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.

Uncrewed aerial vehicles (UAVs) could fly deep into the heart of a raging wildfire, avoiding sudden flare-ups and dodging falling tree branches to gather critical information for rescuers. But in this dangerous situation, the UAV could easily be damaged or destroyed by falling debris.

The UAV — and the rescuers who rely on it — would benefit from a new, autonomous navigation system that is guaranteed to avoid collisions, even in completely unfamiliar surroundings. 

Developed by MIT researchers, this method plans a flight path for a UAV that eludes unknown obstacles that may move in unpredictable ways. It charts an efficient course through an unmapped environment that is mathematically proven to be safe from collisions.

Many popular navigation systems can only offer formal safety guarantees when the environment is static, or when the obstacles are known in advance.

By enabling the UAV to avoid moving obstacles while it is discovering its environment, this trajectory planner, which the researchers call “SANDO” (for “Safe AutoNomous trajectory planning for Dynamic unknOwn environments”) could be especially useful for applications like search and rescue missions into collapsed buildings, mine explorations through networks of hidden tunnels, or package delivery across a crowded neighborhood.

“In the hardest possible environment, where the UAV has no map of the area and there are unknown obstacles moving around, we established a mathematical guarantee of safety. The only thing the planner needs to know is the top speed the obstacles could reach. Given that, you could use it in any environment, without a map, and you know the UAV is not going to crash into anything,” says Kota Kondo SM ’23, PhD ’26, who recently earned his doctorate in aeronautics and astronautics at MIT and is lead author of a paper on this new system.

He is joined on the paper by Jesús Tordesillas PhD ’22, an assistant professor at Comillas Pontifical University in Madrid; MIT graduate students Juan Rached, Lili Sun, and Yixuan Jia; and senior author Jonathan P. How, a Ford Professor of Aeronautics and Astronautics and a principal investigator in the Laboratory for Information and Decision Systems (LIDS) and the Aerospace Controls Laboratory (ACL) at MIT. The research appears in the IEEE Transactions on Robotics.

Safety first

Trajectory planners use images and data from a UAV’s onboard cameras and sensors to chart a flight path that reaches the vehicle’s goal. 

Most existing planners are either designed for unknown static environments, where the obstacles don’t move, or they loosely avoid dynamic obstacles without providing a formal guarantee that the robot won’t crash. 

Formal safety guarantees are important in high-stakes situations, such as if a UAV were delivering medical supplies to the site of a remote natural disaster. But trying to compute every possible crash in a dynamic environment would take too long for real-world deployments.

“In an unknown dynamic environment, you don’t have many assumptions to rely on. In those types of environments, researchers haven’t yet been able to mathematically guarantee that a trajectory is safe,” Kondo explains.

The MIT researchers used a rigorous mathematical approach to develop SANDO, their safe trajectory planner. They theoretically proved the algorithm always computes trajectories which are guaranteed to avoid collisions with moving obstacles in unknown environments.

SANDO starts by mapping out a safety corridor through the robot’s environment. This corridor is a series of connected regions of 3D space the robot can travel through, which are guaranteed not to contain any obstacles. 

But unlike other systems, SANDO creates a time-sensitive safety corridor that considers the possible future movements of dynamic obstacles. It employs a special module that detects, groups, and monitors dynamic obstacles to estimate where they will move next.

While the system doesn’t know exactly where an obstacle will move in the future, it uses that obstacle’s maximum velocity to compute how far it could possibly go in a certain timespan. It puts a sphere around the obstacle that captures the farthest distance it could travel in all directions.

SANDO builds the safety corridor around these spheres to ensure the UAV will not collide with a moving object.

“In the real world, obstacles are going to move, so the safety corridor you create at one point won’t be useful as things move into the corridor. But because we consider this time component, we can now guarantee safety into the future,” Kondo says.

The system uses a heat-map based planner to identify “hot” regions of the environment with many obstacles and guides the UAV away from these dangerous areas. This helps the robot chart a more efficient course around danger zones.

Fast reactions

Once it has established a collision-free safety corridor, SANDO optimizes the trajectory within that corridor to find the fastest path to reach the goal. 

As the robot travels, SANDO adjusts the safety corridor and reformulates the trajectory to ensure the robot’s path remains collision-free until it reaches its goal. 

The researchers employed a few tricks to make the optimization easier to solve so the UAV can rapidly recalculate trajectories using its onboard computer, quickly reacting to sudden changes.

“The most difficult part of developing SANDO was the math,” Kondo says. “When you try to guarantee safety, you need to be rigorous and ensure your theory covers every possible case, even edge cases. Once we had that mathematical guarantee, it was very easy to fly the UAVs.”

In simulations, SANDO reached the robot’s goal faster than several state-of-the-art systems while completely avoiding collisions in all environments. 

SANDO also avoided all dynamic obstacles in 12 test flights with a real UAV, using the robot’s onboard computer and sensors to rapidly replan safe trajectories. 

In the future, researchers could make SANDO more computationally efficient and combine the system with machine-learning models that allow the user to give instructions to a robot using plain language.

A central challenge in autonomous flight is that a path that is safe when it is planned may become unsafe as the environment changes. SANDO addresses this challenge with time-varying safe flight corridors and hard-constrained trajectory optimization that supports frequent onboard replanning. Its combination of spatiotemporal planning, formal safety analysis, and hardware validation provides a practical approach to autonomous flight in complex dynamic environments,” says Fei Gao, an associate professor at Zhejiang University in China, who was not involved with this research.

This research is funded, in part, by the Defense Science and Technology Agency of Singapore.

As artificial intelligence transforms industries and national security, understanding the latest hardware capabilities is important for maintainind technological advantage.

AI accelerators — specialized systems designed to speed up capabilities such as neural networks, deep learning, and machine learning — have been a major area of development for nearly a decade. Since 2018, team from the Lincoln Laboratory Supercomputing Center (LLSC) has been conducting the Lincoln AI Computing Survey (LAICS, pronounced "lace"). Six papers later, LAICS continues to summarize current commercial AI accelerators and compare their peak performance and peak power.

"About eight years ago, we saw a sharp rise in the number of research AI accelerators described in research papers and commercial accelerators being announced, and we started to get questions about them from government sponsors of the laboratory's work. That was motivation enough to start the survey," says Albert Reuther, a staff member at the LLSC, which operates and optimizes the high-performance computing systems used by thousands of laboratory research staff.

Although AI accelerators are frequently used for processes such as machine learning, they also can enable other parallel applications, such as modeling the functions of molecules and speeding up simulations of fluid dynamics — processes that are very computationally expensive.

AI accelerator technology can come in a number of forms: central processing units (CPUs), graphics processing units (GPUs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), and dataflow accelerators. Each type of accelerator has slightly different capabilities. CPUs can be used for general-purpose computing, while ASICs can perform only very specific tasks. Dataflow accelerators, FPGAs, and GPUs are more flexible and can be configured for a variety of workloads. Efficiency and performance vary across the different types of accelerators depending on how they are designed. The goal of LAICS is to survey the technologies currently on the market and compare them to find the best accelerators for certain needs.

Led by Reuther, the LAICS team includes LLSC members Michael Jones, Peter Michaleas, Jeremy Kepner, and Vijay Gadepally. The team also collaborates with researchers across Lincoln Laboratory, including in the Advanced Technology Division and Intelligence, Surveillance, and Reconnaissance and Tactical Systems Division, to learn how accelerators support research and development for their missions.

The first paper in their series studied 57 accelerators, while the latest one looked at more than 120 accelerators. The main metrics the team uses to compare accelerators are the peak performance and power; then they sort accelerators by whether they're on a chip, card, or system. All the data in the papers are drawn from public sources, which can be challenging because some companies prefer to keep their performance and power data private. To keep up to date on the latest in the field, Reuther runs daily news and citation searches that report new technical press articles, company announcements, and industry presentations.

"It continues to surprise me how each year another five to 10 startups get funded and announced, and then release new AI accelerators," Reuther says. "One might think that the landscape is saturated enough, but then another batch of innovative accelerators is introduced."

In addition to summarizing the performance versus peak power of the current accelerators, each paper explores a new aspect of the field. For example, the paper published in 2022 investigated sources of performance increases, finding that they stem from smaller, denser transistor designs and the use of lower numerical precision (i.e., calculating fewer significant digits). The latest paper examined different architectural choices available, analyzing how the addition of certain components, such as more cores per processor or parallel performance, would change the system.

Reuther plans to continue the survey for the foreseeable future, stating that, in just the past few months, six new startups have announced their first AI accelerators.

"AI and the hardware it runs on are such hot topics, and it is important for Lincoln Laboratory to be an unbiased technical advisor for choosing and pursuing the right technologies," Reuther says. "Our AI accelerator surveys have helped many sponsors and government colleagues gain a better understanding of the AI accelerator landscape and make better research and acquisition decisions about them. This survey has also been very valuable to determine which GPUs we should consider for upcoming LLSC system purchases so it not only benefits our sponsors and mission programs, but also benefits all LLSC users."

The full set of papers and the accompanying datasets can be found here.

Chris Bourg, who has served as director of the MIT Libraries since 2015, has been named vice provost and Barbara K. Ostrom (1978) Director of the Libraries, MIT Provost Anantha Chandrakasan announced today.

“The title ‘vice provost’ signals the enduring importance of the MIT Libraries in the digital age,” said Chandrakasan. “While the ways in which the libraries meet the needs of our community might change as technology changes, their mission of protecting access to knowledge and information is as vital as ever.”

Thanks to a generous gift from Barbara K. Ostrom ’78, SM ’78, the position of the director of the libraries now has an endowment to help fund it in perpetuity. Bourg noted that the gift is especially meaningful for her, due to the sense of service she shares with Ostrom: they both completed Reserve Officers' Training Corps (ROTC) during their time as undergraduates — Bourg at Duke University and Ostrom at MIT — before going on to active military service.

“Barbara’s gift is an incredible acknowledgment of the progress the entire MIT Libraries staff has made toward our vision,” said Bourg, who noted that Ostrom had also supported an MIT Libraries initiative to highlight MIT’s women faculty by acquiring, preserving, and making accessible their personal archives. “Her generosity advances the libraries’ ability to support teaching, learning, and community-building at MIT well into the future.”

During her tenure, Bourg has emphasized digital access to content, a more open and equitable scholarly publishing landscape, and expanded support for data-intensive and computational research. She has helped both the libraries and the Institute adapt to AI-driven changes: she co-chaired the Working Group on Scholarly Content and Generative AI, charged with helping the MIT community navigate the legal, technical, and ethical issues involved when scholarly content is used to develop and train generative AI models. And she was a member of the Ad Hoc Committee on AI Use in Teaching, Learning, and Research Training, which issued its final report in August.

Promoting open scholarship has been a longstanding focus of Bourg’s career. In 2018, the libraries launched the Center for Research on Equitable and Open Scholarship, with Bourg serving as founding director. She co-chaired the 2017 MIT Ad Hoc Task Force on Open Access to MIT’s Research; its recommendations led to the creation of the MIT Prize for Open Data, co-sponsored by the School of Science, and the development of the MIT Framework for Publisher Contracts. Guided by the framework, MIT became one of the first major U.S. institutions to cancel a journals contract with Elsevier in 2020, standing by its open scholarship principles while saving the Institute millions of dollars.

Bourg co-chaired the 2016 Ad Hoc Task Force on the Future of Libraries and has guided the MIT Libraries toward the vision laid out by the task force report. Under her leadership, the libraries completed a major renovation of Hayden Library and the Building 14 Courtyard and launched MIT Reads, an Institute-wide reading and discussion program designed to foster empathy and belonging within the MIT community.

Prior to joining MIT, Bourg worked for 12 years in the Stanford University Libraries. Before Stanford, she spent 10 years as an active-duty U.S. Army officer, including three years on the faculty at the United States Military Academy at West Point. She received a BA from Duke, an MA from the University of Maryland, and an MA and PhD in sociology from Stanford. 

Bourg is a member of the board of the Center for Open Science, the external advisory board of the Stanford Data Science Institute’s Center for Open and Reproducible Science, and the board of Annual Reviews, a premier publisher of open review journals in 51 disciplines. She also currently serves on the national academies of Science and Engineering, and the Medicine Corrections and Retractions Committee, tasked with upgrading the scientific record.

MIT is launching a new strategic initiative today: MIT for America, which seeks to strengthen STEM education nationally, at many levels of learning. 

Addressing a critical national need, the initiative ranges across STEM fields and will engage students from kindergarten through community college, preparing them to live and work in a world increasingly shaped by science and technology. MIT for America’s programs will develop new opportunities for student achievement in mathematics, hands-on design and fabrication, and constructive engagement with artificial intelligence. 

President Sally Kornbluth made the announcement today in a letter to the Institute community.

“A natural extension of the Institute’s long record of national service, MIT for America is grounded in our deep belief in potential over pedigree, of the transformative power of learning by doing and in the open sharing of knowledge,” Kornbluth wrote.

The initiative represents an Institute-wide effort to address the national challenge of improving STEM education and helping it adapt to shifting societal needs. MIT for America will leverage the Institute’s longstanding strengths to develop new programs and expand existing projects, while amplifying MIT’s impact on national education.

MIT for America’s faculty leaders emphasize that the initiative will support programs that can be scaled up and that provide direct, face-to-face guidance for learners and educators. 

“The goal is to provide programs that are both scalable and high-touch, and make an impact across the country,” says Professor Eric Klopfer, a faculty co-director of MIT for America and director of the Scheller Teacher Education Program and the Education Arcade at MIT. “All our programs take advantage of things we are well-positioned to do at MIT, and can be distributed around the country. That’s our guiding light.” 

Cynthia Breazeal, faculty co-director of MIT for America, professor of media arts and sciences, and the Benesse Professor of Research in Education, says the initiative “is about bringing high-quality access to STEM and AI education, from kindergarten to community college, across the United States. We live in an innovation-driven world, and it’s important to have an education that gives you access to it, whether that’s so you can be a fully engaged citizen or have the opportunity for economic mobility.”

Breazeal adds: “Talent is everywhere. We think opportunity should be, too.” 

The initiative is addressing large-scale needs in STEM education. Only 29 percent of U.S. students in grades 9-12 build circuits, and only 6 percent of students enroll in computer science classes, even though about 60 percent of high schools offer such courses. MIT for America intends to substantially help communities across the country build the bandwidth needed to further develop STEM knowledge and workplace skills. 

“This effort is about really rolling up sleeves and getting out there and engaging communities, and helping to build capacity across the United States,” Breazeal adds.

MIT for America features programs addressing three high-priority areas:

Mathematical thinking and problem solving: Work here builds on an existing program, the MIT for America Calculus Project, launched a year ago, in which MIT students and alumni volunteer to help provide calculus tutoring to students in school districts across the country. 

Living, learning, and working with AI: This area addresses multiple issues revolving around AI, including teacher education that helps instructors integrate AI into classroom learning in productive ways, and programs that enhance AI skills and STEM education in technical-vocational schools and community colleges. The MIT RAISE program (Responsible AI for Social Empowerment and Education) is a leader in this domain. 

Designing, making, and innovating: MIT for America plans to build out programs that encourage hands-on learning, design work, use of fab labs and makerspaces, and experiential learning, sometimes deploying tools such as national design challenges. 

“Across the U.S., we want to unlock opportunities and experiences where students are,” says Claudia Urrea, head of MIT for America and head of the MIT pK-12 Initiative. “We want to find people who have not had the opportunity to discover their talent.”

In the first case, the MIT for America Calculus Project provides a ready model of outreach with growth potential. About half of U.S. school districts offer calculus, but many of those districts may be under-resourced. The MIT for America Calculus Project has been growing in partnership with an increasing number of school districts, while MIT undergraduates and alumni have enjoyed participating in the program.  

“People have been really excited about the MIT for America Calculus Project, seeing both the impact that it has, and its model, which I think has resonated,” Klopfer says. “That’s really representative of what other programs could look like.”

In the second topic area, MIT for America can leverage burgeoning efforts such as MIT RAISE. The program helps educate teachers and engage students about implementing AI in ways that can complement the developing skills of students, enabling them to use AI in productive ways.

“RAISE is about providing materials and training for teachers that helps them have conversations and productive activities around AI,” Klopfer says. “It’s about understanding how AI works and discussing what the values are in your school. It has to be active, involving the students. If you try to force things, about AI one way or the other, that will fail.”

In an outgrowth of RAISE, MIT also launched a related program, Pathways for AI Training and Hiring (PATH), on Oct. 1. Starting with a partnership with Georgia State University, PATH aims to bring AI training to two-year and four-year colleges, to help students gain AI skills useful for workplaces. 

“RAISE has had a lot of success with scaling already,” Breazeal says, noting that about 1 million students were involved with RAISE’s Day of AI event earlier this year. “At MIT, we want to empower youth voices to shape the future. It’s an excellent example of our ability to do that.”

And in the third topic area, MIT for America can help produce substantial growth in programs aimed at hands-on learning. That may include more projects such as MIT’s Regenerative Futures Challenge, a global program stemming from MIT’s pK-12 Initiative, which give students the opportunity to create climate and sustainability solutions. Additionally, the Fab Foundation, which grew out of MIT’s Center for Bits and Atoms Fab Lab program, offers tools and curriculum to learners at 300 fab labs across the U.S.

“It’s about coming together in places where there are people who can work together, mentor each other, and learn by example,” Klopfer says. “That’s the kind of thing we want to be creating at scale. We want to build on that.”

All told, MIT for America represents a vigorous all-campus effort to enhance education through sustained outreach and knowledge-sharing. At MIT, partners in creating the initiative include the MIT Media Lab; MIT RAISE; the MIT pK-12 Initiative; the Office of the Vice President for Resource Development; and the Office of Innovation and Strategy.

The leaders of MIT for America presented an overview of the program in September at MIT’s Alumni Leadership Conference, and they say alumni activity around the new initiative will be another key to its success. 

“We really appreciate the support and engagement of our alumni,” Breazeal says. “We know they care about K-12 education and access to community colleges.” More broadly, she adds, “We do think this message will resonate with people all across the country.”

“I look forward to watching these programs flourish, evolve, and grow,” Kornbluth wrote to the community. 

Like Earth, most planetary bodies circle their star in stable, detached orbits. These companionable systems can suddenly change when a planet comes too close to its star. In such a close encounter, a star can pull the planet in and swallow it whole. 

Across the galaxy, astronomers have seen plenty of stable, detached planetary systems. They have also observed a handful of stars quickly engulfing their planet. Now, for the first time, scientists have spotted a system that is striking a curious balance between the two extremes. And it’s revealing a new way that stars can interact with planetary companions. 

In a paper appearing today in Nature Astronomy, scientists at MIT and elsewhere have discovered a star leisurely snacking on a closely orbiting brown dwarf — a planet-like object that is more massive than a planet yet not quite as big as a star. 

The new system, named ZTF J0440+2325, is within the Milky Way galaxy, roughly 300 light years from Earth, and represents the first observation of a low-mass object that is slowly and steadily consuming material from another low-mass object. 

The rate at which the star is feeding from the brown dwarf suggests that this slow stellar cannibalism could carry on for hundreds of thousands, or even billions of years.

“When we think of stars interacting with planets or brown dwarfs, the picture is always that the star eventually swallows the other thing,” says Kevin Burdge, assistant professor of physics at MIT. “This is what will happen to the Earth when the sun becomes a red giant. But here, we’ve found an alternative: Instead of swallowing the thing up, the star can gradually eat it, for billions of years.”

The study’s MIT co-authors include Aaron Householder, Kaitlyn Shin, Saul Rappaport, Joheen Chakraborty, and Emma Chickles, along with collaborators from Caltech, the University of Hawaii, the Instituto de Astrofísica de Canarias and the Universidad de La Laguna in Spain, and the Harvard and Smithsonian Center for Astrophysics.

A “weird triangle”

The new system was spotted initially by the Zwicky Transient Facility. The ZTF uses a camera as part of a telescope at the Palomar Observatory, in California, to scan the sky for rapid changes in brightness, which could signal the presence of a supernova, a gamma-ray burst, or colliding neutron stars. 

Several years ago, Burdge was looking through ZTF data when he noticed a strange light curve, or pattern in brightness. Light curves for supernova resemble a bell curve, signaling the gradual brightening and then fading of a star as it bursts. But what Burdge picked out looked more like a triangle, that didn’t appear once, but again and again.

“I remember first looking at this and thinking: Stars don’t make triangular waveforms like this,” he recalls. 

At the time, he and his colleagues were focused on a different signal, which they identified as a “black widow binary” — a system in which an extremely dense, spinning neutron star is slowly consuming a much smaller companion star, similar to how its arachnid namesake plays with its prey. 

Burdge wondered whether the triangle signal might also be from a black widow. But the light from the signal was puzzling. In black widow binaries, the light appears to wobble, as a result of a very light, low-mass object, such as a small companion star, whipping around a much heavier object, such as a neutron star. 

“We weren’t seeing that whipping back and forth here,” Burdge says. “It didn’t make any sense. We couldn’t explain what this was.” 

But they had a hunch: Could the signal be coming not from a wobbly, David-and-Goliath system, but from a more balanced pair of objects, each with a similarly low mass? 

“If you have less mass in the system overall, things can gently orbit each other without whipping back and forth,” Burdge says. “That was the idea. But we never had any proof. And this weird triangle just sat for years.”

A slow and steady fireball

Recently, Burdge and Householder, a graduate student in MIT’s Department of Earth, Atmospheric and Planetary Sciences, decided to revisit the triangle mystery. From the original ZTF signal, they determined the location of its source to be within the Milky Way galaxy, around 300 light years from Earth. They focused multiple telescopes on the source, named ZTF J0440+2325. From these observations, they measured various properties of the source, including its wobble. Compared to black widows and other similar binaries, the wobbling from ZTF J0440+2325 was much smaller — but not insignificant. 

“That was the real clincher for this system,” Householder says. “When we measured that wobble, we found we were not seeing a black widow. This was a low-mass star that’s orbited by a brown dwarf. The wobble was too small in amplitude to be anything else.”

They determined that the star and the brown dwarf are extremely close, with the brown dwarf circling the star every 87 minutes, in an orbit that could fit within the diameter of the sun. Both objects are small by stellar standards. The star is around 85 times as massive as Jupiter, while the brown dwarf is around 25 times as massive. 

With two low-mass objects circling at such close range, the scientists wondered if one object could be pulling material from the other. Such a process, known as accretion, is most often seen around objects that are extremely massive, though small in actual size, such as black holes and neutron stars. When a black hole accretes, or draws material from a much smaller nearby object, it pulls the matter around it in a disk.

“The difference here is: The thing absorbing matter is not a tiny black hole but a star, which is relatively big in size,” Burdge explains. “So matter just pummels directly onto the surface, at very high speeds, like an asteroid hitting the moon.”

The team carried out simulations of possible accretion in ZTF J0440+2325. Taking into account the properties of the star and the brown dwarf, they simulated particles of matter on the brown dwarf, and how these particles should behave within the system over time, according to the laws of physics and equations of motion. 

“When we track those test particles, we see they indeed fall right onto the surface of the star,” Householder says. “This is the first time we’ve caught a low-mass star actively accreting from another low-mass object.”

What’s more, the team calculated that the brown dwarf must be feeding material to its star at a rate of about 1/100,000 of an Earth’s mass each year. That’s about 40 million dump trucks’ worth of material, or roughly 1.3 trillion one-pound burritos every second. While that may seem like a lot of matter to be losing, it is in fact a very small fraction of the brown dwarf. This rate, the researchers estimate, is actually quite slow and steady. Given the size of the system, they say the star could continue leisurely snacking on the brown dwarf, for billions of years. 

This slow accretion, they say, would resemble a steady stream from the brown dwarf, onto the star. The researchers realized that if they were to view the system from afar, the brightness from the system would chart as a triangle, as the brown dwarf and its stream of matter circles its star. 

“It’s like you’ve got this continuous fireball onto one of the objects, and as one orbits the other, that hotspot comes in and out of view, and the peak of the triangle signal is when you’re looking right at the fireball,” Burdge explains. 

With the mystery of the triangle light curve solved, the team hopes to spot similar slow-feeding systems nearby. 

“It’s inspiring a lot of new searches on our part,” Householder says. “I think we’re going to learn a lot about a different kind of way that planets and brown dwarfs interact with their host stars.”

This research was supported, in part, by the National Science Foundation.

MIT formally dedicated its newly transformed Met Warehouse (Building W41) on Wednesday, in an energetic evening ceremony heralding the start of a new era for the practice of design on campus. 

The ceremony highlighted “the essential vision of the Met: connecting people, disciplines, and ideas, and inviting everyone to see where those connections might be,” said Mark Gorenberg ’76, chair of the MIT Corporation, during introductory remarks.

MIT President Sally Kornbluth called the new building “a testament to the transformative power of design, which has been central to MIT from the very beginning.” She also heralded the building as a cornerstone of MIT’s “magnetic new West Campus district for art and design,” which includes the Edward and Joyce Linde Music Building (W18) and the MIT Theater Arts building (W97). 

Hashim Sarkis, dean of MIT’s School of Architecture and Planning, delivered keynote remarks at the event, linking together many themes of the evening and recounting the project’s development in the late 2010s.

In envisioning moving into the Met Warehouse, Sarkis said, the “building spoke to us. It offered us possibilities that we could clearly envision both outside and inside the thick brick walls. … It helped us imagine how we can live and work together as a campus and as a school. Over the months of discussions among the faculty and students across the school, it became clear that it was an idea whose time had come.”

Many members of the MIT Corporation were in attendance for the event, held in the Met Warehouse’s Sidara Auditorium. The ceremony was also an occasion for giving thanks to those who made the new Met Warehouse possible: campus leaders, donors, faculty, architects and designers, contractors and specialized builders, and many others who worked on the remarkable structure in different ways. 

“The list, I promise you, runs longer than the credits at the end of “The Odyssey,” and what an odyssey this has been,” Sarkis quipped. 

From fortress to workshop

The Met Warehouse was originally constructed from 1884 to 1923, as a massive private storage facility with 2-foot thick brick walls, a corner turret, slit-like windows, and other features making it look like a fortress. About 500 feet long, with five stories, the building was long an imposing local curiosity.

MIT acquired the Met Warehouse in the 1970s and in the 2010s began exploring possible new uses for it. By 2018, the idea of making it the new home of MIT’s School of Architecture and Planning had gained enough traction to move forward. 

The high-profile firm Diller Scofidio + Renfro won the competition to become the project architects, and created a variety of solutions to bring natural light into the warehouse and revamp its interior. With permission from the Cambridge Historical Commission, the designers replaced four segments of brick wall on the building’s north side with top-to-bottom glass sheets, which along with skylights bring in abundant light.

At the same time, the architects — led by Elizabeth Diller and Benjamin Gilmartin, who were at the dedication last night — overhauled the building’s interior, while working within many of its structural features. The refurbished Met Warehouse now features double-height design studios, an auditorium, a large entrance lobby, a ground-floor café, offices, and many flexible, reconfigurable classroom spaces designed to help faculty and students collaborate on an immense array of projects. 

The Met Warehouse also features building-long open corridors on all five floors and a central staircase connecting all of them, as elements designed to enhance circulation and connectivity within the building.

In her remarks, Kornbluth heralded the Met Warehouse’s educational potential, noting the challenges artificial intelligence presents for education, as highlighted in an MIT-wide report released in August. 

That report, she outlined, emphasized that education comes from “helping students to value the process of learning as a ‘productive struggle’ and from engaging them in ‘human settings where they … learn how to work with others, communicate their ideas, receive criticism constructively, build confidence, develop judgment, and act as members of a community.’”

With that in mind, Kornbluth said, “that sounds exactly like the kind of hands-on, in-person, collaborative problem-solving the new Met was made for. … This community is not only ready to withstand the educational challenges of AI … it’s also primed to help the rest of MIT meet the moment. And that is very good news for us all!”

Diller also addressed the audience, highlighting some of the key design challenges involved in the project and thanking many of those who worked on it, including Leers Weinzapfel Associates, the project’s associate architects, and Shawmut Design and Construction. The Met Warehouse, she emphasized, is meant to be used in many ways in the future, and was designed with enough flexibility so that it can continue to evolve. 

“This building should remain a work in progress,” Diller said, adding that she would continue to regard it as “definitively unfinished.” 

Professors John Ochsendorf, Caroline Jones, and Lawrence Vale — MIT faculty who are all associate deans in the School of Architecture and Planning — also spoke at the ceremony, outlining the implications of the building for the school’s many forms of research and collaborative work. 

“It’s working,” Ochsendorf said, now that the building is inhabited on an everyday basis by students, faculty, and staff. 

Giving more thanks

As with almost any large, long-term project, credit can be spread in many directions, and the speakers at the dedication ceremony gave ample thanks to those involved — and to the important supporters of the project. 

“Hashim Sarkis has been its greatest champion,” Kornbluth said. “His leadership and imagination shaped not only this building, but the ambitious future it makes possible for the school.” She also thanked MIT President Emeritus L. Rafael Reif, a project supporter during his tenure, “for your foresight, your perseverance, and your insistence that the music, theater, and design communities at MIT deserve facilities worthy of the quality of their world-class work.”

For his part, Sarkis also gave credit to former MIT Corporation Chair Robert Millard ’73, saying, “Without Bob and his wife Bethany, this building would not be here today.” Gorenberg, in his remarks, also made a point of thanking the City of Cambridge for its extensive cooperation with MIT on the project. 

Gorenberg also expressed his deep “gratitude to the Morningside Foundation,” the philanthropic arm of the T.H. Chan family. He cited the “extraordinary generosity” of the founding gift, from family members Gerald and Beryl Chan and Ronnie and Barbara Chan, establishing the Morningside Academy for Design (MAD), a major interdisciplinary center at MIT located in the Met Warehouse. 

Speaking of Gerald L. Chan, Gorenberg added, “We cherish his wisdom and belief in MIT as a leading institution that can do good for the world.”

In a statement sent to MIT News, Chan said: “In this day and age when disciplinary boundaries are ever dissolving, it is important to have initiatives that tie all departments of the Institute together so that students can be facilitated to have broad exposures. Design provides such a possibility, and MAD is the venue.”

Sidara (formerly the Dar Group), a global collaborative of specialist design, engineering, and consulting firms owned by Maha and Talal Shair, supported the establishment of two central public spaces in the building, the Sidara Auditorium, and the Sidara Gallery, on the ground floor. 

“At Sidara, we share MIT’s commitment to improving lives, solving critical challenges, and making room for cultures to shine,” Talal Shair said in a statement to MIT News. “We feel a profound resonance with SA+P across all three dimensions — so it was only fitting that we would support the transformation of the MET into a hub for education, research, and innovation. We trust the Sidara Auditorium and the Sidara Gallery will serve as spaces for idea exchange, inspiring future generations to think broadly and act boldly.”

The LUMA Foundation, a Zurich-based nonprofit founded by Maja Hoffmann in 2004 to support artistic production and the organization behind LUMA Arles, an interdisciplinary creative campus in southern France, also gave an establishing gift for the MIT LUMA Lab, based in the Met Warehouse, for projects combining art, science, technology, conservation, and design. 

“LUMA Foundation has always been grounded in the belief that meaningful change begins by creating the field and conditions for people, disciplines, and forms of knowledge to encounter one another freely, critically, and with mutual respect,” Hoffmann said in a statement for MIT News. “The MET gives this principle a remarkable new context. To see faculty, students, researchers, artists, designers, scientists, and technologists connected through the MIT-LUMA Lab working alongside one another is a powerful expression of what such a place can enable. I am excited and proud to be contributing to this journey and be part of the MIT community and its future.”

Referencing MIT’s motto, “mens et manus,” which is Latin for “mind and hand,” Gorenberg wrapped up the dedication ceremony last night with an additional thought: “This is ‘mens et manus’ at its finest.” 

The Massachusetts Institute of Technology Investment Management Company (MITIMCo) announced today that MIT’s unitized pool of endowment and other MIT funds generated an investment return of 10.3 percent during the fiscal year ending June 30, 2026, as measured using valuations received within one month of fiscal year end. At the end of the fiscal year, MIT’s endowment funds totaled $29.2 billion, excluding pledges. Over the 10 years ending June 30, 2026, MIT generated an annualized return of 11.7 percent.

The endowment is the bedrock of MIT’s finances, made possible by gifts from alumni and friends for more than a century. The use of the endowment is governed by a state law that requires MIT to maintain each endowed gift as a permanent fund, preserve its purchasing power, and spend it as directed by its original donor. Most of the endowment’s funds are restricted and must be used for a specific purpose. MIT uses the bulk of the income these endowed gifts generate to support financial aid, research, and education.

The endowment supports about half of undergraduate tuition, helping to enable the Institute’s need-blind and full-need undergraduate admissions policy, which ensures that an MIT education is accessible to the most talented students in the nation and the world regardless of their financial resources. 

In fiscal 2026, MIT enhanced undergraduate financial aid, ensuring that all students from families with incomes below $200,000 and typical assets have tuition fully covered by scholarships, and that families with incomes below $100,000 and typical assets owe nothing toward their students’ MIT education. Eighty-eight percent of the Class of 2026 graduated with no debt. With our investments in financial aid, parents of MIT undergraduates receiving financial aid now pay on average 10 percent less on a real basis than at the end of the Great Recession.

MIT Student Financial Services works closely with all families of undergraduates who need financial aid to make MIT affordable for them. In 2025-26, the average need-based MIT undergraduate scholarship was $66,155. Fifty-eight percent of MIT undergraduates received need-based financial aid, and 44 percent of MIT undergraduate students received scholarship funding from MIT and other sources sufficient to cover the total cost of tuition.

MIT’s endowment enables it to do more cutting-edge research. Fueled by funding from the endowment, the Institute more than matches the amount of campus-based research funded by the U.S. government and other sponsors — expanding its beneficial impact without asking more from taxpayers. 

MITIMCo is a unit of MIT, created to manage and oversee the investment of the Institute’s endowment, retirement, and operating funds.

MIT’s Report of the Treasurer for fiscal year 2026, which details the Institute’s annual financial performance, was made available publicly today.

As far back as she can remember, Cathy Wu ’12, MNG ’13 wanted to find ways to solve problems to improve people’s lives. Her parents were Taiwanese immigrants, and her father had a long commute to his job, which took him away from the family. On a tight budget, the rest of the family often stayed home on a street that was too busy for playing outdoors. Wu and her siblings ended up playing a lot of computer games. 

Wu says her desire to make the world a better place, her dad’s daily battle against traffic, and the games she played, like “SimCity,” were the seeds of her motivation to design safe, efficient transportation systems. 

Wu is an associate professor in the MIT Department of Civil and Environmental Engineering (CEE) and the Institute for Data, Systems, and Society (IDSS), and a principal investigator in the Laboratory for Information and Decision Systems. Her research focuses on using machine learning and reinforcement learning (RL) to advance reliable strategies for improving a range of complex systems, including transportation.

“Designing transportation systems consists of modeling and analyzing dozens, if not hundreds or thousands, of variants, which means that an evidence-driven approach to designing those systems is simply not within reach of today’s tools,” Wu says. “This is the role that RL plays. If successful, it would free transportation researchers and enable their practitioner partners to design the systems they want.”

Wu credits her older sister with instilling in her the desire to improve people’s lives, and Wu’s interest in transportation fits neatly into that ideal.

“I like transportation because it connects everyone. We all use it, we all experience it, we all have issues with it. So, at some level, we’re all interested in the system being better,” she says.

Wu got interested in applying artificial intelligence to transportation while earning her undergraduate degree at MIT, after attending a lecture on autonomous vehicles by the late professor Seth Teller. The lecture, which Teller gave during an Independent Activities Period robotics competition (that Wu actually won), was the event that honed her particular approach to transportation research, Wu says. She began working with Teller, and when he stopped concentrating on autonomous vehicles, he encouraged Wu to transfer to Professor Daniela Rus, who had done research on robotaxis.

“I’m very grateful to the people who helped me explore those interests and helped me become the person I am now,” she says, specifically naming Teller, Rus, and “my friends at Dropbox,” who invited her to do a second internship focused on transportation issues.

After her master’s degree at MIT, Wu went on to earn her PhD at the University of California at Berkeley. During that time, she observed that transportation researchers were spending years developing optimization methods to model and analyze a single new variant of a system. Her approach as a computer scientist working to develop RL and optimization methodologies to address transportation challenges held the promise of exponentially improved efficiency.

In 2018, Wu’s last year of her PhD at UC Berkeley, she successfully applied RL to a traffic problem: automatically analyzing the potential traffic flow impact of autonomous vehicles in a range of different traffic networks. The research went viral.

While this could have been a “the rest is history” moment for Wu, RL turned out to be a flighty friend. Wu worked on RL theory in a postdoc at Microsoft and came back to MIT as faculty drawn, she says, by the sustainability focus of CEE, and IDSS’s emphasis on infusing data science into other disciplines.

Yet over the next two years, Wu’s further attempts to apply RL to traffic problems failed.

“That was stressful,” Wu says, “it was unclear whether the problem was me (the advisor), my students, the traffic domain, or RL itself.”

Still, the earlier research was a proof-of-concept demonstration that RL could be applied to transportation systems.

And in 2022, she and her students identified that RL algorithms are so sensitive that an algorithm that works on one problem may not on even a closely related one. A key result, which Wu says she is proudest of “because it was like the light at the end of a long tunnel of negative results,” came in 2023. She and her team of researchers devised a way to work around the sensitivity of RL. The team found that while RL may not train well on 90 percent of a group of problems, it can train quite well on 10 percent. And by training RL models on those problems that solve and generalize well, the resultant models collectively perform well on a set of related problems, even those that would not have been solved through direct training. The researchers designed an algorithm to determine which problems to use RL to train, and that algorithm improved training efficiency by up to 30 times, meaning that what would normally have required 100 training models may only require three models.

“This work gave me back the confidence that reinforcement learning can play an important role in solving hard optimization problems, including in transportation,” Wu says. “Now, a good chunk of my group works on the topic of contextual RL, which is the setting where RL seeks to solve a space of related problems.”

Wu’s more recent research applies RL to solve a hard transportation optimization problem with important policy implications: the work shows that eco-driving measures in which vehicle speeds are intelligently controlled to reduce excessive stopping and starting could reduce vehicle emissions by between 11 and 22 percent. The system provides evidence that policies instituting such measures could significantly improve system efficiency, and is “a demonstration that RL can be used to inform transportation policy on problems of practical importance,” Wu says.

“I am a big fan of evidence-based policy and believe it’s the basis for a thriving democratic society, yet our societal systems are so complex,” Wu says. “People can bicker forever about what’s better or worse, but I do believe that there are questions we bicker about that can be analyzed systematically using data and have objective answers. A large part of the reason I am in academia is to better understand how technology can support democratic societal decision-making.”

Wu says that much of the work she and her team have done over the last several years has produced algorithms “to streamline the development of solvers for hard optimization problems, whether they are related to transportation or to other systems, such as logistics, supply chains, manufacturing, and resource allocation.

“This alludes to my preferred style of work,” Wu says, “which is called use-inspired basic research,” explaining that such research addresses a practical problem, developing fundamental knowledge that often translates to other practical problems. Her students start by probing consequential problems ranging from safety to congestion to accessibility, identifying where existing methods fall short, and allowing the problems themselves to shape the direction of the research.

At the same time, Wu’s desire to help others on a more personal level plays out in her teaching.

“I love working with students, both in the classroom and research mentoring,” she says. “It makes my day when I am able to teach someone something — when I see that light bulb go on in a student.”

In addition to earning academic honors, including a 2023 National Science Foundation Faculty Early Career Development Award, Wu has also been formally celebrated for her teaching and mentoring, including with the Ole Madsen Mentoring Award in 2025.

What does she tell students confronting extremely complicated problems?

“Be patient. Start small. Societal impact is a lifelong endeavor, not something to be accomplished in a few years,” Wu says. “It will take years to really understand what’s going on and where the real problems are. In the meantime, try to be helpful. Be curious. Ask many questions.”

Despite the “dot-com crash” in 2000, the tech industry remained an attractive career destination for many who believed technology represented the future. The digital age — defined by global connectivity and computers — had firmly taken hold, and over time, the tech industry emerged as a dominant force in the labor market. High-paying jobs for engineers, designers, and professionals across a wide range of fields became increasingly common. 

MIT PhD student JS Tan SM ’22 was among those who, upon graduating from Brown University and Rhode Island School of Design in 2015, joined the tech industry. 

“A lot of us had this idea that technology, and in particular the technologies related to the internet, had the potential for bringing about a more progressive version of the world,” Tan says. 

Google echoed this ethos to its employees with its once-famous motto, “don’t be evil,” as their informal corporate philosophy and code of conduct guideline. They’ve since dropped the tagline.

But in time, particularly with the start of the first Trump administration, some tech industry employees found themselves questioning if their employers were really intent on supporting policies to support a more democratic world. According to Tan, their willingness to publicly oppose their employers’ actions — at first successfully — is currently experiencing an anti-worker backlash. 

Now, Tan and his former tech industry colleague Clarissa Redwine have published a book on the rise and fall of the tech worker labor movement. “Against Tech Oligarchy: Worker Resistance in the World’s Most Powerful Industry” (Haymarket Books, 2026) chronicles how tech workers organized themselves, the effective strategies they used, and the effect the movement had on Silicon Valley labor politics over the past decade.

Documenting a movement from within

In 2017, Tan was working for Microsoft and Redwine for Kickstarter, when President Donald Trump signed an executive order suspending entry into the United States for nationals from seven predominantly Muslim countries for 90 days, and suspending Syrian refugees from entering the country indefinitely. 

“As a whole, I think the tech sector really pushed back against this,” says Tan. “[OpenAI co-founder] Sam Altman participated in protests of this ban at the airport. In fact, the day before he joined these protests, he wrote in his blog that the tech industry needed to take a stand against the Trump administration, and particularly its immigration policies.” 

Altman’s Jan. 28, 2017, blog post read, in part, “Tech companies go to extraordinary lengths to recruit and retain employees; those employees have a lot of leverage. If employees push companies to do something, I believe they’ll have to. At a minimum, companies should take a public stance. But talking is only somewhat effective, and employees should push their companies to figure out what actions they can take.”

Tan says Altman’s words inspired tech workers across the industry to publicly voice their opposition and “push for the values they believed in.” For the next several years, workers staged walkouts and protested their employers’ contracts with U.S. military and immigration enforcement agencies, as well as workplace policies they considered sexist. 

“That was a time in which a lot of tech workers felt that their companies were walking back the values that they had initially promised,” says Tan.

Their objections initially met with some success. In 2018, following protests by Google employees, the company decided not to renew its contract for Project Maven, a Pentagon initiative using artificial intelligence to analyze drone surveillance footage. Nearly 4,000 employees signed an open letter to their CEO stating, “Google should not be in the business of war.”

Fast forward eight years. Earlier this year, more than 600 employees signed an open letter urging Google’s CEO to reject classified AI work with the Pentagon, citing concerns about potential uses including lethal autonomous weapons and domestic surveillance. 

“The way Google responded to them this time was to say, basically, ‘Too bad, we’re committed to working with the Pentagon,’” says Tan. “So, there is this kind of shift as to how Google is positioning itself politically, as well as to their employees.”

What happened to the tech industry employee leverage? 

Tan’s book outlines several events that he argues have negatively impacted their formerly strong influence. First, following interest rate hikes in 2022, the tech industry lost hundreds of billions in market valuation and set out to cut costs, the most significant of them being the expensive salaries of their employees. In other words, the labor market soured on tech workers, giving employers opportunity to wrest back control, Tan suggests. 

Second, he points to the drastic effect of agentic AI coding systems on the nature of their work, arguing that these tools have deskilled workers and made everyone much more worried about job security.  

“Tech workers had to face these shocks on their own. With a union or some ability to coordinate across workers and bargain as a group, they might’ve had more power to actually push back,” says Tan.  

Documenting the past to support the future

Tan enrolled at MIT in 2020, earning a master’s degree at the MIT Media Lab. He is currently a doctoral student in the Department of Urban Studies and Planning with a focus on the political economy of the tech sector.

At various points in their careers, Tan and his co-author had been involved with organizing in the tech sector. Redwine was a prominent organizer in the union drive at Kickstarter. She was fired from the company in 2019; Redwine said her dismissal was retaliation for her organizing activity, while Kickstarter denied that claim. One reason Tan and Redwine wrote this book is because they saw that the bandwidth for labor organizing among tech workers had hit a new low. 

“Tech workers want to have a say over the way their technologies are designed,” says Tan. “They want to be able to push for the right guardrails around technologies that they’re building. We wanted to use this book as an opportunity to analyze why it was that, within eight years, the tech worker movement had sort of fallen into this state of paralysis.”

They began writing the book in 2024, reliving the highs and lows of the past decade. This is Tan’s first book. He says writing it was an “exhilarating experience” and one that he profoundly enjoyed.

“It’s why, in part, I’m drawn to academia. To a large extent, I believe in the power of research and of writing. To have the opportunity to do this about a subject that I care deeply about has been an amazing experience.”

A book launch and discussion about “Against Tech Oligarchy,” co-hosted by the Department of Urban Studies and Planning, will take place on Oct. 26.

On the first day of class 16.445J/STS.468J (Entrepreneurship in Aerospace and Mobility Systems), David Mindell, MIT professor of aeronautics and astronautics (AeroAstro) and the Frances and David Dibner Professor of the History of Engineering and Manufacturing, asked his students to take a look at an image of a textbook. The cover featured a bright and inspiring collage of rockets, planets, and all manner of futuristic air- and spacecraft. The title: “Entrepreneurship in Aerospace: A Guide for Founders and Investors.”

“This is the most up-to-date guide on the topic,” Mindell said, describing the book’s treatment of the concepts and procedures involved in innovating in aerospace, an industry experiencing a renaissance of entrepreneurship led by venture-backed startups and rapid innovation, and one that, whether or not we realize it, we all depend on every day. 

Konark Chopra, a graduate student in MIT’s Leaders for Global Operations Program, raised his hand. “Sounds great — how can we get a copy?”

“It doesn’t exist,” replied Mindell. “You’re going to write it this semester.”

After conducting over 50 interviews with founders, operators, engineers, and investors across the aerospace industry, the class pulled it off. The 100-page industry report, titled “Entrepreneurship in Aerospace,” provides comprehensive insight into what it actually takes to build an aerospace venture today, and makes predictions about what is needed in the near future.

The challenge to produce the report was inspired by the bestselling guide “Disciplined Entrepreneurship: 24 Steps to a Successful Startup,” by Bill Aulet, professor of the practice in the MIT Sloan School of Management and managing director of the Martin Trust Center for Entrepreneurship. Originally published in 2013, the book provides an outline for entrepreneurs in any industry to cultivate skills for success. “Entrepreneurship in Aerospace” builds on Aulet’s framework to provide an industry-specific guide. “Aerospace is its own special industry with a unique set of constraints and aspirations,” says Mindell.

“Putting together this report let the students learn what they won’t get from a regular class about aerospace entrepreneurship,” he says. “This is the view of the industry from the young people building its future. I’m incredibly proud of what they’ve accomplished.”

Betting on the known unknowns

During spring 2026, while the class was in session, major developments were shaping the aerospace sector, from NASA’s Artemis II mission to SpaceX’s launch of what would become the largest initial public offering in history. “The entire industry landscape shifted like crazy during the semester,” says Mindell, as just one of the reasons that the report was especially timely. 

The report argues that the volume of capital, talent, and policy attention directed toward aerospace is “structurally different from any prior point in the industry’s history,” creating a pivotal moment for the next generation of entrepreneurs and investors.

The report further outlines five predictions about the new space economy: terrestrial infrastructure for compute, manufacturing, and energy will move off-world; autonomous systems, robots, and humans will continue to work together, but with humans in a supervisory capacity; venture capital investment will run ahead of economic justifications; government investment will become the fastest way for young companies to fundraise; and that we are years away from a global regulatory framework for space companies to operate within. Each prediction, or “bet,” includes a section on “where serious people disagree,” laying out relevant counterarguments to their conclusions.

As a guide, the report also translates its findings into practical tools, including a diagnostic for determining how many independent breakthroughs a company needs to succeed. “One of my favorite tools from the report is the miracle count,” says Chopra. “If your company needs one breakthrough to work, that’s a venture bet. If it needs three, that’s a research project pretending to be a startup.”

The report’s findings were informed both by existing research and by interviews with current aerospace professionals. Students were graded, in part, on how many people they spoke with. Their interviews focused on what excites people about the industry, reasons for their optimism (or pessimism), and how people are working within their organizations to address the challenges they see. 

The experience also allowed the students to expand their professional networks, practicing a core entrepreneurial skill while gaining insider perspectives.

“We sought out people from different corners of aerospace and were always asking, ‘Who else should we talk to?’” says Nicole Lee, a graduate student in AeroAstro. “Not only was I able to reconnect with people in my own network, but I got to introduce classmates to those contacts, and then benefit from the networks they brought in, too. That exchange was a big part of what made the interview process, and the class, so special.”

Engineers as entrepreneurs

The report’s authors — 16 classmates from the Department of Aeronautics and Astronautics, MIT Sloan School of Management, and Wellesley College — bring a range of academic backgrounds and career ambitions to the project, using those different perspectives to connect the realities of aerospace engineering with the economic forces impacting the industry. Their research interests and experiences range from spacecraft propulsion and human spaceflight to investment banking and military operations. Collectively, they have worked across organizations like NASA, SpaceX, Blue Origin, Boeing, and a range of startups.

“What I’ll remember most is the team,” says Chopra. “Everyone showed up with their own wisdom and a willingness to challenge each other, learn from each other, and simply have fun. Those are the teams we hope to keep building with.” 

For Lee, those industry experiences support the report’s predictions about where the field itself is headed. “Entrepreneurship in space is going to involve a much broader group of founders, engineers, researchers, policymakers, and operators,” says Lee. “Everyone working in space can take something away from understanding the entrepreneurial mindset and the cultural shift we’re seeing in commercial space. A much wider range of people will be shaping entrepreneurship in the future. I think that shift has already started with us.”

Mindell sees value in that entrepreneurial mindset, regardless of whether the students go on to found companies of their own. “I don’t know if every student in this class is going to found their own company, and I don’t expect them to, but I do expect them to drive their own careers forward,” he says. “And I think for the moment we’re at, providing that opportunity is the best thing MIT can be doing for our students.”

For Chopra, who has had his sights set on founding an aerospace company for as long as he can remember, the findings from the report are immediately applicable. “The heart of a business, a sustainable business, is the demand. What do the customers want? Sure, I could build cool technology, but if we don’t have anyone buying it, it’s a project, not a company.”

Now armed with a clear and evidence-backed picture of the landscape, Chopra wants the report to generate even more activity across the industry. “If our industry report inspires one person to go out and found a company, or invest in a company, or even think about entrepreneurship in aerospace, it’s a pretty big win.”

The book “Disciplined Entrepreneurship” by Bill Aulet, managing director of the Martin Trust Center for MIT Entrepreneurship and the Ethernet Inventors Professor of the Practice at the MIT Sloan School of Management, walks readers through the 24 steps of starting a venture. With more than half a million copies sold, the approach has proven remarkably effective: MIT students who use the framework in the delta v startup accelerator program have a 61 percent survival/acquisition rate and have collectively raised over $3 billion dollars.

But while the framework is taught at hundreds of colleges around the world, it is not designed for younger students who want to learn about entrepreneurship. To fill that gap, the Trust Center created a free, AI-powered youth entrepreneurship platform called Dear Dreamer, made possible through a gift from the Frank and Eileen Foundation. 

Dear Dreamer is open to all students in middle and high school. It adapts the disciplined entrepreneurship framework into a digital, self-paced learning experience featuring short educational videos, interactive exercises, and personalized feedback on the user’s idea. Aulet says the goal is to empower 50,000 young entrepreneurs by 2030.

MIT News spoke with Aulet about the mission of the project and how it came together.

Q: What was the impetus for creating Dear Dreamer?

A: We’ve had a lot of success teaching the disciplined entrepreneurship framework at MIT. Then we made it a course on the online learning platform edX, and we got hundreds of thousands of people taking the class. I used to get emails from people saying, “For the first time in my life, I see myself as an entrepreneur,” or “I see economic security.” At MIT, we often say, “MIT in the world, for the world.” At the Trust Center, we see our mission as, first, train people here at MIT, but also to create more entrepreneurs outside of MIT. 

The platform was inspired by the vision of Audrey McLoghlin, the founder and CEO of apparel brand Frank and Eileen and president of the Frank and Eileen Foundation. In my first meeting with Audrey, we were already asking ourselves, “How do we make more entrepreneurs in the world?” I showed her Jetpack, MIT’s generative AI tool trained on disciplined entrepreneurship, which walks MIT students through the entrepreneurial process, and her eyes lit up. She said, “This is how we create more entrepreneurs: We make the work that you guys are doing here accessible to young people.” She wished she had a tool like this when she first started. She also said her daughter wants to be an entrepreneur, but students don’t get much guidance on entrepreneurship at school. MIT has been a great place to keep making this more accessible.

Q: How does the platform work?

A: It takes the core disciplined entrepreneurship curriculum that we know works from our data, and makes it more interactive and engaging for young people. We spent a lot of time working with younger students to figure out how to make it more digestible to a 10-year old, 12-year old, or 14-year old. We put in different case studies and examples for each step of the framework, and we’ve designed the user interface to make it more like Instagram or YouTube, with videos featuring people that students can relate to. But ultimately the content follows the same path we know works; it’s just presented differently from what you would present to an MIT MBA or PhD.

Q: How might learning about entrepreneurship benefit students?

A: Entrepreneurship is a mindset, a skillset, and a way of operating. It allows you to deal with change, and the world’s rate of change is going faster and faster. It’s really not just about founding companies. Founding companies is a great way to learn the mentality that, ‘We can be different. We can build something. We can achieve a lot.’ Someone once said, ‘If you give a person a job, you give them dignity.’ But if you make someone an entrepreneur, it’s like giving them super dignity. They go from a job seeker to a job creator, and they can focus on the things that they’re most interested in, working with the people they want, in the culture they want. We teach students the entrepreneurial mindset and we train them to systematically take an idea and then come up with a solution in an ambiguous, uncertain environment, and iterate on that. 

Everyone might not start a company, but even if they go to work at bigger companies, there are a lot of benefits to the entrepreneurial mindset. I’m an entrepreneur now and I’m at MIT, which is not a startup. In a world that’s moving faster and faster, everyone has to deal with change and ambiguity. Starting companies is just a great way to learn. It’s kind of like learning to paint from a blank canvas. 

In 2017, a man named Michael walked onto stage in front of a packed Kresge Auditorium at MIT and attempted to draw a spiral, a common test doctors use to diagnose Parkinson’s disease. His tremors, caused by the disease, made the exercise difficult. 

Then, Michael put on a wristband device made by a team of MIT students as part of 2.009 (Product Engineering Processes), who were presenting their prototype that evening.

Michael pressed a button and the device produced a subtle vibration. The vibration sent signals up his wrist and into his brain. His tremors dramatically decreased, and within seconds he was able to draw the spiral with much more precision, to a roaring ovation from the audience.

“When this device is turned on, I feel like I used to feel when I didn’t have Parkinson’s disease,” Michael told the crowd. “It’s an amazing, amazing feeling.”

The performance was so impressive that the student team received requests from classmates and others asking where they could buy the device for family and friends living with tremors. Unfortunately, the students had to explain there was only one — for the time being.

The event set off a near decade-long journey that began by leveraging MIT entrepreneurial resources like MIT Sandbox, MIT FUSE, and the MIT Venture Mentoring Service. In 2020, the student team turned into an official company, Encora Therapeutics. But there were still dozens of hardware iterations ahead. Then there were clinical trials. In one trial, 78 percent of patients reported benefits after 90 days of home use.

This February, nine years after Michael’s brave demonstration, all that work finally paid off: The FDA cleared Encora’s device to help adults with essential tremor, a condition similar to Parkinson’s that causes shaking, often in the hands. 

“It’s been a long and difficult — very difficult — journey, but also very rewarding, especially when we get feedback from patients,” says Daniel Carballo ’18, SM ’20, an Encora co-founder and vice president of strategy. “We hear stories from patients about how they’ve struggled with their condition and how much they benefit from this. It reminds us why we keep going.”

From classroom to commercialization

The three founders of Encora who are still with the company are Carballo, Allison Davanzo ’18, and Kyle Pina ’18. They were each seniors in 2017 when they enrolled in 2.009, MIT’s popular product-design class.

The semester began with a brainstorming session in which groups of about 17 students were asked to come up with dozens of potential product ideas. Carballo proposed a wearable device that used mechanical vibration to send signals to the brain to reduce tremors. The initial idea was to help patients with Parkinson’s disease.

“It was one of hundreds of throwaway ideas,” Carballo recalls. “The initial concept was inspired by classes I had taken in robotics around neural control of movement. I had a preliminary understanding of how an electromechanical device might interact with the body’s control systems and feedback loops that control movement to relieve pathological control of movement.”

The team eventually whittled their long list of ideas down to a few. Carballo’s idea was finally selected by a vote of 16-1 — with Carballo the only dissenting vote.

“I tried to explain to the team that this was so far-fetched that there was no way, in one semester, we would be able to make anything,” Carballo recalls. “Thankfully, I got outvoted.”

Through most of the semester, Carballo’s pessimism looked justified. At every class milestone, the team lagged behind other teams. Then, the week before final presentations, they met Michael, whose severe hand tremors were the result of early-onset Parkinson’s.

“He was a home renovator, so he worked with his hands, but his tremors had progressed to the point that he struggled to turn a screwdriver, use a drill, or even fill out paperwork,” Carballo recalls. “He had become reliant on his wife and daughter not only to run his business, but to help him with everyday activities.”

By this point, the 2.009 team had a prototype that would vibrate to send mechanical feedback to the brain. Carballo described it as “a foamcore box with a Raspberry Pi chip and some wires coming out.” When Michael put on the device and turned it on, his tremors dramatically decreased.

“It was like a light switch turned on and his tremors stopped,” Carballo says. “His wife and daughter started crying. A week later, he was gracious enough to repeat the process on stage for the final presentations.”

The presentation — and the outpouring of interest from people who wanted it for loved ones with Parkinson’s — made the team determined.

“It showed us there were a lot of people with this problem that could really benefit from this,” Carballo says. “A subset of the team became possessed. It would have been such a shame to know this could exist and to have it never leave the classroom.”

Some team members began using MIT’s entrepreneurial resources to commercialize the technology, initially focusing on Parkinson’s. They ran their first clinical trial with 20 Parkinson’s patients in 2022 in collaboration with MassGeneral Brigham. But they soon learned more patients are living with essential tremor.

“It was a greater unmet need,” Carballo says. “There are a lot of drugs being developed for Parkinson’s disease, but essential tremor hasn’t experienced that same innovation.”

Today scientists think tremor is caused by malfunctioning signals in the regions of the brain responsible for interpreting sensory input and coordinating movement.

“In these diseases, neurotransmitter deficiencies result in this pulsed signaling in the brain that manifests as tremors that are basically pulsed motor outputs,” Carballo says.

One current approach to target those signals is surgery that involves drilling holes in the skull to insert electrodes that drive new electrical patterns in the brain. Encora’s watch-like product targets the same brain regions with mechanical vibrations at the wrist.

“We’re applying mechanical stimulus, basically vibration, to stretch receptors in the wrist, which tell your body where it is in space,” Carballo explains. “The stimulation causes the receptors to activate and send a patterned signal to peripheral nerves of the wrist, that then carry the signal to the peripheral nervous system and into the central nervous system, to the same regions of the brain targeted by surgery.”

In 2024, Encora ran a randomized control trial with 47 patients living with essential tremor. Last year, the team ran a 59-person trial where patients used the devices at home. In both trials, more than 70 percent of patients experienced meaningful improvement.

The results were promising enough to gain what’s known as 510(k) clearance from the FDA for use as a medical device, in February of this year.

Helping patients

Most patients see rapid benefit when using Encora’s device. Patients have described the device as life-changing. Some say it allows them to do tasks they haven’t been able to do in years.

“We see some patients using the device 12, 14 hours a day,” Carballo says.

Today, Encora is focused on building a national sales force and working to secure coverage from insurers. As the company ramps up production, the product will finally become available for patients who qualify.

Further down the line, Encora hopes to fulfill its original mission of helping mitigate tremors in patients with Parkinson’s. Carballo believes the approach also holds promise for many other patients.

“There is a surprisingly long list of diseases that this could work for,” Carballo says.  “The most obvious are neurological movement disorders, but the bigger picture of wearable neuromodulation is a rapidly growing field that has seen therapeutic benefit across a broad range of diseases. We see this as a platform technology.”

“What you see is what you get” is a guiding principle for many software engineers — create programs where the content you’re editing looks the same as the final product. But when you’re using generative artificial intelligence systems to 3D print, say, a mug, you’ll likely get a cup that can’t hold your coffee. Why is that?

The issue is that AI models understand how an object should look, but not how it works, leading to impractical designs that undermine an item’s intended use. Even if you want to fix these errors, the models are typically hard to edit, especially for users new to 3D design.

A new approach called “InstructMesh” makes it much easier to design and print household items, accessories, and robots that work in the real world. The design software, which was developed by researchers at MIT’s Computer Science and Artificial Intelligence Laboratory (CSAIL), Google, and Northeastern University, can be prompted to generate a 3D design for a pair of glasses, for instance, and users can then highlight specific parts of the blueprint they’d like refined before 3D printing. It’s an AI-driven interface designed to understand how these designs should look and which edits experts and novices alike want to make, helping them create the objects they actually want to see.

CSAIL researchers used InstructMesh to put a personalized, creative spin on otherwise regular items. For example, the tool produced a mug that appears to be enveloped by a dragon, with its tail being the handle. It also fabricated a shiny blue whistle resembling a shell and a pair of glasses with butterfly wings spreading out just above the lens. Getting even more creative, it made an octopus-like dispenser, with liquids flowing out of each tentacle to distribute drinks into several cups at once.

What makes InstructMesh so adept at following such unique prompts? It pairs Microsoft’s TRELLIS system, which creates 3D models from text and image prompts, with the large language model (LLM) GPT-4, which supports ChatGPT — in other words, visual and textual knowledge combined.

“We wanted to bring together the talents of 3D generators and the reasoning skills of LLMs in an interactive space to make objects that people actually want,” says Faraz Faruqi SM ’22, PhD ’26, lead author on a paper presenting the project, graduate of the Department of Electrical Engineering and Computer Science, and recent CSAIL affiliate. “Language models are great at text and images, while TRELLIS’s talent lies in its ability to create 3D models, since it’s seen so many.”

InstructMesh’s strengths come in handy in other, more surprising areas. MIT scientists used the program to fabricate a knee brace that looks like denim to match a patient’s jeans. InstructMesh can even help create robots — that is, clever enclosures that house wireless components. The researchers made a “bristle bot” that resembles a colorful shrimp to demonstrate this. It has a motor hidden inside, and when switched on, it can slide across surfaces, sort of like a wind-up toy.

Faruqi and his colleagues found that InstructMesh could easily make their desired items. But what would someone who’s never 3D modeled anything think of their program? And could they really detect design flaws before fabrication?

The team has TRELLIS recreate popular 3D models found on Thingiverse, a platform home to millions of 3D printable models, to help them find out. Nearly 80 percent of the models it generated were structurally flawed in some way. CSAIL researchers then asked novices to identify and fix these issues in InstructMesh — and they were able to do both around 90 percent of the time, as reviewed by an expert. What these newcomers lacked in expertise, they made up for in intuition.

InstructMesh scaffolds the actual modeling process, which previously required domain expertise in 3D modeling tools. “With manipulation happening in the latent space of the generative model, InstructMesh supports natural language description of issues, and creates interpretive changes in the geometry for the user to evaluate and approve,” says Faruqi. 

Users then created items resembling things like phone stands and vases, noting that InstructMesh was easy to use. They also found that InstructMesh enabled them to express a wide range of ideas, while the sliders gave them more precision to make certain tweaks, such as enlarging or extruding a particular part of the model.

“The users got what they prompted for and easily tweaked designs where needed,” adds Faruqi. “What they saw is what they got, and the items worked as advertised, so to speak.” 

While users enjoyed using the InstructMesh, Faruqi has an even grander vision for the project. He now works at Google, where he may soon incorporate InstructMesh into an augmented reality (AR) platform. The idea: Prompt the system by explaining what you need using the context of your surroundings, then it’ll rapidly 3D print it (e.g., making a phone case that matches your wallet).

InstructMesh may also begin to incorporate physics simulations to model how your design may react to specific uses, such as whether a bowl breaks when dropped, and which materials would work best. The software might also integrate the more recent TRELLIS.2 to refine even smaller features in 3D models.

Stefanie Mueller, an associate professor of electrical engineering and computer science (EECS) and mechanical engineering at MIT, and a member of CSAIL, is a senior author on the paper. Faruqi and Mueller wrote the paper with Google researchers Ahmed Katary ’23; Fabian Manhardt; Vrushank Phadnis MEng ’13, PhD ’20; Ruofei Du; and Federico Tombari. Other co-authors were Northeastern University Assistant Professor Megan Hofmann along with several CSAIL colleagues: Demircan Tas SM ’24 and SMArchS ’24, a PhD student in EECS and architecture; former visiting researcher Theresa Hradilak; Ning Zhang ’25, a graduate student in EECS; postdoc Jiaji Li; and Martin Nisser SM ’19, PhD ’24.

The researchers’ work was supported, in part, by Google and the MIT-HPI Collaborative Research Program. They will present it at the ACM Symposium on User Interface Software and Technology in November.

MIT Future Fest launched on Wednesday afternoon, featuring a visit from Massachusetts Governor Maura Healey before an opening panel at Kresge Auditorium.

Both Healey and MIT President Sally Kornbluth emphasized the power of curiosity and innovation during introductory remarks, while formally kicking off MIT Future Fest as a new kind of event that opens MIT’s doors to the world.

Spanning five days and featuring talks from leading MIT scholars, panel discussions, open labs, exhibitions, performances, screenings, and much more, MIT Future Fest adds up to a demonstration of campus research, teaching, thinking, and discovery.

“MIT and Massachusetts are known around the world as leaders in innovation,” Healey said. “Future Fest is a chance to celebrate that. But it’s also a chance to connect with each other. To learn from each other, and to imagine what comes next. In life sciences and biotech, robotics, advanced manufacturing, in energy and climate technology and quantum, and in the people who make it all happen.”  

In remarks preceding Healey’s talk, Kornbluth observed that “in a world as complex and interconnected as ours, we aim to invent the future. We need all the help we can get, and that’s why it’s so important to have all of you join us over the next five days in exploration, conversation, and curiosity.”

Future Fest also underscores that at MIT, as Kornbluth put it, “We feel a drive to explore, to understand, to solve, to invent, to design, all in service of society.” 

Since its founding, “MIT has long stood at the forefront of innovation and invention,” Healey noted. “‘The future starts here’ isn’t just a fitting theme for the next few days. It’s something we’ve been doing for the better part of 250 years in Massachusetts.”

Healey later appeared at an event about ocean sciences at the MIT Museum, whose staff have helped develop and organize MIT Future Fest. 

All told, MIT Future Fest features over 100 speakers at more than 50 events, among many other activities. The MIT Museum is free to the public during Future Fest; MIT’s newly opened Met Warehouse is hosting its first public events on Saturday; and Sunday features the Cambridge Science Carnival, a series of family activities. 

The remarks by Healey and Kornbluth were followed by a panel discussion, “The Future Begins Here,” featuring participants Noubar Afeyan PhD ’87, founder of Flagship Pioneering and co-founder of Moderna; Sangeeta Bhatia SM ’93, PhD ’97, the John J. and Dorothy Wilson Professor of Health Sciences and Technology and of Electrical Engineering and Computer Science at MIT, and director of MIT’s Marble Center for Cancer Nanomedicine; and Bob Mumgaard PhD ’15, CEO and co-founder of Commonwealth Fusion Systems. 

Massachusetts Economic Development Secretary Eric Paley moderated the wide-ranging discussion, which touched on the cutting-edge Massachusetts innovation ecosystem, the implications of artificial intelligence for research and industry, and more. 

The panelists noted that a confluence of ingredients — universities, other research institutions such as hospitals, venture capital, and more — has helped make Massachusetts the world leader in biotechnology and other forms of innovation. 

“You can’t be here and not feel like things that the rest of the world feel are not possible are possibly possible,” Afeyan said. “It’s really interesting how the environment makes you feel like you should also do something special.” He added that the various ecosystem elements have created a “critical mass” needed to sustain broad-ranging innovation. 

“This critical mass accumulated between the academic science, the hospital research, the entrepreneurial companies, large companies moved in, the government stepped in, state level, to try to help this community,” Afeyan observed. “It’s an inherent advantage of any ecosystem, it’s the co-location, it’s the connections.”

Mumgaard, whose company is building the first fusion power plant in the U.S., concurred that the combination of elements available in the state ecosystem is crucial for advanced innovation. 

“In many ways the challenge is simply scale,” Mumgaard said. “How do you get enough capital, how do you get enough smart people, how do you get enough people that are hard workers, how do you get the alignment from the institutions that can give you the permissions and tailwinds to be able to go and do that? And we did find that in Massachusetts.” 

He added: “You could not have really have built this company outside of Massachusetts.” 

For her part, Bhatia, a leader in developing nanoscale technologies that have made inroads in battling numerous forms of cancer, observed that part of the inspiration for her career came from visiting the MIT campus as a high school-age student.

“It was just that idea that you could engineer things, you could create instruments to improve human health, that started me on my journey,” Bhatia said. 

As a faculty member, she has helped found eight spinoff companies and helped direct an MIT study about how the Institute — which has already helped generate about 30,000 new companies — can do even more to produce startups and technology transfer. Ultimately, she has concluded, teaching researchers about the tools and skills they need to found companies can help generate even more productive networks of entrepreneurial activity.  

“We can be more intentional. We can create communities. … There are all kinds of ways to do more,” Bhatia said.

The panel discussion included reflections on AI and forecasts about the future of technology 20 years out, while ending with a reminder from Afeyan that, while the future is hard to forecast, it is at least partially shaped by people working consistently hard in pursuit of their goals. 

“We can envision the future a lot better than we can predict it,” Afeyan said, when asked to forecast the effects of technology in a couple of decades, near the end of the discussion.

Afeyan concluded: “My parting thought on this would be to say … I think you can either live in the present, do the things you do, and get the future you deserve, or you can envision the future you want, and do everything in service of that.”

Google.org announced on Sept. 15 that the MIT Transit Lab is a recipient of $2.1 million in funding — one of only 15 projects selected in the worldwide Google.org Impact Challenge: AI for Government Innovation. The funding from Google’s philanthropic arm will support NGOs, social enterprises, and academic institutions as they integrate artificial intelligence-powered solutions across topics like health, resilience, and economy.

The Transit Lab’s winning project, the Public Transit Intelligence Hub (PTIQ), aims to unify public transportation agencies’ real-time monitoring, operations control, and passenger communication systems into a single centralized AI-orchestrated platform that will allow transit control center staff to make better-informed, on-the-spot decisions, and provide riders with more immediate and accurate information.

The control centers of public transportation agencies are similar in appearance to the portrayal of NASA mission control in movies: rooms filled with employees monitoring dozens of radio feeds and computer screens relaying real-time camera data about stations and their operations, transit vehicle locations, riders, traffic, and road conditions. Unfortunately, the information coming in is fragmented, rather than integrated into a centralized system with overall awareness of the network’s conditions. This system creates an intense work environment for the transit staff making operations and communications decisions that can affect thousands of passengers relying on transit to get them where they need to go.

“Public transportation agencies are required to make decisions around the clock regarding real-time operations, control, and passenger communication,” says Awad Abdelhalim, associate director of the Transit Lab, and PTIQ co-principal investigator, project director, and technical lead. “Our goal isn't to automate those decisions, but to make sure the people making them have the best information possible. By unifying and streamlining data and information flow from fragmented and siloed internal systems, PTIQ will improve the experience of both riders and the transit workforce.”

Jinhua Zhao, the MIT Class of 1941 Professor of City and Transportation, head of the MIT Department of Urban Studies and Planning, and founder and director of the MIT Mobility Initiative (MMI), is the other co-principal investigator on the project. The PTIQ program manager is MIT Lecturer Jim Aloisi, who directs the Transit Research Consortium, which will also work on the project. That consortium is comprised of researchers from the Transit Lab, MMI, and Northeastern University, where Professor Haris Koutsopoulos takes the lead. 

In addition to providing funding for the three-year project, Google.org will provide pro bono support from its own engineers and AI product experts.

"AI holds incredible potential to transform public services, but there is often a gap between promise and practice,” says Maggie Johnson, global head of Google.org. “By equipping the 15 selected organizations with funding and pro bono support from Google's own AI experts, we are empowering the people closest to the problem to show what is truly possible. Together, we can ensure that AI makes a profound, positive difference in the everyday lives of communities worldwide."

The project will build on the group’s decades of experience in applied-research collaborations with transit agencies in major metropolitan areas throughout the world. PTIQ’s decision support interface for control center staff will integrate predictive models, optimization engines, and large language model-based contextual reasoning. But ultimately the decision-making based on that information will be left to transit staff, who can better balance the trade-offs of making one decision over another in these often incredibly complex situations.

“The hard part of integrating AI in transit is not the technology; it’s the institution,” Zhao says. “AI is evaluated on benchmarks. Public transit is assessed in the control center and on the streets. Over decades of work with transit agencies in Washington, D.C., Chicago, London, Boston, Tokyo, and Hong Kong, we have learned to ask a different question. Not whether AI can do this, but whether it can work in the organization and whether the staff trust it. PTIQ is designed to ground AI in the institutional reality and behavioral nuances of a transit agency, and bring machine intelligence and human judgment into one place.”

“Currently the evaluation of AI models relies heavily on deterministic, objective tasks, such as solving mathematical equations or generating code,” Abdelhalim explains. “However, the vast majority of real-world operational tasks — like delivering public transit services — are highly dynamic, multi-stakeholder, and lack a single correct objective answer. These complex spatiotemporal environments are the ultimate testbed for evaluating what AI systems can add to society.”

PTIQ aims to transform the transit workforce experience, the transit rider experience, and the overall ability of transit agencies to efficiently respond to disruptions and unexpected events. 

“We expect that PTIQ will take what is largely a siloed environment and connect it in ways that provide powerful benefits for the agency workforce and its riders,” says Aloisi, who is also a former secretary of transportation for the Commonwealth of Massachusetts. “[Doing this by] improving response time, reducing platform and bus stop crowding, providing riders with higher quality and timely information, and supporting agency staff — from dispatchers to vehicle operators and communications staff — with high-quality, reliable, real-time information and solution sets.”

A new AI system that excels at challenging games with hidden information could someday help human decision-makers select ideal strategies to outfox opponents in complicated situations like military maneuvers.

Using advances in machine-learning, researchers from MIT, Carnegie Mellon University, New York University, and Stanford University developed an AI that defeated top-ranked human players of the board wargame Stratego by a large margin — something no AI system had been able to achieve. 

Stratego, a two-player game of imperfect information, in which the opponent’s piece identities remain hidden, is often used as a benchmark to test the strategic thinking abilities of powerful AI models.

To build their model, the researchers combined efficient training algorithms with new techniques tailored for calculated decision-making in hidden information settings. 

The AI system achieved greater performance at Stratego than the next best models, while being far cheaper and less computationally demanding to train. The system also outperformed top human players in other strategic games with different rules and designs, demonstrating how it can be generalized for a variety of use-cases.

The AI system could be adapted to help humans tackle many real-world problems with hidden information, such as business negotiations or cybersecurity. 

“In the kind of imperfect information tasks you would face in reality, you often don’t have the luxury of enumerating through all the possibilities. There are just too many. Having AI algorithms that are general purpose and can provably perform this challenging task so well is a big step forward,” says Gabriele Farina, an assistant professor in the Department of Electrical Engineering and Computer Science (EECS), principal investigator at the Laboratory for Information and Decision Systems (LIDS), and senior author of a paper on this AI system.

He is joined on the paper by lead author Samuel Sokota, a graduate student at Carnegie Mellon; Eugene Vinitsky, an assistant professor at NYU; Zico Kolter, a professor at Carnegie Mellon; Hengyuan Hu, a graduate student at Stanford; and Zhiyuan Fan, an EECS graduate student at MIT. The research appears today in Nature.

Hidden information

The world is full of imperfect information problems. 

In these interactions, some parties possess information others do not. For instance, traders in financial markets may not know the rationale behind the trades of others, while military forces likely don’t have full knowledge of enemy positions. 

With hidden information, the decisions parties make, as well as the decisions they choose not to make, are intertwined in such a way that it is extremely difficult to determine the best steps to take next.

“The more you bluff, the more your opponent expects it, and the less each bluff is worth. It’s not obvious how to reason about that,” Sokota explains. “It’s very different from a setting like chess, where the best move is still the best move no matter how often you’ve played it.”

Stratego is often used to model imperfect information situations. In this board wargame, which resembles military chess, players arrange 40 pieces on their side of a board and then move pieces across the board to capture their opponent’s flag. 

But the identity of all pieces remains secret until they collide, and then the lower-ranking piece is eliminated.

The possible piece configurations number more than 10 to the 66th power — an exponentially greater number than in chess — making Stratego extremely difficult for an AI system to play well. 

Past efforts, such as Google’s DeepMind, relied on sophisticated operations that were computationally demanding and costly. But even with millions of dollars in training costs, these models were still not strong enough to beat top human Stratego players.

“With Stratego, there is an explosion of possible universes you might have to deal with. AI techniques that were developed for games like poker definitely could not scale in this setting,” Farina says.

The MIT researchers set out to develop a full AI system that could achieve superhuman performance for less cost, which they called Ataraxos (a Greek word used to describe one who is unbothered or free from anxiety).

A two-pronged approach

To build Ataraxos, the researchers trained the model using a technique called self-play reinforcement learning. The model plays against itself many times to learn a strong “blueprint strategy” of how to excel at Stratego. 

They designed especially efficient algorithms, which enabled Ataraxos to learn much faster than prior methods while ensuring it didn’t get stuck trying to predict every possible move. This reduces training costs and boosts performance. 

“Our system reaches strictly higher playing strength than DeepNash (DeepMind’s system) while using less than one hundredth of the training examples and less than one thirtieth of the self-play games, indicating a massive improvement in efficiency,” says Farina.

During a game, Ataraxos uses the blueprint strategy as a starting point to set up the board and begin thinking about its next moves at each round of play. 

But before acting, it refines its choices on the fly using a technique called decision-time planning. The system employs a generative model that uses probabilities to estimate the likely identities of the opponent’s hidden pieces, then evaluates future choices before selecting the next move. 

“Rather than just guessing blindly, we use decision-time planning to find the most plausible state of the board. Using this generative model allows us to really zoom in on the specific board and opponent we are facing,” Farina says.

The innovative use of this generative model for decision-time planning was the missing piece that enabled Ataraxos to achieve superhuman performance.

Ataraxos beat the strongest Stratego player in the world by a record margin of 15-1-4 and achieved a 39-2 record against top human players at the Stratego world championship. “Ataraxos is good at calculating risk in a way that humans are not. A human might start freaking out if their most valuable piece is exposed, but the bot can be surprisingly composed. It doesn’t overcorrect and give away its secrets,” Farina says.

The researchers also adapted Ataraxos for other imperfect information games, including Barrage Stratego (a faster-paced variant with fewer pieces), Hanabi (a cooperative card game with many players), and Dou dizhu (a game in which two players cooperate against a third).

The system achieved superhuman performance in each instance, demonstrating the generality of this method.

In the future, the researchers want to build interpretability measures into Ataraxos so the system can explain its decision-making in a way that a human could understand. 

“Humans must have the final say in whether a recommendation is followed, so before adoption can happen, we need a way to audit the model’s decisions. We still have a long way to go, but I hope these algorithms can be the foundation for a lot more work to come,” Farina says.

This research is funded, in part, by the Office of Naval Research, the New York University Department of Civil and Urban Engineering, the C2SMART Center, the National Science Foundation, and a Schmidt Sciences AI2050 Early Career Fellowship.   

About 25 percent of lung adenocarcinomas have mutations of the gene KRAS, which drives uncontrolled cell growth. In recent years, the FDA has approved two KRAS inhibitors to treat patients with KRAS mutations. While these drugs can work well initially, tumors almost always develop resistance to them.

Usually, resistance emerges because cells reactivate KRAS activity, through mutations that prevent drug binding or by increasing KRAS expression that overpowers the effects of the inhibitor. However, in a new study, MIT researchers have modeled an alternative mechanism that cancer cells can use to become resistant to KRAS inhibition.

The researchers found that in some cases, lung tumors undergo transformation from adenocarcinoma to squamous cell carcinoma. Both of these tumor types are commonly found in the lungs, but they are thought to  arise from different cells and have different genetic profiles.

When this transition occurs, tumor cells no longer require KRAS, and appear to turn on alternative signaling pathways that help them continue to grow. Ongoing work to identify those pathways may reveal targets for new drugs that could help prevent resistance to KRAS inhibitors.

“The main takeaway is that there seem to be different routes of resistance to KRAS inhibitors, and so we need to be thinking about how we can address this,” says Carrie Rodriguez, an MIT graduate student and one of the lead authors of the paper.

Nicolas Mathey-Andrews PhD ’25 is also a lead author of the study, which appears today in the journal Nature Genetics. The paper’s senior author is Tyler Jacks, the David H. Koch Professor of Biology and a member of MIT’s Koch Institute for Integrative Cancer Research.

Tissue transformation

The two FDA-approved KRAS inhibitors both target a mutation called KRAS-G12C. These drugs are approved only for use in patients whose tumors have failed to respond to other drugs, and these patients usually have cancer that has spread beyond the lungs.

KRAS inhibitors are effective in about 35 percent of the patients who receive them. However, in those cases, the tumors almost always end up becoming resistant by generating additional copies of the KRAS gene or finding other ways to turn on the MAP kinase signaling pathway, which is usually triggered by KRAS and stimulates cell growth.

“Resistance to targeted therapies is a very serious problem,” Rodriguez says. “Sometimes these KRAS inhibitors can hold cancers at bay, but most cases do end up relapsing.”

A 2021 study from researchers at Dana-Farber Cancer Institute, which analyzed tumors from 17 non-small cell lung cancer patients treated with KRAS-G12C inhibition, identified secondary resistance mutations in a majority of patients. In two of these patients, however, the researchers found that tumors transformed from adenocarcinomas to squamous cell carcinomas, but they did not harbor obvious resistance mutations.

Both adenocarcinomas and squamous cell carcinomas are classified as non-small cell lung cancers (NSCLCs), which are the most common type of primary lung cancer. Adenocarcinomas, the most common type of NSCLCs, often originate from the surfactant-producing cells that line the lungs, while squamous cell carcinomas originate in the cells that line the central airways of the lungs.

Mutations of KRAS are found much more frequently in adenocarcinomas than in squamous cell carcinomas

In this study, the researchers set out to model the factors that might drive the transition from adenocarcinomas to squamous cell carcinomas. To do that, they engineered a mouse lung cancer model to express the mutation that is targeted by the FDA-approved KRAS inhibitors. 

Following treatment with a KRAS-G12C inhibitor, tumors with genetic loss of Nkx2-1, which normally helps maintain alveolar epithelial identity, were able to undergo adeno-to-squamous transition. Turning on a transcription factor called DeltaNp63, which is overactive in many squamous cell carcinomas, also made this transition more likely. Another transcription factor known as SOX2 also helped stimulate the transition, but this gene could not initiate the transition on its own.

Paths to resistance

Tumors that underwent these tissue transformations did not acquire the mutations that typically boost KRAS expression in adenocarcinomas. Instead, KRAS signaling was shut off. The researchers hypothesize that these cells may turn on another signaling pathway that helps them to continue growing.

“There seem to be several different routes where you can get to squamous transformation, either through loss of lung-lineage-defining transcription factors, or overexpression of these squamous master regulators, SOX2 or DeltaNp63. Those resistant squamous tumors no longer respond to KRAS inhibition because they shut off the signaling or at least dampen it significantly,” Rodriguez says.

The researchers are now further exploring what happens to tumor cells as they transition to a squamous state, in hopes of identifying vulnerabilities that could be targeted with new drugs.

“Fundamentally this is a transition that’s poorly understood, and we were happy to see that we were able to model it,” Mathey-Andrews says. “Future directions that have an eye toward translation will utilize those models to understand the process and conditions by which histologic transformation occurs, and then also nominate potential targets downstream.”

The research was funded, in part, by the Koch Institute Support (core) Grant from the National Cancer Institute, a Ruth Kirschstein National Service Research Award, the National Institute of General Medical Sciences, and the Ludwig Center at MIT.

J-PAL North America, a regional office of MIT’s Abdul Latif Jameel Poverty Action Lab (J-PAL), convened the second cohort of the Climate Action Learning Lab this spring with 17 climate leaders from four U.S. government agencies and nonprofit organizations. Participants then engaged in four months of programming designed to strengthen their skills in generating and using evidence, applying research insights to their own programs, and identifying promising policy-relevant interventions for evaluation.

The Climate Action Learning Lab first emerged in 2025 as a response to an urgent need for rigorous research on programs that seek to improve resilience to climate-related hazards and support the transition to a low-carbon economy. By helping participants identify interventions and develop evaluation plans, the Learning Lab aims to generate evidence about which approaches are most effective, and for whom. 

“We have a unique opportunity to embed rigorous evaluation into promising programs as they are implemented in order to accurately measure their impacts on emissions reductions,” says Peter Christensen, scientific advisor of the J-PAL North America Environment, Energy, and Climate Change Sector. “By developing rigorous evaluations, Learning Lab participants can better understand the behavioral mechanisms that drive program impacts and cost-effectiveness, and build an evidence base to inform more effective policymaking.”

Following the success of last year’s Climate Action Learning Lab, which led to multiple research collaborations and the launch of two randomized evaluations, J-PAL North America recruited a second cohort of leaders representing four organizations: the City of Boston’s Sustainability Office, the Hawaii Climate Change Mitigation and Adaptation Commission, the Oregon Department of Environmental Quality, and the Electrification Coalition. 

From May through August, the cohort engaged in a set of offerings including training on impact evaluation, learning how to formulate research questions, and assessing the generalizability of the existing evidence to their own contexts. Participants had the opportunity to explore J-PAL resources, including the recently released Climate Action Evidence Review, which synthesizes existing evidence and highlights important gaps where more research is critical to inform effective, equitable climate action. 

“It was exciting to learn where the gaps in research are and where we have an opportunity to lead. The experience reinforced that we are addressing issues that have not been widely studied, and having access to researchers’ expertise was incredibly valuable,” says Ana Paola De La Vega, from the City of Boston’s Environment Department. Throughout the Learning Lab, the city explored a potential evaluation of its Boston Energy Saver program to better understand its impacts on energy cost savings and energy efficiency incentive uptake among small businesses.

Members of the Climate Action Learning Lab put theory into practice through personalized strategy sessions, where they examined potential programs for evaluation. Researchers from the J-PAL network joined select sessions to advise organizations on which programs to prioritize, considering factors such as research feasibility and existing evidence gaps. Each participating organization selected a target program and explored potential randomization approaches. 

“The Climate Action Learning Lab provided our team with a valuable opportunity to catalog our projects and better understand what makes a program ready for evaluation. It also helped us identify which initiatives are the strongest candidates for rigorous evaluation and how to prioritize them,” says Leah Laramee, Hawaii climate change mitigation and adaptation coordinator. During the Learning Lab, the team assessed three potential programs for evaluation and selected a rebate finder that connects residents with climate-related programs, with a focus on understanding its impact on program uptake, particularly among vulnerable communities.

In August, J-PAL North America hosted a virtual summit to celebrate Learning Lab participants as emerging champions for evidence in the climate space. During the event, cohort members presented their priority research questions and strategic evaluation plans and received feedback from researchers and peers. These presentations highlighted the progress made throughout the engagement and provided an opportunity to discuss next steps for advancing organizations’ evaluation plans beyond the Learning Lab.

“Overall, the Learning Lab has provided our team with a strong foundation to think critically about our own programming when applying for grants, setting up new projects, and determining how to assess impact from the beginning. This knowledge could ultimately help us determine what approaches the Electrification Coalition can take in future policies and programs,” says Ashley Blackwell, deputy director at the Electrification Coalition. 

Although formal Learning Lab programming has concluded, J-PAL North America will continue supporting organizations interested in launching a randomized evaluation through partnership development with researchers and potential funding opportunities. This Learning Lab cohort will join J-PAL North America’s Climate Action Community of Practice, alongside longtime partners and participants from the inaugural Learning Lab cohort. Together, Community of Practice members will continue to exchange ideas, build connections, and explore evidence-informed approaches to mitigation and adaptation strategies.

To learn more about J-PAL North America’s work in the energy, environment, and climate change sector, including our full range of activities, resources, and partnership opportunities, visit the Evidence for Climate Action Project webpage.

Swimming can take a lot of muscle. But as MIT engineers have found, even a single layer of muscle cells can power through water if designed right. 

In a paper appearing today in the journal Advanced Functional Materials, the team presents a design for a thin, muscle-powered swimming robot. The “skeleton” of the aquabot is made from a film of gel that is about the length and width of a stick of gum. The two halves of the gel form the “fins” of the bot. Each fin is covered with a layer of live muscle cells that is much thinner than a single strand of hair. The cells are genetically engineered to twitch in response to light. 

When the researchers shine light on one fin, the muscles on its surface twitch in response, causing the whole fin to flap with enough force to pull the robot through water. By flashing light on one fin or the other, at various intervals, they can control the swimming robot’s direction and speed. 

The engineers showed that the paper-thin bot could swim and swivel through a simple watery maze. At its fastest, the robot can swim a distance of about four times its body length in one minute. That’s a snail’s pace compared to Olympic swimmers, who can cover up to 65 body lengths per minute. But the bot could hold its own against more leisurely swimmers like the cow shark, which explores the ocean at about the same rate.

“It takes a lot of force to move through water versus air,” says study author Ritu Raman, associate professor of mechanical engineering at MIT. “The robot’s quite strong, given its size.”

The new robot is the first example of a very thin, two-dimensional, muscle-powered robot capable of locomotion. 

“Currently, biohybrid robots from our group and others’ are built from bulky, 3D chunks of lab-grown skeletal muscle that require millions of cells to fabricate,” says Raman, who notes that thinner, less bulky designs such as the team’s new bot could be cheaper to build and could move more efficiently. “We believe that biohybrid robots powered by living muscle could one day perform delicate jobs like exploring environments too fragile or unpredictable for conventional hardware, because living tissue is soft, responsive to its surroundings, and can heal itself.”

The study’s MIT co-authors are first author Maheera Bawa, Arielle Berman, Laura Schwendeman, Ferdows Afghah, and Seanbiron Johnson.

Maximizing movement

Last year, Raman’s group developed an iris-inspired disk of artificial muscle tissue. They stamped a disk of gel with a pattern of concentric and radial grooves, and deposited live muscle cells onto the gel’s surface. The cells formed a thin layer that grew along the grooves, and when stimulated with light, the cells moved in patterns that stretched and squeezed the disk, similar to how a human iris dilates and constricts the eye’s pupil. 

That work was the first to demonstrate that muscle cells could be grown in a very thin layer, and in complex patterns that when stimulated could move in multiple, controllable directions. 

“People hadn’t seen this muscle architecture engineered from scratch before,” Raman says. “And the cells were moving in multiple directions. But they only moved about 100 microns. From a robotics perspective, their movements were tiny.” 

In their new work, the team aimed to maximize muscle movements to produce more force — enough, say, to power a swimming robot. The key, they found, was to optimize the skeleton on which the cells grow. 

In their previous iris-inspired design, they grew muscle cells on fibrin, which is a type of ultrasoft gel that the team realized can quickly shrivel in response to the forces generated by the muscles. To better support cells and maximize their force, the researchers focused on tuning the underlying gel by changing three properties: the gel’s composition, its stiffness, and the size and shape of the grooves that are stamped into it. 

“For engineering any type of tissue, it’s known that these are knobs you can tune,” Raman says. “And we wanted to optimize all these parameters to support live muscle cells.”

Tuning a skeleton

To find an optimal “skeleton” on which to grow muscle cells, the team experimented with multiple formulations of gel, of different stiffnesses, and stamped with grooves of different geometries. For instance, one groove type resembled a skinny square trough, where another was more of a long curved valley. They found that when they deposited muscles onto each type of grooved gel, cells settled into alignment in grooves that were more square than curved. More aligned cells tend to fuse into fibers that then form a stronger, more coordinated muscle tissue. Square grooves, they found, were the way to go. 

Instead of using fibrin, they tried gelatin methacrylate (GelMA), a material that is often used in tissue engineering. They made different recipes of GelMA to create skeletons of different stiffnesses and observed how muscle cells grew when deposited on the gel’s surface. They found that cells grew in better alignment, and produced the most force, on stiffer gels. 

The team also varied the gel thickness and found that a half-millimeter-thin film of GelMa offered good support for a single layer of muscle cells. The film was light enough that the cells were able to stick to the gel when they contracted, rather than peeling away. 

Finally, the team “exercised” the muscles, using a training routine of flashing lights to strengthen the muscles. 

With the pumped-up cells and the optimized gel, the team designed a thin, two-finned robot, comprising the gel, stamped on both sides with square-bottomed grooves and lined with muscle cells. The cells fused into fibers, eventually forming a strong, aligned muscle tissue. 

“You can think of the robot as having two independent muscles,” Raman says. “If we shine a light on just one, only that muscle moves. If shining on both, they both flap.”

The researchers submerged the robot in a large petri dish of water and manually maneuvered a light source over the bot. The robot followed the light, flapping its fins in response to navigate through a maze that the team placed in the dish.

The current design is relatively basic as far as its form. The researchers intended first to show that the bot could produce enough force to swim.

“Our next goal is to optimize the body design to enable faster swimming,” Raman says. “But even at slow swim speeds, one could imagine a muscle-powered swimmer being used for purposes like environmental monitoring in aquatic environments.”

This research was supported, in part, by the Office of Naval Research.