin, which flows through a series of graphite pipes, pumps, and flow meters to thermophotovoltaic cells, which turn the light from the glowing hot infrastructure back into electricity.

“You can basically dip the cells into the light and get power, or you can pull them back out and shut it off,” Henry explains. “The liquid metal starts at 2,400 Celsius and then cools as it’s going through the system because it’s giving a bunch of its energy to the photovoltaic, and then it circulates back through the graphite blocks, which act as a furnace, to retrieve more heat.”

From concept to company

Later this year, Fourth Power plans to turn on a 1-megawatt-hour system in its new headquarters in Bedford, Massachusetts. A full-scale system would offer 25 megawatts of power and 250 megawatt hours of storage and take up about half a football field.

“Most technologies you’ll see in storage are around 10 megawatts an acre or less,” Henry explains. “Fourth Power is more like 100 megawatts per acre. It’s very power-dense.”

The power and storage units of Fourth Power’s system are modular, which will allow customers to start with a smaller system and add storage units to extend storage length later. The company expects to lose about 1 percent of total heat stored per day.

“Customers can buy one storage and one power module, and that’s a 10-hour battery,” Henry explains. “But if they want one power module and two storage modules, that’s a 20-hour battery. Customers can mix and match, which is really advantageous for utilities as renewables scale and storage needs change.”

Down the line, the system could also be run as a power plant, converting fuel into electricity or using fuel to charge its batteries during stretches with little wind or sun. It could also be used to provide industrial heat.

But for now, Fourth Power is focused on the battery application.

“Utilities need something cheap and they need something reliable,” Henry says. “The only technology that has managed to reach at least one of those requirements is lithium ion. But the world is waiting for something that’s much cheaper than lithium ion and just as reliable, if not better. That’s what we’re focused on demonstrating to the world.”

Professor John Ochsendorf, a member of the MIT faculty since 2002, is taking on a new role in support of the research efforts of faculty and students in the MIT School of Architecture and Planning (SA+P). At the start of this year, Ochsendorf was appointed to lead an initiative strengthening research strategy, support, and funding across the school.

“John is a bridge-builder by instinct and practice, and we look forward to the bridges he will build between our school and industry, our school and MIT, and between research and pedagogy in our school,” says SA+P Dean Hashim Sarkis. The appointment comes as sponsored research across SA+P continues to grow, expanding opportunities for graduate research assistantships and interdisciplinary collaboration across MIT.

Ochsendorf is the Class of 1942 Professor with dual appointments in the departments of Architecture and Civil and Environmental Engineering in the MIT School of Engineering. At the center of his work is a deep commitment to students and education through research and making. For example, in close collaboration with students and alumni, he has contributed to projects ranging from the Sean Collier Memorial on campus to a recent Martin Puryear sculpture at Storm King Art Center. Since 2022, Ochsendorf has served as the founding director of the MIT Morningside Academy for Design, where he helped establish new models for design research, interdisciplinary collaboration, and student engagement across the Institute.

Ochsendorf describes the new role as both a “challenge and an opportunity” to support the considerable and increasingly broad portfolio of research across SA+P.

“We want to understand the current landscape of our research funding and identify the challenges and inefficiencies impacting faculty,” he notes. “The ultimate goal is to grow our research capacity for a world that needs the best ideas from MIT.”

The effort is consistent with SA+P’s history of pioneering research and pedagogic exploration. The Department of Architecture was among the first in the United States to establish doctoral programs within a school of architecture, including PhDs in history, theory, and criticism and in building technology. The Department of Urban Studies and Planning is home to the largest urban planning faculty in the country and maintains a variety of research labs, while Media Arts and Sciences and the Media Lab has a broad and deep research culture. Each of the school’s departments enjoys the advantage of operating within the context of MIT’s culture of innovation and interdisciplinary study. As new faculty hires have been increasingly research-driven, the time for developing and supporting robust research portfolios is now. 

Ochsendorf and his students’ research have bridged the spectrum from humanistic research supported by organizations such as the National Endowment for the Humanities and the Graham Foundation for Advanced Studies in the Fine Arts to more scientific research supported by the National Science Foundation. In his new role, he will build on that experience to work with faculty and Institute partners to strengthen grant development, clarify research priorities, and expand research capacity across SA+P.

“I’ve always loved being at MIT because of the team spirit here,” says Ochsendorf. “We’re a place where we try to support each other, and it’s because of this environment that I am excited about this new role.”

The United States and China “are the two largest emitters of carbon in the world,” said Nicholas Burns, former U.S. ambassador to the People’s Republic of China, at a recent MIT seminar. “We need to work with each other for the good of both of our countries.” 

During the MITEI Presents: Advancing the Energy Transition presentation, Burns gave insight into the evolving state of U.S.-China relations, its implications for the global order, and its impact on global efforts to advance the energy transition and address climate change.

“We are the two largest global economies,” said Burns, who is now the Goodman Professor of the Practice of Diplomacy and International Relations at Harvard University’s Kennedy School of Government. “These are the only two countries that affect everybody else in the international system because of our weight.”

The relationship between the United States and China can be summarized in three words, according to Burns: competitive, tough, and adversarial — a description that rings true on both sides. He listed four primary areas for this competition: military, technology, trade and economics, and values.

Burns described the especially complicated area of trade and economics. “We both want to be number one. Neither of us — to be honest — is willing to be number two,” said Burns. Outside of North America, China is the United States’ largest trade partner. Outright trade wars — like those in April and October 2025 — create friction. “At one point, you’ll remember, 145 percent tariffs by the United States, and 125 percent by China on the United States. That just grinds a relationship. Those level of tariffs, had they been sustained, would have meant zero trade between the two countries.”

The energy field can be significantly impacted by this area of competition, Burns added. China is dominant in the production and processing of rare earth elements, many of which are critical to products like lithium batteries, solar panels, and electric vehicles. In 2024 and 2025, the United States was not the only country to place tariffs on these products; India, Turkey, South Africa, Mexico, Canada, the EU, and others followed suit. “I think the Trump administration is right, as President Biden was, to try to diversify sources on rare earths,” Burns said.

Burns also noted with interest the dichotomy in the Chinese energy sector between their lead on clean energy technology and their continual use of coal, standing out as an inconsistency in China’s efforts. Burns believes that climate change could be a key area of cooperation between China and the United States, emphasizing the importance of the United States’ participation, both technologically and diplomatically.

Burns also described the significant technological competition between the United States and China — an area of central importance. Throughout his presentation, Burns was quick to praise the emphasis that China puts on education and academic achievement, particularly in STEM fields. Pulling from a recent article in The Economist, he compared the 36 percent of Chinese first-year university students majoring in STEM fields to the 5 percent of American first-year students in STEM. “Think about the volume of graduates and the disparity between our country and China,” he said. “Then think about the percentage of those graduates who go into science and technology.”

Currently, areas like artificial intelligence, quantum computing, and biotechnology are taking center stage in technological innovation. “The Chinese are very skilled in terms of industrial processes and doctrine of adapting quickly,” said Burns. He explained that holding a competitive edge lies not only in who is first on the market, but who adopts the technology first, and who is able to unite that technological progress with policy.

“This is the most important relationship that we have in the world,” said Burns. He believes that the true test is whether the United States and China can manage competition so that interests are protected, while avoiding the use of the massive destructive power both countries possess. “We’ve got to normalize the communication and engagement to prevent the worst from happening,” said Burns.

“We’re at a stage of human history where we’re all linked together, and the fate of everybody in this room and all of our countries is linked together by these huge transnational challenges,” said Burns. “We’ve got to learn to compete and yet live in peace with each other in the process.”

This speaker series highlights energy experts and leaders at the forefront of the scientific, technological, and policy solutions needed to transform our energy systems. Visit MITEI’s Events page for more information on this and additional events.

The early years of faculty members’ careers are a formative and exciting time in which to establish a firm footing that helps determine the trajectory of researchers’ studies. This includes building a research team, which demands innovative ideas and direction, creative collaborators, and reliable resources. 

For a group of MIT faculty working with and on artificial intelligence, early engagement with the MIT-IBM Watson AI Lab through projects has played an important role helping to promote ambitious lines of inquiry and shaping prolific research groups.

Building momentum

“The MIT-IBM Watson AI Lab has been hugely important for my success, especially when I was starting out,” says Jacob Andreas — associate professor in the Department of Electrical Engineering and Computer Science (EECS), a member of the MIT Computer Science and Artificial Intelligence Laboratory (CSAIL), and a researcher with the MIT-IBM Watson AI Lab — who studies natural language processing (NLP). Shortly after joining MIT, Andreas jump-started his first major project through the MIT-IBM Watson AI Lab, working on language representation and structured data augmentation methods for low-resource languages. “It really was the thing that let me launch my lab and start recruiting students.” 

Andreas notes that this occurred during a “pivotal moment” when the field of NLP was undergoing significant shifts to understand language models — a task that required significantly more compute, which was available through the MIT-IBM Watson AI Lab. “I feel like the kind of the work that we did under that [first] project, and in collaboration with all of our people on the IBM side, was pretty helpful in figuring out just how to navigate that transition.” Further, the Andreas group was able to pursue multi-year projects on pre-training, reinforcement learning, and calibration for trustworthy responses, thanks to the computing resources and expertise within the MIT-IBM community.

For several other faculty members, timely participation with the MIT-IBM Watson AI Lab proved to be highly advantageous as well. “Having both intellectual support and also being able to leverage some of the computational resources that are within MIT-IBM, that’s been completely transformative and incredibly important for my research program,” says Yoon Kim — associate professor in EECS, CSAIL, and a researcher with the MIT-IBM Watson AI Lab — who has also seen his research field alter trajectory. Before joining MIT, Kim met his future collaborators during an MIT-IBM postdoctoral position, where he pursued neuro-symbolic model development; now, Kim’s team develops methods to improve large language model (LLM) capabilities and efficiency. 

One factor he points to that led to his group’s success is a seamless research process with intellectual partners. This has allowed his MIT-IBM team to apply for a project, experiment at scale, identify bottlenecks, validate techniques, and adapt as necessary to develop cutting-edge methods for potential inclusion in real-world applications. “This is an impetus for new ideas, and that’s, I think, what’s unique about this relationship,” says Kim.

Merging expertise

The nature of the MIT-IBM Watson AI Lab is that it not only brings together researchers in the AI realm to accelerate research, but also blends work across disciplines. Lab researcher and MIT associate professor in EECS and CSAIL Justin Solomon describes his research group as growing up with the lab, and the collaboration as being “crucial … from its beginning until now.” Solomon’s research team focuses on theoretically oriented, geometric problems as they pertain to computer graphics, vision, and machine learning. 

Solomon credits the MIT-IBM collaboration with expanding his skill set as well as applications of his group’s work — a sentiment that’s also shared by lab researchers Chuchu Fan, an associate professor of aeronautics and astronautics and a member of the Laboratory for Information and Decision Systems, and Faez Ahmed, associate professor of mechanical engineering. “They [IBM] are able to translate some of these really messy problems from engineering into the sort of mathematical assets that our team can work on, and close the loop,” says Solomon. This, for Solomon, includes fusing distinct AI models that were trained on different datasets for separate tasks. “I think these are all really exciting spaces,” he says.

“I think these early-career projects [with the MIT-IBM Watson AI Lab] largely shaped my own research agenda,” says Fan, whose research intersects robotics, control theory, and safety-critical systems. Like Kim, Solomon, and Andreas, Fan and Ahmed began projects through the collaboration the first year they were able to at MIT. Constraints and optimization govern the problems that Fan and Ahmed address, and so require deep domain knowledge outside of AI. 

Working with the MIT-IBM Watson AI Lab enabled Fan’s group to combine formal methods with natural language processing, which she says, allowed the team to go from developing autoregressive task and motion planning for robots to creating LLM-based agents for travel planning, decision-making, and verification. “That work was the first exploration of using an LLM to translate any free-form natural language into some specification that robot can understand, can execute. That’s something that I’m very proud of, and very difficult at the time,” says Fan. Further, through joint investigation, her team has been able to improve LLM reasoning­ — work that “would be impossible without the IBM support,” she says.   

Through the lab, Faez Ahmed’s collaboration facilitated the development of machine-learning methods to accelerate discovery and design within complex mechanical systems. Their Linkages work, for instance, employs “generative optimization” to solve engineering problems in a way that is both data-driven and has precision; more recently, they’re applying multi-modal data and LLMs to computer-aided design. Ahmed states that AI is frequently applied to problems that are already solvable, but could benefit from increased speed or efficiency; however, challenges — like mechanical linkages that were deemed “almost unsolvable” — are now within reach. “I do think that is definitely the hallmark [of our MIT-IBM team],” says Ahmed, praising the achievements of his MIT-IBM group, which is co-lead by Akash Srivastava and Dan Gutfreund of IBM.

What began as initial collaborations for each MIT faculty member has evolved into a lasting intellectual relationship, where both parties are “excited about the science,” and “student-driven,” Ahmed adds. Taken together, the experiences of Jacob Andreas, Yoon Kim, Justin Solomon, Chuchu Fan, and Faez Ahmed speak to the impact that a durable, hands-on, academia-industry relationship can have on establishing research groups and ambitious scientific exploration.

When patients undergo general anesthesia, doctors can choose among several drugs. Although each of these drugs acts on neurons in different ways, they all lead to the same result: a disruption of the brain’s balance between stability and excitability, according to a new MIT study.

This disruption causes neural activity to become increasingly unstable, until the brain loses consciousness, the researchers found. The discovery of this common mechanism could make it easier to develop new technologies for monitoring patients while they are undergoing anesthesia.

“What’s exciting about that is the possibility of a universal anesthesia-delivery system that can measure this one signal and tell how unconscious you are, regardless of which drugs they’re using in the operating room,” says Earl Miller, the Picower Professor of Neuroscience and a member of MIT’s Picower Institute for Learning and Memory.

Miller, Edward Hood Taplin Professor of Medical Engineering and Computational Neuroscience Emery Brown, and their colleagues are now working on an automated control system for delivery of anesthesia drugs, which would measure the brain’s stability using EEG and then automatically adjust the drug dose. This could help doctors ensure that patients stay unconscious throughout surgery without becoming too deeply unconscious, which can have negative side effects following the procedure.

Miller and Ila Fiete, a professor of brain and cognitive sciences, the director of the K. Lisa Yang Integrative Computational Neuroscience Center (ICoN), and a member of MIT’s McGovern Institute for Brain Research, are the senior authors of the new study, which appears today in Cell Reports. MIT graduate student Adam Eisen is the paper’s lead author.

Destabilizing the brain

Exactly how anesthesia drugs cause the brain to lose consciousness has been a longstanding question in neuroscience. In 2024, a study from Miller’s and Fiete’s labs suggested that for propofol, the answer is that anesthesia works by disrupting the balance between stability and excitability in the brain.

When someone is awake, their brain is able to maintain this delicate balance, responding to sensory information or other input and then returning to a stable baseline.

“The nervous system has to operate on a knife’s edge in this narrow range of excitability,” Miller says. “It has to be excitable enough so different parts can influence one another, but if it gets too excited it goes off into chaotic activity.”

In that 2024 study, the researchers found that propofol knocks the brain out of this state, known as “dynamic stability.” As doses of the drug increased, the brain took longer and longer to return to its baseline state after responding to new input. This effect became increasingly pronounced until consciousness was lost.

For that study, the researchers devised a computational model that analyzes neural activity recorded from the brain. This technique allowed them to determine how the brain responds to perturbations such as an auditory tone or other sensory input, and how long it takes to return to its baseline stability.

In their new study, the researchers used the same technique to measure how the brain responds to not only propofol but two additional anesthesia drugs — ketamine and dexmedetomidine. Animals were given one of the three drugs while their brain activity was analyzed, including their response to auditory tones.

This study showed that the same destabilization induced by propofol also appears during administration of the other two drugs. This “universal signature” appears even though the three drugs have different molecular mechanisms: propofol binds to GABA receptors, inhibiting neurons that have those receptors; dexmedetomidine blocks the release of norepinephrine; and ketamine blocks NMDA receptors, suppressing neurons with those receptors.

Each of these pathways, the researchers hypothesize, affect the brain’s balance of stability and excitability in different ways, and each leads to an overall destabilization of this balance.

“All three of these drugs appear to do the exact same thing,” Miller says. “In fact, you could look at the destabilization measure we use and you can’t tell which drug is being applied.”

The researchers now plan to further investigate how each of these drugs may give rise to the same patterns of brain destabilization.

“The molecular mechanisms of ketamine and dexmedetomidine are a bit more involved than propofol mechanisms,” Eisen says. “A future direction is to do a meaningful model of what the biophysical effects of those are and see how that could lead to destabilization.”

Monitoring anesthesia

Now that the researchers have shown that three different anesthesia drugs produce similar destabilization patterns in the brain, they believe that measuring those patterns could offer a valuable way to monitor patients during anesthesia. While anesthesia is overall a very safe procedure, it does carry some risks, especially for very young children and for people over 65.

For adults suffering from dementia, anesthesia can make the condition worse, and it can also exacerbate neuropsychiatric disorders such as depression. These risks are higher if patients go into a deeper state of unconsciousness known as burst suppression.

To help reduce those risks, Miller and Brown, who is also an anesthesiologist at MGH, are developing a prototype device that can measure patients’ EEG readings while under anesthesia and adjust their dose accordingly. Currently, doctors monitor patients’ heart rate, blood pressure, and other vital signs during surgery, but these don’t give as accurate a reading of how deeply the patient is unconscious.

“If you can limit people’s exposure to anesthesia, if you give just enough and no more, you can reduce risks across the board,” Miller says.

Working with researchers at Brown University, the MIT team is now planning to run a small clinical trial of their monitoring device with patients undergoing surgery.

The research was funded by the U.S. Office of Naval Research, the National Institute of Mental Health, the Simons Center for the Social Brain, the Freedom Together Foundation, the Picower Institute, the National Science Foundation Computer and Information Science and Engineering Directorate, the Simons Collaboration on the Global Brain, the McGovern Institute, and the National Institutes of Health.

Zora Neale Hurston remains one of America’s best-known authors. Charles Henry Turner developed landmark studies about the behavior of bees and spiders. Brian Wilson founded the Beach Boys. George Nissen invented the trampoline. What do they all have in common?

Well, for one thing, they were all innovative Americans — creators and discoverers, producing work no one anticipated. For another, they are all now celebrated as such, in verse, by Joshua Bennett.

That’s right. Bennett — an MIT professor, lauded poet, and literary scholar — is marking the 250th anniversary of the founding of the U.S. with a book-length work of poetry about the country and some of its distinctive figures. In fact, 50 of them: Bennett has written a substantial work featuring remarkable people or inventions from each of the 50 states, meditating on their place in cultural fabric of the U.S.

“There’s so much to be said for a country where you and I are possible, and the things we do are possible,” Bennett says.

The book, “We (The People of the United States),” is published today by Penguin Books. Bennett is a professor and the Distinguished Chair of the Humanities at MIT.

Bennett’s new work has some prominent Americans in it, but is no gauzy listing of familiar icons. Many of the 50 people in his book overcame hardship, poverty, rejection, or discrimination; some have already been rescued from obscurity, but others have not received proper acclaim. Few of them had a straightforward, simple connection with their times.

“It’s about feeling that you have a life in this country which is undeniably complex, but also has this remarkable beauty to it,” Bennett says of the work. “A beauty you helped to create, and that no one can take away from you.”

The figures that Bennett writes about are sources of fascination, and inspiration, demonstrating the kinds of lives it is possible to invent in the U.S.

“We’re in a moment that calls for compelling, historically grounded stories about what America is, what it has been, and what it can be,” Bennett adds. “Can we build a life-affirming vision for the future and those who will inherit it? I’m trying to. I work on it every day.”

Taking flight

“We (The People of the United States)” is inspired, in part, by Virgil’s “Georgics,” pastoral poems by the great Roman poet. Bennett encountered them while a PhD student in literature at Princeton University.

“The poet Susan Stewart, my professor at Princeton, introduced me to Virgil’s Georgics,” Bennett says. “I eventually started to think: What would it look like for me to cover Virgil?” Adding to his interest in the concept, one of his favorite poets, Gwendolyn Brooks, had spent time recasting Virgil’s ancient epic, “The Aeneid,” for her Pulitzer Prize-winning work, “Annie Allen.” She also translated the original work from Latin as a teenager. Moreover, Bennett’s writing has long engaged with the subject of people working the land in America.

“I decided to start writing all these poems about agriculture,” Bennett says. “But then I thought, this would be interesting as an epic poem about America.” As he launched the project, its focus shifted some more: “I started to think about the book as an ode to invention.”

Soon Bennett had worked out the structure. An opening section of the work is about his own family background, becoming a father, and the process of building a life here in Massachusetts.

“Where does my influence, my aspiration, end and the child begin?” Bennett writes in one poem. That section prefigures further themes in the collection about the domestic environments many of its figures emerged from. For the rest of the work, with one innovator or innovation for each of the 50 states, Bennett adopted a regular writing schedule, producing at least one new poem per week until he was finished. 

Hurston, one of several famous authors and artists featured in the book, represents Florida. From Ohio, entomologist Charles Henry Turner was the first Black person to receive a PhD from the University of Chicago, in 1907, before conducting a wide range of studies about the cognition and behavior of spiders and bees, among other things.

George Nissen, alternately, was a University of Iowa gymnast who built the first trampoline in the 1930s in his home state — something Bennett calls a “magical device” that brings to life “the scene in your mind of the leap/and of the leap itself, where you are airborne, illuminated/quickly immortal.” Whether these innovations appear through rigorous academic exploration or became mass-market goods that produce flights of fancy, Bennett has a keen eye for people who break new ground and fire our own feelings of wonder.

“We actually are all bound up in it together,” Bennett says. “These different figures, from various fields, eras, and lifelong pursuits are in here together precisely because they helped weave the story of this country together. It’s a story that is still unfolding.”

Bennett is straightforward about the struggles many of his subjects faced. His choice to represent North Carolina is the poet George Moses Horton, an enslaved man who not only learned to read and write in the early 1800s — the state later made that illegal for enslaved persons, in 1830 — but made money selling poems to University of North Carolina students. Indeed, Horton’s work was published in the 1820s. Bennett writes that Horton’s public performance of his poetry was “an ancient art revived in the flesh of a prodigy in chains.”

Bennett’s unblinking regard for historical reality is a motif throughout the work. “To me it’s not only about exploring a history that a reader might feel connected to or want to learn more about,” he says. “It’s about honoring those who lived that history, who helped make some of the most beautiful parts of the present possible, through an engagement with the substance of their lives.”

Just my imagination

Many figures in “We (The People of the United States)” are artists, but of many forms. From watching VH1 as a child, Bennett got into the Beach Boys, and he devotes the California entry in the poem to them. Or as Bennett puts it, he was “newly initiated into a sound/I do not understand until I am old enough to be nostalgic/for windswept locales, and singular moments in time/I never lived through.”

Bennett was learning about the Beach Boys while growing up in Yonkers, New York, far from any California beaches. But then, Brian Wilson wasn’t a surfer either — he grew up in an industrial suburb of Los Angeles. Imagination was the coin of the realm for Wilson, something Bennett understood when Beach Boys songs would veer off in unexpected directions.

“I’ve always been drawn to moments of great surprise, or revelation, in the works of art I love,” Bennett says. “Which is part of why I’ve dedicated my life to poetry. You think one thing is happening in a poem, and suddenly that shock comes, that unexpected turn, or volta. Brian Wilson always had a great understanding of that. It works in pop music. Surprise, sometimes, is a shift in register that takes you higher.”

Various poems in the collection have down-to-earth origins. Bennett remembers his father often fixing things in the family home, from toys to the boiler, saying, “Pass me the Phillips-head,” when he needed a screwdriver. Thus Oregon appears in the book: Portland is where the Phillips-head screwdriver was invented.

In conversation, Bennett notes the hopeful disposition of his father, who after living through Jim Crow and serving in the Vietnam War, worked 10-hour shifts at the U.S. Postal Service to support his family. Even with all the difficulty he experienced in his life, Bennett’s father always encouraged his son to pursue his dreams.

“I’m grateful that I inherited a profound sense of belonging, and dignity, from my parents,” Bennett says. “There was always this feeling that we were part of a much larger story, and that we had a responsibility to tell the truth about the world as we knew it.”

And that’s really what Bennett’s new book is about.

“We can reckon with our history in its fullness and work, tirelessly, toward a world that’s worthy of the most vulnerable among us,” Bennett says. “Like Toni Morrison, we can ‘dream the world as it ought to be.’ And then make it real. That’s my vision.”