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.

A new short film from MIT Open Learning explores the origin, influence, and global reach of MIT OpenCourseWare, reflecting on its role in establishing MIT, in 2001, as the first higher education institution to make educational resources freely available to learners across the world.

Part of MIT Open Learning, MIT OpenCourseWare helped spark a global movement that continues to shape how knowledge is shared across the world. The film, titled “The Courage to Be Open: MIT OpenCourseWare and the Democratization of Knowledge,” captures both the vision behind this work and the lasting impact it has had on expanding access to learning at scale.

Video by MIT Open Learning | 15 minutes, 22 seconds

For many graduate students, waking up at noon after a 4 a.m. bedtime is a sign of a night well spent. For a group of MIT students, it was simply the start of their workday — timed not to the sun, but to the aurora.

Their goal was simple: to study plasma phenomena using the aurora borealis as a natural laboratory. The process, less so; working largely in darkness in Fairbanks, Alaska, the students conducted experiments in temperatures that dipped as low as -25 degrees Fahrenheit, using red headlamps for visibility. The sun set before 3 p.m., and even at its warmest, temperatures barely reached 20 F.

The aurora provides a rare opportunity to observe plasma behavior directly, as charged particles that interact with Earth’s magnetic field produce visible, large-scale structures in the night sky. As Fairbanks is situated beneath a region of especially frequent auroral activity, it is one of the most reliable places in the world to observe these phenomena, though the conditions come with real constraints. 

For one thing, the extreme cold directly impacted the instrumentation. “Our laptops went from full battery to nearly empty in 10 minutes because of the cold,” says Leonardo Corsaro, a PhD student in physics at the Plasma Science and Fusion Center (PSFC) at MIT. “We were trying to transfer data as fast as possible before everything shut down; it was a race against time!”

The challenges extended beyond the cold itself. “The cold can be managed,” says Leon Nichols, a PhD student in physics at PSFC. “With good planning, you can stay comfy in -20 F. The real difficulty was movement when deploying cameras far away from the roads. Walking through thick snow can burn up to 900 calories in an hour. We used cross-country skis to access some of the more remote terrain that would have taken hours to reach otherwise.”

But the conditions were more than worth it: During their time in Alaska, the group witnessed the strongest solar storm in the past two decades, bringing the aurora to life in ways few will ever experience. “It felt like we were the only ones there,” Sydney Menne, a PhD student in nuclear science and engineering, recounts, “removed from the Earth and just entirely surrounded by the aurora, fully immersed in it.” 

The team was granted access to observation facilities at Poker Flat Research Range through the University of Alaska Fairbanks Geophysical Institute. Over the course of the trip, students deployed multiple all-sky camera systems across distances of up to 100 miles, enabling simultaneous observations of auroral structures from different locations. These cameras, which capture 360-degree images of the night sky, were paired with magnetometers to correlate visual auroral features with changes in Earth’s magnetic field. 

By combining spatially distributed imaging with magnetic field measurements, the team aimed to capture how auroral structures change across space, with the long-term goal of supporting three-dimensional reconstructions of the aurora. This year’s campaign also expanded the measurements beyond imaging, using muon detectors to explore possible correlations between visual auroral activity, magnetic field changes, and particle detections, offering a potential window into how high-energy particles in the upper atmosphere relate to visible auroral activity.

Despite decades of study, many aspects of the aurora remain poorly understood, and each observation offers an opportunity to better characterize the behavior of plasma in near-Earth space. The team also observed a pulsating aurora, a relatively rare phenomenon in which strips of light stretching across the sky blink on and off multiple times per second. By combining instruments not traditionally applied to these problems and deploying low-cost systems at scale, the team is exploring new approaches to studying these phenomena. Insights from these observations can help improve our understanding of space weather, including how solar activity affects satellites, communications systems, and power infrastructure on Earth.

For some participants, the experience reshaped how they think about plasma physics itself. Corsaro explains, “In my research, it is easy to associate these phenomena with colorful plots and simulations, losing touch with the physical process. Seeing structures in the aurora, electric currents and flows forming and shifting overhead, brought a sense of reality to those concepts, and served as a reminder that real plasmas are far less neat and intuitive than theory suggests.”

The experience is part of a broader effort. This group of students represented the third iteration of the Geophysical Plasma Observation Expedition (GPOE), a project involving MIT students from the Plasma Science and Fusion Center, along with collaborating departments, that sends a cohort to Fairbanks, Alaska, each year. Faculty members now provide support for the expedition, while continuity is maintained through its student-driven structure, with each cohort including a mix of returning and new participants. The expedition is organized and led entirely by students and operates on an intensive, compressed timeline. Students are responsible not only for data collection, but also for instrument design, site selection, logistics, and post-processing, completing a full research cycle within a matter of months.

This year’s cohort included graduate students Leonardo Corsaro and Leon Nichols of PSFC; Sydney Menne of the Department of Nuclear Science and Engineering; and Noah Wolfe and Oleksandra “Sasha” Lukina of the Laser Interferometer Gravitational-Wave Observatory (LIGO) Laboratory and the MIT Kavli Institute for Astrophysics and Space Research. The group was accompanied by Professor Matthew Evans, professor of physics at MIT, who is affiliated with the LIGO Laboratory and the Kavli Institute. 

“This is an opportunity to go from concept to data analysis in just a few months,” says John Ball, a PhD student in nuclear science and engineering at PSFC. “That kind of compressed scientific cycle is rare, especially in our field.”

The program itself has relatively recent and somewhat unusual origins. It began in 2023, when graduate student Shon Mackie, frustrated by the lack of hands-on plasma diagnostic opportunities, noticed the solar cycle was approaching its peak and saw an opportunity to study plasma phenomena more directly. He drafted a short proposal to PSFC leadership, and the response from then-Director Dennis Whyte was two lines: “Sounds cool, literally! PSFC will fund this.” 

Since its launch in 2023, GPOE has evolved from a single-camera effort into a multi-instrument, multi-site campaign with growing participation, with each cohort building on the work of previous years by refining instrumentation, expanding observational coverage, and improving data collection strategies. 

This hands-on, student-driven approach has also created opportunities to extend the experience beyond MIT. In 2024, the program expanded to include a new outreach collaboration with the MIT Museum and the MIT Nord Anglia Collaboration, bringing approximately 65 high school students from around 20 schools worldwide to MIT to help design and build components of the all-sky camera systems used in the field. Working within a set of technical constraints, students developed and tested designs, ultimately producing 13 cameras that were deployed during the Alaska expedition.

The program has also begun to produce results beyond the expedition itself. Students have presented their work at major conferences, including the American Geophysical Union, and published findings in peer-reviewed journals such as Earth and Space Science. The group’s low-cost all-sky camera and magnetometer design is now being adopted by other research teams and community science initiatives, extending its impact beyond MIT.

Beyond its scientific goals, participants emphasized the broader impact of the experience. 

“Standing outside at midnight in Alaska, staring up at sheets of glowing plasma stretching thousands of kilometers across the sky, really brings home just how small and delicate our own place in the universe is,” says Ball. 

As the program continues to grow, students hope to expand both its technical capabilities and its reach, including more permanent instrumentation and expanding outreach partnerships. For many involved, the expedition represents not just a research opportunity, but a reminder of the scale and immediacy of the phenomena they study.

“Science is an adventure,” Corsaro says. “This kind of work reminds you why you became a scientist in the first place.”

MIT economist Whitney Newey PhD ’83, the Ford Professor of Economics, emeritus, has received the 2026 Erwin Plein Nemmers Prize in Economics.

The biennial Nemmers prizes from Northwestern University recognize top scholars for their lasting contributions to new knowledge, outstanding achievements, and the development of significant new modes of analysis.

The university cited Newey — whose research has focused on econometrics — for producing “a body of work that has shaped the field of semiparametric econometrics, guided both econometricians and empirical researchers over several decades, and helped lay the foundations for modern machine learning-based inference.”

Newey will interact with Northwestern faculty and students through programming scheduled to occur during the 2026-27 academic year. The prize also includes a $300,000 award.

“I am delighted, deeply honored, and very grateful,” says Newey of the honor. “I am thrilled to have worked on and now work on ideas and approaches that are important for modern, machine learning-based inference and modern empirical economics more generally, most of this with such capable collaborators. This prize will expedite this work.”

Newey has been a leading figure in econometric theory for more than four decades, shaping both research and training in the field. He has done pathbreaking work on variance estimation, nonparametric simultaneous equations, consumer surplus estimation with general heterogeneity, and debiased machine learning.

“My colleagues and I are thrilled to see Whitney’s incredible career recognized with this high honor,” says Jonathan Gruber, the Ford Professor of Economics and head of the Department of Economics. “His research has given birth to many of the econometric methods that are now second nature to economists, and those of us in his orbit also know him as a source of sage, comprehensive, and generous advice. Whitney symbolizes, through both his pathbreaking research and his great generosity, what has made MIT economics so great for so many years.”

Newey is a distinguished fellow of the American Economic Association, a member of the American Academy of Arts and Sciences, and a fellow of the Econometric Society. He is also a fellow of the International Association of Applied Econometrics, of CEMP at Jinan University, and an international fellow of the Centre for Microdata Methods and Practice (CEMMAP) at University College London. He earned a BA and a PhD in economics from Brigham Young University and MIT, respectively.

Newey was named a 2020 Distinguished Fellow by the American Economic Association. He has been a fellow of the Center for Advanced Study in the Behavioral Sciences and received a Sloan Foundation Research Fellowship. He has served as co-editor of Econometrica — a journal produced by the Econometric Society — and as program co-chair for the World Congress of the Econometric Society. He has also served on the Econometric Society’s Executive Committee. He previously taught economics at Princeton University and at MIT, and also previously served as the head of MIT’s Department of Economics. He has been a visiting scholar, professor, and lecturer at institutions across the world.

Even in the primary visual cortex, a brain region named for its specialized role in processing basic features of what the eyes see, not every neuron ends up answering the call to process properties of visual input. Maybe that’s because each neuron receives a wide variety of inputs via thousands of circuit connections, or “synapses,” and has to opt to respond to the visual information versus something else. In a new study in mice, neuroscientists at The Picower Institute for Learning and Memory at MIT reveal how neurons that perform visual processing bring order to this input to get the job done.

Neuroscientists are keenly interested in what inputs, from among so many choices, will compel neurons to participate in the brain’s computations and functions, says senior author Mriganka Sur, Newton Professor of Neuroscience in the Picower Institute and MIT’s Department of Brain and Cognitive Sciences. Neurons ultimately participate in brain circuits by “firing” an electrical action potential.

“The configuration of inputs, the kind of organization, the assembly of neurons that modulate each other to generate an action potential is the essence of how brain circuits process information,” Sur says. “These (visual cortex) cells are a microcosm of this very profound and big picture of neuroscience.”

In the open-access study in iScience, led by postdoc Kyle Jenks, the research team achieved their findings by meticulously imaging how not only neurons’ cell bodies, but also their individual synapses, formed on protrusions known as dendritic spines, responded as mice viewed moving images. They did this imaging for not only visually responsive neurons, but also for unresponsive neurons that nevertheless have visually responsive spines. That allowed them to analyze many key properties that might influence where a particular synapse forms, and how it influences responses at the cell body.

“This pulls together a lot of things that have been looked at in isolation and looks at them in one collective paper,” Jenks says. “We can compare how the neuron and the spines on that neuron respond to the same stimuli, and we can do this for both visually responsive and unresponsive neurons.”

In visual cortex layer 2/3, Jenks and the team genetically engineered neurons such that their individual dendritic spines would glow when surges of calcium indicated increased activity by the synapses on the spines. The scientists did the same for the cell body, or “soma,” to keep track of how the cell responded and even signaled its overall responses back out to the synapses. This way, as the mice watched black and white gratings at varying angles drift by their eyes in different directions, the scientists could keep track of each spine’s and each cell’s overall response to that patterned visual input.

In all, they tracked 11 neurons that responded to the visual input and 11 others that seemingly ignored it. That enabled them to find several rules:

Distance from the soma matters: On cells that responded to visual input, the responses of individual spines were much more likely to correlate with the activity of the soma the closer the spine was to the soma. In the same vein, the soma’s signal back out to spines, which is believed to influence the spines’ alignment with the soma’s preferences, was more likely to be detectable closer to the soma than farther away. 

Local clustering: On neurons that responded to visual input, spines formed distinct little enclaves of correlated responses with each other. Specifically, spines within 5 microns (five one-millionths of a meter) acted in concert. But then, right outside that 5-micron boundary, spines were less likely than chance to join in that activity. Sur speculates that these isolated pockets of activity sharpened the response from each enclave.

“Apical” vs. “basal:” The neurons the team studied have two distinct kinds of dendrites. Apical dendrites, which are very long and protrude from the top, or “apex,” of the neuron, tend to get a wide variety of inputs from across the cortex. Basal dendrites, which are shorter and extend out from the bottom, typically get more raw visual input. While basal dendrites indeed received more visual input than apical dendrites overall, Jenks found that apical dendrites on visually responsive neurons had significantly more visually responsive spines than those on non-responsive neurons. And both types of dendrites equally obeyed the rules above about distance from the soma.

Orientation selectivity matters most: Jenks, Sur, and the team used statistical modeling to determine which of many factors (the stimulus selectivity, reliability of the response, a spine’s distance from the soma, apical versus basal, etc.) most explained how correlated a spine’s responsiveness was with that of the soma. By a wide margin, how selective a spine was to the orientation of its preferred grating was the most important single factor.

“Our results reveal that synaptic inputs to excitatory layer 2/3 neurons in mouse (visual cortex) are not randomly arranged, but organized and distributed in a manner that correlates with multiple factors including somatic responsiveness, somatic tuning, branch type, distance from the soma, local correlations, and stimulus selectivity,” the researchers wrote.

The team’s findings can help advance studies of vision in the brain in multiple ways, Jenks and Sur say. Certain genetic mutations that affect how neurons connect in circuits can affect visual cortex neurons and vision, Sur says. Documenting these rules provides researchers with a baseline to compare against when examining the effects of such mutations. Jenks adds that the findings could inform efforts to model how neurons integrate synaptic inputs in their computations.

In addition to Sur and Jenks, the paper’s other authors are Gregg Heller, Katya Tsimring, Kendyll Martin, Asrah Rizvi, and Jacque Pak Kan Ip.

The National Institutes of Health, the Simons Foundation Autism Research Initiative, and the Freedom Together Foundation provided support for the study.

The National Academy of Sciences (NAS) has elected 120 members and 25 international members for 2026, including six MIT faculty members and 10 additional alumni. 

Among MIT professors, Bengt Holmström, Michale Fee, Gareth McKinley ’91, Keith Nelson, Fan Wang, and Catherine Wolfram ’96 were elected in recognition of their “distinguished and continuing achievements in original research.” 

Additional alumni who were elected include Christopher J. Chang PhD ’02 (Chemistry); Cynthia J. Ebinger SM ’86, PhD ’88 (Earth, Atmospheric and Planetary Sciences); Andrew Gelman ’85, ’86 (Mathematics and Physics); Richard L. Greene ’60 (Physics); Chuan He PhD ’00 (Chemistry); Pardis C. Sabeti ’97 (Biology/Life Sciences); Robert J. Shiller SM ’68, PhD ’72 (Economics); Daniel M. Sigman PhD ’97 (EAPS); Eero Simoncelli SM ’88, PhD ’93 (Electrical Engineering and Computer Science); and Salil P. Vadhan PhD ’99 (Mathematics).

Membership in the National Academy of Sciences is one of the highest honors a scientist can receive in their career. The NAS is a private, nonprofit institution that was established under a congressional charter signed by President Abraham Lincoln in 1863. It recognizes achievement in science by election to membership, and — with the National Academy of Engineering and the National Academy of Medicine — provides science, engineering, and health policy advice to the federal government and other organizations.

Bengt Holmström is the Paul A. Samuelson Professor of Economics, emeritus. He received his doctoral degree from the Stanford Graduate School of Business in 1978 and held faculty positions at Northwestern University and Yale University before joining the MIT faculty in 1994 with a joint appointment in economics and management.

Holmström is best known for his foundational research on the theory of contracting and incentives, for which he received the 2016 Sveriges Riksbank Prize in Economic Sciences in Memory of Alfred Nobel (together with Oliver Hart of Harvard University). His extensive contributions to contract theory as applied to the theory of the firm, corporate governance, and liquidity problems in financial crises have had wide-ranging impacts, while bringing contract theory into mainstream economic thought.

In addition to the Nobel, Holmström’s research has been recognized with the Stephen A. Ross Prize in Financial Economics and the Grand Cross of the Order of the Lion of Finland. He is a member of the American Academy of Arts and Sciences, the Econometric Society, and the American Finance Association. Holmström is also an elected foreign member of the Royal Swedish Academy of Sciences and a member of the Finnish Academy of Sciences and Letters.

Michale S. Fee is the Glen V. and Phyllis F. Dorflinger Professor of Neuroscience, head of the MIT Department of Brain and Cognitive Sciences (BCS), and investigator at the McGovern Institute for Brain Research. His research explores how the brain learns and generates complex sequential behaviors. Using the zebra finch as a model system, Fee investigates the neural mechanisms underlying birdsong — a behavior that young birds learn from their fathers through trial and error, much as human infants learn to speak through babbling. His research extends far beyond birdsong — the neural circuits controlling birdsong learning are closely related to human brain circuits disrupted in Parkinson’s and Huntington’s diseases. Insights from Fee’s research could reveal new clues to the causes and potential treatments of these complex brain disorders.

After receiving his BE with honors in engineering physics at the University of Michigan in 1985, Fee studied applied physics at Stanford University, where he carried out his PhD thesis work in the laboratory of Steven Chu. In 1992, he began working as a postdoc in David Kleinfeld’s lab in the Biological Computation Research Department at Bell Laboratories. Four years later, he became a permanent member of the technical staff at Bell Labs and began working on the mechanisms of vocal sequence generation in the songbird. In 2003, he became an investigator at the McGovern Institute and a faculty member in BCS. In 2021, he was appointed BCS department head, continuing the department’s tradition of being led by scientists whose exemplary work makes MIT a world leader in brain science. Fee is a member of the American Academy of Arts and Sciences and a recipient of multiple undergraduate and graduate teaching awards at MIT.  

Gareth H. McKinley ’91 is the School of Engineering Professor of Teaching Innovation in the Department of Mechanical Engineering at MIT, former associate head and interim head of the department, and co-founder of Cambridge Polymer Group. McKinley’s research interests include non-Newtonian fluid dynamics, microfluidics, extensional rheology, field-responsive materials, super-hydrophobicity, drag reduction, and the wetting of nanostructured surfaces. His work focuses on understanding the rheology of complex fluids such as surfactants, biomaterials, gels, and polymers, which are ubiquitous in foods and consumer products. 

McKinley has made outstanding contributions to viscoelastic fluid mechanics, understanding flow instabilities and stretching flows. His research group has developed novel instrumentation and customized rheological analysis techniques that have driven the field of rheology for complex and soft fluids. His instrumentation and testing algorithms, along with freely-distributed code for analyzing large amplitude oscillatory shear flow, and broad-band “chirp” rheometry, are used worldwide in industry and academia . 

McKinley is the author of over 390 technical publications. He has won the Publication Award of the Society of Rheology twice (2007 and 2022), as well as the 2021 Walters Award from J. Non-Newtonian Fluid Mechanics. He was awarded the Bingham Medal of The Society of Rheology in 2013, the Gold Medal from the British Society of Rheology in 2014, and the G.I. Taylor Medal from the Society for Engineering Science in 2022. In 2019, he was elected to the National Academy of Engineering and was also inducted as a fellow of the Royal Society of London. In 2023, he was awarded an honorary doctorate from the Katholieke University of Leuven, and in 2024 became a corresponding member of the Australian Academy of Sciences. In 2025, he was elected to the American Academy of Arts and Sciences and also became a foreign fellow of the Indian National Academy of Engineering.  

Keith A. Nelson, the Haslam and Dewey Professor of Chemistry, earned his BS in chemistry from Stanford University. After completing his doctoral studies in physical chemistry, also at Stanford, he conducted postdoctoral research with John P. McTague at the University of California at Los Angeles. In 1982, Nelson joined the MIT Department of Chemistry as an assistant professor.

His distinguished career has been recognized with numerous honors, including the William F. Meggers Award, the Bomem-Michelson Award, and the Frank Isakson Prize for Optical Effects in Solids. Research in the Nelson Group focuses on the time-resolved optical study and control of  collective transformations in condensed matter, using pulses of light in the THz, optical, and X-ray spectral ranges and laser-generated strain waves to drive the modes of motion through which these changes occur.

Fan Wang is a professor of Brain and Cognitive Sciences, investigator at the McGovern Institute, and co-director of the K. Lisa Yang and Hock E. Tan Center for Molecular Therapeutics at MIT. She investigates the neural circuits that govern the dynamic interactions between brain and body, exploring how the brain generates sensory perceptions and controls movement. Wang uses cutting-edge techniques including optogenetics, in vivo electrophysiology, and in vivo imaging to make discoveries with profound clinical implications.

By developing innovative tools to study how brain circuits work, Wang discovered distinct populations of neurons activated by anesthesia that can suppress pain without blocking sensation, and can calm anxiety by regulating automatic body functions like heart rate. She also identified the brain circuits controlling rhythmic movements essential for exploration and communication. Together, these findings reveal how emotion, physiology, movement, and consciousness are deeply interconnected.

Before coming to MIT, Wang obtained her PhD from Columbia University working with Richard Axel, and received her postdoctoral training at the University of California at San Francisco and Stanford University with Marc Tessier-Lavigne. She became a faculty member at Duke University in 2003, where she was later appointed Morris N. Broad Professor of Neurobiology. Wang became an investigator at the McGovern Institute and a faculty member in the Department of Brain and Cognitive Sciences at MIT in 2021. She is a member of the American Academy of Arts and Sciences and a recipient of multiple undergraduate teaching and graduate mentorship awards at MIT.  

Catherine D. Wolfram ’96 is the William Barton Rogers Professor in Energy and professor of applied economics in the MIT Sloan School of Management. Before coming to MIT Sloan, Wolfram previously served as the Cora Jane Flood Professor of Business Administration at the Haas School of Business at the University of California at Berkeley. From March 2021 to October 2022, she served as the deputy assistant secretary for climate and energy economics at the U.S. Treasury, while on leave from UC Berkeley. Before leaving for government service, she was the program director of the National Bureau of Economic Research’s Environment and Energy Economics Program and a research affiliate at the Energy Institute at Haas. Before joining the faculty at UC Berkeley, she was an assistant professor of economics at Harvard University. She received a PhD in economics from MIT in 1996 and an BA from Harvard in 1989.

Wolfram has published extensively on the economics of energy markets. Her work has analyzed rural electrification programs in the developing world, energy efficiency programs in the United States, the effects of environmental regulation on energy markets, and the impact of privatization and market restructuring in the United States and United Kingdom. She is currently working on projects at the intersection of climate, energy, and trade, including work on carbon border adjustment mechanisms and oil market sanctions. Since March 2025, Wolfram has served on the COP30 President’s Council on Economics, Finance, and Climate, and has chaired a working group on climate coalitions.

This spring, six reporters from The Associated Press’ climate desk traveled from cities across the United States to work with students from the MIT Graduate Program in Science Writing. Students developed and pitched local climate stories, then, over a four-day intensive weekend, worked with visual journalists from the AP to report and produce their pieces. Articles cover a broad spectrum of environmental topics, ranging from area kelp harvests that are used to produce biofuels to efforts to restore cranberry bogs in environmentally friendly ways, and include visual elements, like photography and videography.

The four collaborative pieces include:

• “How a retired cranberry bog helped change the game for wetland restoration,” by Jamie Jiang and Julia Vaz
• “Planes and ships could run on kelp someday, but there are serious hurdles,” by Zoe Beketova and Ana Georgescu
• “Massachusetts is dumping sewage into waterways. Grassroots organizations are fighting back,” by Ashley D’Souza and Lucie McCormick
• “One of America’s oldest weather observatories shows people the science behind our climate,” by Laura Martin Agudelo and Alex Megerle

“This workshop was an intense few days that offered a unique opportunity for MIT journalists to get feedback while in the field, reporting. The students brought enthusiasm and passion to the reporting, heading out before the sun came up and working long into the nights over the weekend for stories in the Boston area and beyond,” says AP’s Climate Photo Editor Alyssa Goodman, the workshop’s lead organizer. “For the AP team members who participated, it was also a rewarding opportunity, allowing us to share our passion for climate storytelling while getting to know these students, watching them build strong stories and gain experiences that will help them as they continue in journalism.”

The collaboration is unique, even among journalism programs. The Associated Press is one of the most prestigious, longest-running news wire services in existence. Nearly 4 billion people worldwide come in contact with AP journalism every day. The publication has won 59 Pulitzer Prizes, including 36 in photojournalism.

MIT student reporters say that the opportunity to directly work with journalists from the AP’s climate team and have their own stories published has been a highlight of their time in the Graduate Program in Science Writing.

“It was great to be in the field with a reporter and photographer from the AP News team, learning directly from her as the reporting unfolded,” says Zoe Beketova, whose story focused on kelp biofuels. “That kind of expertise is difficult to get in a static classroom setting, and I think my team learned a lot.”

Ana Georgescu says that the experience of working with the AP team was “like stepping into a real newsroom.” Georgescu adds that coordinating with AP editors and reporting teams in real time and under tight deadlines provided valuable on-the-ground experience.

“What made the biggest difference for me was being in the field alongside an experienced photojournalist and seeing how they read a scene in practice,” she says. “We were able to get immediate feedback on how we directed subjects, which scenes we chose, and how we integrated photography into the reporting process. That kind of hands-on, in-the-moment experience was incredibly helpful, and it’s made me really excited to keep exploring climate stories, as well as the visual side of journalism.”

About 2 billion people — just under a quarter of the world’s population — lack regular access to clean drinking water. And roughly 800,000 people annually die from illnesses associated with unsanitary water.

Drinking water access is a fundamental problem for human and economic development. The U.N., for instance, highlighted the issue in its Sustainable Development Goals of 2015, an ambitious 17-point agenda that specified safe drinking water as a basic global aim.

Past research shows that democracies, in comparison to other forms of government, tend to be more successful at delivering this kind of public good, which benefits a large portion of the population. This is likely due to accountability measures that include elections, greater transparency, and more freedom in civil society.

But now a study led by an MIT professor shows that across nearly 100 countries with developing economies, that dynamic has become more complex in the 21st century. While democracies are slightly ahead of non-democracies when it comes to providing at least some water, they have been falling behind when it comes to ensuring that there is safe water on tap. 

“Among low- and middle-income countries, which have not done as well economically, we found there wasn’t really a big difference between democracies and non-democracies in the provision of what is called basic drinking water,” says MIT political scientist Evan Lieberman, co-author of a new paper detailing the results. “But for safe drinking water, we found, surprisingly, that democratic countries were becoming less good at extending access.” 

While the study does not pinpoint the precise reasons for this, it suggests a lens for viewing the problem. Democracies tend to be better at delivering visible public goods, the kinds of things citizens can literally see — like infrastructure that delivers water. But the difference between safe and unsafe water is not necessarily visible and obvious, so public officials may not be as responsive.

“This is likely a big part of the equation, that the invisibility of safe water makes it a less compelling public good for politicians,” says Lieberman, the Total Professor of Political Science and Contemporary Africa, and director of MIT’s Center for International Studies.

The paper, “Beyond the tap: The limited value of democracy for delivering universal safe water access in low- and middle-income countries,” is published in the journal World Development. The authors are Lieberman, and Naomi Tilles, a doctoral student in political science at Stanford University.

Seeing is believing

To conduct the study, the scholars analyzed drinking water data recorded by the World Health Organization/UNICEF Joint Monitoring Programme. That provides information for basic availability to water, defined as access to an improved water source with no more than 30 minutes of collection time; and access to safe drinking water, defined as an improved water source that is available on premises, available when needed, and free from potentially disease-producing contaminants, which range from fecal matter to harmful chemicals.

Examining 96 low- and middle-income countries, the researchers looked at a variety of measures pertaining to its democratic or non-democratic features, and ran 39,000 regressions to see how the form of government related to its provision of water. Overall, Lieberman and Tilles found that democratic governance is modestly associated with an increase in the basic availability of water, compared to non-democracies. However, the effect is not particularly robust.

The good news is that between 2000 and 2024, 81 of the 90 countries with data available in both years made gains in safe drinking water access. However, democratic countries have been less successful than their non-democratic counterparts in advancing the goal of achieving universal access. 

“Moreover, the gap between democracies and non-democracies seems to be getting a little bit larger over time,” Lieberman observes. 

Because the study is focused on establishing the overall empirical situation, the scholars do not claim to have determined why this trend has been emerging. Many newer democracies have struggled to establish high-functioning governance in some regions, which may influence their overall results. 

More broadly, Lieberman suggests, visibility matters. Past scholarship has shown that democracies perform relatively well in delivering visible public goods, especially in countries with little information in the public sphere. Delivering water generates attention for politicians in a way that keeping water safe does not. 

“Politicians may figure out they should do things citizens like, to stay in office, such as bringing water to an area,” Lieberman says. “You can have a ribbon-cutting ceremony, and people feel it really happened. But water quality is often invisible.

It’s a more difficult challenge to ensure safe water: You have to do testing, prevent people from polluting, and you may need to treat the water.”

In any case, Lieberman notes, “Given what we find, what is clear is that the incentives are not aligned under the current systems for advancing safe-water access within all democracies. That provides opportunities for human agency to create incentives for citizens, nongovernment agencies, and governments to do what is needed.” 

Development for all

Lieberman comes to the topic of water access as an expert on African politics. His most recent book, “Until We Have Won Our Liberty” (Princeton University Press, 2022), examines the vicissitudes of South African democracy. In the book and in general, he suggests that democracy is the most viable path toward development with “dignity,” meaning economic growth accompanied by liberties and equal treatment under the law. 

“I think democracy provides dignified development, by granting people recognition and participation, and that’s an extremely valuable thing,” Lieberman says.

Still, when it comes to the performance of many countries with regard to safe water, he says, “I think we just need to be clear-eyed about real problems.”

In some countries, he suggests, the time horizon of elected officials may also be relatively short-term, and they may be more oriented toward simpler problems than water safety. At the same time, other members of society need to find ways to make water safety a bigger issue in the eyes of the public. 

“There are important lessons for democracies to learn, and citizens in civil society who are aware of this challenge need to figure out ways to get people to care about it, to recognize the connection between illness and unsafe water, and to use political campaigns to advance their longer-term interests,” Lieberman says.

Overall, he adds, “There is something intrinsically important about democratic government. Then the question becomes how to make it work better to deliver really important outcomes like safe water.”

At any given time, technology does two things to employment: It replaces traditional jobs, and it creates new lines of work. Machines replace farmers, but enable, say, aeronautical engineers to exist. So, if tech creates new jobs, who gets them? How well do they pay? How long do new jobs remain new, before they become just another common task any worker can do?

A new study of U.S. employment led by MIT labor economist David Autor sheds light on all these matters. In the postwar U.S., as Autor and his colleagues show in granular detail, new forms of work have tended to benefit college graduates under 30 more than anyone else. 

“We had never before seen exactly who is doing new work,” Autor says. “It’s done more by young and educated people, in urban settings.” 

The study also contains a powerful large-scale insight: A lot of innovation-based new work is driven by demand. Government-backed expansion of research and manufacturing in the 1940s, in response to World War II, accounted for a huge amount of new work, and new forms of expertise. 

“This says that wherever we make new investments, we end up getting new specializations,” Autor says. “If you create a large-scale activity, there’s always going to be an opportunity for new specialized knowledge that’s relevant for it. We thought that was exciting to see.” 

The paper, “What Makes New Work Different from More Work?” is forthcoming in the Annual Review of Economics. The authors are Autor; Caroline Chin, a doctoral student in MIT’s Department of Economics; Anna M. Salomons, a professor at Tilburg University’s Department of Economics and Utrecht University’s School of Economics; and Bryan Seegmiller PhD ’22, an assistant professor at Northwestern University’s Kellogg School of Management.

And yes, learning about new work, and the kinds of workers who obtain it, might be relevant to the spread of artificial intelligence — although, in Autor’s estimation, it is too soon to tell just how AI will affect the workplace.

“People are really worried that AI-based automation is going to erode specific tasks more rapidly,” Autor observes. “Eroding tasks is not the same thing as eroding jobs, since many jobs involve a lot of tasks. But we’re all saying: Where is the new work going to come from? It’s so important, and we know little about it. We don’t know what it will be, what it will look like, and who will be able to do it.”

“If everyone is an expert, then no one is an expert”

The four co-authors also collaborated on a previous major study of new work, published in 2024, which found that about six out of 10 jobs in the U.S. from 1940 to 2018 were in new specialties that had only developed broadly since 1940. The new study extends that line of research by looking more precisely at who fills the new lines of work. 

To do that, the researchers used U.S. Census Bureau data from 1940 through 1950, as well as the Census Bureau’s American Community Survey (ACS) data from 2011 to 2023. In the first case, because Census Bureau records become wholly public after about 70 years, the scholars could examine individual-level data about occupations, salaries, and more, and could track the same workers as they changed jobs between the 1940 and 1950 Census enumerations. 

Through a collaborative research arrangement with the U.S. Census Bureau, the authors also gained secure access to person-level ACS records. These data allowed them to analyze the earnings, education, and other demographic characteristics of workers in new occupational specialties — and to compare them with workers in longstanding ones.

New work, Autor observes, is always tied to new forms of expertise. At first, this expertise is scarce; over time, it may become more common. In any case, expertise is often linked to new forms of technology.

“It requires mastering some capability,” Autor says. “What makes labor valuable is not simply the ability to do stuff, but specialized knowledge. And that often differentiates high-paid work from low-paid work.” Moreover, he adds, “It has to be scarce. If everyone is an expert, then no one is an expert.”

By examining the census data, the scholars found that back in 1950, about 7 percent of employees had jobs in types of work that had emerged since 1930. More recently, about 18 percent of workers in the 2011-2023 period were in lines of work introduced since 1970. (That happens to be roughly the same portion of new jobs per decade, although Autor does not think this is a hard-and-fast trend.) 

In these time periods, new work has emerged more often in urban areas, with people under 30 benefitting more than any other age category. Getting a job in a line of new work seems to have a lasting effect: People employed in new work in 1940 were 2.5 times as likely to be in new work in 1950, compared to the general population. College graduates were 2.9 percentage points more likely than high school graduates to be engaged in new work. 

New work also has a wage premium, that is, better salaries on aggregate than in already-existing forms of work. Yet as the study shows, that wage premium also fades over time, as the particular expertise in many forms of new work becomes much more widely grasped. 

“The scarcity value erodes,” Autor says. “It becomes common knowledge. It itself gets automated. New work gets old.”

After all, Autor points out, driving a car was once a scarce form of expertise. For that matter, so was being able to use word-processing programs such as WordPerfect or Microsoft Word, well into the 1990s. After a while, though, being able to handle word-processing tools became the most elementary part of using a computer.

Back to AI for a minute

Studying who gets new jobs led the scholars to striking conclusions about how new work is created. Examining county-level data from the World War II era, when the federal government was backing new manufacturing in public-private partnerships throughout the U.S., the study shows that counties with new factories had more new work, and that 85 to 90 percent of new work from 1940 to 1950 was technology-driven. 

In this sense there was a great deal of demand-driven innovation at the time. Today, public discourse about innovation often focuses on the supply side, namely, the innovators and entrepreneurs trying to create new products. But the study shows that the demand side can significantly influence innovative activity. 

“Technology is not like, ‘Eureka!’ where it just happens,” Autor says. “Innovation is a purposive activity. And innovation is cumulative. If you get far enough, it will have its own momentum. But if you don’t, it’ll never get there.”

Which brings us back to AI, the topic so many people are focused on in 2026. Will AI create good new jobs, or will it take work away? Well, it likely depends how we implement it, Autor thinks. Consider the massive health care sector, where there could be a lot of types of tech-driven new work, if people are interested in creating jobs.

“There are different ways we could use AI in health care,” Autor says. “One is just to automate people’s jobs away. The other is to allow people with different levels of expertise to do different tasks. I would say the latter is more socially beneficial. But it’s not clear that is where the market will go.” 

On the other hand, maybe with government-driven demand in various forms, AI could get applied in ways that end up boosting health care-sector productivity, creating new jobs as a result. 

“More than half the dollars in health care in the U.S. are public dollars,” Autor observes. “We have a lot of leverage there, we can push things in that direction. There are different ways to use this.” 

This research was supported, in part, by the Hewlett Foundation, the Google Technology and Society Visiting Fellows Program, the NOMIS Foundation, the Schmidt Sciences AI2050 Fellowship, the Smith Richardson Foundation, the James M. and Cathleen D. Stone Foundation, and Instituut Gak.

MIT scientists Sven Dorkenwald and Whitney Henry have been named 2026 Searle Scholars, an award given annually to 15 exceptional early-career researchers in the fields of biomedical sciences and chemistry. Dorkenwald is an assistant professor of brain and cognitive sciences and an investigator at the McGovern Institute for Brain Research. Henry is the Robert A. Swanson (1969) Career Development Professor of Life Sciences and an intramural faculty member at the Koch Institute for Integrative Cancer Research.

In addition, MIT alumni Irene Kaplow ’10 and Jared Mayers PhD ’15 were also honored.

Chosen by a scientific advisory board, Searle Scholars are considered among the most creative young researchers pursuing high-risk/high-reward research. The Searle Scholars Program is funded through the Searle Funds at The Chicago Community Trust and administered by Kinship Foundation. Each scholar will each receive $450,000 in flexible funding to support their work over the next three years.

Sven Dorkenwald

Sven Dorkenwald is a computational neuroscientist investigating the organizational principles of neuronal circuits. The synaptic connectivity of neurons, their connectome, is fundamental to how networks of neurons function. Dorkenwald develops computational and collaborative tools to map, analyze, and interpret synapse-resolution connectomes. His work has led to large connectomic reconstructions of the fruit fly brain and parts of mammalian brains. He uses these connectomes to investigate the architecture of neuronal circuits and how their structure supports complex computations.

“As I establish my new lab, the Searle Scholars Award will help us launch ambitious projects and set our long-term scientific direction,” says Dorkenwald. “I am deeply grateful for the support from the Kinship Foundation and look forward to interacting with this amazing cohort of Searle Scholars.”

Dorkenwald joined the faculty of MIT in 2026 as an assistant professor in the Department of Brain and Cognitive Sciences and an investigator at the McGovern Institute. He earned a BS in physics and an MS in computer engineering from the University of Heidelberg, followed by a PhD in computer science and neuroscience at Princeton University in 2023 under the mentorship of Sebastian Seung and Mala Murthy. Dorkenwald completed his postdoctoral training as a Shanahan Research Fellow at the Allen Institute and the University of Washington, while serving as a visiting faculty researcher at Google Research.

Whitney Henry

Whitney Henry investigates the potential of ferroptosis, an iron-dependent form of cell death, for developing novel therapies that target subpopulations of cancer cells that are highly metastatic, therapy-resistant, and therefore critical instigators of tumor relapse. Her research is focused on uncovering the molecular factors influencing ferroptosis susceptibility, investigating its effects on the tumor microenvironment, and developing innovative methods to manipulate ferroptosis resistance in living organisms, drawing from functional genomics, metabolomics, bioengineering, and a range of in vitro and in vivo models.

“I am incredibly grateful to the Kinship Foundation for supporting our research and giving us the freedom to ask bold, curiosity-driven scientific questions,” says Henry. “This support allows us to pursue ambitious ideas, take creative risks, and embark on new research directions.”

Henry joined the MIT faculty in 2024 as an assistant professor in the Department of Biology and a member of the Koch Institute, and is currently an HHMI Freeman Hrabowski Scholar. She received her bachelor's degree in biology with a minor in chemistry from Grambling State University and her PhD from Harvard University. Following her doctoral studies, she worked in the lab of Robert Weinberg at the Whitehead Institute for Biomedical Research and was supported by fellowships from the Jane Coffin Childs Memorial Fund for Medical Research and the Ludwig Center at MIT.

Alumni also honored

Irene Kaplow ’10, a graduate of the MIT Department of Mathematics, is an assistant professor in the Department of Biology and the Ray and Stephanie Lane Computational Biology Department at Carnegie Mellon University. Her selection as a Searle Scholar is for “deciphering transcriptional regulatory mechanisms underlying mammalian dietary phenotype evolution and their relationships to transcriptional regulatory responses to changes in diet.”

Jared Mayers PhD ’15, who earned his doctorate from the MIT Department of Biology, is an assistant professor at the Fred Hutchinson Cancer Center at the University of Washington. His selection as a Searle Scholar is for “a reverse-translational framework to decipher metabolic vulnerabilities of bacterial pathogens.”

In 2023, the Center for Computational Science and Engineering (CCSE), an academic unit in the MIT Schwarzman College of Computing, introduced a new standalone PhD degree program. This interdisciplinary PhD program blends both coursework and a thesis, enabling students to pursue research in cross-cutting methodological aspects of computational science and engineering.

PhD candidate Emily Williams is poised to be the first graduate of the program. With a technical background in aerospace engineering and applied mathematics, her research interests include stochastic and generative modeling for multiscale chaotic systems. She earned a BS in aerospace engineering from the University of Illinois Urbana-Champaign and an MS in aeronautics and astronautics from MIT. She was awarded the Department of Energy Computational Science Graduate Fellowship, which funded her doctoral research. Here, she discusses her experience with the program and its impact on her career trajectory.

Q: What has been a highlight of the CCSE degree program?

A: I found the program curriculum to be extremely thoughtful and intentional. In particular, the program of study was constructed to cover many important areas of computational science and engineering research and education, from engineering and mathematical modeling to scientific and parallel computing. I found a lot of overlap with the DoE CSGF program of study, so I was given a lot of freedom to pursue very interesting technical electives that fit within CSE that I might not have been able to explore if I had been in a discipline-centric program.

Q: Why is this program so impactful, especially in the context of having a stand-alone PhD program?

A: I think a stand-alone PhD program helps to further establish the MIT CCSE as a leader in CSE research and education. The joint programs give graduate researchers more opportunity to learn and apply leading CSE methodologies to their disciplinary areas and primarily stay within their home department. For me, I’ve found that I’ve had more opportunities for collaboration, in potentially applying my methods to a wide range of different exciting applications. I think this theme of collaboration will continue to foster through those advancing through the standalone program in particular.

Q: What advice would you give to students considering this program?

A: I think my advice would be to keep an open mind. My interest in CSE was shaped by common threads in my education and research interests over the years that I didn’t think were connected at all. Through my fellowship and the standalone program, I felt like I was able to create my own path to my degree and take courses that excited me and fit within the CSE themes of our program of study.

A high-performance steel with MIT origins has come full circle. 

After proving its worth in Formula One and Baja 1000 race cars, the computationally designed material has now been incorporated into the 2026 electric race car built by the student-run MIT Motorsports team.

The MIT car is scheduled to race against cars from other universities in the Formula SAE Electric competition in June.

Designing materials

Gregory B. Olson, professor of the practice in the MIT Department of Materials Science and Engineering, founded the MIT Steel Research Group (SRG) in 1985 with the goal of using computers to accelerate the hunt for new materials by plumbing databases of those materials’ fundamental properties. It was the beginning of a new field — computational materials design — that would eventually lead to the Materials Genome Initiative, a national program announced by President Barack Obama in 2011.

In 1985, however, “nobody knew whether we could really do this,” says Olson. Olson and colleagues eventually showed that the approach worked, and around 1990 the Army Research Office funded an SRG project aimed at developing high-performance steels for the gears in helicopters. That work came to the attention of producers at “Infinite Voyage,” a science documentary that ran on the Public Broadcasting System.

“When “Infinite Voyage” came to see me about the helicopter gear steels,” Olson remembers, “we got into a discussion about my interest in race cars” and whether the steels might have an application there.

The answer was yes, and Olson found himself connecting with the Newman/Haas racing team that Michael and Mario Andretti were driving for. Newman/Haas was also featured in the “Infinite Voyage” program, so “my first discussion with their chief engineer was on live television,” says Olson, who is also affiliated with the MIT Materials Research Laboratory.

He and colleagues went on to design a novel gear steel that could withstand the extreme conditions associated with a race car. They did the work over a weekend. “The surface hardness was the same as for a conventional gear steel, but we gave it the core properties of an armor steel,” Olson says.

Introducing Ferrium C61

That steel, which became known as Ferrium C61, was commercialized through QuesTek Innovations, the materials-design company Olson co-founded. It became the company’s first product.

Although it was never used in Newman/Haas cars, QuesTek pitched it to Baja 1000 off-road racers.

“We particularly focused on the 1600 class of those racing dune buggies. They would go flying over a sand dune with the wheels spinning in the air. And when they land, there would be a tremendous jolt to the drive gears,” Olson says. The result: The racers’ gears made with conventional steel regularly failed.

“The average life for conventional drive gears was point-six race,” says Olson (meaning on average they lasted for only 60 percent of a race). “With Ferrium C61, we changed it from point-six to six races.” The gears could now complete an average six races before failing.

QuesTek brought that data to meetings with different Formula One teams “to try to get C61 into other racing classes,” Olson says. 

Enter Red Bull, the British-licensed Formula One team. “The leading mechanical failure in Formula One racing is gearbox failures,” Olsen says. The gearbox houses the gearset, or collection of gears, in a car’s engine. “Once Red Bull adopted our steel for the gearset, they never had any gearbox failures, and they were world champions four times in the last decade.”

MIT Motorsports heard of this history and within the past year approached Olson about getting a sample of C61. “QuesTek had some stock available, and sold it at a high discount to the MIT team with, of course, instructions on how to heat-treat it,” Olson says. 

Because, of course, the students, who are mostly undergraduates, made the gears — and the car — themselves.

Among all of the possible chemical compounds, it’s estimated that between 1020 and 1060 may hold potential as small-molecule drugs.

Evaluating each of those compounds experimentally would be far too time-consuming for chemists. So, in recent years, researchers have begun using artificial intelligence to help identify compounds that could make good drug candidates. 

One of those researchers is MIT Associate Professor Connor Coley PhD ’19, the Class of 1957 Career Development Associate Professor with shared appointments in the departments of Chemical Engineering and Electrical Engineering and Computer Science and the MIT Schwarzman College of Computing. His research straddles the line between chemical engineering and computer science, as he develops and deploys computational models to analyze vast numbers of possible chemical compounds, design new compounds, and predict reaction pathways that could generate those compounds. 

“It’s a very general approach that could be applied to any application of organic molecules, but the primary application that we think about is small-molecule drug discovery,” he says.

The intersection of AI and science

Coley’s interest in science runs in the family. In fact, he says, his family includes more scientists than non-scientists, including his father, a radiologist; his mother, who earned a degree in molecular biophysics and biochemistry before going to the MIT Sloan School of Management; and his grandmother, a math professor.

As a high school student in Dublin, Ohio, Coley participated in Science Olympiad competitions and graduated from high school at the age of 16. He then headed to Caltech, where he chose chemical engineering as a major because it offered a way to combine his interests in science and math.

During his undergraduate years, he also pursued an interest in computer science, working in a structural biology lab using the Fortran programming language to help solve the crystal structure of proteins. After graduating from Caltech, he decided to keep going in chemical engineering and came to MIT in 2014 to start a PhD.

Advised by professors Klavs Jensen and William Green, Coley worked on ways to optimize automated chemical reactions. His work focused on combining machine learning and cheminformatics — the application of computation methods to analyze chemical data — to plan reaction pathways that could make new drug molecules. He also worked on designing hardware that could be used to perform those reactions automatically. 

Part of that work was done through a DARPA-funded program called Make-It, which was focused on using machine learning and data science to improve the synthesis of medicines and other useful compounds from simple building blocks.

“That was my real entry point into thinking about cheminformatics, thinking about machine learning, and thinking about how we can use models to understand how different chemicals can be made and what reactions are possible,” Coley says.

Coley began applying for faculty jobs while still a graduate student, and accepted an offer from MIT at age 25. He received a mix of advice for and against taking a job at the same school where he went to graduate school, and eventually decided that a position at MIT was too enticing to turn down.

“MIT is a very special place in terms of the resources and the fluidity across departments. MIT seemed to be doing a really good job supporting the intersection of AI and science, and it was a vibrant ecosystem to stay in,” he says. “The caliber of students, the enthusiasm of the students, and just the incredible strength of collaborations definitely outweighed any potential concerns of staying in the same place.”

Chemistry intuition

Coley deferred the faculty position for one year to do a postdoc at the Broad Institute, where he sought more experience in chemical biology and drug discovery. There, he worked on ways to identify small molecules, from billions of candidates in DNA-encoded libraries, that might have binding interactions with mutated proteins associated with diseases.

After returning to MIT in 2020, he built his lab group with the mission of deploying AI not only to synthesize existing compounds with therapeutic potential, but also to design new molecules with desirable properties and new ways to make them. Over the past few years, his lab has developed a variety of computational approaches to tackle those goals. 

“We try to think about how to best pair a challenge in chemistry with a potential computational solution. And often that pairing motivates the development of new methods,” Coley says. One model his lab has developed, known as ShEPhERD, was trained to evaluate potential new drug molecules based on how they will interact with target proteins, based on the drug molecules’ three-dimensional shapes. This model is now being used by pharmaceutical companies to help them discover new drugs.

“We’re trying to give more of a medicinal chemistry intuition to the generative model, so the model is aware of the right criteria and considerations,” Coley says.

In another project, Coley’s lab developed a generative AI model called FlowER, which can be used to predict the reaction products that will result from combining different chemical inputs. 

In designing that model, the researchers built in an understanding of fundamental physical principles, such as the law of conservation of mass. They also compelled the model to consider the feasibility of the intermediate steps that need to take place on the pathway from reactants to products. These constraints, the researchers found, improved the accuracy of the model’s predictions.

“Thinking about those intermediate steps, the mechanisms involved, and how the reaction evolves is something that chemists do very naturally. It’s how chemistry is taught, but it’s not something that models inherently think about,” Coley says. “We’ve spent a lot of time thinking about how to make sure that our machine-learning models are grounded in an understanding of reaction mechanisms, in the same way an expert chemist would be.”

Students in his lab also work on many different areas related to the optimization of chemical reactions, including computer-aided structure elucidation, laboratory automation, and optimal experimental design.

“Through these many different research threads, we hope to advance the frontier of AI in chemistry,” Coley says.

Justin Solomon, associate professor in the MIT Department of Electrical Engineering and Computer Science (EECS), has been appointed associate dean of engineering education in the MIT School of Engineering, effective July 1.

In this new role, Solomon will focus on advancing innovation in engineering education across the school. He will help shape new pedagogical approaches in the context of an AI-enabled world and will explore experiential, hands-on, and other modes of learning. Working closely with academic departments, Solomon will serve as a thought partner in integrating AI into curricula and will help facilitate interdisciplinary and shared teaching opportunities across departments and other schools. He will also play a key role in helping the school implement relevant recommendations from the Committee on AI Use in Teaching, Learning, and Research Training. 

Solomon will explore opportunities to build industry collaborations, including new models for internships and industry-engaged learning on campus. Collaborating with department heads and the School of Engineering leadership team, he will also support faculty in designing new courses and evolving existing programs to meet emerging opportunities in engineering.

“Justin’s interdisciplinary approach will be especially valuable as we continue to evolve engineering education to meet new opportunities and challenges. His extensive experience applying AI across a wide range of domains will help each academic department thoughtfully integrate AI and new educational models into their curricula,” says Paula T. Hammond, dean of the School of Engineering and Institute Professor. “I look forward to the vision and perspective he will bring to the school’s leadership team.”

A dedicated educator, Solomon has played a central role in shaping computing education at MIT. He is a key contributor to the Common Ground for Computing, where he co-teaches the core class 6.C01 (Modeling with Machine Learning: From Algorithms to Applications) with Regina Barzilay, the Delta Electronics Professor in the MIT Department of Electrical Engineering and Computer Science and affiliate faculty member at the Institute for Medical Engineering and Science. Within EECS, he teaches 6.7350 (Numerical Algorithms for Computing and Machine Learning) as well as 6.8410 (Shape Analysis). He is also the founder of the Summer Geometry Initiative, a six-week program that introduces students to geometry processing through intensive training, collaboration, and research experiences.

Solomon’s dedication to teaching and helping students has been honored with various awards, including the EECS Outstanding Educator Award and the Burgess (1952) and Elizabeth Jamieson Prize for Excellence in Teaching. He is the author of “Numerical Algorithms,” a textbook that presents a modern approach to numerical analysis for computer science students.

Solomon is a principal investigator at MIT’s Computer Science and Artificial Intelligence Laboratory (CSAIL), where he leads the Geometric Data Processing Group. His research sits at the intersection of geometry and computation, with applications spanning computer graphics, autonomous navigation, political redistricting, physical simulation, 3D modeling, and medical imaging. He is also a core faculty member of the MIT-IBM Watson AI Lab, contributing to research that advances the foundations and applications of artificial intelligence.

His scholarly contributions have been recognized with numerous distinctions, including the 2023 Harold E. Edgerton Faculty Achievement Award for exceptional contributions in teaching, research, and service. In 2025, he was named a Schmidt Polymath, supporting interdisciplinary research across areas such as acoustics and climate that rely on large-scale simulation of physical systems.

Solomon joined the MIT faculty in 2016. He previously held an NSF Mathematical Sciences Postdoctoral Research Fellowship in Princeton University’s Program in Applied and Computational Mathematics. He earned his bachelor’s, master’s, and doctoral degrees from Stanford University. While studying at Stanford, he also worked as a research assistant at Pixar Animation Studios.

Urbanization in the Asia-Pacific region of the world is occurring at an alarmingly rapid pace, with more than 2.2 billion people now living in cities in the region, and an additional 1.2 billion projected to migrate to cities by 2050, according to a February 2026 report from the U.N. Economic and Social Commission for Asia and the Pacific, the Asian Development Bank, and the U.N. Development Program.

Such rapid growth places stress on nearly every aspect of urban areas, including housing, drinking water and sewage sources, roads and other transportation modes, and often results in environmental degradation and an increased vulnerability to climate-related disaster. But the situation also presents opportunities for doing things differently by deploying improved urban planning and management approaches, economic development strategies, as well as innovative technologies in real estate development and investment.

With a keen awareness of this ongoing urbanization and the pressures it brings, the MIT Center for Real Estate (CRE) within the MIT School of Architecture and Planning established the MIT Asia Real Estate Initiative (AREI) in 2022. The AREI mission is to serve as a platform for collaborative research, education, and industry engagement that will help urban areas across the Asia-Pacific region and the Gulf Corridor adapt to these ongoing challenges and allow their growing populations to thrive.

The AREI is co-directed by Professor Siqi Zheng, faculty director of the CRE and director of the MIT Sustainable Urbanization Lab, and James Scott MS ’16, a lecturer who is director of industry and professional programs for the CRE and director of the MIT Real Estate Transformation Lab.

“Imagine a region building the equivalent of a Boston every 40 days,” says Zheng, the STL Champion Professor of Urban and Real Estate Sustainability. “Asia is not just urbanizing. It’s redefining city life on a planetary scale.

“Drawing on MIT CRE’s deep roots in the region — more than half of international students in our MSRED program hail from Asia, and we have a robust 40-year alumni network spanning the Asia-Pacific countries and Gulf Corridor countries of West Asia — the AREI will naturally extend MIT’s role as a global convening point for real estate thought leaders.”

The initiative’s work will center on three pillars tailored to Asia’s urban needs: sustainable cities and real estate, urban vibrancy and dynamics, and technology and innovation.

Zheng is a leading scholar of sustainable urban development, real estate markets, and environmental quality, with particular expertise in China and Asia. She currently serves as president of the American Real Estate and Urban Economics Association and is a former president of the Asia Real Estate Society. Her research, which has appeared in leading journals across urban and real estate economics, environmental science, and urban studies, examines the tensions and synergies between fast urbanization and quality of life in cities, and how cities can develop their resilience against future uncertainties. She is now coauthoring a book with Matthew Kahn titled, “The Triumph of Asian Cities: Growth, Risk and Resilience in the 21st Century” (Harvard University Press).

Co-director Scott specializes in technology and innovation in the built environment. While attending MIT as a graduate student, his focus quickly moved in this direction. He has since played a pivotal role in advancing innovation and adoption of technology across some of the largest and most forward-thinking real estate organizations. Much of his work now is in PropTech, an inclusive phrase referring to new technologies in all areas of real estate, including financing, construction, sales, and materials lifespan, among others. His focal areas are Japan, South Korea, the United Arab Emirates, and other West Asian countries.

Scott credits the quick uptake of new PropTech for helping advance the speed of development in these regions.

“The sheer scale and pace of development across the Asia-Pacific and Gulf Corridor regions is extraordinary, from landmark projects like the Burj in Dubai to the transformative mega-developments in Saudi Arabia and the remarkable urban expansion seen in cities like Beijing and Shanghai over the past 10 to 15 years,” Scott says.

“Boston, by contrast, reflects a more incremental but equally important model of urban evolution,” he says. “The difference is not one of ambition, but of tempo and scale, and it underscores the diverse ways cities around the world are driving innovation in the built environment. This also highlights how the AREI can foster two-way learning across different development contexts, creating a platform for shared insights between rapidly evolving markets and more incremental urban ecosystems.”

In addition to its MIT headquarters, the initiative has two regional hubs — one in Tokyo, the other in Dubai, with a third planned for Hong Kong. The hubs will serve as loci for regional research, as well as provide a means of organizing the CRE’s many alumni in these areas for professional opportunities. For this reason, successful alumni have been selected to head each of the hubs.

Taka Kiura MS ’00 is director of the Toyko hub. After working for the global real estate development firm Heitman for more than 20 years, Kiura last year founded Base-K, a real estate and venture capital investment firm. He also is CEO and founder of HyStat, an investment firm that backs and accelerates the adoption of next-generation technologies.

The Dubai Hub is directed by Ocean Saleem Jangda MS ’25, who works on development innovation partnerships at Majid Al Futtaim Properties, one of the largest mixed-use developers in the Gulf Region.

This spring, Zheng is co-taught course 15.S67 (Special Seminar in Management) in the MIT Sloan-CRE Real Estate Lab. The course, co-taught with Hong Ru, a visiting associate professor in the MIT Sloan School of Management, deployed interdisciplinary student teams to work on applied projects, one of which is in Singapore. Zheng also has partnered with MIT International Science and Technology Initiatives, a program under the MIT Center for International Studies, that is offering student internships with the AREI Hong Kong hub next January.

Another of the steps in the directors’ goal of coalescing CRE alumni in these areas will be organized by Ryan Othman, who will return to Saudi Arabia following completion of his master’s this month to launch his real estate development business in mid-sized market residential and industrial projects.

Othman, who also holds a BS in civil engineering and an SM in finance, will lead an MSRED/AREI trek to introduce next year’s MSRED students to alumni and other business and government officials in Saudi Arabia. “The MIT’s master’s in real estate development is the oldest in the country,” he says. “It’s a powerful program with an amazing alumni network, which I’d like to help expand.”

“Asian cities have become the defining arena for global economic growth, environmental change, and human welfare in the 21st century,” Zheng explains. “Their future depends on durable, place-based infrastructure, real estate investments shaped by regional integration, human capital, and how these cities interact with each other and the rest of the world.

“The outcome of this incredible growth will largely determine global living standards and environmental consequences for the remainder of this century. I believe the MIT Asia Real Estate Initiative is a great platform for the MIT community to make its intellectual contribution to these mega-dynamics.” 

Senior MBA student Patrick Yeung came to MIT Sloan School of Management wanting to be surrounded by a community of builders. 

“I come from a consulting background, which has its own strengths and gives you a specific toolkit, but I felt like I was not very technical, and so I wanted to be surrounded and inspired by people who had that knowledge and experience,” he says.

“MIT Sloan’s Sustainability Initiative provides a great platform to help a generalist like myself become more specialized in this space, whether it be the Sustainability lunch series that they run every Thursday, the annual conference that gets organized, or the class catalog that aligns with the Sustainability Certificate.”

Yeung eventually hopes to join a climate tech scale-up to help formalize the business and scale, using what he’s learned at MIT Sloan to make a real impact.

“I've come to appreciate the systems thinking approach to sustainability that MIT Sloan has, especially in the context of the tech and lab-scale tech spinout ecosystem that MIT more broadly has. The technology is obviously an important piece of both climate mitigation and adaptation, but we will also need other techno-economic regime changes to be able to truly change our planet for the better — that takes policy and legal changes, that takes leadership and courage, and ultimately it takes a willingness to fail, over and over, in order to iterate.”

The following photo gallery provides a snapshot of what a typical day for Yeung has been like as an MIT student.

The Haystack 37m Telescope has been a landmark in radio astronomy and radar studies of the solar system since its first light in 1964. Over the following four decades, it supported NASA's Apollo landings on the moon, made planetary radar maps of the surface of Venus, contributed to experimental tests of Einstein's general relativity, supported the development of VLBI, and conducted foundational studies of quasars and star-forming regions. 

Recently, the Haystack 37m Telescope — a 37-meter radio and millimeter-wavelength antenna at MIT Haystack Observatory in Westford, Massachusetts — made its return to front-line astronomical research following an extended period of system upgrades. These observations reconnect this instrument with its long tradition of scientific discovery and open a new chapter.

On Dec. 8, 2025, Haystack scientists observed the supermassive black hole system at the center of the galaxy Messier 87 (M87) using a technique called very long baseline interferometry (VLBI) that links telescopes across continents to achieve extraordinary resolution. These observations mark the return of one of America's most storied radio telescopes to its historical scientific and educational mission.

The observations targeted the powerful jet of energy and matter launched from M87’s central black hole, M87*. This jet, driven by a black hole six-and-a-half billion times the mass of our sun, extends thousands of light years into intergalactic space and is one of the most energetic phenomena in the known universe. 

Previous international campaigns, namely those led by the Event Horizon Telescope, have imaged the black hole's immediate “shadow.” The Haystack 37m Telescope observations, performed in collaboration with the telescopes of the Very Long Baseline Array (VLBA) and the Greenland Telescope (GLT), help to probe the larger-scale structure of the jet, investigating how energy is transported far beyond the black hole's vicinity. Understanding this process is central to explaining how supermassive black holes shape the galaxies that surround them.

“The Haystack 37m Telescope’s exceptional sensitivity enables the intercontinental telescope array to detect faint emission from around the distant M87* black hole,” says Paul Tiede, principal investigator of the M87 study. “In tandem with the GLT and the VLBA, Haystack is helping create the first multifrequency movies of M87*’s faint jet, greatly improving our understanding of black hole physics.”

The upgraded Haystack 37m Telescope opens multiple new lines of research. At MIT, Saverio Cambioni and Richard Teague of the Department of Earth, Atmospheric and Planetary Sciences (EAPS) plan to use the instrument within MIT’s Planetary Defense Project to measure asteroid sizes and shapes, characterizing objects that could pose a hazard to Earth and deepening our understanding of the solar system’s formation. Associate Professor Brett McGuire of the Department of Chemistry plans to search for complex organic molecules in space, work that speaks to the question of how the chemical precursors to life arise.

“We are thrilled to provide the research community with a powerful telescope at a time where few such instruments are available,” says Jens Kauffmann, principal investigator of the Haystack 37m Telescope Astronomy Program, who uses the telescope to study the formation of stars and their planets. “Even more exciting are the prospects this generates for the next generation of astronomers. Hands-on training opportunities on world-class research telescopes have become exceptionally rare worldwide, and now we can offer this singular advanced workforce development program right here in Massachusetts.”

Student involvement with the Haystack 37m Telescope has already resumed: Undergraduate interns at Haystack Observatory played an active role in developing the telescope’s control systems and data analysis algorithms. This work exemplifies Haystack’s role as a hands-on research and training environment where students contribute directly and gain practical experience with a frontline research instrument.

The return to research-focused observations is the result of more than 10 years of careful, sustained work. From 2010 to 2014, the Haystack 37m Telescope underwent a major upgrade and refurbishment that enhanced its ability to observe at millimeter wavelengths. This work was primarily done to improve the antenna’s capability as a space radar. The dish now primarily serves U.S. government agencies in that capability, and astronomy was temporarily a secondary activity. 

But work to restore the telescope's science capability never stopped. Initial support from the National Science Foundation (NSF) in 2015 modernized systems for data analysis and radio signal processing. The first successful engineering-oriented VLBI experiments with the new dish were conducted at the same time. Additional NSF funding in 2019, provided in the context of the Next Generation Event Horizon Telescope (ngEHT) program, enabled a more general and sustained effort to upgrade receiver equipment and computing systems. Support from private donors to Haystack also aided in this longer-term effort.

Several recent developments, particularly in 2025, proved significant. With support from MIT's Jarve Seed Fund for Science Innovation, scientists and engineers removed lingering technical limitations with astronomy systems and expanded the telescope's scientific reach. Other funding for projects led by the Smithsonian Astrophysical Observatory enabled the M87 campaign and commissioning of the next-generation digital back end, a highly advanced signal-processing system developed for the ngEHT. Together, these advances made the December 2025 observations possible. MIT Haystack Observatory is now pursuing support from both private and federal sources for further improvements under the Haystack 37m Telescope Astronomy Program.

“The upgraded Haystack 37m Telescope empowers MIT students and researchers to pursue fundamental questions relating to our origins and our solar system,” says Richard Teague, professor at MIT EAPS. “With privileged access to such a powerful facility, we can undertake ambitious observational programs previously impossible to schedule. This is the beginning of what we expect will be an exciting era of new discoveries with the Haystack 37m Telescope.”

Using a powerful single-molecule imaging method they developed, a research team from the Broad Institute of MIT and Harvard has unveiled a dynamic view of how some cancer-related proteins interact in living cells. 

The technique relies on highly stable nanoparticle probes that brightly illuminate individual molecules for long periods of time. The researchers used their method to observe, for the first time, individual receptors as they move around the cell membrane, attaching to and then letting go of other receptors to alter signaling within the cell.

Described in the journal Cell, the work demonstrates the method’s potential for investigating other receptors and molecules, and for improved drug screening to better understand the effects of therapeutics on living cells.

“With our photostable probes, we can map out the entire lifespan of these molecules in their native environment and see things that have never been observable before,” says study leader Sam Peng, a Broad Institute core institute member and assistant professor of chemistry at MIT.

Molecular movies

Peng’s method solves a problem with existing contrast agents used in single-molecule tracking, such as dyes. Under the laser light that’s used to excite these dyes, they burn out after a few seconds in a phenomenon known as photobleaching, which means that scientists could only use them to take a few snapshots of cell receptors, and not follow them over the entirety of the signaling process.

For a longer and richer view, Peng’s lab developed long-lasting probes, known as upconverting nanoparticles, which emit signals that remain stable under laser excitation. The nanoparticles contain rare-earth ions that continue to luminescence for minutes, hours, and potentially years. In addition, by altering the type and doses of the ions, scientists can engineer probes emitting in many different colors, enabling tracking of many targets in a single experiment.

In the current study, the researchers aimed to uncover new biology by focusing on the EGFR family of cell receptors, which have been linked to several kinds of cancer. They collaborated with EGFR experts Matthew Meyerson and Heidi Greulich of the Broad’s Cancer Program. They knew that EGFR receptors need to pair up, or “dimerize,” in order to initiate signaling within the cell, but they wanted to learn more about the dynamics of these pairings — what the receptors partner with, how long they stay together, and how they find new partners.

For a better and more sustained look at the receptors, the research team customized their upconverting nanoparticles to tag EGFR and related receptors HER2 and HER3, which are linked to cancer, and used them to track the molecules in living human cells.

A new view of protein pairings

In this study, Peng and his team observed that, when activated with a stimulating molecule, EGFR receptors can pair up and stay dimerized for several minutes, something not observable using traditional dyes. Excessive and prolonged dimerization can lead to too much cell growth and cancer.

A microscopy video shows upconverting nanoparticles tagged to EGFR receptors (labeled pink and green), which track individual receptors as they dimerize. Image courtesy of the researchers.

When the EGFR molecules carried cancer-related mutations, the dimers became more stable, with the more stabilizing mutations linked to more potent cancers in people. In addition, the mutated receptors could form stable dimers even without an external stimulus prompting them to dimerize. The finding helps explain how EGFR mutations can lead to uncontrolled cell growth and cancer, and could inform efforts to target this process therapeutically.

The team discovered several other new and surprising details about how HER2 and HER3 form stable pairings with themselves, which helps illuminate the role of these molecules in related cancers.

When the research team tagged all three receptor types in one experiment, they observed a vibrant scene with receptors navigating the cell surface, finding partners, unpairing, and then finding new partners, over and over again.

Beyond shedding light on EGFR biology, the scientists hope that collaborators in other fields will apply their method to ask new scientific questions about other proteins of interest. “We think this technique could be transformative for studying molecular biology, because it enables dynamic biological processes to be observed with high spatiotemporal resolution over unprecedented timescales,” says Peng.

They are also planning to explore the method’s use in studying the mechanism of drug action, to reveal how potential therapeutics alter individual molecules over time. In addition, they will continue to improve their methods, such as making the probes smaller, brighter, and able to emit more colors.

In the aftermath of a devastating earthquake, unpiloted aerial vehicles (UAVs) could fly through a collapsed building to map the scene, giving rescuers information they need to quickly reach survivors. 

But this remains an extremely challenging problem for an autonomous robot, which would need to swiftly adjust its trajectory to avoid sudden obstacles while staying on course.

Researchers from MIT and the University of Pennsylvania developed a new trajectory-planning system that tackles both challenges at once. Their technique enables a UAV to react to obstacles in milliseconds while staying on a smooth flight path that minimizes travel time. 

Their system uses a new mathematical formulation that ensures the robot travels safely to its destination along a feasible path, and that is less computationally intensive than other techniques. In this way, it generates smoother trajectories faster than state-of-the-art methods.

The trajectory planner is also efficient enough for real-time flight using only the robot’s onboard computer and sensors. 

Named MIGHTY, the open-source system does not require proprietary software packages that can cost hundreds of thousands of dollars. It could be more readily deployed in a wider variety of real-world settings.

In addition to search-and-rescue, MIGHTY could be utilized in applications like last-mile delivery in urban spaces, where UAVs need to avoid buildings, wires, and people, or in industrial inspection of complex structures, such as wind turbines.

“MIGHTY achieves comparable or better performance using only open-source tools, which means any researcher, student, or company — anywhere in the world — can use it freely. By removing this cost barrier, MIGHTY helps democratize high-performance trajectory planning and opens the door for a much broader community to build on this work,” says Kota Kondo, an aeronautics and astronautics graduate student and lead author of a paper on this trajectory planner.

Kondo is joined on the paper by Yuwei Wu, a graduate student at the University of Pennsylvania; Vijay Kumar, a professor at UPenn; 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 IEEE Robotics and Automation Letters.

Overcoming trade-offs           

When Kondo was a child, the Fukushima Daiichi nuclear accident occurred following the Great East Japan Earthquake. With school cancelled, Kondo was stuck at home and watched the news every day as workers explored and secured the reactor site. Some workers still had to enter hazardous areas to contain the damage and assess the situation, exposing them to high doses of radioactive material.

“I became passionate about creating autonomous robots that can go into these dynamic and dangerous situations, then come back and report to humans who stay out of harm’s way,” Kondo says.

This task requires a strong trajectory planner, which is software that decides the path a robot should follow to safely get from point A to point B. 

But many existing systems force tradeoffs that limit performance. 

While some commercial systems can rapidly generate smooth trajectories, they can cost hundreds of thousands of dollars. Open-source alternatives often underperform compared to commercial solvers or are difficult to use.      

With MIGHTY, Kondo and his colleagues developed an open-source system that produces high-quality, smooth trajectories while reacting to obstacles in real-time, and which runs fast enough for flight using only onboard components.

To do this, they overcame a key challenge that limits many open-source systems. 

These methods usually estimate how long it will take the robot to get from point A to point B as a first step. From that fixed estimation of travel time, the planner finds the best path to reach the destination.

While using a fixed travel time allows the planner to rapidly generate a trajectory, it has drawbacks. For one, if the UAV must go far out of its way to avoid obstacles, it could be forced to crank up the speed to meet the fixed travel-time budget. This makes it harder to avoid sudden hazards.

A MIGHTY method

Instead, MIGHTY uses a mathematical technique, called a Hermite spline, that optimizes the travel time and flight path together, in a single step, to form a smooth trajectory that can be precisely controlled.

“Optimizing the spatial and temporal components together gets us better results, but now the optimization becomes so much bigger that it is harder to solve in a feasible amount of time,” Kondo says.

The researchers used a clever technique to reduce this computational overhead. 

Instead of generating a trajectory from scratch each time, MIGHTY makes an initial guess of a trajectory. Then it refines the trajectory through an iterative optimization, using a map of the scene generated by the UAV’s lidar sensors.

“We can make a decent guess of what the trajectory should be, which is a lot faster than generating the entire thing from nothing,” Kondo says.

This enables MIGHTY to react in real-time to unknown obstacles while keeping the trajectory smooth and minimizing travel time. The system utilizes the UAV’s onboard components, which is important for applications where a robot might travel far from a base station.

In simulated experiments, MIGHTY needed only about 90 percent of the computation time required by state-of-the-art methods, while safely reaching its destination about 15 percent faster than these approaches. 

When they tested the system on real robots, it reached a speed of 6.7 meters per second while avoiding every obstacle that appeared in its path.

“With MIGHTY, everything is integrated in one piece. It doesn’t need to talk to any other piece of software to get a solution. This helps us be even faster than some of the commercial solvers,” Kondo says.

In the future, the researchers want to enhance MIGHTY so it can be used to control multiple robots at once and conduct more flight experiments in challenging environments. They hope to continue improving the open-source system based on user feedback.

“MIGHTY makes an important contribution to agile robot navigation by revisiting the trajectory representation itself. Hermite splines have already been successfully used in visual simultaneous localization and mapping, and it is nice to see their advantages now being exploited for trajectory planning in mobile robots. By enabling joint optimization of path geometry, timing, velocity, and acceleration while retaining local control of the trajectory, MIGHTY gives robots more freedom to compute fast, dynamically feasible motions in cluttered environments,” says Davide Scaramuzza, professor and director of the Robotics and Perception Group at the University of Zurich, who was not involved with this research.

This research was funded, in part, by the United States Army Research Laboratory and the Defense Science and Technology Agency in Singapore.

The brain’s capacity to use and understand language expands rapidly in the first years of life, as babies start to make sense of the words they hear and eventually begin to piece together sentences of their own. The language-processing parts of the brain that make this possible continue to evolve in older children, as they expand their vocabularies and learn to use language more flexibly. 

MIT brain researchers have captured snapshots of the developing language-processing network in brain scans of hundreds of children and adolescents. Their data, reported May 16 in the journal Nature Communications, show that the network continues to mature, becoming better integrated and increasingly responsive until around age 16. But they also found that a key feature of the adult language network is established early on: its localization in the left side of the brain. 

Language lateralization 

It is well known that using language is mostly the job of the left hemisphere. As adults, we call on the language-processing regions there when we read, write, speak, or listen to others talk. But there was some question as to whether this left lateralization is established early in life, or might instead emerge as the language network matures, with both sides of the brain contributing to language in childhood. 

To find out, researchers needed to see young brains in action — and several MIT labs had collected exactly the right kind of data. Groups led by Evelina Fedorenko, an associate professor of brain and cognitive sciences; John Gabrieli, the Grover Hermann Professor of Health Sciences and Technology; and Rebecca Saxe, the John W. Jarve (1978) Professor of Brain and Cognitive Sciences, teamed up to share brain scans from children, adolescents, and adults and compare how their brains responded to language. Fedorenko, Gabrieli, and Saxe are also investigators at the McGovern Institute for Brain Research. 

In studies aimed at better understanding a variety of cognitive functions and developmental disorders, the three teams had all collected functional MRI data while subjects participated in “language localizer” tasks — an approach the Fedorenko lab developed to map the language-processing network in a person’s brain. By monitoring brain activity with functional MRI as people engage in both language tasks and non-linguistic tasks, researchers can identify parts of the brain that are exclusively dedicated to language processing, whose precise anatomic location varies across individuals. 

To activate the language network, the researchers had children listen to stories inside the MRI scanner. Depending on their age, some heard excerpts of “Alice in Wonderland,” some listened to podcasts and TED talks, and others heard shorter, simpler stories. To watch their brains during a non-linguistic task, the researchers had the children listen to nonsense words. 

Across the data from the three labs, which included children between the ages of 4 and 16, as well as adults for comparison, the team saw clear developmental changes in the brain’s response to language. “The integration of the system — how well different subregions of the system correlated with each other and worked together during language processing — was stronger in older children as compared to younger children,” says Ola Ozernov-Palchik, a research scientist in Gabrieli’s lab and a research assistant professor at Boston University. The system was also more strongly activated by language in older children, which may reflect their growing comprehension of what they hear. 

But strikingly, almost all language processing happened on the left side of the brain, even in the youngest subjects. “From age 4 on, it seems just as lateralized as in an adult,” Gabrieli says. 

Language and developmental disorders 

The researchers say this finding has implications for understanding developmental conditions that impact language, including autism and dyslexia. The right side of the brain frequently gets more involved in language processing in people with these conditions than it does in typically developing children. “Almost every single developmental disorder that’s associated with language has a theory that’s related to language lateralization,” Ozernov-Palchik says. 

The reason for more bilateral language processing in some disorders is debated. One idea has been that some people might use both sides of their brain for language processing because their brains are less mature. If the right side of the brain processes language early in life, scientists had reasoned, it might simply continue to do so for longer in people with autism or dyslexia than it does in neurotypical individuals. But if most people use the left side of their brains for language even when they are young, the difference can’t be attributed to a developmental delay. Other developmental differences might cause bilateral language processing instead. 

The researchers don’t have the full picture yet; they still need to know what parts of the brain process language in children younger than 4. Likewise, they would like to know what the brain areas that become the language network are doing in the first months of life, when infants aren’t using language yet. They are eager to find out, both to understand fundamentals of brain development and to shed light on developmental disorders. “I think understanding that normal trajectory is really critical for interpreting what a deviation from that trajectory is,” says Amanda O’Brien, a former graduate student in Gabrieli’s lab who is now a postdoc at Harvard University. 

One reason people thought lateralization might develop gradually is because damage to the left hemisphere of the brain impacts language abilities differently, depending on when it occurs. “If you have damage to the left hemisphere as an adult, you’re very likely to end up with some form of aphasia, at least temporarily,” Fedorenko explains. “But a lot of the time, with early damage to the left hemisphere, you grow up and you’re totally fine. The language can just develop in the right hemisphere.” 

Some scientists suspected that the right side of the brain was able to take over language processing in children who suffered early-life brain damage because it was already participating in this function at the time. But the team’s findings suggest the developing brain may be nimbler than that. “Our data tell you that this early plasticity apparently happens in spite of the fact that by age 4, we see these very strongly lateralized responses already,” Fedorenko says.