General News from MIT - Massachusetts Institute of Technology

MIT News
MIT News is dedicated to communicating to the media and the public the news and achievements of the students, faculty, staff and the greater MIT community.
Building AI models that understand chemical principles

Connor Coley works at the interface of chemistry and machine learning, to discover and design new drug compounds.

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 appointed associate dean of engineering education

MIT faculty member in electrical engineering and computer science to focus on innovation in engineering education and new pedagogical approaches.

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.

MIT Asia Real Estate Initiative expands its footprint in booming Asian cities

The initiative plans to engage industry leaders and MIT alumni with hubs in Tokyo, Dubai, and Hong Kong.

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.” 

A day in the life of MIT MBA student Patrick Yeung

MIT Sloan’s Sustainability Initiative provides opportunities to lead in ways that will help build a more sustainable future.

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: A new era of astrophysical research

The legendary radio astronomy telescope returns to its science and educational mission at MIT Haystack Observatory.

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.”

Single-molecule tracker illuminates workings of cancer-related proteins

Researchers use custom-built microscopy and nanotechnology to tag and follow the activity of individual proteins in real-time.

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.

New research enables a robot to chart a better course

By rapidly generating a smooth path plan that cuts travel time and avoids obstacles, the open-source “MIGHTY” system could streamline disaster recovery and parcel delivery.

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.

Language development in the brain

The brain’s language network is still evolving in adolescence. But by age 4, language processing is already handled by the left side of the brain, new research finds.

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.

A bet that has paid off 500 million times over

Twenty-five years ago, MIT opened its curriculum to the world with the launch of OpenCourseWare. MIT Open Learning continues to build on the legacy of that bold decision.

In 2001, at the dawn of the digital age, MIT made a bold decision: to open its curriculum to the world. Through MIT OpenCourseWare — now part of MIT Open Learning — the Institute began sharing materials from nearly all of its courses online for free.  

A quarter of a century later, that decision has impacted the lives of more than 500 million people across the world who have used OpenCourseWare’s resources to expand their knowledge and develop new skills. 

“When MIT opens its doors, the world walks in,” said Dimitris Bertsimas, vice provost for open learning, at OpenCourseWare’s recent 25th Anniversary Symposium. “Twenty-five years ago, MIT made a bet on openness, generosity, and on the belief that knowledge is a public good. That bet has paid off 500 million times over.”

The impact of that bet took center stage as nearly 200 people gathered on campus for the symposium on April 8. The daylong celebration brought together faculty and staff, OpenCourseWare learners and educators, new and early funders of the program, MIT President Sally Kornbluth, Bertsimas, and others to reflect on OpenCourseWare’s global impact and the future of free and open education. 

The occasion also marked the premiere of “The Courage to Be Open: MIT OpenCourseWare and the Democratization of Knowledge.” Produced by MIT Open Learning’s Emmy Award-winning video team, the short documentary explores the origin, influence, and global reach of OpenCourseWare.

Initially announced as a 10-year initiative, MIT OpenCourseWare now offers more than 2,500 courses that span the undergraduate and graduate curriculum. Learners can freely access lecture notes, syllabi, problem sets, exams, and video lectures through the MIT Learn platform, the OpenCourseWare website, and its YouTube channel, which has grown into the platform’s most popular higher education channel with more than 6 million subscribers. To extend that reach even further, the OpenCourseWare Mirror Site Program provides free copies of course content on hard drives to educational organizations with limited or costly internet access.

From an idea to a global movement

In launching OpenCourseWare, MIT sparked a global movement, inspiring other universities to create their own open course initiatives and solidifying grassroots open education efforts into worldwide communities like OE Global. “Today, [OpenCourseWare] is cited in national education strategies, by nonprofit initiatives, and by international development programs — proof that openness scales when you lead with vision and courage,” Kornbluth said.

That impact lives on in the learners who turn to the Institute’s free course materials every day — from a community college student in Boston to a teenager in Australia to medical students in Turkey. OpenCourseWare has expanded the reach of MIT’s life-changing knowledge to nearly every corner of the world and opened doors to learners of all ages and backgrounds.

For many, that access is transformative. High school senior Hinata Yamahara and Andrea Henshall, a veteran of the United States Air Force, shared how OpenCourseWare helped fuel their curiosity, support their studies, and advance their goals.

“OpenCourseWare [reduces] the barrier to entry to more specialized topics,” said Yamahara, who discovered the resources while exploring an interest in urban planning, and now credits an MIT workshop with helping him pass the Federal Aviation Administration’s Private Pilot Knowledge Test.

From access to agency

What emerges across stories is that MIT’s decision to give away its course materials exemplified its mission to advance knowledge in service of the nation and the world. Openness, noted speakers, is part of the Institute’s DNA. “It’s written into our values,” said Chris Bourg, director of libraries at MIT, where she is also the founding director of the Center for Research on Equitable and Open Scholarship (CREOS).

Those values have also drawn thousands of supporters — from alumni and individual learners to businesses and the world’s leading philanthropic foundations — to help underwrite the initiative, and Open Learning more generally.

By making course materials not only free, but open, the Institute enables anyone to download, copy, modify, reuse, remix, and redistribute its resources for educational, non-commercial uses. “Access is powerful and absolutely necessary,” said Curt Newton, director of OpenCourseWare. “But openness goes further. It invites participation.”

For educators like Elizabeth Siler, a professor at Worcester State University in the department of business administration and economics, and Victor Odumuyiwa, an associate professor in computer science at the University of Lagos, OpenCourseWare offers a window into how MIT designs learning experiences and a foundation to bring those approaches into their own classrooms.

“I applied the same approach back home and, sincerely, I’ve gotten a lot of positive feedback from people getting jobs in global companies after taking the course that I designed,” Odumuyiwa said. 

For faculty on MIT’s campus, OpenCourseWare has also been transformative, fostering interdisciplinary collaborations and innovative uses of digital educational tools. Referencing the United Nations Sustainable Development Goals, Christopher Capozzola, the Elting E. Morison Professor of History at MIT, pointed to quality education (goal 4), reduced inequalities (goal 10), and peace, justice, and strong institutions (goal 16) as a guiding equation for open education. “I believe that MIT, through OpenCourseWare and all of our open education initiatives, has committed to solving that problem,” he said. “I just wanted to roll up my sleeves and be part of that.”

A new era for open education

If the rise of the internet in the early 2000s catalyzed MIT’s decision to “open its doors to the world without requiring a key,” said Kornbluth, artificial intelligence now presents a new moment to lead.

Building on that legacy, MIT Open Learning is leading the way with the launch of MIT Learn, an AI-enabled hub for the Institute’s non-degree learning opportunities. The platform brings together innovations like AskTIM — an AI assistant that helps learners discover relevant offerings and, in select offerings, enhances understanding with guided support — and new self-paced, modular online learning experiences that prepare learners to take on complex global challenges, including AI and climate. Together, these advances move MIT closer to a future of truly personalized education at global scale, grounded in faculty expertise and research.

“Sometime in the next five years, I’m looking for a moment that rhymes with what happened in 2001,” Newton said.

With the launch of MIT Learn and Open Learning’s goal of reaching 1 billion learners in the next decade, that next chapter is already taking shape.

“The future of open learning is bright, and belongs to all of us,” Bertsimas said.

Startup making reusable emergency housing wins MIT $100K competition

Uplift Microhome’s modular housing units can provide their own power and water, for faster deployments.

A startup making emergency housing cheaper and faster to deploy won this year’s MIT $100K Entrepreneurship Competition on May 12.

Uplift Microhome is building reusable, modular housing units to provide housing on demand to people affected by natural disasters and other emergencies. Each of the company’s homes has its own batteries and water reservoir, allowing them to quickly be transported and placed off-grid.

“Every year, millions of Americans are displaced by natural disasters,” said co-founder Charlie Nitschelm, who is in MIT’s Leaders for Global Operations program, earning a master’s in engineering and an MBA. “If they're lucky, they can stay with friends or family. If they’re not so lucky, they could end up in a homeless shelter. But disasters aren’t just two-week problems. It takes months, sometimes years, to get back to what life was like before. Bottom line: We lack dignified and affordable housing after disasters.”

Uplift Microhome was one of seven teams chosen to pitch at the final event, which took place inside a packed Kresge Auditorium. Each team got five minutes to pitch their startups before a few minutes of questioning from judges.

This year’s competition started in April with more than 80 applications. The program’s judges selected 16 teams to compete in the semifinal before whittling that number down to the finalist teams for Tuesday’s event.

“This competition isn’t just about one big night,” $100K managing director and MIT Sloan School of Management student Celine Christory said. “It’s a year-long journey for our organizers and students. It kicks off with the ‘Pitch’ event in December, moves to ‘Accelerate’ in March, and culminates in the ‘Launch’ event.”

In the pitch that won the $100,000 Danny Lewin Grand Prize, Nitschelm said it takes an average of four months for the U.S. Federal Emergency Management Agency (FEMA) to deploy single-use housing after a disaster. That’s because these homes require power and utilities in addition to extensive foundation preparation.

“As a result, less than 1 percent of survivors actually receive a physical home,” Nitschelm said. “The rest get a check and are told to go figure it out. This isn’t just our opinion. The Department of Homeland Security audited FEMA and recommended providing a cost-effective housing alternative that allows disaster survivors to stay close to their home.”

Uplift’s homes can be transported on the back of a tractor trailer and deployed using a standard forklift. In addition to its battery and water reservoir, the homes feature self-leveling bases that allow them to be deployed on uneven terrain.

“That dramatically simplifies delivery, installation, and deactivation to the point where you can economically recover, refurbish, and redeploy the unit,” says co-founder Trevor O’Leary, a student at Harvard Business School.

The company has already built a home and believes it can manufacture each unit at a cost similar to the cheapest tractor trailer while delivering housing in hours. The company expects the marginal cost of reusing each unit to be an order of magnitude less expensive than current solutions. Down the line, it plans to deploy homes to combat housing insecurity, for seasonal workers and during construction projects. It plans to manufacture its homes in the United States.

The second-place $50,000 David T. Morgenthaler Founder’s Prize was awarded to the startup Mohan, which is using generative artificial intelligence to map the Earth’s subsurface in three dimensions. The company is deploying its technology to help mining companies decide where to drill, starting by targeting copper deposits.

“Everyone is talking about AI and chips, but no one is talking about what they sit on: copper,” said co-founder Hongze Bo, a PhD student in MIT’s Department of Earth, Atmospheric and Planetary Sciences. “Every cable, GPU, and data center depends on copper. By 2030, we’re going to be 4 million tons of copper short. But we don't know where the next deposit is. Today we just drill and hope.”

The core of Mohan’s technology is a diffusion AI model that iteratively removes noise from subsurface data to create underground scans. The company also develops its own subsurface data.

“We built a full, 3D subsurface model using generative AI,” explained Bo. “It’s the same technology behind [image generation tools] Sora and Midjourney.”

The third place $5,000 prize went to Iceberg Systems, which is using autonomous AI agents to predict how risk cascades across the economy. The company invented a new class of AI systems at MIT that coordinates millions of AIs to simulate how risks emerge through interaction. It has been working with the Department of Energy.

“Iceberg simulates behaviors across millions of market participants, from brokers to consumers to institutions, to simulate and predict how shocks cascade through their interactions and create systemic risk in the economy,” says co-founder and MIT PhD student Ayush Chopra.

The $5,000 Audience Choice Prize went to Pixology, an agentic AI platform that creates on-brand, sponsor-ready sports content to help monetize live moments.

The other finalists that presented at this year’s event were:

• NeuralPhysics, which is building foundation physics models and agents for hardware design simulation and manufacturing;
• DesignFlowAI, a design intelligence app embedded in computer-aided design software to give engineers insights in real time; and
• Auto Lab, an autonomous AI platform that helps teams build better models faster. 

The $100K Entrepreneurship Competition is one of MIT’s annual flagship entrepreneurial events. It began more than 30 years ago when a group of students, along with the late Ed Roberts, who was the founder and chair of the Martin Trust Center for MIT Entrepreneurship, decided to start a startup pitch competition.

The prize started at $10,000 then grew to $50,000 before reaching today’s $100,000 grand prize. Past participants include HubSpot, Akamai, and Lightmatter.

In addition to the prizes, teams received mentorship from venture capitalists, serial entrepreneurs, corporate executives, and attorneys; funding for prototypes; business plan feedback; and more.

MIT practicum connects students with Ukrainian city leaders on economic development

Students in a Department of Urban Studies and Planning course work with leaders from Vinnytsia, Ukraine, exploring innovation ecosystems, infrastructure, and workforce development amid constraints of war.

MIT graduate students are working with leaders from the Ukrainian city of Vinnytsia to explore strategies for economic development, infrastructure, and innovation during wartime conditions.

As part of the MIT Department of Urban Studies and Planning (DUSP) spring course 11.S941 (Innovating in Ukraine), DUSP hosted a delegation of five Ukrainian leaders from Vinnytsia, a city region of 400,000 people located approximately 280 kilometers from Kyiv in central Ukraine. The course, taught by professor of the practice Elisabeth Reynolds, is a practicum in which students work with a “client” for the semester on specific projects or issues the city would like to address and provide a final report or deliverable.

The city of Vinnytsia, which had two representatives on the trip, has focused on building out its “innovation ecosystem” across key parts of its economy. Amid the ongoing war with Russia, the country has accelerated its long-time expertise in information technology in both civilian and military contexts. Examples include the digitalization of government services, such that many services are accessible by cellphone through the e-governance app Diia, as well as the development of a rapidly evolving drone industry.

The 13 graduate students, who draw from the School of Architecture and Planning and the MIT Sloan School of Management, as well as Harvard University’s Kennedy School and Graduate School of Design, have worked with members of the city government and Vinnytsia National Technical University on a range of projects focused on the city’s future growth. The projects include developing an agro-food cluster to facilitate Ukraine’s integration into the European Union; transportation and logistics to support economic growth in the city and enhance its role as a regional hub; improving the city’s and country’s electronic waste management; and developing the city’s creative and entrepreneurial talent to retain and attract workers.

While in Cambridge for the week, the visitors and students toured a number of places and organizations that engage in innovation. A trip to Boston City Hall to meet with Kairos Shen, Boston’s chief city planner and a former professor of the practice at the MIT Center for Real Estate, highlighted the ways in which the built environment can facilitate activities and interactions to foster a more innovative city. Tours of the Cambridge Innovation Center in Kendall Square, Greentown Labs in Somerville, and MassChallenge in Boston provided examples of the myriad ways the region supports entrepreneurs through shared workspace, incubators, and network development.

“We are very interested in partnering with some of these organizations,” said Dmitry Sofyna, CEO and co-founder of WINSTARS.AI, an R&D center in Ukraine focused on AI applications. “We want to transform Ukraine from a major player in engineering and scientific outsourcing into a hub for creating large-scale tech companies in defense, medicine, and energy.” Vinnytsia is currently building Crystal Technology Park, one of the largest technology parks in Ukraine.

Usually during a practicum, students travel to the host location to spend a week during Independent Activities Period (IAP) or spring break learning about the city or region. In the case of the collaboration with Vinnytsia — an outgrowth of the MIT-Ukraine initiative and the Ukraine Community Recovery Academy, with which DUSP has been working for two years — the students are unable to travel to Ukraine due to the war. With the help of a generous alumnus, DUSP instead brought the Ukrainian delegation to Cambridge so that there could be in-person exchange between the students and the Vinnytsia partners.

“It’s been an amazing trip,” said Yanna Chaikovska, director of Vinnytsia’s Institute for Urban Development. “We are planning for the future because that is what we must do. Ukraine has faced many challenges in the past and always worked in small and big ways to move forward. MIT is helping us do this.”

Nick Durham, a joint DUSP/MIT Sloan master’s student, added: “I am continually inspired by the resilience of the Ukrainian people and how they are finding creative ways to build a better future. In many ways, Ukrainian innovation is serving as a model for reimagining industries and complex economic systems.”

The collaboration reflects a broader effort within DUSP to engage with cities facing complex economic and geopolitical challenges through applied, practice-based research. Hashim Sarkis, dean of the School of Architecture and Planning, spoke of this effort during a panel discussion with the Ukrainian visitors, noting that “with so much conflict in the world today, SA+P must create new ways to help cities rebuild, whether in Ukraine or elsewhere.” 

Big strides in cancer detection and treatment from the tiniest technologies

The MIT Marble Center for Cancer Nanomedicine looks back at 10 years of turning big ideas about nanotechnology into transformative advances for cancer patients.



How the war in the Middle East is impacting global energy systems

Expect energy disruptions and economic damage, especially in developing countries, and prepare to build a more resilient, sustainable energy system, says International Energy Agency executive director.

One day after the announcement of a ceasefire between the United States and Iran, the head of the International Energy Agency (IEA) outlined the implications of the war in the Middle East on the global energy system and the world’s economy, offering his expertise to an MIT audience.

“This is the largest energy crisis we’ve ever had in the world,” Fatih Birol, the executive director of the IEA, said at the MIT Energy Initiative’s (MITEI) Earth Day Colloquium on April 8. Birol put the current disruption of the world’s energy markets into historical perspective, shared what he believes will be the long-term impacts of this war — even in the best-case scenario where the ceasefire paves a path toward peace — and emphasized the need to create a more sustainable, resilient system moving forward.

In 1973, and again in 1979, there were oil crises that led the world economy into recession, with many countries — especially those with developing economies — spiraling into debt. More recently, Russia’s invasion of Ukraine led to a natural gas crisis. “The current crisis, the amounts of oil and gas we’ve lost, is bigger than all those three put together,” Birol stated. According to data received two hours before the seminar, Birol confirmed that 80 energy facilities in the Middle East had been damaged, with over one-third of those having been severely damaged.

The IEA has played a significant role in the global response to the war. “Our job is to have a real-world impact,” said Birol. Earlier in the conflict, after making clear to policymakers and members of the press the scale of the problem at hand, the IEA turned to its member countries — which are required to have significant oil stock reserves — to bring their reserves to the market. “Since the disruption was so big, we brought all the countries together, which is not easy,” Birol said. “We released 400 million barrels of oil, which is the highest we have ever done. This calmed markets and put downward pressure on prices.” The IEA also released a suite of recommendations for conserving oil quickly, many of which countries around the world are already implementing, said Birol.

The implications of this crisis are far-reaching, and will vary in severity depending on how long the war lasts and how quickly normal operations resume afterwards — which could take some time, considering the extent of the damage to the Middle East’s energy infrastructure, Birol said.

Birol explained the more immediate impacts of the war on the gas industry. Although the natural gas industry has presented itself as a reliable, affordable, and flexible energy source, Birol highlighted that the two major gas crises in the last four years have brought that assertion into question.

“Is [natural gas] still reliable? Is it still flexible? Is it still affordable? After these two big crises, the natural gas industry needs to work hard to regain its brand,” he said.

Birol also outlined three potential outcomes that this shift may bring to the renewable energy sector. First, there is historical precedent for building up nuclear power plants in response to the oil crises of the 1970s. “Around 45 percent of nuclear power plants operating today were built as a response to those crises,” said Birol. He believes there will be another large push for nuclear power, including small nuclear reactors.

Second, renewables may be the biggest beneficiaries of this situation, he said. “In Europe, after Russia’s invasion of Ukraine, the renewable annual installations increased by a factor of three,” he said.

Third, especially in Asia, we will likely see an increase in the market penetration of electric vehicles, Birol said. This is especially important to note because Asia is the center of current oil demand growth, but the adoption of more electric vehicles could have an impact on that, he suggested. Previous crises have also led to car manufacturers improving the fuel efficiency of their cars.

“The energy security premium will be a factor of the energy trade in the future, in addition to the cost of energy,” said Birol, speaking to the longer-term effects on the global energy market. “Countries will be more careful now with whom they are trading.”

Addressing the current crisis also necessitates changes to our energy system going forward, according to Birol. He explained that the entire global economy is being held hostage by the 50 kilometers of the Strait of Hormuz, which is a critical path not only for oil and gas shipments, but for materials used to make fertilizer, which are needed to feed the world’s population, and materials such as helium, which are needed to manufacture products like cell phones.

“I'm afraid that after this is finished, some of the countries will come back faster because they have stronger financial muscles, better engineering capabilities, and better technologies, whereas other countries will suffer,” he said. “It will be, in my view, not easy for the global economy. I believe who will be suffering under this economic damage will be mainly developing countries.”

The burden on developing countries will not only come in the form of energy prices, but also lasting impacts on fertilizer consumption, food security, and food prices, which Birol emphasized is a global problem. “What should be the response to have a more secure, but also more sustainable, future for everybody?” he asked.

Birol suggested the best possible outcome to the current global energy and economic disruption would be if the ceasefire leads to a peaceful settlement of the war. Still, this “best possible outcome” includes significant risk for much of the world.

If there is a settlement of peace, Birol said he expects oil and the gas production in the region to restart. He noted that there are about 200 fully laden oil tankers and 15 loaded liquid natural gas ships that could leave the Gulf fairly quickly if the Strait of Hormuz fully reopens.

“But I don’t think that in a very short period of time we will go back where we were before the war,” Birol said. “And this may keep the prices at elevated levels. This is surely not good news, especially in the emerging world. I would be surprised if we don’t see significant inflationary pressures in Asian developing countries, in Africa, and in Latin America,” Birol said. “In addition to that, the petrochemical industry, fertilizers, we will discover how important those commodities are for the supply chains we have … I expect a bit of volatility in the markets.”

This speaker series highlights energy experts and leaders at the forefront of the scientific, technological, and policy solutions needed to transform our energy systems. Visit the MIT Energy Initiative’s events page for more information on this and additional events. The series will return this fall.

Two from MIT named 2026 Knight-Hennessy Scholars

The prestigious fellowship funds graduate studies at Stanford University.

MIT master’s student Sunshine Jiang ’25 and Rupert Li ’24 are recipients of this year’s Knight-Hennessy Scholarship. Now in its ninth year, the highly competitive scholarship provides up to three years of financial support for graduate studies at Stanford University. 

Sunshine Jiang  ’25

Sunshine Jiang, from Hangzhou, China, graduated from MIT in 2025 with a bachelor’s degree as a double major in physics and electrical engineering and computer science, along with minors in mathematics and economics. She will receive her master of engineering degree this month and will start her PhD in computer science at Stanford School of Engineering this fall. 

Jiang researches embodied artificial intelligence and robotics, developing data-efficient, adaptive systems for general-purpose robots that broaden accessibility. She has presented her research at major conferences, including the Conference on Robot Learning, the International Conference on Robotics and Automation, and the International Conference on Learning Representations. 

Jiang led the development of AI-powered systems that provide access to traditional Chinese art in rural classrooms, founded cross-country programs that expand girls’ access to STEM education, and created a Covid-19 documentary amplifying community voices, which was featured on China Daily.

Rupert Li ’24

Rupert Li, from Portland, Oregon, is currently pursuing a PhD in mathematics at Stanford School of Humanities and Sciences. He graduated from MIT in 2024 with a bachelor’s degree, double majoring in mathematics and computer science, economics, and data science. Along with his bachelor’s degree, he also received a master’s degree in data science. Li then traveled to the United Kingdom as a Marshall Scholar, where he earned a master’s degree in mathematics from the University of Cambridge.

Li’s research interests lie in probability, discrete geometry, and combinatorics. He enjoys serving as a mentor for MIT PRIMES-USA, a high school math research program, and previously served as an advisor for the Duluth REU, an undergraduate math research program. In addition to the Knight-Hennessy Scholarship and the Marshall Scholarship, he has been awarded the Hertz Fellowship, P.D. Soros Fellowship, and the Goldwater Scholarship, and he received honorable mention for the Frank and Brennie Morgan Prize.

Building “hardcore” advanced machines

In 2.72/2.270 (Elements of Mechanical Design), “if it doesn’t break the laws of physics, it’s possible; you just have to figure out how to engineer it.”

MIT class 2.72/2.270 (Elements of Mechanical Design) offers undergraduate and graduate students advanced study of modeling, design, and integration, along with best practices for use of machine elements like bearings, bolts, belts, flexures, and gears.

“[Students] learn how to use basically everything from the MechE undergraduate curriculum to build hardcore advanced machines,” says Martin Culpepper, the Ralph E. and Eloise F. Cross Professor in Manufacturing and professor of mechanical engineering (MechE) at MIT.

The course employs modeling and analysis exercises based on rigorous application of physics, mathematics, and core mechanical engineering principles, which are then reinforced through lab experiences and a mechanical system design project.

Culpepper, known to students and colleagues as Marty, says one of his main goals in the course is to “make students into stronger engineers.” His methods involve a mix of teaching and coaching techniques that push students to explore the bounds of what’s possible. 

“Marty likes to say that ‘as long as something doesn't break the laws of physics, it’s possible. You just have to figure out how to engineer it,’” says Yasin Hamed, a teaching assistant for the course.

For the system design projects, students build a lathe that can meet repeatability, accuracy, and functional requirements, and that can also “pass ‘Marty’s death test,’” says MechE graduate student Sarah Stoops. “What that means practically,” explains fellow graduate student Amber Velez, “is, at the end of class, Marty takes all our lathes and drops them and hits them with a hammer, and if they explode, you don’t pass the class.”

This final test may seem harsh, but it is an important part of the process and helps build to additional, critical skills: resilience and perseverance.

“The students are very resilient. They learn to persevere and take some time to try and figure things out, and through that process … you learn so much,” says Hannah Gazdus, a teaching assistant for the course.

Before the so-called “death test,” students tackle two other challenges: precision and material removal. “All of our lathes are required to cut to within 50 microns of precision,” explains Velez. In the material removal rate competition, teams compete to see who can turn down a piece of stock by one inch the fastest. Velez’s team completed the later task in approximately 27 seconds.

“The core classes are important — things like mechanics, materials, dynamics, controls — but many of them have a degree of abstraction that separates the content within those courses from the mechanical elements that you use in designing an actual machine,” says Hamed. “I feel like this class serves very well to bridge that [and] inspire that confidence as working engineers.”

From technical solution to systems change: Tackling the problem of plastic waste

Akorfa Dagadu, an MIT senior in chemical engineering, learns the importance of community-engaged research and innovation through the PKG Center for Social Impact.

When Akorfa Dagadu arrived at MIT, she had a solution in mind: a mobile app to improve recycling and environmental engagement in her home country of Ghana. The project, called Ishara, aimed to make it easier for people to participate in local recycling systems while creating economic opportunities.

“I grew up in what people often call the trash capital of Accra,” she recalls. “I thought I knew what would fix it. So [my Ishara co-founders and I] built a solution — an app — behind some desk in a library … We did what I thought was market research, but looking back, we were basically asking people what they thought about our idea instead of asking how things actually worked … Implementation humbled us very quickly.”

On the ground, Dagadu encountered a reality very different than she anticipated.

“Informal networks of waste pickers and aggregators were already doing the work,” she explains. They’d developed a system that was already working, but it was “invisible, undervalued, and excluded from larger recycling conversations.” 

From technical solutions to systems change 

Soon after arriving at MIT, Dagadu discovered the PKG Center for Social Impact as a place that could help her pivot, taking a step back from her technical solution to understand the systemic context of the problem she was trying to solve.

As a first-year student, Dagadu received a PKG Fellowship, which provides funding and mentorship for students to pursue community-engaged research and development. This early support positioned Dagadu to apply to PKG’s IDEAS Social Innovation Incubator to further refine her social enterprise, Ishara. Dagadu was one of few first-year students selected for IDEAS among an applicant pool dominated by MBA and other graduate students. 

“At MIT, there are a lot of opportunities focused on entrepreneurship. But not as many that emphasize how you can do something for the environment or your community,” says Dagadu. IDEAS trains technical founders in systems change for social impact and community-engaged innovation.

Dagadu obtained another PKG Fellowship to iterate on Ishara the following summer, and was accepted to the IDEAS incubator a second time. Eventually, she refined her app from a technical solution the community didn’t need to one that connects existing recycling networks to the broader value chain, in ways that are transparent and fair, using a blockchain-enabled buyback center. 

“The biggest thing PKG has given me is a way of thinking,” Dagadu explains. “The systems thinking mindset really stays with you. You start to see everything as connected. Technical solutions are not just technical; they have social and economic implications. I find myself applying that in all my classes. Whether I am designing a reactor system or working through a materials problem, I am always asking how this fits into the larger system and who it affects.” 

Community-engaged chemical engineering

Dagadu says that “PKG has shaped both how I do research and how I think about it.” She grew to understand the importance of research grounded in local partnerships, and points to her collaboration with Chanja Datti, a recycling company in Nigeria, as a prime example. 

“That collaboration has directly informed my research,” says Dagadu. “What started as a PKG-supported exploration has now grown into a full undergraduate-led research project at MIT, supported by D-Lab, focused on one of the hardest questions in recycling: what to do with multilayer plastic waste.”

“This is where my chemical engineering and materials background comes in,” explains Dagadu, who studies how random heteropolymers can stabilize enzymes for plastic degradation through the Alexander-Katz Lab. “Thinking about polymer structure, processing, and what is actually feasible,” is critical to her work on the ground. “But it is also shaped by everything PKG emphasizes. You cannot separate the material from the system it lives in.”

Dagadu also appreciates the personal community she’s developed through her journey at MIT, especially as her venture evolved and her co-founders stepped away. “I went from being part of a strong team of three to building Ishara largely on my own,” she recalls. “That’s when I understood what people mean by entrepreneurship being lonely. The doubt, the weight of decisions — it became very real, very quickly.”

She drew on relationships developed through PKG and the Kuo Sharper Center for Prosperity and Entrepreneurship, where Dagadu is a student fellow, to ground her and remind her of her personal mission. “It’s not just about having a team,” she realized. “It’s about having a community that can hold you through the moments when things fall apart.” 

The PKG Center’s assistant dean, Alison Hynd, who supported Dagadu through multiple PKG Fellowships, sees Dagadu’s ability to create community as a tremendous asset: “As a first-year student, she came through the door with an intellectual vision and drive to do this work, but at MIT, she’s found her voice to pull other people into it.”

Same question, different scale

Next year, Dagadu will broaden her community still more, as a Schwarzman Scholar at Tsinghua University in Beijing. While the context of her studies will change, her motivation remains the same as when she entered MIT.

“I want to keep asking the same question that’s shaped so much of my work so far,” she says, “not just how we design better materials, but how we design systems where those materials can actually work. That means zooming out and exploring the policy and economics of material flow.” 

Through Ishara, Dagadu’s social enterprise, she’s seen how systems intersect and function on the ground in the case of recycling in Ghana. “Now, I want to understand forces at a much larger scale,” she says, “and I can’t think of a better place to explore this question than in China, the manufacturing hub of the world.”

3Q: Why science is curiosity on a mission

VP for Communications Alfred Ironside describes how a new initiative from MIT seeks to remind Americans of the value and power of curiosity-driven research.

This week, MIT launches a new initiative — titled Science Is Curiosity on a Mission — to make the case for the long-horizon, curiosity-driven science that has powered generations of American innovation. Through stories of scientists pursuing open-ended questions, the project highlights how fundamental discovery research sparks advances in medicine, technology, national security, and economic growth.

MIT News spoke with Alfred Ironside, the Institute’s vice president for communications, about what inspired the effort, what’s at stake for the U.S. research enterprise, and why curiosity remains one of America’s greatest strengths.

Q: What is “Science Is Curiosity on a Mission,” and why launch it now?

A: Science has been under threat for some time now, and public investment in discovery science has been flagging. We want to remind people in Washington and across the country what curiosity-driven science is all about, and why it matters so much in our individual lives and in the life of the country. 

Science begins with curiosity — someone asking a question and refusing to let it go. History’s most important discoveries did not begin with a commercial objective or a guaranteed outcome. They began because someone wanted to understand how the world works. Think Ben Franklin and his kite: This drive to discover goes back to the beginnings of the United States. 

That’s the story we want to tell, but in today’s terms. We’re spotlighting researchers whose years-long pursuit of core questions has seeded breakthroughs that have changed lives for the better.

We’re launching this storytelling initiative now because public investment is declining, and in all the debates about funding what’s gotten lost is an appreciation for the incredible gifts of curiosity-driven discovery science. 

Over generations, the United States became the world’s scientific leader by investing in research of this kind, especially at universities, where long-term scientific undertakings have time and space to thrive. In turn, those investments have created an extraordinary pipeline of innovation, the envy of the world.

When public investment in basic science falters, the long-term losses start right away — and cascade. Labs close. Young scientists leave the field. Entire avenues of discovery go unexplored. Those losses are not always immediately visible, but eventually we feel them through what’s missing: treatments that never arrive, industries that never emerge, talent that migrates elsewhere.

Other countries understand this. They’re watching us stumble — and they’re growing their research investments aggressively. America’s scientific leadership has been built over decades — and maintaining it requires similar commitment.

It’s important to note that while this initiative to tell the story of discovery science was sparked at MIT, it is not about MIT. We want to spotlight university-based scientists across the country whose work is critical in advancing discovery, educating talent, and fueling innovation that benefits all of us.

Q: Why emphasize the idea of “curiosity”?

A: We start with curiosity for two reasons. First, it’s a human experience we’ve all had, so everyone can relate to it. Everyone knows the feeling of just wanting to know why something happens or how something works. Second, it’s the essential fuel that drives discovery science. 

There’s sometimes a tendency to talk about science in terms of outputs: breakthroughs, startups, commercial applications. Those things matter enormously, but they usually come much later. The beginning is more human. It’s someone wondering why something behaves the way it does, or whether a seemingly impossible problem might have an answer.

Some of the most transformative breakthroughs arose from questions that once appeared disconnected from practical use. MRI technology grew from research on atomic nuclei. The foundations of immunotherapy came from scientists trying to understand how the immune system works. GPS depends on what was once viewed as purely theoretical physics.

Curiosity fuels scientific discovery by pushing people to keep pursuing deep questions because they simply need to know: How does the brain work? How does cancer start? What is the universe made of?

That’s why the second half of the phrase matters: “on a mission.” University researchers are not indulging in idle speculation. They are pursuing knowledge to expand our understanding — and that new knowledge can be the key to startling new solutions.

Universities are uniquely important environments for this work. They bring together people from different disciplines and backgrounds who challenge assumptions and generate new questions. That concentration of talent and openness is extraordinarily productive.

After World War II, the American research university system became one of the most successful engines of discovery in human history. Public investment in university research has helped produce new medicines, computing technologies, communications networks, energy systems, and entire industries that shape modern life.

This effort aims to reconnect all of us with that story.

Q: What’s at stake if the U.S. fails to sustain support for basic research?

A: What’s at stake is not just scientific leadership, but the future pace of American innovation and opportunity.

The innovation pipeline operates across long time horizons. The discoveries powering today’s companies and medical treatments often crystallized 10, 20, or 30 years ago. The breakthroughs that will define the 2040s and 2050s are being explored in laboratories right now.

Basic research is the foundation of that pipeline, and private-sector innovation depends on it. Private investment plays a critical role, but it naturally gravitates toward projects with clearer commercial returns. Public funding supports the earliest, highest-risk stages of inquiry, where outcomes are uncertain but the potential benefit to society is enormous.

If that pipeline dries up, the consequences are stark. Fewer discoveries lead to fewer technologies, startups, and industries. We also risk losing scientific talent to countries that are watching our shifting national priorities — and making larger and more sustained investments in advancing science.

At the same time, there is enormous reason for optimism. The American scientific enterprise remains one of the great achievements of the modern era. It has delivered extraordinary gains in health, prosperity, and quality of life. Millions of people are alive today because of advances rooted in publicly supported research.

This system was built through sustained national commitment across generations. The question now is whether the country will continue investing in curiosity, discovery, and the people pursuing the new knowledge that will allow us to solve the intractable problems of tomorrow.

When curiosity is given room to run, the results can be life-changing for us all.

“I have yet to meet a professor that cares more for their students”

Associate Professor Daniel “Danny” Hidalgo, a political scientist who studies elections, democracy, and political behavior in Latin America, is honored as “Committed to Caring” for graduate student mentorship.

Since joining the faculty of MIT’s Department of Political Science in 2012, F. Daniel Hidalgo, known to many as “Danny,” has built a reputation as both a meticulous quantitative scholar and one of the department’s most generous and steadfast mentors.

A member of the 2025–27 Committed to Caring cohort, Hidalgo is recognized for a style of mentorship that combines intellectual intensity with humility, approachability, and a willingness to show up for students. A quantitative political scientist whose research focuses on elections, democratic accountability, and political behavior in Brazil and Latin America, his scholarship uses statistical and experimental methods to study how institutions shape political outcomes. According to his students, the rigor he brings to his research is matched by an equally strong commitment to the people he mentors.

Hidalgo’s reputation is illuminated repeatedly in nominations. One student, reflecting on years of mentorship, aptly summed this up by saying, “I have yet to meet a professor that cares more for their students.”

Showing the mess, not just the map

Most MIT political science PhD students encounter Hidalgo in their first year, when he teaches the department’s quantitative methods sequence. For many, the course is a turning point — an introduction to causal inference and the logic of experimentation that reshapes how they think about political science itself.

While the material is demanding, students describe a classroom that feels captivating, rather than intimidating. Even during the height of Covid-19-era Zoom courses, one student reflected on the ways in which Hidalgo “made the class engaging and interesting,” injecting energy into even the most complex statistical concepts. “It is no surprise that for many of us, the final papers we wrote for this class laid the foundation … for our subsequent research trajectories,” the student added.

Hidalgo’s approach to mentorship begins with demystifying research by exposing the process behind final products. If he had to articulate a guiding principle, he says, it would be this: “Show students the mess, not just the map.” Graduate students too often see only the polished journal article, not the abandoned drafts, failed models, or questions that had to be rebuilt from scratch. Hidalgo makes a point of bringing students into that disorganization early, normalizing uncertainty as part of scholarship.

That transparency reshapes both how students conceive of research, and how they intentionally practice it. As one student explained, Hidalgo’s mentorship creates “a space where we can share even our messiest ideas,” knowing they will be met with thoughtful feedback rather than judgment. His classroom and office are often described as rare environments where rigor and creativity coexist without fear.

A boundless capacity for mentorship

It is no secret within the department that Hidalgo advises a large number of students, providing one-on-one mentorship in addition to leading a growing research group. Despite this, students consistently describe weekly meetings where he gives their work his full attention. He reads drafts carefully and responds with detailed, constructive feedback, whether on a fellowship application, a conference paper, or a dissertation chapter.

Hidalgo’s mentorship is not confined to his formal advisees. Students who are not on his committee can still rely on him for advice on quantitative methods, knowing that he will make time for them. Over time, this has earned him a department-wide reputation as approachable, steady, and kind.

His advisees’ research spans the discipline: business politics in China, applied machine learning, nationalism in Europe, and electoral politics in Latin America. As one student put it, mentees are “united not by a single topic, but by [Hidalgo’s] generous and inclusive mentorship.” Although his own scholarship centers on Brazil and Latin America, students say he tackles every project with genuine curiosity and intellectual investment, connecting them to literature they might never have encountered and sharpening their arguments’ credibility.

At an institution where quantitative research is often the default, Hidalgo encourages methodological grounding that goes beyond the dataset. He pushes students to immerse themselves in the contexts they study: spend time in the field, talk to people, and absorb local political realities. Immersion, he argues, does not replace rigorous analysis — it sharpens it.

Building community in a solitary profession

Dissertation work can be isolating. In response, Hidalgo has launched a biweekly research group for his mentees. The group, now more than 10 students strong, meets throughout the semester to workshop ideas at any stage of development.

Students describe it as a rare low-stakes space where early drafts are welcome and half-formed ideas encouraged. Discussions are intellectually demanding, but never hostile. The diversity of projects — across regions, methods, and topics — broadens everyone’s perspective.

Hidalgo’s care for his students also emerges in small but meaningful ways. He brings snacks to meetings, organizes informal gatherings, and creates opportunities for connection beyond formal advising. During the isolation of the Covid-19 pandemic, he engaged students through reading groups and small gatherings. When visiting scholars arrive, he folds them in. When global or personal events weigh heavily, he checks in.

One student recalled the morning after a deeply contentious U.S. presidential election. Rather than proceed as usual, Hidalgo canceled class and invited students to gather in his office. There were pastries and a space to talk — “a small, deeply touching gesture” that made an anxious day more bearable.

Standing by students in moments of uncertainty

Several nominations speak not only to academic mentorship, but to Hidalgo’s response during moments of personal and professional difficulty.

One advisee described hitting a breaking point in their fourth year: stalled research ideas, a failed fieldwork trip, deteriorating mental health, and a departmental warning about insufficient progress. Rather than stepping back, Hidalgo leaned in — helping generate new project ideas, structuring attainable plans, and encouraging another attempt at fieldwork, which ultimately proved successful.

Another student, pursuing an unconventional joint program bridging political science and statistics, described feeling academically isolated. Recognizing that need, Hidalgo helped create a reading group aligned with the student’s interests and encouraged collaboration across departments. As the student recalled, he “[put] the maximum trust in me to make decisions while always giving me the strong feeling that he [had] my back.”

When students choose paths outside academia, Hidalgo is equally supportive — encouraging them to align their research and professional development with their goals, without diminishing the value of their work.

His mentorship leaves a lasting imprint not only on students’ research, but on how they understand what it means to support others in turn. Across these experiences, a consistent theme emerges: Hidalgo challenges students to meet high standards while ensuring they never navigate those expectations alone. 

Elazer Edelman receives the 2026-2027 Killian Award

The professor of medical engineering and science is honored for medical research that has led to better treatments for cardiovascular disease.

Elazer R. Edelman ’78, SM ’79, PhD ’84, an engineer and cardiologist who helped develop cardiovascular stents that have been used by more than 100 million people, has been named the recipient of the 2026-2027 James R. Killian Jr. Faculty Achievement Award.

The award committee recognized Edelman, the Edward J. Poitras Professor in Medical Engineering at MIT’s Institute for Medical Engineering and Science, for his work at the interface of engineering, science, and medicine. In addition to his work on stents, he has made significant contributions to tissue engineering and to deciphering the fundamental biological processes underling cardiovascular disease.

A member of the MIT faculty for more than 30 years, Edelman is renowned as a teacher and mentor. He is also a professor of medicine at Harvard Medical School and a critical care cardiologist at Brigham and Women’s Hospital, and he served as director of MIT’s Institute for Medical Engineering and Science from 2018 to 2024.

“He is a clinician of the highest order who has touched the lives of many, a teacher of greatest passion who has mentored hundreds and taught thousands, and an engineer whose work has reached around the globe,” states the award citation, which was presented at today’s faculty meeting by Xuanhe Zhao, chair of the Killian Award Selection Committee and a professor of mechanical engineering at MIT.

The Killian Award was established in 1971 to recognize outstanding professional contributions by MIT faculty members. It is the highest honor that the faculty can give to one of its members.

“It’s deeply meaningful that your colleagues think enough of you to want to recognize your life’s work. This is an incredibly awe-inspiring group, and for them to feel that way is a truly special honor,” Edelman told MIT News after learning that he had been selected for the award.

Edelman, who grew up in Brookline, Massachusetts, got his first MIT experience as a high school student, taking classes as part of the Institute’s High School Studies Program. That experience led him to apply to MIT, where he earned two bachelor’s degrees, in applied biology and electrical engineering and computer science, followed by a master’s in bioelectrical engineering and a PhD in medical engineering and medical physics. He also earned an MD from Harvard Medical School through the Harvard-MIT Program in Health Sciences and Technology.

As a graduate student, Edelman was one of the first students to join the lab of Robert Langer, the David H. Koch Institute Professor at MIT. Working with Langer, he developed mathematical approaches to guide the design of controlled drug-delivery systems.

“Bob opened my eyes to what it really means to use MIT science to make the world a better place,” Edelman says.

Early in his career, Edelman brought a scientist’s eye to one of medicine’s most urgent clinical challenges: how to address diseased blood vessels without provoking further injury. His studies of the cellular and molecular mechanisms of atherosclerosis and vascular healing — work that continues to this day — coupled with fundamental insights from engineering and physics, helped enable the optimization of bare-metal stents and the development of drug-eluting stents. 

Roughly 90 percent of the more than 100 million stents implanted worldwide now release drugs through principles his work helped define and advance, saving countless lives and improving quality of life for patients around the globe.

Edelman’s work reflects a continuing cycle of discovery: Basic insights in biology shaped transformative medical technologies, and the challenges posed by those technologies, in turn, continue to push biology, science, technology, and engineering together toward new discoveries and clinical advances.

“His landmark work on the cellular mechanisms underlying atherosclerosis and on the biology of cell-material interfaces established the scientific foundations that transformed bare-metal cardiovascular stents from a promising mechanical concept into a biologically informed and clinically transformative therapy with enduring legacy — paving the way for a cascade of innovations that changed the landscape of medicine,” the award committee wrote.

More recently, Edelman’s lab has designed novel heart valves and other innovative approaches to mechanical organ support.

During his tenure as the director of IMES, he led an MIT-wide effort to provide personal protective equipment to health care workers and emergency responders in the early stages of the Covid-19 pandemic. 

“One of the things I’m most proud of is working with many people at MIT in the Covid response. At the height of Covid, we were supplying 23 percent of all PPE throughout New England,” he says. “Every single person who could possibly contribute contributed.”

As director of MIT’s Center for Clinical Translational Research and faculty lead for the Hood Pediatric Innovation Hub, he is now working to help clinical research thrive at MIT and to address the inequities in technology access for society’s most vulnerable population — children.

Throughout his career, Edelman has devoted himself to mentoring students and trainees.

“I’m really proud of what our students have accomplished, not only scientifically, but on a personal level, and not only with me, but everything they’ve done afterwards. The greatness of a place like MIT is that you enable people to grow beyond their potential. That’s really the extraordinary thing about our community,” he says.

In recognition of his scientific achievements, Edelman has been elected a fellow of the American College of Cardiology, the American Heart Association, the Association of University Cardiologists, the American Society of Clinical Investigation, American Institute of Medical and Biological Engineering, the American Academy of Arts and Sciences, National Academy of Inventors, the Institute of Medicine/National Academy of Medicine, and the National Academy of Engineering.

“The Selection Committee is delighted to have this opportunity to honor Professor Elazer Edelman for his exceptional contributions to medical engineering and science, to MIT, and to the world,” the award citation concludes.

MIT chemists discover and isolate a new boron-oxygen molecule

The discovery of dioxaborirane could expand the chemistry of boron-based reagents, providing new tools for oxidation reactions in synthesis and materials science.

Oxygen is a cornerstone of chemistry, largely because it is so good at building the organic molecules that make up our world. Some oxygen-based compounds, called peroxides, are famous for being highly reactive — they act like oxygen delivery trucks, transferring atoms to other molecules. This process is essential for everything from creating new medicines to industrial manufacturing.

In an open-access study published April 24 in Nature Chemistry, researchers from the labs of MIT professors Christopher C. Cummins and Robert J. Gilliard, Jr. have revealed a brand-new type of peroxide containing boron. This molecule, called a dioxaborirane, represents a major advance in a field where such structures were long-proposed, but considered too unstable to actually isolate.

Room-temperature breakthrough

Dioxaborirane forms when a specially engineered boron molecule reacts with oxygen gas. What makes this discovery remarkable is that the reaction happens almost instantly at room temperature. Usually, creating strained oxygen-containing rings like this requires extreme, “punishing” conditions — like freezing temperatures or high pressure — to keep the molecule from falling apart.

Using advanced tools such as crystallography and computational modeling, the team proved the existence of a highly strained, three-member ring made of one boron and two oxygen atoms.

A molecule with two personalities

The most exciting part of the discovery is how the molecule behaves. Depending on its electrical charge, it acts in two very different ways:

• The builder: It can donate oxygen atoms to help construct new chemical compounds.
• The trapper: It can react with carbon dioxide, potentially offering a new way to capture and transform greenhouse gases.

“By showing that these compounds can be generated under mild conditions, our work opens the door to entirely new types of chemistry,” says Chonghe Zhang, the first author of the paper and an MIT chemistry graduate student co-advised by Cummins and Gilliard. “In the long term, these findings could provide us with powerful new tools for oxidation reactions in synthesis and materials science.”

Additional co-authors on the paper are Noah D. McMillion and Chun-Lin Deng of MIT and Junyi Wang of Baylor University. The work was funded, in part, by the U.S. National Science Foundation.

Researchers “reprogram” materials by quickly rearranging their atoms

A new method for precisely moving columns of individual atoms within a material could give rise to exotic quantum properties.

It’s been 37 years since scientists first demonstrated the ability to move single atoms, suggesting the possibility of designing materials atom by atom to customize their properties. Today there are several techniques that allow researchers to move individual atoms in order to give materials exotic quantum properties and improve our understanding of quantum behavior.

But existing techniques can only move atoms across the surface of materials in two dimensions. Most also require painstakingly slow processes and high-vacuum, ultracold lab conditions.

Now a team of researchers at MIT, the Department of Energy’s Oak Ridge National Laboratory, and other institutions has created a way to precisely move tens of thousands of individual atoms within a material in minutes at room temperature. The approach uses a set of algorithms to carefully position an electron beam at specific locations of a material, then scan the beam to drive atomic motions.

“The results demonstrate the ability to deterministically move atoms repeatedly within a material’s 3D atomic lattice,” says MIT Research Scientist Julian Klein, who conceived of and directed the project. “We can reprogram materials to create defects at will, realizing entirely artificial states of matter not found in nature with a wide range of potential applications, including sensing, optical, and magnetic technologies. There are so many opportunities enabled by these techniques.”

“It’s like a photocopier that can create columns of identical atomic defects,” says Frances Ross, MIT’s TDK Professor in Materials Science and Engineering. “It’s especially useful because you can move a few atoms to form defects, and do it again and again to build atomic arrangements in three dimensions that have tunable functions in a system that is more robust because the defects exist beneath the surface.”

In a Nature paper appearing today, the researchers described their approach and how they used it to create more than 40,000 quantum defects in a crystalline semiconductor material.

The researchers say the approach offers a new way to study quantum behavior in materials. It could also one day lead to improvements in systems that leverage quantum defects, like quantum computers, dense magnetic memory, atomic-scale logic devices, and more.

Joining Klein and Ross on the paper are Kevin Roccapriore and Andrew Lupini, researchers at Oak Ridge National Laboratory; Mads Weile, a former MIT visiting student; Sergii Grytsiuk, a former Radbound University researcher; Malte Rösner, a professor at Bielefeld University in Germany; Zdenek Sofer, a professor at the University of Chemistry and Technology Prague in the Czeck Republic; Dimitar Pashov, a research associate at King’s College London; and Mark van Schilfgaarde and Swagata Acharya, researchers at the National Laboratory of the Rockies.

Designing matter

In a now-famous 1989 demonstration, IBM researchers used a scanning tunneling microscope to arrange 35 atoms on the surface of a chilled crystal to spell out “IBM.” It was the first time atoms had been precisely positioned, and an important milestone. The approach enabled scientists to engineer specific defects, such as atom-sized vacancies and surface atoms in crystalline materials, leading to major advances in quantum science. But placing those 35 atoms had taken researchers many hours, if not days.

In parallel with those developments, researchers also developed two additional approaches for manipulating atoms in a vacuum, using optical tweezers to trap neutral atoms and oscillating electric fields to trap ions.

While those approaches have enabled remarkable progress, they remain limited to either surfaces or highly controlled experimental systems. Another factor limiting the design of materials for applications such as quantum computers is the inability of atomic manipulation techniques to move atoms in three dimensions: The patterns are created on the surface of a material, where they are exposed to the environment and cannot survive outside tightly controlled laboratory settings.

Engineering usable materials with custom quantum properties would require researchers to rearrange many more atoms, preferably on the interior of materials. The MIT researchers demonstrated that capability in their Nature study.

“We were trying to improve the number of atoms we could move in a reasonable length of time,” Ross explains. “You want to place the atoms close to each other so they can interact, and you want to have a lot of them arranged as you’d like — thousands or millions of atoms in specific locations you’ve chosen. That’s been challenging with existing techniques.”

The researchers used high-performance microscopes at the Department of Energy’s Oak Ridge National Laboratory for their work. Their new technique uses a sophisticated set of algorithms to direct an electron beam at a target atom with a precision of a few picometers (one trillionth of a meter). The beam does a tight loop to help zero in on its target, then sends a beam of electrons through the material in a carefully designed oscillating path, spending about a second at each location. 

“We developed algorithms that allow us to quickly obtain information on where the beam is in the material,” Klein explains. “The trick is to use very few electrons in the process of getting that information, so the whole process is fast and does not unintentionally damage your crystal. It took many years to develop these algorithms and determine the minimum required information needed to infer where the atoms are located with the highest precision.”

The motion of the beam as it delivers electrons, an oscillating path devised by the researchers, pushes entire columns of atoms to new locations the way you might swipe a screen on your phone.

In their experiments, the researchers used this approach to direct the movement of columns of chromium atoms in a stable semiconductor material, chromium sulfide bromide, using a crystal about 13 nanometers thick. The beam created atom-sized vacancies in the material, each vacancy paired with the displaced atom, that they calculated would give the crystal exotic quantum properties.

To show how well their approach scaled, the researchers created over 40,000 defects in about 40 minutes, creating vacancies and interstitials across different distances and in different patterns, calculating that different atomic arrangements should give rise to different quantum mechanical properties.

“Each of these defects has certain ways to interact with its neighbors,” Ross says. “If you place them in a pattern, you could essentially simulate the interactions between the electrons within a molecule, so the whole electronic structure of that molecule can, in a sense, be mapped onto a pattern that you can write into a solid material.”

Probing quantum systems

The success of the approach was likely aided by the way chromium binds within the semiconductor, which has a unique electronic structure. The researchers are further investigating other crystals in which this might work, though they suspect it will be applicable to a diverse range of materials. 

In the materials where it works, the approach has several advantages over existing techniques.

“Moving atoms within solids enables the creation of quantum properties in materials that are stable in the air outside of vacuum conditions,” Klein explains. “And this approach is also scalable to many atomic manipulations, so moving thousands or millions of atoms to create artificial structures would represent completely new physics. We’d like to study those systems.”

The researchers say their technique lays the foundation for a new class of programable matter, which could aid the development of a range of stable quantum devices.

“This is a way of accessing physical phenomena that involve a lot of atoms placed in a certain specified arrangement, and can’t be done by self-assembly,” Ross says. “You can create individually tuned atomic arrangements, and you can have so many of them, each arranged exactly how you like over areas that are tens and hundreds of nanometers. That leads to collective physics we are excited to explore.”

The work was supported, in part, by the Department of Energy and the National Science Foundation.

A new approach to cancer vaccination yields more powerful T cells

Using immune-remodeling mRNA molecules, researchers generated T cells that can slow tumor growth and, in some cases, eradicate tumors.

MIT engineers have developed a new way to amplify the T-cell response to mRNA vaccines — an advance that could lead to much more powerful cancer vaccines and stronger protection against infectious diseases.

Most vaccines generate both antibodies and T cells that can target the vaccine antigen by activating antigen-presenting cells, such as dendritic cells. In this study, the researchers boosted the T-cell response with a new type of vaccine adjuvant (a material that can help stimulate the immune system). The new adjuvant consists of mRNA molecules encoding genes that turn on immune signaling pathways and promote a supercharged T-cell response. 

In studies in mice, this mRNA-encoded adjuvant enabled the immune system to completely eradicate most tumors, either on its own or delivered along with a tumor antigen. The adjuvant also boosted the T-cell response to vaccines against influenza and Covid-19.

“When these adjuvant mRNAs are included in the vaccines, the number of antigen-targeted T cells is substantially increased. These T cells play an important role in the immune response, assisting in the clearance of virally infected cells or, in the case of cancer, killing cancerous cells,” says Daniel Anderson, a professor in MIT’s Department of Chemical Engineering and a member of MIT’s Koch Institute for Integrative Cancer Research and Institute for Medical Engineering and Science.

Anderson and Christopher Garris, an assistant professor at Harvard Medical School and Massachusetts General Hospital, are the senior authors of the study, which appears today in Nature Biotechnology. The paper’s lead authors are Akash Gupta, a former Koch Institute research scientist who is now an assistant professor at the University of Houston; Kaelan Reed, an MIT graduate student; and Riddha Das, a research fellow at Harvard Medical School and MGH. Robert Langer, the David H. Koch Institute Professor at MIT, and Ralph Weissleder, a professor of radiology and systems biology at MGH and Harvard Medical School, are also authors.

More powerful vaccines

Vaccines that stimulate the body’s immune system to attack tumors have shown promise in clinical trials, and a handful have been FDA-approved for certain cancers. In some patients, these vaccines stimulate a strong response, but in others, a weak response fails to kill the cancerous cells.

The MIT-MGH team wanted to find a way to make those immune responses more powerful. One way to do that is to deliver immune-stimulating molecules called cytokines along with a vaccine. However, cytokines can overstimulate the immune system, leading to potentially severe side effects.

As an alternative approach, the researchers decided to deliver mRNA strands encoding two genes, IRF8 and NIK, which are involved in antigen presentation and can switch immune cells into a more active state.

NIK is an enzyme that activates a signaling pathway involved in immunity and inflammation, while IRF8 is a transcription factor that helps program dendritic cells, particularly a subset called cDC1, which are especially effective at activating T cells. These antigen-presenting cells can digest foreign antigens and present them to T cells, stimulating the T cells to mount an immune response against the antigen.

“We see that the dendritic cells start shifting toward a more cDC1 phenotype, which is the most important dendritic cell phenotype and can generate a stronger T-cell response,” Gupta says. 

The researchers packaged the mRNA in lipid nanoparticles similar to those used to deliver mRNA Covid vaccines, but with a different chemical composition that promotes their delivery to the spleen after being injected intravenously. 

Inside the spleen, the particles encounter antigen-presenting cells, including dendritic cells. Within 24 hours, these cells begin expressing IRF8 and NIK, and both of these pathways help drive dendritic cells to mature and become activated so that they can prime an anti-tumor response. 

Over a few days to a week, the T-cell population expands. These T cells, along with other immune cells such as natural killer (NK) cells, can then recognize and attack tumors.

“Most cancer immunotherapies rely on external signals to activate immune cells. We take a different approach — reprogramming immune cells from within by targeting their internal signaling machinery, enabling a more potent and durable anti-tumor response,” Das says. 

Stronger T cells

The researchers tested the immune-remodeling mRNAs in several mouse models of cancer, including an aggressive bladder cancer, colon carcinoma, melanoma, and metastatic lung cancer. In nearly all of these mice, the injected mRNA stimulated a strong T-cell response that significantly slowed tumor growth and in many cases completely eradicated the tumors. This happened even when the mice were not given a vaccine against a specific cancer antigen. When they were, the response was even stronger.

“We showed that you can get an anti-cancer response with these adjuvants without including the antigen, just by activating the immune system. However, cancer-specific antigens with the adjuvants in a vaccine further improved the responses,” Anderson says.

The mRNA adjuvant also enhanced the immune response to immunotherapy drugs called checkpoint blockade inhibitors. These drugs, which work by lifting a brake that tumor cells put on T cells, are FDA-approved to treat several kinds of cancer. These drugs don’t work for all patients, but combining them with the mRNA vaccine adjuvant could offer a way to make them more effective, the researchers say.

“The microenvironment of solid tumors is often hostile to T cells and represents a major barrier to effective immunotherapy. We find that immune remodeling with these adjuvants creates a T cell–permissive environment and promotes tumor rejection,” Garris says.

The researchers also explored whether their new adjuvant could boost the immune response to vaccination against viral infection. When they delivered the mRNA particles along with Covid or flu vaccines, they found that the vaccine generated a 10-to-15-fold stronger T cell response in the mice.

The researchers now plan to test this approach in additional animal models, in hopes of developing it for use in both cancer and infectious diseases. 

“While there are differences between the mouse systems that we’ve worked in and humans, we are optimistic that these adjuvants will work in humans and could improve a range of different vaccines,” Anderson says.

The research was funded by Sanofi, the National Institutes of Health, the Marble Center for Cancer Nanomedicine, and the Koch Institute Support (core) Grant from the National Cancer Institute.

3 Questions: Shedding light on why power grids go dark

Pablo Duenas-Martinez, a MITEI research scientist, describes the “death spiral” of events that caused the 12-hour Iberian peninsula power outage in 2025, and five lessons learned.

On April 28, 2025, the power grid serving continental Spain and Portugal went down, causing gridlock in cities, cutting communications networks, and stranding people on trains, in airports, and in elevators all across the Iberian peninsula and briefly in a small area in southwest France close to the Spanish border. The unprecedented, massive blackout lasted as long as 12 hours in some areas, including in the capital city, Madrid. Not surprisingly, placing blame for the outage was rapid. Quick reactions pointed to cyberattack, sabotage, and natural phenomena such as solar flares. 

But such theories were quickly laid to rest, and a panel of experts was formed to determine exactly what caused the blackout. After a year following the outage — and after much analysis by many experts — there isn’t a simple answer: In short, no one technology was to blame. While solar and wind generation was high, experts agree that the renewables weren’t at fault. 

In this Q&A, Pablo Duenas-Martinez, a research scientist at the MIT Energy Initiative and an assistant professor at Universidad Pontificia Comillas in Madrid, provides an update.

Q: How does a proper, well-functioning power grid behave, and what does the system operator do to help?

A: There are two components to the flows on a power grid. One is “active power” — the part that lights up our light bulbs and runs our engines. With active power, the demand on the grid must always equal supply. The other component is “reactive power,” the part we can’t see but controls the voltage at which the power is delivered so it suits our devices. If voltage is too low, lights will flicker. If voltage is too high, devices may not only fail to work, but be damaged beyond repair.

The operator of the transmission system — the TSO — must control both components, and that can be tricky. Active power supply and demand are largely coordinated through markets. But controlling reactive power is harder. The main way the TSO can control it is to call on operators of conventional power generators, so generators burning natural gas, or coal, or nuclear plants. Those systems can be adjusted to either absorb or inject reactive power as needed to control voltage on the power grid — indeed, they are typically required by law to provide “reactive power control.”

In contrast, solar and wind generators always absorb reactive power. The large solar and wind sources can provide reactive power control when it’s needed, but doing so is costly for them — and in Spain, unlike in most countries, it’s not mandated by law, so they typically don’t do it. Meanwhile, there are many small solar systems — imagine lots of rooftop solar installations and small solar farms. Those small systems are directly connected to the distribution system. As a result, they’re not controlled by the TSO; the TSO may not even know whether they’ve shut down or are still running and absorbing reactive power.

Sometimes, fluctuations in voltage called “oscillations” can happen on a power grid: for example, when a transmission line or a generator is connected or disconnected. Oscillations can increase and decrease the voltage rapidly, and if voltage gets too high, generators and user devices can start “tripping” — that is, automatically disconnecting to prevent being damaged. Operators have standard protocols to follow to bring oscillations under control.

Q: So what happened on April 28 of last year?

A: The Spanish grid is loosely connected to the French grid and in practice is merged with the grid serving Portugal. Within Spain, we have many large solar and wind farms and lots of small installations of solar systems, many located in the southwestern area of the country. On April 28 — as on most spring days, when demand is low — about two-thirds of the power on the grid came from renewable sources. The rest came from a mix of nuclear and natural gas plants.

The day before the blackout, the TSO confirmed that there were no conventional generators scheduled to run. So, to ensure safe operation the next day, the TSO took steps that included dispatching 12 conventional generators, 10 of them to provide reactive power control. One of the units in the south called him back and said, “I won’t be available. I cannot switch on tomorrow.” The TSO thought he had things under control and continued operations with only nine units available to provide reactive power control.

During the morning on April 28, several small oscillations on the power grid were detected coming from Europe, plus one from Spain. To stabilize the weakened grid, the TSO connected additional transmission lines and took other technical actions.

At 12:19 p.m., a major oscillation was detected on the grid, again coming from Europe. In response, the TSO — again following standard protocol — reduced exports to Portugal, switched the flows to France from alternating current to direct current, and connected five more transmission lines within Spain. While those steps stabilized the voltage, the TSO recognized that there was now limited capacity on the system to control voltage. So, he called on a different conventional generator to begin running. But that unit couldn’t be available for an hour.

Suddenly, as a consequence of the previous actions, the voltage increased dramatically, and generating units began to trip. Within half-a-second, many of the small solar generators — especially prone to damage from high voltages — automatically shut down. Twenty milliseconds later, a big solar plant in southwestern Spain tripped. Because the solar plants were no longer absorbing reactive power, voltage on the system went up even more, and more systems shut down. The grid went into what some have called a death spiral, resulting in a total blackout across the Iberian peninsula and some areas of southern France.

Q: What have we learned from this Iberian blackout, and have changes been implemented to ensure that the same won’t happen again — or happen elsewhere?

A: A resilient power system must prevent, mitigate, respond, and recover. In this case, the first three components clearly failed. Preventive mechanisms were insufficient; they initially mitigated the oscillatory events, but left the system in a weakened state, and the response triggered the death spiral that led to the final blackout.

The good news is that the recovery was quick. The northern and southern sections of the peninsula had power back within a few hours. I live in the suburbs of Madrid, and I had power back just six hours later. My parents live downtown, so that was far more challenging — a big city with a large, complex load. Even so, they had power back in 12 hours — and 12 hours is quick for such a major, widespread blackout.

In the end, experts and analysts have agreed that the blackout was caused by a series of events that were all happening in the same place, at the same time. And the experience did provide a number of valuable learnings:

Lesson 1

The experience clearly demonstrated the importance of having a sufficient number of conventional power plants prepared to provide reactive power control, or to turn on right away when called on. There’s a recommendation calling for a set ratio between conventional generators and renewables on a power grid. Conventional facilities such as nuclear, hydroelectric, and fossil fuel plants rely on heavy metal wheels to generate electricity. Those massive rotating wheels have high inertia, so they’ll keep running and can help stabilize frequency and voltage even when solar and wind plants shut down. Before the blackout, Spain had a sufficient number of “rotating units” to meet the recommended ratio. However, in southern Spain, there was just one such unit — well below the recommended number, given the huge number of small solar units plus several large solar units in the area.

The message here is that you can't just look at the country as a whole. You have to look at regions. Voltage is a local problem that can propagate at the system level. Before the blackout, southern Spain typically had at most three conventional power plants. Now the region usually has six or seven at the ready to help with reactive power control.

Lesson 2

The rules or protocols for controlling reactive power and dealing with oscillations were not well designed. By law, rotating generators must automatically — and without being paid — do a defined amount of reactive power control. But making the needed operational change costs money, and a plant can do less than the required amount and not incur any kind of penalty. However, the TSO doesn’t know in advance how much reactive power control a given plant will actually do. Now that loophole in the law has been reviewed by the regulator.

The main rules have been updated, and now also require large solar and wind power plants — those above 5 megawatts — to provide reactive power control. More importantly, voltage control will be auctioned and remunerated, incentivizing rotating conventional generators and bringing in a new money stream for solar and wind power plants. Those power plants that do not upgrade their installation for voltage control might be disconnected by the TSO if local voltage issues arise.

Lesson 3

Another learning concerns the many small solar power generators and the protections that cause them to trip. The TSO doesn’t know in advance when this may happen because the small solar sources are directly connected to the distribution system, and therefore are under the umbrella of the distribution system operator. So, the learning here is that there should be more communication and coordination between the operator of the transmission system — the TSO — and the operator of the distribution system.

Lesson 4

In most countries, laws dictate a range of voltage that is approved. In Spain, the upper limit is high — in fact, it’s very near a voltage at which equipment may be damaged. And the Spanish grid tends to hover close to that upper limit, even during normal operation, and that can be a big problem: If there are strong oscillations — as there were leading up to the blackout — voltage can reach that upper limit, and protections on devices will automatically trip. The panel of experts has strongly recommended to lower this upper limit in Spain and align it with the rules in neighboring countries, including Portugal and France. The TSO is still studying the recommended change.

Lesson 5

During normal operation, the TSO controls voltage by activating rotating generators that can provide reactive power control. But as we saw in conditions leading up to the blackout, the TSO doesn’t always have rotating generators available.

Theoretically, TSOs have two more ways to control voltage. They can connect a device called a shunt reactor, which absorbs reactive power — a means of dealing with voltage rise. And they can regulate voltage directly using a “STATCOM,” a special device that provides rapid, dynamic voltage control.

However, neither the shunt reactors nor the STATCOM could help prevent the blackout. The shunt reactors available at that time were operated manually, and collapse of the grid happened so quickly that the TSO didn’t have time to connect them. And at that time, there was a single STATCOM device on the Spanish system. Planning was under way to install three more devices — and that installation is being rapidly completed.

From newspaper articles and off-the-record conversations, I’ve learned that the system has — due to similar external circumstances — been close to blackout again during the past year. But in part due to the learnings and to changes that have been implemented as a result, it didn’t happen again.

A new unit of measurement to honor an influential MIT alumnus

In a nod to the prank that first introduced the smoot, an MIT team rolls out the “klein” in homage to Martin Klein ’62 and playfully renames a beloved Charles River span the “Shortfellow Bridge.”

The hallowed history of student pranks (often known as hacks) at MIT includes the annual Baker House Piano Drop and the MIT weather balloon at the Harvard-Yale football game in 1982. One hack that has shown remarkable staying power in local lore is the 1958 measurement of the Massachusetts Ave. Bridge in “smoots,” a now accepted unit of meausrement named for the 5-foot, 7-inch Oliver R. Smoot Jr. ’62. Then a first-year pledge at the Lambda Chi Alpha fraternity, Smoot famously laid down hundreds of times across the span one storied night as his peers painted markers across the bridge, totaling 364.4 smoots (plus 1 ear). Nearly 70 years later, the smoot markings remain.

On April 4, an MIT team set out on a similar journey across the Charles River to pull off a new hack, this time measuring the Longfellow Bridge in “kleins.” This new measurement is named after Smoot’s classmate Martin Klein ’62. One klein (4 feet, 9.5 inches) is equal to 0.85820896 smoots. The expedition was undertaken in honor of both Smoot and the 85th birthday of Klein.

Known as the father of commercial side-scan sonar, Martin Klein serves on the MIT Sea Grant Advisory Board and the MIT Museum Collections Committee. He is a life fellow of both the Marine Technology Society and the Explorers Club, an international organization dedicated to the advancement of field exploration and scientific inquiry. His sonar technology has been used worldwide to help locate countless famous shipwrecks, including the Titanic, the World War I ocean liner RMS Lusitania, and the treasure-laden Nuestra Señora de Atocha.

Appropriately, the MIT team used a “side-scan” method to survey the Longfellow Bridge. Reclined on a custom-engineered wooden cart topped with a mission-specific chaise lounge pillow, Klein himself acted as the official observation device — by looking to the sides — as the team pulled him along the bridge. Some of the noted anomalies and discoveries included a Duck Boat passing underneath, a mermaid tail, a kayak paddle, a sleeping goose, and a tenacious survey team.

The initiative was spearheaded by Makenna Reilly, a second-year undergraduate in mechanical engineering, and Andrew Bennett ’85, PhD ’97, MIT Sea Grant education administrator and senior lecturer in the Department of Mechanical Engineering (MechE). Over a dozen surveyors joined the expedition, including alumni, faculty, and staff from MechE, MIT Sea Grant, MIT Edgerton Center, MIT Museum Hart Nautical Collections, Harvard Extension School, and Woods Hole Oceanographic Institution. MIT students also joined the effort, including senior Teagan Sullivan, junior Adrienne Lai, and graduate students Ansel Garcia-Langley, Erin Menezes, Manuel Valencia, and Gerardo Berlanga Molina.

The Longfellow Bridge was determined to be 442 kleins (plus 2 legs) and was celebrated as the “Shortfellow Bridge” in a ceremony following the event. 

One klein = 57.5 inches = 146.05 centimeters = 1.4605 meters = .0009075126 miles = 1.597222 yards = 4.791667 feet = .0007886069 nautical miles = .007260087 furlongs = 0.7986111 fathoms = 172.5 barleycorns = 292,100,000 beard seconds = 647.4421 Ligne = 14.375 horse hands = 4.819655 shaku = .85820896 smoots.

Additional participants in the event include:

• Elisabeth (Libby) Meier, assistant curator for the Hart Nautical Collections at the MIT Museum;
• Dana Yoerger, PhD ’82, senior scientist applied ocean physics and engineering at WHOI;
• Professor George Buckley, assistant director of sustainability at Harvard University Extension School and diver of the year of the Boston Sea Rovers;
• Paul K. Matthias, senior program manager of the Ocean Observatories Initiative at the WHOI;
• Jim Bales, associate director of the Edgerton Center at MIT;
• John Freidah of MechE; and
• Joice Himawan ’83.

A new way to spot signs of dark matter

Gravitational waves emitted by colliding black holes may bear imprints of dark matter, which physicists could detect with a new model.

Dark matter is thought to make up most of the matter in the universe, but the only way it interacts with its surroundings is through gravity. If two colliding black holes spiral through a dense region of dark matter and merge, gravitational waves rippling across space and time could carry an imprint of that dark matter.

Now, physicists may be able to spot such imprints of dark matter in gravitational waves that are detected on Earth. 

Researchers at MIT and in Europe have developed a method that makes predictions for what a gravitational wave should look like if it were produced by black holes that moved through dark matter, rather than empty space. They applied the technique to publicly available gravitational-wave data previously recorded by LIGO-Virgo-KAGRA (LVK), the global network of observatories that detect gravitational waves from black hole mergers and other far-off astrophysical sources.

The researchers looked through the gravitational-wave signals recorded over the LVK’s first three observing runs. From 28 of the clearest signals, the team found that 27 originated from black holes that merged in a vacuum, as physicists expected. But the pattern of one signal, GW190728, showed possible signs of a dark matter imprint. 

The scientists emphasize that they have not detected dark matter. Rather, the new method offers a new way to screen gravitational-wave data for hints of dark matter, which physicists can then follow up and confirm with other techniques. 

“We know that dark matter is around us. It just has to be dense enough for us to see its effects,” says Josu Aurrekoetxea, a postdoc in the MIT Department of Physics. “Black holes provide a mechanism to enhance this density, which we can now search for by analyzing the gravitational waves emitted when they merge.”

Aurrekoetxea and his colleagues report their results in a study appearing today in Physical Review Letters. The study’s co-authors are LVK member Soumen Roy of Université Catholique de Louvain (UCLouvain) in Belgium, Rodrigo Vicente of the University of Amsterdam, Katy Clough of Queen Mary University of London, and Pedro Ferreira of Oxford University. 

A dark pull

Dark matter is an invisible, hypothetical form of matter that, unlike normal everyday matter, has no interactions with the electromagnetic force. Dark matter can pass through light, magnetic fields, and any other form of energy along the electromagnetic spectrum without leaving a trace. The only evidence that dark matter exists is through its apparent interaction with one other force: gravity. 

By observing how gravity bends around distant galaxies, astronomers have surmised that there must be an extra force, outside of the galaxies’ own gravitational pull, to explain the bending fields, or “lensing.” This extra force, physicists suspect, is dark matter, which could account for over 85 percent of the matter in the universe. But exactly what dark matter is is a matter of huge debate, with theories for dark matter particles that range widely in particle size and properties. 

One class of proposed dark matter consists of “light scalar” particles, whose masses are many orders of magnitude lighter than an electron. Theorists predict that such dark matter should behave not just as particles, but also as coordinated waves when moving near black holes.

When waves of dark matter come in contact with a rapidly spinning black hole, physicists predict that the black hole's rotational energy can be transferred to the dark matter, amplifying it. This phenomenon, known as superradiance, would whip up the waves to extremely high densities of dark matter, akin to churning cream into butter.

At high enough densities, light scalar dark matter, which is invisible by all other accounts, should leave an imprint on the gravitational waves that reverberate from the colliding black holes. 

But exactly what would that imprint look like? And could such an imprint be detectable in gravitational waves that arrive on Earth, from black holes that merged many millions of light years away? 

For answers to those questions, Aurrekoetxea and his colleagues developed a model to predict the gravitational waveform, or the pattern of gravitational waves that two black holes would produce, if they collided in an environment of dark matter, versus in a vacuum (empty space, with no dark matter). 

An imprint’s prediction

For their new study, the team performed detailed numerical simulations to predict the gravitational wave that would be produced given various properties of two colliding black holes — a system known as a “black hole binary.” They considered black hole binaries across a range of scenarios and properties, for example, varying the size and mass of each black hole, the environment of dark matter that the black holes might pass through, and the density of the dark matter that the black holes would spin up. 

They designed the model to predict what a gravitational wave from a black hole binary would look like if it carried an imprint of dark matter, and furthermore, what that wave would look like if it traveled a given distance across space and time, to eventually arrive at a detector on Earth.

With their model, they looked to see whether any gravitational-wave signals that have been detected on Earth match their predicted patterns of dark matter imprints. To do so, they applied the model to publicly-available data recorded by LVK over the observatories’ first three observing runs. The observatories have picked up hundreds of gravitational-wave signals during this period. For their purposes, the researchers focused on the clearest signals, comprising gravitational waves from 28 separate events. 

For each event, the team compared the pattern of the actual gravitational wave against their model of what the signal would look like if it were generated by the same event in an environment of dark matter. They also compared the gravitational wave to the more expected scenario in which the signal was produced in a vacuum. 

Of the 28 clearest signals that they analyzed, 27 were solidly within the predictions for having been produced in a vacuum. However, the pattern of one event, GW190728, showed a “preference,” or an agreement with the team’s dark matter model. In other words, the signal may carry an imprint of dark matter. 

GW190728 is a gravitational wave that is named after the date that it was detected — on July 28, 2019. Scientists previously determined that the gravitational wave originated from a black hole binary with a total mass of about 20 times the mass of the sun. With their model, the team showed that such a system could have merged through a dense cloud of dark matter and produced a similar gravitational wave to GW190728. 

“The statistical significance of this is not high enough to claim a detection of dark matter, and further checks should be performed by independent groups,” Aurrekoetxea says. “What we think is important to highlight is that without waveform models like ours, we could be detecting black hole mergers in dark matter environments, but systematically classifying them as having occurred in vacuum.”

“We now have the potential to discover dark matter around black holes as the LVK detectors keep collecting data in the coming years,” says co-author Soumen Roy, who led the data analysis part of the work. “It is an exciting time to search for new physics using gravitational waves.”

“Using black holes to look for dark matter would be fantastic,” adds co-author Rodrigo Vicente, who developed the analytical model of the signal. “We would be able to probe dark matter at scales much smaller than ever before.”

This work was supported, in part, by the U.S. National Science Foundation and MIT’s Center for Theoretical Physics — a Leinweber Institute.

Powerful shrinking technique could enable devices that compute with light

MIT researchers created tiny 3D photonic devices with features small enough to channel visible light.

Using a new technique that can create vacancies at any site across a material and then shrink it to about 1/2,000 of its original volume, MIT researchers have designed nanotechnology devices that could be used for optical computing and other applications involving the manipulation of visible light.

The new fabrication technique, known as “implosion carving,” allows researchers to imprint features throughout a hydrogel using photopatterning. If patterned with a resolution of about 800 nanometers, these features can then be shrunk to less than 100 nanometers. 

Because that resolution is smaller than the wavelength of light, the devices can bend light in specific ways that allow them to perform optical computations.

“In order to enable nanophotonic applications in visible light, we need to make nanostructures with feature sizes with a resolution less than 100 nanometers. Only in that way can we precisely create the structure that can manipulate visible light,” says Quansan Yang, a former MIT postdoc, now an assistant professor at the University of Washington, and one of the lead authors of the new study.

In their paper, the researchers demonstrated a photonic device that can perform a simple digit-classification task, but future versions could be used for high-speed imaging and information processing, they say.

Gaojie Yang, a former MIT postdoc, is the co-lead author of the paper, which appears today in Nature Photonics. The paper’s senior authors are Peter So, director of the MIT Laser Biomedical Research Center (LBCR) and an MIT professor of biological engineering and mechanical engineering, and Edward Boyden, the Y. Eva Tan Professor in Neurotechnology at MIT and a professor of biological engineering, media arts and sciences, and brain and cognitive sciences. Boyden is also a Howard Hughes Medical Institute investigator and a member of MIT’s McGovern Institute for Brain Research, the Yang Tan Collective, and Koch Institute for Integrative Cancer Research.

Nanoscale feature sizes

Photonic devices, which transmit and manipulate light, hold potential for use as optical computer chips that could offer an energy-efficient alternative to semiconductor chips. However, existing techniques for creating 3D photonic devices haven’t yet achieved the 100-nanometer resolution that is needed to channel visible light, which has wavelengths between 380 and 750 nanometers.

Using an additive manufacturing technique called two-photon lithography, researchers can use light to create 3D nanoscale features, but with a resolution larger than 100 nanometers. Another technique, known as electron-beam lithography, can be used to etch smaller-resolution features onto a silicon chip, but it doesn’t generate 3D structures. 

To make 3D devices with the necessary feature size, the researchers extended the concept of “implosion fabrication,” which Boyden’s lab developed in 2018, to create a new variant called “implosion carving.” In implosion carving, a laser creates vacancies — tiny voids where the hydrogel material has been removed — at precisely targeted locations. These vacancies exhibit different optical properties than the surrounding hydrogel. The hydrogel is then shrunk to bring the patterned features down to the nanoscale.

The carving process begins with immersing the hydrogel in a photosensitizing dye. Then, the researchers use a laser to excite the photosensitizer at specific places in the gel, which in turn generates reactive oxygen species that cut the bonds holding the hydrogel together. This creates a vacancy in that spot.

Once the desired vacancy pattern has been carved into the hydrogel, the researchers shrink it using a two-step process. First, they soak it in a solution containing ions, which causes it to shrink about tenfold in each dimension. To shrink it a little more, and to remove the watery solution, the hydrogel then undergoes a process called supercritical drying, which can remove liquid from a gel without damaging it.

At the end of the process, the hydrogel has been shrunk more than tenfold in each dimension, leading to a 2,000-fold reduction in volume. 

Computing with light

To demonstrate the versatility of this technique, the researchers used it to create several 3D shapes, including a helix and a structure inspired by a butterfly wing. Some of these structures are too thin, and have too high an aspect ratio, to be stably created using conventional two-photon lithography.

The researchers also created a device that could perform a simple calculation known as digit classification, a task that is traditionally used to test the performance of neural networks. During this task, the device was presented with a digit, such as 1 or 5, and had to light up a specific location to indicate which number was detected.

To achieve this, the researchers patterned vacancies throughout the device so that it would act like a neural network. The pattern of vacancies would diffract input light as it passed through many layers of patterned hydrogel, so that the output light was determined by the shape of the digit that was entered into the system.

“This is a purely optical system that effectively performs optical computing,” So says. 

“One of the very attractive features of this technology is that you can manipulate the property of the material at every tiny location,” says Dushan Wadduwage, an assistant professor at Old Dominion University and former MIT postdoc, who is also an author of the paper. “You have millions of different locations that you need to decide the property of, and that turns into a really interesting design problem where we can use deep-learning algorithms to find designs over these millions of parameters and come up with parts that go into optical systems in new ways.”

The researchers now plan to use the same principles to build optical devices that could classify cells based on their state as they flow through a microfluidic device. This could help identify rare cells such as circulating tumor cells in a blood sample, they say. 

This approach could also enable the creation of high-throughput imaging techniques for applications such as analyzing tissue samples from biopsies or surgical specimens. And, if adapted to work with other materials such as hydrophobic polymers, it could also be used to create channels within 3D nanofluidic devices. 

Other authors of the paper include Gaojie Yang, Takahiro Nambara, Hiroyuki Kusaka, Yuichiro Kunai, Alex Matlock, Corban Swain, Brett Pryor, Yannick Salamin, Daniel Oran, Hasindu Kariyawasam, Ramith Hettiarachchi, and Marin Soljacic. 

The research was funded, in part, by the MIT-Fujikura Partnership Fund, the U.S. Army Research Office through the Institute for Soldier Nanotechnologies at MIT, Lisa Yang and Y. Eva Tan, John Doerr, the Open Philanthropy Project, the Howard Hughes Medical Institute, and the U.S. National Institutes of Health.

Improving the reliability of circuits for quantum computers

A new technique helps scientists measure a phenomenon that can cause quantum circuits to perform differently than expected, increasing the error in computations.

Quantum computers could someday solve pressing problems that are too convoluted for classical computers, such as modeling complex molecular interactions to streamline drug discovery and materials development. 

But to build a superconducting quantum computer that is large and resilient enough for real-world applications, scientists must precisely engineer thousands of quantum circuits so they perform operations with the lowest possible error rate.

To help scientists design more predictable circuits, researchers from MIT and Lincoln Laboratory developed a technique to measure a property that can unexpectedly cause a superconducting quantum circuit to deviate from its expected behavior. Their analysis revealed the source of these distortions, known as second-order harmonic corrections, leading to underperforming circuit architectures.

The MIT researchers fabricated a device to detect second-order harmonic corrections, identify their origin, and precisely measure their strength. This technique could help scientists deliberately design quantum circuits that can counteract the effects of these deviations.

This is especially important in larger and more complicated quantum circuits, where the negative impact of second-order harmonic corrections can be amplified. 

“As we make our quantum computers bigger and we want to have more precise control over the parameters of these devices, identifying and measuring these effects is going to be important for us to have a precise understanding of how these systems are constructed. It is always important to keep diving down into the circuit to see if there is an effect you didn’t expect, which impacts how your device is performing,” says Max Hays, a research scientist in the Engineering Quantum Systems (EQuS) group of the Research Laboratory of Electronics (RLE) and co-lead author of a paper on this research.

Hays is joined on the paper by co-lead author Junghyun Kim, an electrical engineering and computer science (EECS) graduate student in the EQuS group; senior author William D. Oliver, the Henry Ellis Warren (1894) Professor of EECS and professor of physics, leader of the EQuS group, director of the Center for Quantum Engineering, and associate director of RLE; as well as others at MIT and Lincoln Laboratory. The research appears today in Nature Physics.

A pair-wise problem

In a quantum computer that utilizes superconducting circuits, which is one of many potential computing platforms, Josephson junctions are critical elements that enable the transfer and manipulation of information. These devices utilize two superconducting wires that are brought very close together, with a nanometer-scale barrier between them. Like a traditional circuit, the electric charge in Josephson junctions is carried by electrons. 

But in a superconducting circuit, charge-carrying electrons pair up, forming what are called Cooper pairs. These Cooper pairs can “quantum tunnel” through the barrier between the two wires, transporting current from one wire to the other.

Cooper pairs can usually only tunnel one pair at a time, which is a key property that makes quantum computation possible. 

“If you try to force more Cooper pairs through, it just doesn’t work. This non-linear effect is extremely important for all our circuits. If we didn’t have that effect, then we wouldn’t be able to control or manipulate any quantum information that we store in these circuits,” Hays explains.

But sometimes, Cooper pairs can unexpectedly squeeze through the barrier two at a time, an effect that is known as a second-order harmonic correction. This effect limits the performance of a quantum circuit that has been configured to only allow single-pair tunneling.

“If two Cooper pairs tunnel at the same time, then the assumption we used to build our circuit doesn’t apply anymore. We need to fix the circuit so it can handle that,” Kim says.

But before they can fix the circuit, scientists need to know the source and strength of these distortions.

To obtain this information, the MIT researchers fabricated a quantum circuit so it would be very sensitive to these effects. Essentially, the device is designed to suppress the quantum tunneling process of single Cooper pairs, while allowing the two-pair tunneling process to continue. 

In this way, they can detect the presence of second-order harmonic corrections and precisely measure their strength. 

Straight to the source

They can also use this circuit to pinpoint the source of these harmonics, which helps researchers identify the best way to correct for them. 

There are two potential sources of second-order harmonics — one source is intrinsic to the dynamics of the Josephson junction and the other is caused by the wires connecting the junction to other circuit elements. 

While prior research had indicated the second-order harmonics could be due to the dynamics of the junction, the MIT researchers found that additional inductance — the tendency to oppose changes in the flow of electric current —from wires in the circuit was the actual source in their devices. 

“This is important because, if we know where the second-order harmonic correction is coming from, we can predict how strong it is likely to be, and use that information to engineer more predictable circuits that will hopefully perform better,” Hays says.

In the future, the researchers want to design experiments that more accurately predict how a device will perform when second-order harmonic corrections occur. They also want to study other sources of second-order harmonic corrections and whether those sources could have negative impacts on a circuit under different fabrication conditions.

This work is funded, in part, by the U.S. Department of Energy, the U.S. Co-design Center for Quantum Advantage, the U.S. Air Force, the Korea Foundation for Advanced Studies, and the Intelligence Community Postdoctoral Research Fellowship Program at MIT. 

For most US drivers, EVs offer emissions benefits and cost savings

When it comes to emissions, individual driving patterns matter as much as how “green” the regional electricity mix is, MIT researchers report.

Despite regional variability in climate, electricity sources, congestion, and the wide variation in individual driving patterns, electric vehicles generate less greenhouse gas emissions and do not cost more than comparable gas-powered vehicles for drivers and vehicle fleet owners in most parts of the United States, according to a new study by MIT researchers.

The team’s approach captures many key factors that contribute to regional and individual differences in the life-cycle emissions and ownership cost of electric vehicles, including meteorological data, the distance and duration of trips, and fuel prices.

To paint a fuller picture of emissions and costs than was previously available, the researchers sourced data from thousands of U.S. zip codes and drilled down to the level of individual drivers within those locations. Their study considers time-averaged fuel prices so as not to be overly influenced by fluctuations in prices at any one point in time. They finalized their analysis at the end of 2024 and early 2025.

Their results indicate that a person’s driving behaviors can matter as much as regional factors like the local electricity mix when it comes to the emissions savings of an electric vehicle, compared to a similar gas-powered vehicle. In most locations, a battery-electric vehicle reduces emissions between 40 and 60 percent, with larger impacts in urban areas. 

They also found that colder climates do not reduce overall emission benefits as much as some media reports assume.

The researchers utilized this detailed analysis to update a public tool they previously developed, carboncounter.com, which enables individuals to compare the life-cycle emissions and total ownership costs of nearly any car on the market. A new version of carboncounter.com is also being released today.

“There are a lot of statements being thrown around, like that electric vehicles don’t reduce emissions very much in cool climates, and we wanted to analyze these factors systematically and evaluate these statements against one another simultaneously. Rather than simply asking, ‘Are EVs better?’, this paper helps answer ‘better for whom, and under what conditions?’” says Marco Miotti PhD ’20, a senior researcher at ETH Zurich who completed this research while a graduate student in the Institute for Data, Systems, and Society (IDSS) at MIT. 

He is joined on the paper by senior author Jessika Trancik, a professor in IDSS. The research appears today in Environmental Research Letters.

A holistic approach

Many prior studies that compare emissions and costs of electric vehicles (EVs) to combustion-engine vehicles cover a few factors, like the amount of renewable energy in the grid and how gas prices impact affordability, Miotti says.

“To our knowledge, there have been few efforts so far that bring all these factors together. But if someone wants to buy a car and have a better understanding of the factors that affect emissions and costs, this holistic approach is important,” he adds.

The researchers focused on two types of EVs: battery-electric vehicles, which only operate on electricity, and plug-in hybrid electric vehicles, which also have a combustion engine that works in tandem with the battery to optimize fuel savings.

The team expanded and improved a set of previously developed vehicle cost and emissions models to incorporate a wider variety of factors and data types.

For instance, they refined an existing model that estimates energy use and gas mileage so it could capture more nuances of local climate variability. 

“But the real effort was not just in extending these different models, but in bringing together all these different data and making them work with the models in a consistent manner,” Miotti says.

The team sourced data on a wide variety of factors for each U.S. zip code, such as typical drive cycles, the amount of traffic, local gas and electricity prices, makeup of the regional electricity mix, meteorological profiles, and more. They used statistical approaches to amalgamate different types of data. 

For example, the team used a probabilistic matching technique to combine data on how often people drive, which was drawn from nationwide travel surveys, with more detailed GPS data that includes factors like drivers’ acceleration patterns and the distance they usually drive on each day of the week.

The researchers designed their analysis to focus on the spatial picture of emissions and costs, based on U.S. zip codes, while simultaneously considering the impact of the size and features of each specific vehicle model.

“At the end of the day, it’s the vehicle and fleet owners who make decisions about vehicle purchases. So, we wanted to make sure to consider their wide-ranging individual perspectives rather than simply performing a region-by-region comparison,” says Trancik.

Lower emissions, comparable costs

In the end, their modeling framework revealed that all factors they analyzed matter about equally in determining emissions-reduction potential of EVs compared to internal combustion vehicles. 

EVs reduce emissions the most in areas with a cleaner electricity mix, denser traffic, higher annual travel distances, and a mild climate, in decreasing order of importance. In each area, emission reductions increase for drivers who drive more often, drive larger vehicles, and are more frequently stuck in traffic. 

In a colder area like North Dakota, fuel economy of battery-electric vehicles might be reduced by as much as 50 percent on a particularly frigid night, but the effect on annual emission benefits is minimal. 

“We even did a sensitivity study to see if the range is reduced in very cold climates, and we found that, even in the most unfavorable conditions, EVs still reduce emissions by a substantial amount,” Miotti says.

On the cost side, the models show that, in most places across the U.S., EVs are competitive with comparable combustion-engine vehicles in terms of lifetime ownership cost, even without clean vehicle tax credits. And in areas where electricity is relatively affordable, battery-electric vehicles tend to cost less than their plug-in hybrid or combustion-engine counterparts.

In the future, the researchers want to expand this analysis to include a temporal dimension, so the framework also considers how changes in vehicle, fuel, and electricity prices affect emissions and costs over time. 

“While we found that the electricity mix is a big driver of the spatial variation in emissions savings of EVs, the electricity grid is decarbonizing everywhere. As that happens, emissions savings across space will become more homogenous for EVs, but the differences across one driver to another will remain,” Miotti says.

They could also use the framework to explore regions outside the United States or incorporate data on hybrid-electric vehicles that cannot be plugged in.

This work was funded, in part, by the MIT Martin Family Society of Fellows for Sustainability.

Mapping the ocean with autonomous sensors

Founded by Ravi Pappu SM ’95, PhD ’01, Apeiron Labs is deploying low-cost ocean sensors to improve storm forecasts, detect endangered species, and more.

In late October 2025, Tropical Storm Melissa moved through the Caribbean Sea with moderate winds that didn’t get much attention. But on Oct. 25, aided by a patch of warm ocean, the storm rapidly intensified. By the time it made landfall in Jamaica, it was one of the strongest Atlantic hurricanes on record, uprooting trees, tearing the roofs from buildings, and causing catastrophic flooding and power outages.

Ravi Pappu SM ’95, PhD ’01 blames the surprise on our inability to gather high-quality ocean data.

“The storm intensified because of a small pool of hot water in the Caribbean Ocean that fed it energy,” Pappu explains. “These pools are everywhere. They can be hundreds of kilometers wide and are literally invisible to us. If we knew about that pool, we could say very precisely how the hurricane would intensify and better deal with it.”

Pappu thinks he has a way to solve that problem. He is the founder of Apeiron Labs, a company deploying low-cost autonomous ocean sensors to capture more data, in more places, and at a lower cost than is possible today. The company’s devices roam the ocean up to a quarter mile below the surface and continuously gather data on temperature, acoustics, salinity, and more, providing a real-time look at one of the planet’s last known mysteries. He says the sensors can do for the ocean what small, modular CubeSat satellites did for Earth observation from space.

When the devices are ready to be recharged, trackers make it easy to scoop them from the ocean surface. Pappu envisions the recovery process being done by autonomous boats in the future.

“Humanity needs ocean measurements, and we need them at a scale that has never been attempted before,” Pappu says. “It’s a massively hard problem. In the last century, oceanographers resigned themselves to calling it the century of undersampling. If we are successful, we will have a much more fine-grained understanding of our oceans and how they impact humans. That’s what drives us.”

Homework

Pappu came to MIT after completing a 10-year homework assignment. It started when he was a child in India in the 1980s, when he saw a hologram on the cover of National Geographic for the first time.

“I was so taken by it that I decided I needed to learn how to make those three-dimensional images,” Pappu recalls. “I learned what I could by reading books and papers. I didn’t know who invented the hologram until I read a book about MIT’s Media Lab. The book named the person who invented the rainbow hologram, so I wrote him a letter. I didn’t know his address, so I just wrote on the envelope, ‘Steve Benton, holography researcher, MIT, USA.’”

To Pappu’s surprise, the letter reached Benton, and the former Media Lab professor even wrote back with some further topics he needed to learn about.

Pappu never forgot that. He earned a bachelor’s degree in electrical engineering in India, then earned his master’s degree at Villanova University, taking all the optics classes he could.

“Eventually, about 10 years after I saw my first hologram, I wrote to Steve and I said, ‘I did all these things you asked me, now I want to study with you,’” Pappu says. “That’s how I got into MIT.”

Pappu studied under Benton for the next three years. He also studied under Professor Neil Gershenfeld as part of his PhD. Following graduation, Pappu and four classmates started ThingMagic, a consulting company that eventually sold RFID readers. ThingMagic was acquired 2010. Pappu returned to MIT for two years as a visiting scientist around the time of the acquisition.

Following that experience, Pappu worked at In-Q-Tel, an organization that invested in ThingMagic and other companies with potential to advance national security. It was there that Pappu realized how badly the world needed large-scale, inexpensive ocean sensing.

“All of the ocean sensing up to that point, and even today, was about making a really expensive thing that cost $20 million, goes to the bottom of the ocean, and stays there for five years,” Pappu says. “We needed things that are cheap and scalable to deploy wherever you need them for as long as you want.”

Pappu officially founded Apeiron Labs in 2022.

“What we’re focused on is figuring out how the ocean works,” Pappu says. “How warm is it? What is the pH? How salty is it? These things vary from place to place every 10 kilometers or so. It varies over time, and it varies by season. If we knew the details of the ocean with the same fidelity we have for the atmosphere, we would be able to tell exactly when and where hurricanes hit. It would mean less uncertainty.”

Apeiron’s ocean-sensing devices are each 3 feet long and about 20 pounds. They’re designed to be dropped off a boat or plane with biodegradable parachutes and stay in the ocean for six months. Each device continuously sends data to the cloud, is controllable through a cloud-based ocean operating system, and is accessible on a mobile phone.

“We lower the carbon footprint and cost of gathering ocean data because everything else needs a diesel ship — and a fully crewed ship costs $100,000 a day,” Rappu says. “By the time you collect the first data in the old model, you’ve already committed to a lot of money in addition to millions of dollars for the sensors. “

The company’s devices currently have two types of sensors: one for measuring salinity, temperature, and depth, and the other that uses a hydrophone to passively listen for things like submarines and whales.

That could be used to detect the low-frequency calls and clicks of endangered whales and other fish species. Currently, fishermen must look for whales manually with spotters on ships or planes. The data could also be used to improve weather forecasts, monitor noise from offshore energy projects, and track currents.

“Currents are determined by temperature and salinity, so if there’s an oil spill, our data could help determine where that spill is going,” Pappu says. “Or if you’re a fisherman, knowing where the water changes from warm to cold, which is where the fish hang out, is very useful.”

An ocean of possibilities

Apeiron Labs has worked with government defense agencies including the U.S. Navy over the last two years. The company has also tested its devices off the coast of California and in the Boston Harbor.

“The most important thing is, when we show people our approach and what we’ve demonstrated so far, they are no longer asking, ‘Can it be done?’ they’re asking, ‘What can we do with it?’” Pappu says. “Our customers have spent decades working in the ocean and they understand how novel these capabilities are.”

Of all the possibilities, improved storm forecasting could be the one Pappu is most excited about.

“Our mission is to lower the barriers to ocean data,” Pappu says. “The ocean is a huge determinant of weather, climate, and short-term forecasting. Despite our best efforts to predict the intensity of storms, sudden changes are still the norm, and much of that comes down to a lack of understanding of our oceans. If we were monitoring these things over long periods of time and finer spatial scales, we could see these storms coming much earlier with more certainty.”

MIT student Jack Carson named 2026 Udall Scholar

The Udall Foundation identifies and rewards future leaders in tribal public policy, Indigenous health policy, and the environment.

Jack Carson, a second-year undergraduate at MIT majoring in electrical engineering and computer science, has been named a 2026 Udall Scholar, one of up to 65 undergraduates nationally to receive the prestigious $7,500 award. 

The Udall Scholarship honors students who have demonstrated a commitment to the environment, Indigenous health care, or tribal public policy. Carson is only the third MIT student to win this award, and the first to win for tribal policy.

Carson, a member of the Cherokee Nation and resident of Oklahoma, exemplifies the multidisciplinary approach to problem-solving that the Udall Scholarship seeks to honor. His work spans artificial intelligence, biomedical research, Indigenous community development, and ethics.

"Jack is the type of leader the Udall Foundation exists to support," says Kim Benard, associate dean for distinguished fellowships. "He's not only conducting cutting-edge research, but he's actively creating opportunities for Indigenous students to enter tech fields."

At MIT's Computer Science and Artificial Intelligence Laboratory (CSAIL), Carson works in the Barzilay Lab, developing multiomics models for personalized therapeutic target identification. His work on deep learning and statistical physics has resulted in a sole-author paper published at the International Conference on Machine Learning (ICML).

Carson founded Code.Tulsa, a summer technology program designed to introduce Indigenous high school students to computer science and tech careers. The initiative addresses a significant gap: Indigenous communities remain highly underrepresented in technology fields, despite the potential for tech to advance tribal sovereignty and economic development.

This year, Carson won the Elie Wiesel Prize in Ethics Essay Contest. He is an accomplished musician who has performed at Carnegie Hall and with the National Opera, a motorcycle racer, and a self-described philosopher deeply committed to questions of justice and responsibility.

Photonics advance could enable compact, high-performance lidar sensors

With a novel design, MIT researchers overcame a stubborn problem that has limited the effectiveness of chip-based systems for lidar.

Lidar systems use pulses of infrared light to measure distance and map a 3D scene with high resolution, allowing autonomous vehicles to rapidly react to obstacles that appear in their path. But traditional lidar sensors are expensive, bulky systems with many moving parts that degrade over time, limiting how the sensors can be deployed.

A new study from MIT researchers could help to enable next-generation lidar sensors that are compact, durable, and have no moving parts. The key advance is a novel design for a silicon-photonics chip, which is a semiconductor device that manipulates light rather than electricity. 

Typically, such silicon-photonics chip-based systems have a restricted field of view, so a silicon-photonics-based lidar would not be able to scan angles in the periphery. Existing workarounds to this problem increase noise and hamper precision.

To avoid these drawbacks, the MIT researchers designed and demonstrated an array of integrated antennas that minimizes unwanted crosstalk between the antennas. Their innovation allows a lidar chip to scan a wider field of view while maintaining low-noise operation compared to other silicon-photonics-based approaches.

This novel demonstration could fuel the development of advanced lidar sensors for demanding applications like autonomous vehicle navigation, aerial surveying, and construction site monitoring.

“The functionality we demonstrated in this work solves a fundamental problem for integrated optical-phased-array technology, enabling future lidar sensors that can achieve significantly higher performance than we could demonstrate previously,” says Jelena Notaros, the Robert J. Shillman Career Development Associate Professor of Electrical Engineering and Computer Science (EECS) at MIT, a member of the Research Laboratory of Electronics, and senior author of a paper on this innovation.

She is joined on the paper by lead author and EECS graduate student Henry Crawford-Eng as well as EECS graduate students Andres Garcia Coleto, Benjamin M. Mazur, Daniel M. DeSantis, and Tal Sneh. The research appears today in Nature Communications.

Adjusting an antenna array

Many traditional lidar systems map a scene using a bulky box that spins to send pulses of light in multiple directions. The light bounces off nearby objects and returns to the sensor, providing data that are used to reconstruct the environment. 

Instead, silicon-photonics-based lidar sensors systematically scan an emitted light beam in multiple directions non-mechanically using a system called an integrated optical phased array (OPA).

Key to an OPA is an array of integrated antennas that have tiny perturbations placed periodically along their length. These corrugations allow the antenna to scatter light from an input source up and out of the photonic chip.

By adjusting the phase of light routed to each antenna, the researchers can change the angle at which the light is emitted out of the array. In this way, they can steer the beam with no moving parts.

But if engineers place the antennas too close together, the antennas will couple with each other and the light they emit will get jumbled. To avoid this, scientists typically space the antennas farther apart, but this also has downsides.

If the antennas are spaced too far apart, the array will emit multiple copies of the light beam at different angles. The researchers can only steer the primary beam so far in either direction until it is undiscernible from its neighboring copies.

“This limits our field of view, so the autonomous vehicle now only knows what is in front of it for a certain angular range,” Garcia Coleto explains.

These beam copies, known as grating lobes, can cause false positives by confusing the sensor. They also waste power.

The MIT researchers solved this problem by designing a set of reduced-crosstalk antennas that can be placed close together without causing a significant coupling effect.

In a standard OPA, all the antennas have the same design, meaning the same arrangement of corrugations. These identical antennas couple very strongly when placed close together.

To address this fundamental roadblock, the MIT researchers designed a set of three antennas with different geometries, varying the width of each antenna and the size and arrangement of corrugations. With varied geometries, each antenna has a different propagation coefficient, which determines how light travels down the antenna.

“Because the antennas have very different propagation coefficients, when we put them close together, essentially each antenna doesn’t ‘see’ the antenna next to it. Therefore, it won’t couple with its neighbor,” Garcia Coleto says. 

A photonic balancing act

But even though the antennas have different propagation coefficients, the researchers still need them to emit light in the same way. 

They achieved this by carefully designing the antennas to meet three parameters. 

First, each antenna must emit the same amount of light. Second, each antenna must emit a beam at the same angle for the same wavelength of light. Third, the emission angle must change uniformly across the array as the researchers steer it.

“We have this challenge where we require the antennas to have different geometries to reduce the crosstalk, but we need to simultaneously design the antennas to have the same emission characteristics. While it is possible to engineer this, it is extremely difficult because, typically, when antennas are designed with different geometries, they tend to behave differently,” Crawford-Eng says.

The researchers first developed the fundamental electromagnetic theory behind how radiative modes couple. They used that theory as a guide to design and simulate their antennas.

Building on those analyses, they fabricated the OPA with reduced-crosstalk antennas spaced significantly closer than they would be in a traditional OPA, then experimentally tested the system.

While a typical OPA would have coupling of about 100 percent in this experiment, their OPA reduced coupling to about 1 percent while generating a single, precise beam. Using this design, they demonstrated accurate beam steering across a wide field of view without any grating lobes. 

In the future, the researchers plan to further improve their technique to enable an even wider field of view. In addition, they are exploring a new potential solution to wide field-of-view functionality that they discovered while developing the underlying theory.

“This work addresses a longstanding challenge in integrated optical phased arrays: simultaneously achieving both a wide field of view, which requires dense antenna spacing, and high beam quality, which requires low crosstalk between neighboring antennas. The authors solve this problem with an elegant antenna design. Their innovation is an important step forward for chip-scale, solid-state beam-steering technology,” says Joyce Poon, professor of electrical and computer engineering at the University of Toronto and director of the Max Planck Institute of Microstructure Physics, who was not involved with this work.

This research was supported, in part, by the Semiconductor Research Corporation, the National Science Foundation, an MIT MathWorks Fellowship, the U.S. Department of War, and the MIT Rolf G. Locher Endowed Fellowship.

The work was conducted, in part, using MIT.nano facilities.

Study: Firms often use automation to control certain workers’ wages

MIT economists found US companies tend to target employees earning a “wage premium,” which increases inequality but not necessarily productivity.

When we hear about automation and artificial intelligence replacing jobs, it may seem like a tsunami of technology is going to wipe out workers broadly, in the name of greater efficiency. But a study co-authored by an MIT economist shows markedly different dynamics in the U.S. since 1980. 

Rather than implement automation in pursuit of maximal productivity, firms have often used automation to replace employees who specifically receive a “wage premium,” earning higher salaries than other comparable workers. In practice, that means automation has frequently reduced the earnings of non-college-educated workers who had obtained better salaries than most employees with similar qualifications. 

This finding has at least two big implications. For one thing, automation has affected the growth in U.S. income inequality even more than many observers realize. At the same time, automation has yielded a mediocre productivity boost, plausibly due to the focus of firms on controlling wages rather than finding more tech-driven ways to enhance efficiency and long-term growth.

“There has been an inefficient targeting of automation,” says MIT’s Daron Acemoglu, co-author of a published paper detailing the study’s results. “The higher the wage of the worker in a particular industry or occupation or task, the more attractive automation becomes to firms.” In theory, he notes, firms could automate efficiently. But they have not, by emphasizing it as a tool for shedding salaries, which helps their own internal short-term numbers without building an optimal path for growth.

The study estimates that automation is responsible for 52 percent of the growth in income inequality from 1980 to 2016, and that about 10 percentage points derive specifically from firms replacing workers who had been earning a wage premium. This inefficient targeting of certain employees has offset 60-90 percent of the productivity gains from automation during the time period.

“It’s one of the possible reasons productivity improvements have been relatively muted in the U.S., despite the fact that we’ve had an amazing number of new patents, and an amazing number of new technologies,” Acemoglu says. “Then you look at the productivity statistics, and they are fairly pitiful.”

The paper, “Automation and Rent Dissipation: Implications for Wages, Inequality, and Productivity,” appears in the May print issue of the Quarterly Journal of Economics. The authors are Acemoglu, who is an Institute Professor at MIT; and Pascual Restrepo, an associate professor of economics at Yale University.

Inequality implications

Dating back to the 2010s, Acemoglu and Restrepo have combined to conduct many studies about automation and its effects on employment, wages, productivity, and firm growth. In general, their findings have suggested that the effects of automation on the workforce after 1980 are more significant than many other scholars have believed. 

To conduct the current study, the researchers used data from many sources, including U.S. Census Bureau statistics, data from the bureau’s American Community Survey, industry numbers, and more. Acemoglu and Restrepo analyzed 500 detailed demographic groups, sorted by five levels of education, as well as gender, age, and ethnic background. The study links this information to an analysis of changes in 49 U.S. industries, for a granular look at the way automation affected the workforce. 

Ultimately, the analysis allowed the scholars to estimate not just the overall amount of jobs erased due to automation, but how much of that consisted of firms very specifically trying to remove the wage premium accruing to some of their workers. 

Among other findings, the study shows that within groups of workers affected by automation, the biggest effects occur for workers in the 70th-95th percentile of the salary range, indicating that higher-earning employees bear much of the brunt of this process. 

And as the analysis indicates, about one-fifth of the overall growth in income inequality is attributable to this sole factor.

“I think that is a big number,” says Acemoglu, who shared the 2024 Nobel Prize in economic sciences with his longtime collaborators Simon Johnson of MIT and James Robinson of the University of Chicago.

He adds: “Automation, of course, is an engine of economic growth and we’re going to use it, but it does create very large inequalities between capital and labor, and between different labor groups, and hence it may have been a much bigger contributor to the increase in inequality in the United States over the last several decades.” 

The productivity puzzle

The study also illuminates a basic choice for firm managers, but one that gets overlooked. Imagine a type of automation — call-center technology, for instance — that might actually be inefficient for a business. Even so, firm managers have incentive to adopt it, reduce wages, and oversee a less productive business with increased net profits.

Writ large, some version of this seems to have been happening to the U.S. economy since 1980: Greater profitability is not the same as increased productivity.

“Those two things are different,” says Acemoglu. “You can reduce costs while reducing productivity.” 

Indeed, the current study by Acemoglu and Restrepo calls to mind an observation by the late MIT economist Robert M. Solow, who in 1987 wrote, “You can see the computer age everywhere but in the productivity statistics.” 

In that vein, Acemoglu observes, “If managers can reduce productivity by 1 percent but increase profits, many of them might be happy with that. It depends on their priorities and values. So the other important implication of our paper is that good automation at the margins is being bundled with not-so-good automation.” 

To be clear, the study does not necessarily imply that less automation is always better. Certain types of automation can boost productivity and feed a virtuous cycle in which a firm makes more money and hires more workers. 

But currently, Acemoglu believes, the complexities of automation are not yet recognized clearly enough. Perhaps seeing the broad historical pattern of U.S. automation, since 1980, will help people better grasp the tradeoffs involved — and not just economists, but firm managers, workers, and technologists. 

“The important thing is whether it becomes incorporated into people’s thinking and where we land in terms of the overall holistic assessment of automation, in terms of inequality, productivity and labor market effects,” Acemoglu says. “So we hope this study moves the dial there.”

Or, as he concludes, “We could be missing out on potentially even better productivity gains by calibrating the type and extent of automation more carefully, and in a more productivity-enhancing way. It’s all a choice, 100 percent.”

Method for stress-testing cloud computing algorithms helps avoid network failures

The “MetaEase” technique provides a heads-up to potential scenarios that could cause long wait-times or outages.

Researchers from MIT and elsewhere have developed a more user-friendly and efficient method to help networking engineers identify potential system failures before they cause major problems, like a cloud service outage that leaves millions of users unable to access applications. 

The technique uncovers hidden blind spots that might cause a shortcut algorithm to fail unexpectedly when it is deployed. 

This new approach can identify worse-case scenarios that an engineer might miss if they use a traditional method that compares an algorithm against a set of human-designed past test cases. It is also less labor-intensive than other verification tools that require engineers to rewrite an algorithm in a complex mathematical code each time they want to test it.

Instead of needing a mathematical reformulation, the new method reads the algorithm’s source code directly and automatically searches for worse-case scenarios that lead to the highest level of underperformance.

By helping engineers quickly and easily stress-test a networking algorithm before deployment, the method could catch failure modes that might otherwise only appear in a real outage. The technique could also be used to analyze the risks of deploying AI-generated code.

“We need to have good tools to measure the worse-case scenario performance of our algorithms so we know what could happen before we put them into production. This is an easy-to-use tool that can be plugged into current systems so we can find the best algorithm to use and ensure the worse-case scenarios are identified in advance,” says Pantea Karimi, an electrical engineering and computer science (EECS) graduate student and lead author of a paper on this new technique. 

She is joined on the paper by senior authors Mohammad Alizadeh, an associate professor of EECS and a member of the Computer Science and Artificial Intelligence Laboratory (CSAIL); and Behnaz Arzani, a principal researcher at Microsoft Research; along with Ryan Beckett, Siva Kesava Reddy Karkarla, and Pooria Namyar, researchers at Microsoft Research; and Santiago Segarra, a professor at Rice University. The research will be presented at the USENIX Symposium on Networked Systems Design and Implementation. 

Assessing algorithms

In large systems like cloud servers, the tried-and-true algorithms that route data from one place to another or are often too computationally intensive to run in a feasible amount of time. 

So, engineers and researchers develop suboptimal algorithms called heuristics that can run much faster. However, there could be unexpected but plausible circumstances that will cause a heuristic to underperform or fail when deployed.

A heuristic can route millions of data requests across a cloud network in seconds, but under the wrong conditions — like an unusual traffic pattern or a sudden spike in demand — the shortcut can break down in ways the designer never anticipated.

When these problems occur, a company may have no choice but to drop some requests that can’t be processed. 

The firm could also deliberately allocate more resources in advance to head-off a potential disaster, leading to higher overall costs and wasted electricity from underutilization.

“This is really bad for a company because, either way, they are going to lose a lot of money. If this particular scenario hasn’t happened before and was never tested, how would a developer know in advance before it happens?” Karimi says.

Stress-testing heuristics typically involves running a new algorithm in simulation using a set of human-designed test cases and manually comparing the performance with a previous algorithm. But this is time-consuming and can leave blind spots if an engineer doesn’t know to test for certain situations.

Alternatively, engineers could use a verification tool to evaluate the performance of their heuristic more systematically. However, these tools require the engineer to encode the algorithm into a complex, mathematical formula that can take days to flesh out. The process, which doesn’t work for every type of heuristic, must be repeated each time the engineer changes the code.

Instead, the researchers developed a more user-friendly and efficient verification tool, called MetaEase, that analyzes the heuristic’s existing implementation code directly to identify the biggest risks of deploying it.

“This would reduce the friction of using these heuristic analysis tools,” Karimi says.

She began this work during an internship at Microsoft Research, where the team previously developed MetaOpt, a heuristic analyzer that requires engineers to rewrite their algorithms as formal optimization models. MetaEase grew out of the desire to remove that barrier.

Maximizing the gap

MetaEase is driven by two key innovations. First, it uses a technique called symbolic execution to map out the different decision points in the heuristic's code. These are places where the algorithm might behave differently depending on the input.

This technique produces a set of representative starting points, each corresponding to a distinct behavior the heuristic could exhibit.

Second, from these starting points, MetaEase utilizes a guided search to systematically move toward inputs that make the heuristic perform as poorly as possible, compared to the optimal algorithm.

In machine learning, for instance, an input could be a set of user queries to an AI chatbot at a given time.

“In this way, we have exploited every possible heuristic behavior and used special techniques to move in the direction where we think the performance gap is going to increase,” Karimi explains.

In the end, MetaEase identifies the input that maximizes the performance gap between the heuristic and an optimal benchmark.

With this information, a heuristic developer could inspect the input to understand what went wrong and incorporate safeguards that will prevent the problem from happening during deployment.

In simulated experiments, MetaEase often identified inputs with larger performance gaps than traditional methods — pinpointing more catastrophic worse-case scenarios. And it did so much more efficiently. 

It was also able to analyze a recent networking heuristic that no state-of-the-art method could handle.

In the future, the researchers want to enhance MetaEase so it can process additional types of types of data, like categorical inputs. They also want to improve the scalability of their method and adapt MetaEase to evaluate more complex heuristics.

“Reasoning about the worst-case performance of deployed heuristics is a hard and longstanding problem. MetaEase makes tangible progress by analyzing heuristics directly from source code, eliminating the need for formal models that have historically limited who can use such analysis tools. I was pleasantly surprised that it handles non-convex and randomized heuristics by combining symbolic execution with gradient-based search in a practical and effective way,” says Ratul Mahajan of the University of Washington Paul G. Allen School of Computer Science and Engineering, who was not involved with this research.

This research was funded, in part, by a Microsoft Research internship and the U.S. National Science Foundation (NSF).

Astronomers pin down the origins of a planetary odd couple

New measurements of a hot Jupiter and its mini-Neptune companion suggest both planets formed surprisingly far away from their host star.

Across the Milky Way galaxy, a planetary odd couple is circling a star some 190 light years from Earth. A normally “lonely” hot Jupiter is sharing space with a mini-Neptune, in a rare and unlikely pairing that’s had astronomers puzzled since the system’s discovery in 2020.

Now MIT scientists have caught a glimpse into the atmosphere of the mini-Neptune, which is circling inside the orbit of its Jupiter-sized companion, and discovered clues to explain the origins of this unusual planetary system.

In a study appearing today in Astrophysical Journal Letters, the scientists report on new measurements of the mini-Neptune’s atmosphere, made using NASA’s James Webb Space Telescope (JWST). It is the first time astronomers have measured the composition of a mini-Neptune that resides inside the orbit of a hot Jupiter.

Their measurements reveal that the smaller planet has a “heavy” atmosphere that is rich with water vapor, carbon dioxide, sulfur dioxide, and hints of methane. Such a heavy atmosphere would not have been acquired by the planet if it had formed in its current location, very close to its star.

Instead, the scientists say their findings point to an alternate origin story: Both the mini-Neptune and the hot Jupiter may have formed much farther away, in the colder region of the protoplanetary disk. There, the planets could slowly build up atmospheres of ice and other volatiles. Over time, the planets were likely drawn in toward the star in a gradual process that kept them close, with their atmospheres intact.

The team’s results are the first to show that mini-Neptunes can form beyond a star’s “frost line.” This boundary refers to the minimum distance from a star where the temperature is low enough that water instantly condenses into ice.

“This is the first time we’ve observed the atmosphere of a planet that is inside the orbit of a hot Jupiter,” says Saugata Barat, a postdoc in MIT’s Kavli Institute for Astrophysics and Space Research and the lead author of the study. “This measurement tells us this mini-Neptune indeed formed beyond the frost line, giving confirmation that this formation channel does exist.”

The team consists of astronomers around the world, including Andrew Vanderburg, a visiting assistant professor at MIT, and co-authors from multiple other institutions including the Harvard and Smithsonian Center for Astrophysics, the University of South Queensland, the University of Texas at Austin, and Lund University.

A “one-of-a-kind” system

As their name implies, mini-Neptunes are planets that are less massive than Neptune. They are considered to be gas dwarfs, which are made mostly of gas, with an inner, rocky core. Mini-Neptunes are the most commonly found planet in the Milky Way, though, interestingly, no such world exists in our own solar system. Astronomers have observed many planets circling a wide variety of stars in a range of planetary systems. Mini-Neptunes, then, are generally considered to be garden-variety planets.

But in 2020, Chelsea X. Huang, then a Torres Postdoctoral fellow at MIT (now on the faculty at University of South Queensland), discovered a mini-Neptune in a rare and puzzling circumstance: The planet appeared to be circling its star with an unlikely companion — a hot Jupiter.

The astronomers made their discovery using NASA’s Transiting Exoplanet Survey Satellite (TESS). They analyzed TESS’ measurements of TOI-1130, a star located 190 light years from Earth, and detected signs of a mini-Neptune and a hot Jupiter, orbiting the star every four and eight days respectively.

“This was a one-of-a-kind system,” says Huang. “Hot Jupiters are ‘lonely,’ meaning they don’t have companion planets inside their orbits. They are so massive, and their gravity is so strong, that whatever is inside their orbit just gets scattered away. But somehow, with this hot Jupiter, an inner companion has survived. And that raises questions about how such a system could form.”

A spot-on snapshot

The 2020 discovery of TOI-1130 and its odd planetary pair inspired Huang, Vanderburg, and their colleagues to take a closer look at the planets, and specifically, their atmospheres, with JWST. In its new study, the team reports its analysis of TOI-1130b — the inner-orbiting mini-Neptune.

Catching the planet at just the right time was their first challenge. Most planets circle their star with a regular, predictable period, like the tick of a clock. But the mini-Neptune and the hot Jupiter were found to be in “mean motion resonance,” meaning that each can affect the other’s motion, pulling and tugging, and slightly varying the time each takes to orbit their star. This made it tricky to predict when JWST could get a clear view.

The team, led by Judith Korth of Lund University, assembled as many past observations of the system as they could, and developed a model to predict when each planet would pass by the star at an angle that JWST could observe.

“It was a challenging prediction, and we had to be spot-on,” Barat says.

In the end, the team was able to catch a direct and detailed snapshot of both planets.

“The beauty of JWST is that it does not observe just in one color, but at different colors, or wavelengths,” Barat explains. “And the specific wavelengths that a planet absorbs can tell you a lot about the composition of its atmosphere.”

From JWST’s measurements, the team found that the planet absorbed wavelengths specifically for water, carbon dioxide, sulfur dioxide, and to a lesser degree, methane. These molecules are heavier than hydrogen and helium, which constitute lighter atmospheres. Astronomers had assumed that, if mini-Neptunes formed very close to their star, they should have light atmospheres.

But the team’s new results counter that assumption and offer a new way that mini-Neptunes could form. Since heavier molecules were found in the atmosphere of TOI-1130b, which resides very close to its star, the scientists say the only possible explanation for its composition is that the planet formed much farther out than its current location.

The planet likely accumulated its heavy atmosphere of water and other volatiles such as carbon dioxide and sulfur dioxide in the icy region beyond the star’s frost line. In this much colder environment, water condenses onto bits of dust to form icy pebbles, which an infant planet can draw into its atmosphere. The water evaporates as it slowly migrates in closer to its star.

Barat says the team’s detection of heavy molecules in the atmosphere of TOI-1130b confirms that the planet — and likely its hot Jupiter companion — formed in the outskirts of the system. Through gradual migration, the two planets would be able to stay close together and keep their atmospheres intact.

“This system represents one of the rarest architectures that astronomers have ever found,” Barat says. “The observations of TOI-1130b provide the first hint that such mini-Neptunes that form beyond the water/ice line are indeed present in nature.”

This work was supported, in part, by NASA.

The tech revolution that wasn’t

Associate Professor Dwai Banerjee’s new book examines the visionaries who wanted to turn India into a world power at making computers.

In 1960, engineers at India’s Tata Institute of Fundamental Research (TIFR) built what they called an “Automatic Calculator,” the country’s first working computer. It had the same type of ferrite-core memory as IBM’s world-leading machines, and at a glance, appeared to herald a new age of tech advances in India.

Constructed with a fraction of the resources Western computer engineers had, the TIFRAC, as they called it, was a remarkable feat.

“The people working on it had never really seen an actual functioning computer,” says Dwai Banerjee, an associate professor of science, technology, and society, and the author of a new book about computing in India. “You had this ambitious group of engineers building a state-of-the-art machine with very, very, limited resources. The fact they could build this is staggering.”

However, the TIFRAC was never even replicated, let alone produced at scale. The visionaries behind it wanted to turn India into an independent computing nation: a place that would produce its own equipment and become an industry power. Instead, the TIFRAC became a technological cul-de-sac, and India’s tech industry took on a very different shape. Instead of exporting equipment, it exports talent, sending skilled engineers and executives around the globe.

Now Banerjee explores those issues in the book, “Computing in the Age of Decolonization: India’s Lost Technological Revolution,” published by Princeton University Press. In it, he examines the country’s pursuit of technological self-sufficiency, and the global forces that prevailed against this vision. As a result, the country is “the world’s leading provider of inexpensive outsourcing and offshoring services, yet enjoys minimal benefits from more profitable advances in research, manufacturing, and development,” Banerjee writes.

“This book is about understanding how the current landscape of technological power came to be and the unequal way in which power is distributed across the world when it comes to anything to do with computing,” Banerjee says. “Basically, the historical conditions of the mid-20th century period are essential to understanding why the world of computing looks the way it does today.”

Computing and the geopolitics of knowledge

When India became a sovereign nation in 1947, many of its leaders believed “rapid technology-driven industrialization was the only way out of centuries of colonial underdevelopment,” as Banerjee writes. Some leapt into action, such as the remarkable nuclear physicist Homi J. Bhabha, who helped establish the TIFR.

Initially, Indian leaders hoped to gain cooperation for the U.S. and international organizations in making technological advances, but quickly ran into Cold War politics. Computing was heavily bound up with defense matters; India was not always fully aligned with U.S. political interests, so the flow of knowledge from the U.S. to India was distinctly limited.

“This is very much an external constraint story,” Banerjee says. “You need blueprints and not just working papers, and that’s what was guarded by the U.S. for a very long time.”

Still, the TIFR research team toiled away as its computing projects until the TIFRAC was up and running — making national headlines.

“The achievement it represents is mind-boggling,” Banerjee emphasizes. “A computer in the U.S. would have cost more to run than this entire institute in India.”

As Banerjee details in the book, the TIFRAC machine was built to grow. Its engineers matched the speed of IBM machines and planned to import larger ferrite-core memory stacks as their workload expanded. But when IBM released the FORTRAN programming language in 1957, it required four times the memory the TIFRAC machine was equipped with. India’s 1958 foreign exchange crisis then shaped the machine’s fate: The World Bank convened a U.S.-led creditor consortium that conditioned rescue loans on the opening of Indian markets to Western capital. Importing larger memory stacks became unaffordable, rendering the TIFRAC obsolete almost as soon as it was completed.

“It’s a geopolitics-of-knowledge question, not that they made a mistake,” Banerjee says of the Indian engineers. “They didn’t know IBM was about to reshape software.”

Exit IBM, enter services

Though IBM’s jump forward after the release of Fortran left the TIFRAC project stalled out, Indian advocates for computer manufacturing did not give up their dream. For one thing, they looked around for partnerships and other ways of moving their domestic tech industry forward. And then in 1978, India, uniquely, banned IBM from the country, on account of its business practices.

That might have set the stage for India’s computer manufacturing industry to flourish. But at the same moment, countervailing forces took hold, including a widespread turn toward the private sector as an increasing source of activity, rather than public-private enterprises.

“For a moment you have this imagination come to a sort of fruition,” Banerjee observes. “But by the late 1970s and 1980s, there is a new group of people arguing for quick profits through software services, saying that this route feels less painful than setting up manufacturing, R&D, and firms for a decade or more.”

This turn toward private-sector services rather than government-involved manufacturing ultimately became a decisive factor in shaping India’s tech-sector trajectory. Rather than seeking to make machines domestically, the country became part of the global tech-services sector, while many of its engineers migrated to Silicon Valley and other tech hotspots. Global tech firms used their reach to advance the idea that many countries would develop independent industries. This is not the outcome India’s leaders and technologists once envisioned.

“It still surprises me because of the one thing India did that no other country in the world managed to do, and that’s kick out IBM,” Banerjee says. “The fact that this vision fades is part of changing government ambition.”

Beyond the mavericks

In writing the book, Banerjee has multiple goals. One is simply shedding more light on the rich details of India’s initial computing efforts. Another is contesting the idea that India somehow naturally found a role providing services and exporting talent; that is not what many people once hoped.

Still another motif in Banerjee’s work is that the history of computing too often centers on innovators who are cast as mavericks, shrugging off conventions to upend business and society — whereas the large-scale forces of global capital and geopolitics matter greatly in technological development.

“This book suggests we often overplay those stories of individual genius, because you can be a genius with all the right ideas, but if you don’t have all the institutions supporting you, it means nothing,” Banerjee says.

Other scholars have praised “Computing in the Age of Decolonization.” Matthew L. Jones, a professor of history at Princeton University, has stated that Banerjee’s book is a “scrupulous accounting of ultimately failed Indian efforts to secure technological sovereignty in the wake of independence,” which “joins the best recent accounts of computing worldwide and transforms how we think through diverse national trajectories through the Cold War and beyond.”

For his part, Banerjee hopes a wide variety of readers will be interested in the book — and recognize that the specific case of India and computing can tell us a lot about the challenges of new types of economic growth in many places.

“India stands in for a lot of countries in the mid-20th century that had recently gained formal political independence and were thinking of ways to catch up with the rest of the advanced industrialized world,” Banerjee says. “But the power structures tied to technological and scientific advancement did not disappear. They were replaced by newer structures, including foreign policy with very specific ideas about what different countries should be doing with regard to technology. That’s where the story starts.”

Biologist Joey Davis explores how cells build complex structures

His studies have shed light on the assembly instructions that govern ribosomes, the critical protein-building machines of the cell.

Ribosomes, the cellular machines that assemble proteins, are made from dozens of proteins and RNA molecules. Putting all of those pieces together is a complex puzzle — one that MIT Associate Professor Joey Davis PhD ’10 revels in trying to solve.

Understanding how these structures form and later break down could help researchers learn more about how disruptions of these fundamental processes can lead to disease. But, as Davis points out, it’s also an interesting biological question.

“Our long-term goal is to really understand how the natural world assembles these huge complexes rapidly and efficiently. It’s a fundamentally interesting question to think about how these things get put together,” he says.

His work has helped reveal that unlike building a house, which happens in a prescribed sequence of steps — pouring the foundation, building the frame, putting on the roof, then doing electrical and plumbing work — ribosomes can be assembled in a more flexible way. Cells can even skip an assembly step and then come back to it later.

“In these natural systems, it seems like the assembly pathways are much more dynamic and flexible,” he says. “It appears that evolution has selected pathways that aren’t strictly ordered in the way we would think about an assembly line, where you always put in one component, then the next, and then the next. We’re excited to understand the selective advantages of such approaches.”

A love of discovery

Davis’ interest in how things are put together developed early in life, inspired by his father, a carpenter who framed houses. During the mid-1980s, the family moved from Colorado to Southern California, where his father worked in construction during a housing boom there.

“I was always interested in building things, which I think probably came from being around my dad and other builders,” Davis says.

As an undergraduate at the University of California at Berkeley, where he majored in computer science and biological engineering, Davis’ interests turned toward smaller scales, in the realm of cells and molecules. During his junior year, he started working in the lab of chemistry professor Michael Marletta, who studies molecular-level biological interactions.

In the lab, Davis investigated how enzymes that contain heme are able to preferentially bind to either oxygen or nitric oxide, two gases that are very similar in structure. That work kindled a love of studying the natural world and pursuing discoveries in fundamental science.

“Being in the Marletta lab and seeing students and postdocs that were really passionate about these problems had a big impact on me,” Davis says. “The goal was to understand the fundamentals of how molecular discrimination works, and the idea of discovery for the sake of discovery was thrilling.”

After graduating from Berkeley, Davis spent another year working in Marletta’s lab, and then a year working odd jobs, before heading to MIT to pursue a PhD in biology. There, he worked with Professor Bob Sauer, now emeritus, who studied the relationship between protein structure and function, with a particular focus on the molecular machines that degrade or remodel proteins.

Davis’ thesis research centered on enzymes called AAA proteases, which remove damaged proteins from cellular membranes and send them to cell organelles that break them down. In addition to studying the structure and function of the proteases, Davis worked on ways to engineer them to tag specific proteins for destruction.

That work led him into synthetic biology, which he used to develop genetic parts that drive production of proteins of interest. Some of those parts ended up being used by the biotech startup Ginkgo Bioworks, where Davis took a job as a senior scientist after graduating.

Working at Ginkgo Bioworks allowed Davis to stay in Boston while his partner finished her PhD. The couple then moved back to California, where Davis worked as a postdoc at Scripps Research, which was home to one of the first direct electron detection cameras for cryo-electron microscopy (cryo-EM). These detectors allow researchers to generate structures with near atomic resolution. At Scripps, Davis began using them to study ribosomes as they were being assembled.

Peering into the ribosome

After joining the MIT faculty in 2017, Davis continued his work on ribosomes and assembled a lab group that includes students from a variety of backgrounds who work together to develop new ways to explore biological phenomena.

“I have a mix of method developers and biologists in the group, and the work from each of them informs each other,” Davis says. “My lab goes back and forth between building sets of tools to answer biological questions, and then as we’re answering those questions, it motivates the next generation of tool development.”

During ribosome assembly, RNA molecules fold themselves into the correct shapes, creating docking sites for proteins to attach. Then, more RNA molecules come in and fold themselves into the structure.

“It’s a beautifully coupled process by which the cell folds hundreds of RNA helices and binds on the order of 50 proteins, and it does it in two minutes from start to finish. E. coli does this 100,000 times per hour, and it’s amazing how rapid and efficient the process is,” Davis says.

Cryo-EM allows scientists to capture this process in minute detail. It can be used to take hundreds of thousands of two-dimensional images of ribosome samples frozen in a thin layer of ice, from different angles. Computer algorithms then piece together these images into a three-dimensional representation of the ribosome.

To gain insight into how ribosomes are assembled, researchers can stall the process at different points and then analyze the resulting structures. In 2021, Davis’s lab developed a new method called CryoDRGN, which uses neural networks to analyze cryo-EM data and generate the full ensemble of structures that were present in the sample.

This work has shown that when certain steps of ribosome assembly are blocked, many different structures result, suggesting that the assembly can occur in a variety of ways.

In future work, Davis aims to dramatically increase the throughput of cryo-EM to generate datasets of protein structures that could help improve the AI-based models that are now used to predict protein structures.

“There are still huge swaths of sequence space that these models are very poor at predicting, but if we could collect data on those sequences en masse, that could potentially serve as key training data for a next-generation protein structure prediction method that could fill out that space,” he says.

How chromatin movement helps control gene expression

By monitoring these chromosomal structures over many timescales, MIT researchers found that chromatin helps bring genes closer to their regulatory elements.

Gene expression is controlled, in part, by the interactions between genes and regulatory elements located along the genome. Those interactions depend on the ability of chromatin — a mix of DNA and proteins — to move around within a crowded space.

In a new study, MIT researchers have measured chromatin movement at timescales ranging from hundreds of microseconds to hours, allowing them to rigorously quantify those dynamics for the first time.

Their analysis revealed that chromatin can exist in two different categories: In one, chromatin moves in a constrained way that allows it to primarily contact only neighboring regions of the genome; in the other, chromatin moves more freely and contacts regions that are farther away, but only over longer timescales.

The findings offer insight into how gene expression is regulated, as well as how chromatin segments come together for other processes such as DNA repair, the researchers say.

“Because we were able to look at chromatin dynamics for the first time at these very fast timescales, and also for the first time across the full dynamic range, we were able to observe chromatin motion over a range that just wasn’t possible before,” says Anders Sejr Hansen, an associate professor of biological engineering at MIT and the senior author of the new study, which appears today in Nature Structural and Molecular Biology.

The paper’s lead authors are MIT postdoc Matteo Mazzocca, Domenic Narducci PhD ’25, and Simon Grosse-Holz PhD ’23. Jessica Matthias, chief commercial officer of Abberior Instruments, and Tatiana Karpova, manager of the National Cancer Institute Optical Microscopy Core, are also authors of the paper.

Constrained movement

In textbooks, chromatin is often depicted as a static structure within the cell nucleus, but in reality, it is constantly moving. Those movements are necessary for genes to interact with DNA regulatory sequences such as enhancers, which can be as far as 1 million base pairs away. They also ensure that when DNA breaks occur, the two ends of DNA can encounter each other to be repaired.

“Chromatin dynamics are foundational to all processes in the nucleus, and especially processes that involve two things finding each other. That’s important in DNA repair, gene regulation, recombination, or moving a particular gene to the right compartment of the nucleus,” Hansen says.

The movement of any particular location on the genome, or locus, is constrained by the fact that DNA is a polymer. After moving in any direction, a locus will be pulled back by the DNA on either side of it.

“Chromosomes are polymers. They’re held together by many nucleotides of DNA. Being part of DNA is a little bit like running while holding hands with other people. If a hundred people are holding hands and you, in the middle of the chain, try to run in one direction, you’ll get pulled back,” Hansen says.

This type of behavior is known as subdiffusive movement. Previous studies have yielded conflicting reports on how subdiffusive chromatin is, mainly because the studies were not able to track the movement over a long enough period of time to obtain statistically robust measurements. Because the movements are so small, on the order of nanometers, data needs to be obtained over long dynamic ranges — from milliseconds to hours.

In those earlier studies, researchers used imaging techniques that can track the position of a single molecule over time by comparing images frame by frame. These are useful but can only be used over a small dynamic range because of the limitations of conventional microscopy.

To generate more statistically robust data, the MIT team used MINFLUX — a super-resolution light microscopy technique that can track the movement of tiny objects such as proteins over longer periods of time. This technique was recently developed by Stefan Hell of the Max Planck Institute, a Nobel laureate for his work in super resolution microscopy. In this study, the MIT team became the first to apply this technique to chromatin in living cells.

“MINFLUX allowed us to get around the limitations of conventional microscopy, letting us measure chromatin movement faster and for a longer period of time than ever before,” Narducci says. “To our knowledge, it’s the first time this technique has been used this way.”

Using MINFLUX, the researchers were able to study cells over timescales that covered four orders of magnitude — from 200 microseconds to 10 seconds. And by combining MINFLUX with two traditional imaging techniques, they could track chromatin movement over seven orders of magnitude across time, from hundreds of microseconds to several hours.

“Region of influence”

These studies, performed across several different mouse and human cell types, allowed the researchers to identify two distinct classes of chromatin dynamics. In both classes, over short and intermediate timescales (up to 200 seconds), any given locus tends to move only within about 200 nanometers. This suggests that the subdiffusive pull is stronger than had been previously thought.

“One of the main takeaways is that you have this region of influence where a genomic locus has access to other genomic loci, and this is roughly a couple hundred nanometers large,” Grosse-Holz says. “If loci are much closer together than a couple hundred nanometers, they’re effectively in contact all the time. You get a cutoff at a couple hundred nanometers where everything within that region around a given locus can see that locus, and everything outside cannot.”

This constant contact is likely beneficial for DNA repair, as the broken strands remain in close proximity to each other. The findings also suggest that for genes and regulatory elements that are within about 100,000 base pairs, they don’t need any extra help to find each other — they will do so routinely through their normal movement.

“If they are closer than 100,000 bases, and most regulatory elements are, then those elements are going to find their target gene within a few milliseconds or a few minutes,” Mazzocca says. “These are timescales that are completely consistent with transcription.”

In the other class of chromatin dynamics that the researchers identified, chromatin is able to move over a wider range, but only at longer timescales (a few minutes to hours). This class of chromatin appeared in some types of cells but not others, for reasons that are not yet understood.

“It would be reasonable to assume that the behavior would be more or less the same in all cell types, but that’s not at all what we found,” Hansen says. “It’s very different in different cell types, with no obvious way of categorizing things.”

He adds that the strength of the subdiffusive pull that the researchers found in this study can’t be explained with existing models that have been developed to study chromatin dynamics — the Rouse model and the fractal globule model. This suggests that the models may need to incorporate factors that were previously left out, such as the interactions between chromatin and the crowded nucleoplasm it sits within.

“These findings are significant for two key reasons,” says Luca Giorgetti, a group leader at the Friedrich Miescher Institute for Biomedical Research in Switzerland, who was not involved in the study. “First, they rigorously confirm longstanding but anecdotal observations that chromatin motion is strongly subdiffusive. Second, they demonstrate that this behavior is consistent across multiple cell types and persists across all measured timescales.”

The research was funded, in part, by the National Institutes of Health, a National Science Foundation CAREER Award, a Pew-Stewart Scholar for Cancer Research Award, and the Bridge Project, a partnership between the Koch Institute for Integrative Cancer Research at MIT and the Dana-Farber/Harvard Cancer Center.

Found Industries aims to strengthen America’s industrial supply chains

Founded by Peter Godart ’15, SM ’19, PhD ’21, the company has developed technologies for extracting critical metals and making fuel out of aluminum.

Found Industries has gone through several distinct phases in the four years since it was originally formed as Found Energy. There was the scrappy startup stage, in which the company was primarily housed in the basement of founder Peter Godart ’15, SM ’19, PhD ’21. Then there was the demonstration phase, in which the company worked to productize its technology for transforming aluminum into high-density fuel for industrial operations.

Now, after confronting supply chain vulnerabilities related to critical metals in its aluminum fuel business, the company is launching a new division, Found Metals, to extract the critical metal gallium from mineral refineries — a move that builds on its original technology while addressing a major national security need.

Gallium is a critical material in the defense, semiconductor, and energy sectors. In 2024, China produced 99 percent of the world’s primary supply — market dominance the country takes advantage of through export controls.

Godart’s company developed an electrochemical gallium extraction technology for internal use after realizing how dependent it would be on China for the catalyst material at the center of its aluminum fuel reactors. Now, with support from the U.S. Department of Energy, Found is hoping to use that technology to create a new domestic supply chain for gallium and a host of other important metals.

Found Industries is still committed to its aluminum fuel operations, now under its Found Energy division. It is already running a 100-kilowatt-class demonstration plant and is preparing for industrial pilot deployments next year. But with its expansion, which was announced April 21, the company is also working to meet the moment for critical metals production.

“Gallium is the world’s most critical metal, as it’s 99 percent controlled by China,” Godart says. “When you produce 99 percent of something, you also produce 99 percent of the tools required to extract it. We couldn’t get our hands on some of those tools, so we were forced to come up with a new technology. Now we believe we can deploy this at scale to become one the first major Western suppliers of these metals.”

From fuel to metals

Godart focused on robotics as an undergraduate in MIT’s Department of Mechanical Engineering and Department of Electrical Engineering and Computer Science. Following graduation, he worked at NASA’s Jet Propulsion Laboratory, where he explored systems for tapping into high-density fuels like aluminum on other planets.

“I had this crazy idea that you could use aluminum, which is already a common construction material for aerospace, as a fuel on other planets,” Godart says. “You don’t need most of the aluminum on a spacecraft once you land on another planet. Aluminum is around 40 times more energy-dense than lithium-ion batteries, and if you have an oxidizer, like water on an icy moon for example, then you can react that aluminum with water and extract energy as heat and hydrogen.”

Luckily for people who might spill water on aluminum while cooking, the metal is normally very stable when exposed to air. In order to tap into aluminum’s stored energy, it needs to undergo a chemical reaction. Godart began exploring catalyst materials to create that reaction at NASA. He continued that work with professor of mechanical engineering Douglas Hart when he returned to MIT in 2017, this time for applications a little closer to home.

“If we want to think about moving humanity to other planets, we have some problems to solve here first,” Godart says. “That was the impetus for me to go back to MIT to study using aluminum as a fuel for energy distribution on Earth.”

Around 70 million tons of aluminum are already transported around the globe every year. Godart says that gives aluminum an easier path to scale. During his PhD, he created a process for coating aluminum with a gallium-containing alloy to help tap into aluminum’s embodied energy.

“We found a catalyst that, when mixed with aluminum scraps, enabled aluminum to react with water very rapidly and at orders of magnitude higher power density than what had been possible before,” Godart says. “That meant you could use aluminum as a fuel and get megawatt-scale power from compact reactor systems.”

By the time he finished his PhD in 2021, Godart and his collaborators had developed a system that mixes aluminum fuel with those catalysts to continuously produce electricity at the kilowatt scale through a hydrogen fuel cell.

Godart launched Found Energy in 2022, licensing part of his research from MIT’s Technology License Office and receiving support from MIT’s Venture Mentoring Service. The company received an Activate fellowship, and after quickly outgrowing Godart’s basement, moved into its current 20,000 square foot facility in Charlestown, Massachusetts.

Today, Found Energy is working with industrial companies that have abundant aluminum scrap.

“When you invent a fuel, you then have to invent the engine,” Godart says. “Our engine is called a catalyzed aluminum water reactor. You feed in aluminum that’s been treated with the catalyst and water, and you get a steam-hydrogen gas mixture. We call that our power stream. We use it to cogenerate industrial heat and electricity. The reaction byproduct is a hydrated aluminum oxide that can be sold into various industries or recycled back into aluminum, which is the long-term vision.”

As Godart worked to build more of the systems, he became concerned about Found’s reliance on Chinese supply chains for its catalyst material. So, in 2024, he developed a new way to extract gallium from Bayer liquor, an industrial process stream used to produce aluminum. Traditional methods for extracting gallium rely on foreign-controlled organic chemicals or resins to bind and concentrate the gallium.

Found uses a continuous electrochemical process to recover the gallium directly from Bayer liquor and other industrial feedstocks, even at low concentrations.

“We thought of it as a way to future-proof what we were doing,” Godart says. “Necessity was the mother of invention.”

Then, toward the end of 2024, China began restricting the export of critical metals including gallium.

“We realized we had already developed a technique for producing these restricted metals that could be very quickly adapted,” Godart recalls.

Scaling for national security

On April 14, the Department of Energy’s Office of Critical Minerals and Energy Innovation selected Found as part of its $5.4 million program to recover gallium from domestic feedstocks. The company plans to start extracting gallium, along with other critical metals like indium and germanium, by the end of 2027.

Meanwhile, Found is already running a 100-kilowatt-class aluminum fuel demonstration system in Charlestown and is working through a orders of several megawatts from large public companies.

“For our fuel technology, the vision is to go as big as possible,” Godart says. “We envision major power plants. Aluminum refineries today, for example, consume hundreds of megawatts of continuous thermal power. That’s what we aim to deliver.”

Godart says he spends most of his time now on gallium extraction, but both branches of the business could make supply chains more secure in the future.

“The big focus now is critical metals, because the government needs this,” Godart says. “We’re also making these metals for ourselves, so we’re vertically integrating our own supply chain, which is table stakes now for companies that deal in physical goods. You need to be able to control your inputs. By focusing on metals, it improves the likelihood of success for our aluminum fuel business.”

Beacon Biosignals is mapping the brain during sleep

Founded by Jake Donoghue PhD ’19 and former MIT researcher Jarrett Revels, the company is creating an AI-driven platform to help diagnose and treat disease.

The human brain remains one of the most fascinating and perplexing mysteries in medicine. Scientists still struggle to match neurological activity with brain function and detect problems early, slowing efforts to treat neurological disorders and other diseases.

Beacon Biosignals is working to make sense of the brain by monitoring its activity while people sleep. The company, which was founded by Jake Donoghue PhD ’19 and former MIT researcher Jarrett Revels, developed a lightweight headband that uses electroencephalogram (EEG) technology to measure brain activity while people enjoy their normal sleep routines at home. Those data are processed by machine-learning algorithms to monitor the effects of novel treatments, find new signs of disease progression, and create patient cohorts for clinical trials.

“There’s a step-change in what becomes possible when you remove the sleep lab and bring clinical-grade EEG into the home,” says Donoghue, who serves as Beacon’s CEO. “It turns sleep from a constrained, facility-based test into a scalable source of high-quality data for diagnostics, drug development, and longitudinal brain health.”

Beacon partners with pharmaceutical companies to accelerate its path to patients. The company’s FDA 510(k)-cleared medical device has already been used in over 40 clinical trials across the globe as part of studies aimed at treating conditions including major depressive disorder, schizophrenia, narcolepsy, idiopathic hypersomnia, Alzheimer’s disease, and Parkinson’s disease.

With each deployment, Beacon learns more about how the brain works — insights it is using to create a “foundation model” of the brain.

“It’s our belief that the dataset that’s going to transform brain health doesn’t exist yet — but we are rapidly creating it,” Donoghue says. “Our platform can characterize the heterogeneity of disease progression, generating dynamic insights that are impossible to fully capture through static modalities like sequencing or imaging. The brain is an electric organ and changes through synaptic plasticity, so tracking brain function across many diseases at scale will allow us to discover novel subgroups of diseases and map them over time.”

Illuminating the brain

Donoghue trained in the Harvard-MIT Program in Health Sciences and Technology, conducting clinical training for an MD while completing his PhD in neuroscience at MIT under the guidance of Earl Miller, MIT's Picower Professor in Brain and Cognitive Sciences and The Picower Institute for Learning and Memory. While in the program, Donoghue trained at Massachusetts General Hospital and Boston Children’s Hospital, where he helped care for patients, including in oncology, during the rise of genomic sequencing to guide precision cancer therapies. He later worked in neurology and psychiatry, where care often relied on more iterative approaches — highlighting an opportunity to bring similarly data-driven precision to brain health.

“What struck me most was the inability to measure brain function in the ways that cardiologists can longitudinally monitor cardiac function in patients from home,” Donoghue says. “At MIT, I built this conviction that processing a lot of brain data and working to correlate that with brain function would be transformative to how these neurological diseases are identified and treated.”

Toward the end of his training, Donoghue began developing his ideas further, engaging with mentors including HST and Harvard Medical School professors Sydney Cash and Brandon Westover. He had met Revels, who was working as a research software engineer in MIT’s Julia Lab, during his PhD, and convinced him to co-found Beacon with him in 2019.

“We decided building a business to understand the organ of interest — the brain — would be a great start to understanding heterogeneous neuropsychiatric diseases and building better treatments,” Donoghue recalls.

Beacon began as a computation and analytics company building wearable devices to expand clinical impact and reach. From its early days, Beacon has been partnering with large pharmaceutical companies running clinical trials, offering a less invasive way to watch brain activity and learn how their drugs are impacting the brain as well as how patients sleep.

“It was clear sleep was the right window to understand the brain,” Donoghue says. “Neural activity during sleep can be an order of magnitude higher and more structured, almost like a language. It’s a great surface area for understanding brain function and how different drugs affect the brain.”

Donoghue says Beacon’s devices can collect lab-grade data on each patient for multiple sequential nights, resulting in higher quality assessment. The company uses machine learning to extract insights, such as the time patients spend in different sleep stages and the number of small awakenings that occur throughout the night. It can also detect subtle sleep architecture changes that might lead to cognitive decline.

“We’re starting to take features of sleep activity and link them to outcomes in a way that’s never been done with this level of precision,” Donoghue says.

To date, Beacon has taken part in clinical trials for sleep and psychiatric disorders as well as neurodegenerative diseases, where sleep changes can emerge years before the presentation of symptoms.

“We do a lot of work in areas like Alzheimer’s disease and Parkinson’s, which affected my grandfather,” Donoghue says. “We’re analyzing features of rapid-eye-movement and slow-wave sleep to detect early changes that precede clinical symptoms. It’s an opportunity to move these diseases from late recognition to much earlier, data-driven detection.”

Improving brain treatments for millions

Last year, Beacon acquired an at-home sleep apnea testing company that serves more than 100,000 patients each year across the U.S., accelerating access to high-quality, comprehensive testing in the home and expanding the reach of its platform. Then in November, the company raised $97 million to accelerate that expansion.

“The vision has always been to reach patients and help people at scale,” Donoghue says. “What’s powerful is that we’re building a longitudinal record of brain function over time,” Donoghue says. “A patient might come in for sleep apnea screening, but if they develop Parkinson’s years later, that earlier data becomes a window into the disease before symptoms emerged. That turns routine testing into a foundation for entirely new prognostic biomarkers — and a path to detecting and intervening in brain disease earlier, potentially before symptoms ever begin.”

Study: Gene circuits reshape DNA folding and affect how genes are expressed

When genes are transcribed, they suppress or activate their neighbors, coupling expression between the two genes.

When a gene is turned on in a cell, it creates a ripple effect along the DNA strand, changing the physical structure of the strand. A new study by MIT researchers shows that these ripples can stimulate or suppress neighboring genes.

These effects, which result from the winding or unwinding of neighboring DNA, are determined by the order of genes along a strand of DNA. Genes upstream of the active gene are usually turned up, while those downstream are inhibited.

The new findings offer guidance that could make it easier to control the output of synthetic gene circuits. By altering the relative ordering and arrangement of genes, or “gene syntax,” researchers could create circuits that synergize to maximize their output, or that alternate the output of two different genes.

“This is really exciting because we can coordinate gene expression in ways that just weren’t possible before,” says Katie Galloway, an assistant professor of chemical engineering at MIT. “Syntax will be really useful for dynamic circuits. Now we have the ability to select not only the biochemistry of circuits, but also the physical design to support dynamics.”

Galloway is the senior author of the study, which appears today in Science. MIT postdoc Christopher Johnstone PhD ’26 is the paper’s lead author. Other authors include MIT graduate student Kasey Love, members of the lab of Brandon DeKosky, an MIT associate professor of chemical engineering, and researchers from Peter Zandsta’s lab at the University of British Columbia and the labs of Christine Mummery and Richard Davis at Leiden University Medical Center in the Netherlands.

Gene syntax

When a gene is copied into messenger RNA, or “transcribed,” the double-stranded DNA helix must be unwound so that an enzyme called RNA polymerase can access the DNA and start copying it. That unwinding leads to physical changes in the structure of DNA strand.

Upstream of the gene, DNA becomes looser, while downstream, it becomes more tightly wound. These changes affect RNA polymerase’s ability to access the DNA: Upstream of an active gene, it’s easier for the enzyme to attach; downstream, it’s more difficult.

In a study published in 2022, Galloway and Johnstone performed computational modeling that explored how these biophysical changes might influence gene expression. They studied three different arrangements, or types of syntax: tandem, divergent, and convergent.

Most synthetic gene circuits are designed in a tandem arrangement, with one gene followed by another downstream. In a divergent arrangement, neighboring genes are transcribed in opposite directions (away from each other), and in convergent syntax, they are transcribed toward each other.

The modeling suggested that the divergent arrangement was most likely to produce circuits where both genes are expressed at a high level. Tandem arrangements were predicted to result in the downstream gene being suppressed by the upstream gene.In the new study, the researchers wanted to see if they could observe these predicted phenomena in human cells.

“Normally, we think about gene circuits and pieces of DNA as these lines that we draw, but they’re polymers that have physical characteristics,” Galloway says. “The thing that we were trying to solve in this paper was: When you put two genes on the same piece of DNA, how does their physical interaction become coupled?”

The researchers engineered circuits that each contained two genes, in either a tandem, divergent, or convergent configuration, into human cell lines and human induced pluripotent stem cells.

The results confirmed what their modeling had predicted: In divergent circuits, expression of both genes was amplified. In tandem circuits, turning on the upstream gene suppressed the expression of the downstream gene.

These effects produced as much as a 25-fold increase or decrease in gene expression, and they could be seen at distances of up to 2,000 base pairs between genes.

Using a high-resolution genome mapping technique called Region Capture Micro-C, the researchers were also able to analyze how the DNA structure changed when nearby genes were being transcribed.

As predicted, they found that the DNA regions downstream from an active gene formed tightly twisted structures known as plectonemes, similar to the tangles seen in a twisted telephone cord. These structures make it harder for RNA polymerase to bind to DNA.

To engineer these cells, the researchers used a new system they developed with the LUMC team called STRAIGHT-IN Dual, which allows them to efficiently insert two genes into the same DNA strand at both alleles. This system is being reported in a second paper published today, in Nature Biomedical Engineering.

Precise control

The new findings could help guide the design of synthetic gene circuits, which are usually designed to be controlled by biochemical interactions with activator or repressor molecules. Now, circuit designers can also perform biophysical manipulations to enhance or repress genes expression.

“Everyone thinks about the components they need, and the biochemical properties they need to build a circuit,” Galloway says. “Now, we have added the physical construction of those components, which is going to change how those biochemical units are interpreted.”

As a demonstration of one potential application, the researchers built synthetic circuits containing the genes for two segments of a novel antibody discovered by the Dekosky lab, used to treat yellow fever, and incorporated them into human cells. As they expected, the divergent syntax produced larger quantities of the yellow fever antibody.

Galloway’s lab has also used this approach to optimize the output of synthetic gene circuits they previously reported that could be used to deliver gene therapy or to reprogram adult cells into other cell types.

This strategy could also be used to build a variety of other types of dynamic synthetic circuits, such as toggle switches, oscillators, or pulse generators, for any application that requires precise control over gene expression.

“If you want coordinated expression, a divergent circuit is great. If you want something that’s either/or, you can imagine using a convergent or tandem circuit, so when one turns on, the other turns off, and you can alternate pulses,” Galloway says. “Now that we understand the syntax, I think this will pave the way for us to program dynamic behaviors.”

The research was funded, in part, by the National Institutes of Health, the National Institute for General Medical Sciences, a National Science Foundation CAREER Award, the Pershing Square Foundation, the Air Force Research Laboratory, and the Koch Institute Support (core) Grant from the National Cancer Institute.

The hidden structure behind a widely used class of materials

Relaxor ferroelectrics have been used in electronics and sensors for decades, but the source of their unique properties was a mystery until now.

Materials called relaxor ferroelectrics have been used for decades in technologies like ultrasounds, microphones, and sonar systems. Their unique properties come from their atomic structure, but that structure has stubbornly eluded direct measurement.

Now a team of researchers from MIT and elsewhere has directly characterized the three-dimensional atomic structure of a relaxor ferroelectric for the first time. The findings, reported today in Science, provide a framework for refining models used to design next-generation computing, energy, and sensing devices.

“Now that we have a better understanding of exactly what’s going on, we can better predict and engineer the properties we want materials to achieve,” says corresponding author James LeBeau, MIT’s Kyocera Professor of Materials Science and Engineering. “The research community is still developing methods to engineer these materials, but in order to predict the properties those materials will have, you have to know if your model is right.”

In their paper, the researchers describe how they used an emerging technique to reveal the distribution of electric charges in the material, with a surprising result.

“We realized the chemical disorder we observed in our experiments was not fully considered previously,” says co-first authors Michael Xu PhD ’25 and Menglin Zhu, who are both postdocs at MIT. “Working with our collaborators, we were able to merge the experimental observations with simulations to refine the models and better predict what we see in experiments.”

Joining Zhu, Xu, and LeBeau on the paper are Colin Gilgenbach and Bridget R. Denzer, MIT PhD students in materials science and engineering; Yubo Qi, an assistant professor at the University of Alabama at Birmingham; Jieun Kim, an assistant professor at the Korea Advanced Institute of Science and Technology; Jiahao Zhang, a former PhD student at the University of Pennsylvania; Lane W. Martin, a professor at Rice University; and Andrew M. Rappe, a professor at the University of Pennsylvania.

Probing disordered materials

Leading simulations of relaxor ferroelectrics suggest that when an electric field is applied, the interactions of positively and negatively charged atoms in different nanoregions of the material help give rise to exceptional energy storage and sensing capabilities. The details of those nanoregions have been impossible to directly measure to date.

For their Science paper, the researchers studied a relaxor ferroelectric material used in sensors, actuators, and defense systems that is a lead magnesium niobate-lead titanate alloy. They used an emerging measurement technique, called multi-slice electron ptychography (MEP), in which researchers move a nanoscale-sized probe of high-energy electrons over a material and measure the resulting electron diffraction patterns.

“We do this in a sequential way, and at each position, we acquire a diffraction pattern,” Zhu explains. “That creates regions of overlap, and that overlap has enough information to use an algorithm to iteratively reconstruct three-dimensional information about the object and the electron wave function.”

The technique revealed a hierarchy of chemical and polar structures that spanned from atomic to mesoscopic scales. The researchers also found that many regions of differing polarization in the material were much smaller than predicted by the leading simulations. The researchers then fed their new data back into those computer simulations and refined the models to better reflect their findings under different conditions.

“Previously, these models basically had random regions of polarization, but they didn’t tell you how those regions correlate with each other,” Xu says. “Now we can tell you that information, and we can see how individual chemical species modulate polarization depending on the charge state of atoms.”

Toward better materials

Zhu says the paper demonstrates the potential of electron ptychography to study complex materials and opens up new avenues of research into complex, disordered materials.

“This study is the first time in the electron microscope that we’ve been able to directly connect the three-dimensional polar structure of relaxor ferroelectrics with molecular dynamics calculations,” Xu says. “It further proves you can get three-dimensional information out of the sample using this technique.”

The researchers also believe the approach could one day help engineer materials with advanced electronic behaviors for a range of improved memory storage, sensing, and energy technologies.

“Materials science is incorporating more complexity into the material design process — whether that’s for metal alloys or semiconductors — as AI has improved and our computational tools have become more advanced,” LeBeau says. “But if our models aren’t accurate enough and we have no way to validate them, it’s garbage in garbage out. This technique helps us understand why the material behaves the way it does and validate our models.”

The work was supported, in part, by the U.S. Army Research Laboratory, the U.S. Office of Naval Research, the U.S. Department of War, and a National Science Graduate Fellowship. The researchers also used MIT.nano facilities.

Making the case for curiosity-driven science

President Sally Kornbluth spoke in front of a packed crowd about growing challenges to the U.S. research ecosystem as funding for America’s top research universities becomes increasingly strained.

“The thing that really struck me when I came to MIT and strikes me every single day is the stuff that’s going on here is amazing. The science, the engineering … every day I hear something that makes my jaw drop,” remarked President Sally Kornbluth during a live discussion with Lizzie O’Leary of Slate’s “What Next: TBD” podcast.

Kornbluth spoke about everything from the importance of curiosity-driven science and why basic science is critical to our nation’s future, to AI and education, and even bravely joined O’Leary in a rendition of the Williams College song, “The Mountains,” in honor of their shared alma mater.

“We are in this time of incredible uncertainty,” said Kornbluth of the current state of higher education and funding for scientific research. “What we are trying to do is keep the science robust.”

Bouncing back to her time at Duke and her love of college basketball, she noted it’s a combination of zone coverage and man-to-man defense when trying to address skepticism about higher education in Washington. She emphasized: “As one of the top institutions in the world it’s part of our responsibility to articulate the importance of science. Behind the scenes, I am — along with many other [university] presidents — I am in D.C. all the time now. I want to speak to Congressmen and women, Senators, people in the executive branch to explain the importance of what we are doing.”

Kornbluth emphasized that the pipeline of basic science that flows from U.S. universities is a critical asset for our country, cautioning that to keep straining this pipeline could have enormous negative ramifications for the U.S. down the line.

“If you think about research done in this country, it’s done in in universities, it’s done in national labs, and it’s done in industry,” said Kornbluth. Universities are where most of the science with a long pathway to impact, requiring patience, starts. She pointed to immunotherapy for cancer, which began 30-40 years ago in basic immunotherapy research, as an example. With that pipeline being drained, what does the future hold for new cancer therapies or new AI and quantum technologies?

Kornbluth also underscored that uncertainty and lost funding are having a “huge impact on the talent pipeline,” delving into the unique role universities play in training graduate students, who are the next generation of scientific researchers. “We hear, ‘Oh it would be okay if research was more in industry.’ I say, ‘Would you fly on a plane with a pilot who had never flown?’ How do they think people learn how to do research? We are training the next generation … and we are losing funding for them.” She added: “I think we are going to see reverberations for many decades if we don’t rectify that issue.”

When asked how she and her colleagues are working to keep research moving forward, Kornbluth explained that at MIT, “we have tried to find alternative ways to elevate the science. We have a series of presidential initiatives that cut across the whole campus in things like health and life sciences, quantum, humanities and social sciences. The notion is that we are trying to create new opportunities.”

Still, she acknowledged that losses from the endowment tax and diminished federal funding are painful. “There are only four schools right now that are subject to the 8 percent endowment tax, which is a tax on our earnings. For us, that means $240 million dollars a year plus other losses in grants. So, let’s say the whole thing is, we budgeted for a loss of $300 million a year on a $1.7 billion budget. … That has definitely had an impact on us. No question about it. 

“The other thing about it is again there’s all this uncertainty. Our investigators are writing a ton of grants. They don’t know if they’re going off into the void or they really have the sort of competitive opportunities they’ve always had in the past.”

Asked why universities did not see this moment coming, Kornbluth offered a few thoughts. “Look at MIT — 30,000 companies have come from MIT. When you look at something like that, why would you think any government that wants economic flourishing in their country would come after MIT?” she reflected. “It just never would have occurred to us.”

Turning toward the rapid advances in AI, and how the field is impacting education, Kornbluth noted that at MIT and other universities, “we have to focus on the human element, we have to educate our students, they need to know how to write and do mathematics … they have to view AI as a tool to augment their capabilities. That is how we are thinking about it.”

In the course of the conversation, Kornbluth also expressed her unwavering support for international students, noting that most want the opportunity to stay and contribute to research in the U.S. after graduation. “The talent brought to us through our international community is unbelievable. We can attract the very best in the world. You can bet when they talk about competitiveness with China, for example, in AI, quantum, etc., they are not sitting around in China saying, ‘Oh it’s great America is taking all our students.’ They’re thinking, ‘It’s great that America doesn’t want to take as many of our students anymore because we can train them.’ It’s a competitive issue that we really should lean into.”

Study: Immigrants help address the US eldercare shortage

Economists find that in metro areas with more immigration, nurses are spending more time with elderly patients.

Good caregivers are often in short supply, but after the Covid-19 pandemic hit the U.S. in early 2020, staff levels at nursing homes dropped by 10 percent. What was a simple personnel shortage has moved closer to being a nursing-care crisis.

“We have an aging population, care for them is labor-intensive, and there are shortages everywhere in that supply chain,” says MIT economist Jonathan Gruber.

As it happens, about one-fifth of health care support workers in the U.S. are immigrants. And as a newly published study of the nation’s metro areas shows, changes in immigration levels can affect how much nursing care the elderly receive.

“When immigration rises in a city, it significantly increases the health care workforce,” says Gruber, co-author of the study and a paper detailing its findings.

Overall, Gruber and his colleagues determined that when there is more immigration, registered nurses and other aides work more hours at nursing homes, without displacing already-employed caregivers, while patient outcomes improve. Essentially, a 10 percent increase in female immigrants in a given metro area leads to a 1.1 percent increase in hours that registered nurses spend with elderly patients, while hospitalizations for those patients drop, among other things.

“Even if immigration actually increases labor supply to the medical sector, it was an open question if that would improve outcomes, and it does,” adds Gruber, the Ford Professor of Economics and head of the MIT Department of Economics.

The paper, “Immigration, the Long-Term Care Workforce, and Elder Outcomes in the U.S.,” appears in the American Journal of Health Economics. The authors are Gruber; David C. Grabowski, a professor in the Department of Health Care Policy at Harvard Medical School; and Brian E. McGarry, an assistant professor in the Department of Medicine and the Department of Public Health Sciences at the University of Rochester.

More care, fewer hospitalizations

To conduct the study, the researchers tapped into multiple data sources, including immigration information from 2000 to 2018 appearing in the U.S. Census Bureau’s American Community Survey. Extensive nursing home data came from different types of reports that facilities are required to file in order to maintain Medicare and Medicaid eligibility, allowing the scholars to examine care staffing levels and patient outcomes.

All told, the study encompasses 16 million Medicare beneficiaries in over 13,000 nursing homes in metropolitan statistical areas of the U.S., and evaluates immigrations flows for two decades.

“One of the key groups that’s taking care of our nation’s elders is immigrants,” Gruber says. “So I thought it would be fascinating to understand how much does immigration actually matter for elder care.”

More specifically, the scholars find that for every 10 percent increase in immigration above the norm in metro areas, in addition to the 1.1 percent increase in registered nurse hours, there is a 0.7 percent increase in hours of care provided by certified nurse assistants. There is a 0.6 percent decline in hospitalizations for patients making short-term stays, of up to a month, in nursing homes.

Beyond that, the study yielded other markers showing that patient outcomes improve in these situations. The roughly 1 percent increase in hours of care was accompanied by a decline in the use of physical restraints needed for patients, who also needed less psychiatric medication prescriptions and had fewer urinary tract infections, among other things.   

The fact that those outcomes improved in more immigrant-staffed situations is among the new insights provided by the research.

“There’s a lot of evidence that providing more labor supply to the elderly sector improves patient outcomes,” Gruber says. “But it wasn’t clear whether more immigrants would work the same way, because of language issues or other factors.”

A new lens

The study comes as immigration policy has become a major issue in the U.S., something that Gruber says helped spur his curiosity about its health care implications — although he did not know what the study would reveal, one way or another. In this case, he notes, the impact of immigration on eldercare may be another factor to be considered in the larger debates about the subject.

“I think it provides a new lens on the debate over immigration,” Gruber says. “The debate over immigration has been solely about what will it do to native workers, what will it do to the crime rate, what will it do to tax collection. This adds a new element, which is: What will it do to our citizens’ care? By having more immigration, we provide more care.”

Gruber, Grabowski, and McGarry are continuing to study this issue. In a new working paper, released in February, they found that increases in immigration are consistent with a reduction in the mortality rate, in part by allowing more elderly people the opportunity to receive care at home.

Gruber recognizes that there will continue to be sharp policy disagreements over immigration. Still, as the just-published paper states, to this point, when it comes to nursing care, the “results paint a consistent picture of improved quality of care resulting from increased immigration.”

The MIT-IBM Computing Research Lab launches to shape the future of AI and quantum computing

Building on a long-standing MIT–IBM collaboration, the new lab will chart the convergence of AI, algorithms, and quantum computing.

The following is a joint announcement by the MIT Schwarzman College of Computing and IBM.

IBM and MIT today announced the launch of the MIT-IBM Computing Research Lab, advancing their long-standing collaboration to shape the next era of computing. The new lab expands its scope to include quantum computing, alongside foundational artificial intelligence research, with the goal of unlocking new computational approaches that go beyond the limits of today’s classical systems.

The MIT-IBM Computing Research Lab builds on a distinguished history of scientific excellence at the intersection of research and academia. Evolving from the MIT-IBM Watson AI Lab, which originated in 2017 on MIT’s campus, the new lab reflects a transformed technology landscape — one which AI has entered mainstream deployment, and quantum computing is rapidly advancing toward practical impact. Together, MIT and IBM aim to help lead research in AI and quantum and to redefine mathematical foundations across both domains.

“We expect the MIT-IBM Computing Research Lab to emerge as one of the world’s premier academic and industrial hubs accelerating the future of computing,” says Jay Gambetta, director of IBM Research and IBM Fellow, and IBM chair of the MIT-IBM Computing Research Lab. “Together, the brightest minds at MIT and IBM will rethink how models, algorithms, and systems are designed for an era that will be defined by the sum of what’s possible when AI and quantum computing come together.”

“For a decade, the collaboration between MIT and IBM has produced leading-edge research and innovation, and provided mentorship and supported the professional growth of researchers both at MIT and IBM,” says Anantha Chandrakasan, MIT’s provost, who, as then-dean of the School of Engineering, spearheaded the creation of the MIT-IBM Watson AI Lab and will continue as MIT chair of the lab. “The incredible technical achievements sets the bar high for our work together over the next 10 years. I look forward to another decade of impact.”

Addressing the next frontiers in computation

The MIT-IBM Computing Research Lab will serve as a focal point for joint research between MIT and IBM in AI, algorithms, and quantum computing, as well as the integration of these technologies into hybrid computing systems. The lab is designed to accelerate progress toward powerful new computational approaches that take advantage of rapid advances in AI and quantum-centric supercomputing, including those that combine maturing quantum hardware with classical systems and advanced AI methods.

This research initiative will include improving capabilities and integrating AI with traditional computing, alongside pursuing advances in small, efficient, modular language model architectures, novel AI computing paradigms, and enterprise-focused AI systems designed for deployment in real-world environments, where reliability, transparency, and trust are essential.

In parallel, the lab will rethink the mathematical and algorithmic foundations that underpin the next era of computing by accelerating the development of novel quantum algorithms for complex problems, with impacts in areas such as materials science, chemistry, and biology.

Additionally, the lab will investigate mathematical and algorithmic foundations of machine learning, optimization, Hamiltonian simulations, and partial differential equations, which are used to approximate the behaviors of dynamical systems that currently stump classical systems beyond limited scales and accuracy. Innovations from the lab could have wide implications for global industries, from more accurate weather and air turbulence prediction to better forecasts of financial market performance. Similarly, with improved optimization approaches, research from the lab could help lower risks in areas like finance, predict protein structures for more targeted medicine, and streamline global supply chains.

With its focus on AI, algorithms, and quantum, the MIT-IBM Computing Research Lab will complement and enhance the work of two of MIT’s strategic initiatives, the MIT Generative AI Impact Consortium and the MIT Quantum Initiative. MIT President Sally Kornbluth launched these strategic initiatives to broaden and deepen MIT’s impact in developing solutions to serious global challenges. The MIT-IBM Computing Research Lab will also leverage IBM’s longtime leadership and expertise in quantum computing. As part of its ambitious roadmap, IBM has laid out a clear path to delivering the world’s first fault-tolerant quantum computer by 2029, and is working across industries to drive value from quantum-centric supercomputing, tightly integrating quantum computers with high-performance computing and AI accelerators to solve the world’s toughest problems.

Deep integration with scientific domains

The MIT-IBM Computing Research Lab will also continue to serve as a foundation for training the next generation of computational scientists and innovators. It will do so by engaging faculty and students across MIT departments, enabling new computational approaches to accelerate discoveries in the physical and life sciences.

The lab will continue to be co-directed by Aude Oliva, senior research scientist at MIT’s Computer Science and Artificial Intelligence Laboratory, and David Cox, vice president of AI Foundations at IBM Research. MIT and IBM have appointed leads for each of the lab’s three focus areas — AI, algorithms, and quantum. Jacob Andreas, associate professor in the Department of Electrical Engineering and Computer Science (EECS), and Kenney Ng, principal research scientist at IBM Research and the MIT-IBM science program manager, will co-lead AI; Vinod Vaikuntanathan, the Ford Foundation Professor of Engineering in EECS, and Vasileios Kalantzis, IBM Research senior research scientist, will co-lead algorithms; and Aram Harrow, professor of physics, and Hanhee Paik, IBM director of Quantum Algorithm Centers, will co-lead quantum.

“The MIT-IBM Computing Research Lab reflects an important expansion of the collaboration between MIT and IBM and the increasing connections across AI, algorithms, and quantum. This deepened focus also underscores a strong alignment with the MIT Schwarzman College of Computing’s mission to advance the forefront of computing and its integration across disciplines,” says Dan Huttenlocher, dean of the MIT Schwarzman College of Computing and MIT co-chair of the lab. “I’m excited about what this next chapter will enable in these three areas, and their impact broadly.”

Building on nearly a decade of collaboration

The MIT-IBM Watson AI Lab helped pioneer a model for academic-industry research collaboration, aligning long-term scientific inquiry with real-world impact. Since its inception, the lab has funded over 210 research projects involving over 150 MIT faculty members and over 200 IBM researchers. Collectively, the projects have led to over 1,500 peer-reviewed articles. The lab also helped shape the career growth of a number of MIT students and junior researchers, funding more than 500 students and postdocs.

“The true measure of this lab is not just innovation, but transformation of a field. Hundreds of students have contributed to thousands of publications in top conferences and journals, demonstrating their capabilities to address meaningful problems,” says Oliva. “The MIT-IBM Computing Research Lab builds on an extraordinary legacy of impact to advance a trusted collaboration that will redefine the future of AI and quantum computing in a way never seen before.”

“By coupling academic rigor with industrial scale, the lab aims to define the computational foundations that will power the next generation of AI, quantum, and scientific breakthroughs,” says Cox. “By bringing together advances in AI, algorithms, and quantum computing under one integrated research effort, we’re creating the conditions to rethink the mathematical and computational foundations of science and engineering.”

The MIT-IBM Computing Research Lab will capitalize on this foundation, expanding both the scientific scope and the ecosystem of collaborators across the Cambridge-Boston region and beyond.

MIT engineers’ virtual violin produces realistic sounds

Based on the physics of how the instrument produces sound, the model could help violin makers in the design process.

There is no question that violin-making is an art form. It requires a musician’s ear, a craftsperson’s skill, and an historian’s appreciation of lessons learned over time. Making a violin also takes trust: Violin makers, or luthiers, often must wait until the instrument is finished before they can hear how all their hard work will sound.

But a new tool developed by MIT engineers could help luthiers play around with a violin’s design and tweak its sound even before a single part is carved.

In a study appearing today in the journal npj Acoustics, the MIT team reports on a new “computational violin” — a computer simulation that captures the detailed physics of the instrument and realistically produces the sound of a violin when its strings are plucked.

While there are software programs and plug-ins that enable users to play around with virtual violins, their sounds are typically the result of sampling and averaging over thousands of notes played by actual violins.

In contrast, the new computational violin takes a physics-based approach: It produces sound based on the way the instrument, including its vibrating strings, physically interacts with the surrounding air.

As a demonstration, the researchers applied the computational violin to play two short excerpts: one from “Bach’s Fugue in G Minor,” and another from “Daisy Bell” — a nod to the first song that was ever produced by a computer-synthesized voice.

The computational violin currently simulates the sound of plucked strings — a type of playing that musicians know as “pizzicato.” Violin bowing, the researchers say, is a much more complicated interaction to model. However, the computational violin represents the first physics-based foundation of a strung violin sound that could one day be paired with a model of bowing to produce realistic, bowed violin music.

For now, the team says the new virtual violin could be used in the initial stages of violin design. Luthiers can tweak certain parameters such as a violin’s wood type or the thickness of its body, and then listen to the sound that the instrument would make in response.

“These days, people try to improve designs little by little by building a violin, comparing the sound, then making a change to the next instrument,” says Yuming Liu, senior research scientist at MIT. “It’s very slow and expensive. Now they can make a change virtually and see what the sound would be.”

“We’re not saying that we can reproduce the artisan’s magic,” adds Nicholas Makris, professor of mechanical engineering at MIT. “We’re just trying to understand the physics of violin sound, and perhaps help luthiers in the design process.”

Makris and Liu’s MIT co-authors include Arun Krishnadas PhD ’23 and former postdoc Bryce Campbell, along with Roman Barnas of the North Bennet Street School.

Sound matrix

The quality of a violin’s sound is determined by its dimensions and design. The instrument is made from thoughtfully crafted parts and materials that all work to generate and amplify sound. In recent years, scientists have sought to understand what artisans have intuited for centuries, in terms of what specific parameters shape a violin’s sound.

In one early effort in 2006, scientists, as part of the Strad3D project, put a rare Stradivarius violin through a CT scanner. The violin was crafted in 1715 by the master violinmaker Antonio Stradivari, during what is considered the “Golden Age” of violin making. To better understand the violin’s anatomy and its relation to sound, the scientists scanned the instrument and produced 600 “slices,” or views, of the violin.

The CT scans are available online for people to view and use as data for their own experiments. For their study, Makris and his colleagues first imported the CT scans into a solid modeling software program to generate a detailed three-dimensional model of the violin. They then ran a finite element simulation, essentially dividing the violin into millions of tiny individual cubes, or “elements.”

For each cube, they noted its material type, such as if a cube from the violin’s back plate is made from maple or spruce, or if a string is made from steel or natural fibers. They then applied physics-based equations of stress and motion to predict how each material element would move in relation to every other element across the instrument.

They also carried out a similar process for the air surrounding the violin, dividing up a roughly cubic-meter volume of air and applying acoustic wave equations to predict how each tiny parcel of air would move and contribute to generating sound.

“The entire thing is a matrix of millions of individual elements,” explains Krishnadas. “And ultimately, you see this whole three-dimensional being, which is the violin and the air all connected and interacting with each other.”

A plucky model

The team then simulated how the new computational violin would sound when plucked. When a violinist plucks a string, they pull the string sideways and let it go, causing the string to vibrate. These vibrations travel across the instrument and inside it; the air’s vibrations are amplified as they travel out of the violin and into the surroundings, where a listener hears the vibrations as sound.

For their purposes, the engineers simulated a simple string pluck by directing one of the virtual violin’s strings to stretch out and then rebound. The simulation computed all the resulting motions and vibrations of the millions of elements in the violin, and the sound that the pluck would produce.

For notes that require pressing down on a violin’s fingerboard, they simulated the same plucking, and in addition, included a condition in which the string is held fixed in the section of the fingerboard where a violinist’s finger would press down.

The researchers carried out this computational process to virtually pluck out the notes in several measures of “Daisy Bell” and “Bach’s Fugue in G Minor.”

“If there’s anything that’s sounding mechanical to it, it’s because we’re using the exact same time function, or standard way of plucking, for each note,” says Makris, who is himself a lute player. “A musician will adapt the way they’re plucking, to put a little more feeling on certain notes than others. But there could be subtleties which we could incorporate and refine.”

As it is, the new computational model is the first to generate realistic sound based on the laws of physics and acoustics. The researchers say that violin makers could use the model to test how a violin might sound when certain dimensions or properties are changed. For instance, when the researchers varied the thickness of the virtual violin’s back plate or changed its wood type, they could hear clear differences in the resulting sounds.

“You can tweak the model, to hear the effect on the sound,” Makris says. “Since everything obeys the laws of physics, including a violin and the music it makes, this approach can add an appreciation to what makes violin sound. But ultimately, we get most of our inspiration from the artisans.”

This work was supported, in part, by an MIT Bose Research Fellowship.

Enabling privacy-preserving AI training on everyday devices

A new method could bring more accurate and efficient AI models to high-stakes applications like health care and finance, even in under-resourced settings.

A new method developed by MIT researchers can accelerate a privacy-preserving artificial intelligence training method by about 81 percent. This advance could enable a wider array of resource-constrained edge devices, like sensors and smartwatches, to deploy more accurate AI models while keeping user data secure.

The MIT researchers boosted the efficiency of a technique known as federated learning, which involves a network of connected devices that work together to train a shared AI model.

In federated learning, the model is broadcast from a central server to wireless devices. Each device trains the model using its local data and then transfers model updates back to the server. Data are kept secure because they remain on each device.

But not all devices in the network have enough capacity, computational capability, and connectivity to store, train, and transfer the model back and forth with the server in a timely manner. This causes delays that worsen training performance.

The MIT researchers developed a technique to overcome these memory constraints and communication bottlenecks. Their method is designed to handle a heterogenous network of wireless devices with varied limitations.

This new approach could make it more feasible for AI models to be used in high-stakes applications with strict security and privacy standards, like health care and finance.

“This work is about bringing AI to small devices where it is not currently possible to run these kinds of powerful models. We carry these devices around with us in our daily lives. We need AI to be able to run on these devices, not just on giant servers and GPUs, and this work is an important step toward enabling that,” says Irene Tenison, an electrical engineering and computer science (EECS) graduate student and lead author of a paper on this technique.

Her co-authors include Anna Murphy ’25, a machine-learning engineer at Lincoln Laboratory; Charles Beauville, a visiting student from Ecole Polytechnique Fédérale de Lausanne (EPFL) in Switzerland and a machine-learning engineer at Flower Labs; and senior author Lalana Kagal, a principal research scientist in the Computer Science and Artificial Intelligence Laboratory (CSAIL) at MIT. The research will be presented at the IEEE International Joint Conference on Neural Networks. 

Reducing lag time

Many federated learning approaches assume all devices in the network have enough memory to train the full AI model, and stable connectivity to transmit updates back to the server quickly.

But these assumptions fall short with a network of heterogenous devices, like smartwatches, wireless sensors, and mobile phones. These edge devices have limited memory and computational power, and often face intermittent network connectivity.

The central server usually waits to receive model updates from all devices, then averages them to complete the training round. This process repeats until training is complete.

“This lag time can slow down the training procedure or even cause it to fail,” Tenison says.

To overcome these limitations, the MIT researchers developed a new framework called FTTE (Federated Tiny Training Engine) that reduces the memory and communication overhead needed by each mobile device.

Their framework involves three main innovations.

First, rather than broadcasting the entire model to all devices, FTTE sends a smaller subset of model parameters instead, reducing the memory requirement for each device. Parameters are internal variables the model adjusts during training.

FTTE uses a special search procedure to identify parameters that will maximize the model’s accuracy while staying within a certain memory budget. That limit is set based on the most memory-constrained device.

Second, the server updates the model using an asynchronous approach. Rather than waiting for responses from all devices, the server accumulates incoming updates until it reaches a fixed capacity, then proceeds with the training round.

Third, the server weights updates from each device based on when it received them. In this way, older updates don’t contribute as much to the training process. These outdated data can hold the model back, slowing the training process and reducing accuracy.

“We use this semi-asynchronous approach because want to involve the least powerful devices in the training process so they can contribute their data to the model, but we don’t want the more powerful devices in the network to stay idle for a long time and waste resources,” Tenison says.

Achieving acceleration

The researchers tested their framework in simulations with hundreds of heterogeneous devices and a variety of models and datasets. On average, FTTE enabled the training procedure to reach completing 81 percent faster than standard federated learning approaches.

Their method reduced the on-device memory overhead by 80 percent and the communication payload by 69 percent, while attaining near the accuracy of other techniques.

“Because we want the model to train as fast as possible to save the battery life of these resource-constrained devices, we do have a tradeoff in accuracy. But a small drop in accuracy could be acceptable in some applications, especially since our method performs so much faster,” she says.

FTTE also demonstrated effective scalability and delivered higher performance gains for larger groups of devices.

In addition to these simulations, the researchers tested FTTE on a small network of real devices with varying computational capabilities.

“Not everyone has the latest Apple iPhone. In many developing countries, for instance, users might have less powerful mobile phones. With our technique, we can bring the benefits of federated learning to these settings,” she says.

In the future, the researchers want to study how their method could be used to increase the personalized performance of AI models on each device, rather than focusing on the average performance of the model. They also want to conduct larger experiments on real hardware.

This work was funded, in part, by a Takeda PhD Fellowship.

With a swipe of a magnet, microscopic “magno-bots” perform complex maneuvers

MIT researchers’ new fabrication technique can produce soft, microscopic structures with magnetically activated moving parts.

Under a microscope, a bouquet of lollipop-like structures, each smaller than a grain of sand, waves gently in a petri dish of liquid. Suddenly, they snap together, like the jaws of a Venus flytrap, as a scientist waves a small magnet over the dish. What was previously an assemblage of tiny passive structures has transformed instantly into an active robotic gripper.

The lollipop gripper is one demonstration of a new type of soft magnetic hydrogel developed by engineers at MIT and their collaborators at the École Polytechnique Fédérale de Lausanne (EPFL) in Switzerland and the University of Cincinnati. In a study appearing today in the journal Matter, the MIT team reports on a new method to print and fabricate the gel, which can be made into complex, magnetically activated three-dimensional structures.

The new gel could be the basis for soft, microscopic, magnetically responsive robots and materials. Such magno-bots could be used in medicine, for instance to release drugs or grab biopsies when directed by an external magnet.

Making objects move with magnets is nothing new, at least at the macroscale. We can, for example, wave a refrigerator magnet over a pile of paper clips that will trail the magnet in response. And at the microscale, scientists have designed a variety of magnetic “micro-swimmers” — components that are smaller than a millimeter and can be directed remotely by a magnet to squeeze through small spaces. For the most part, these designs work by mixing magnetic particles into a printable resin and pulling the entire swimmer in the direction of an external magnet.

In contrast, the MIT team’s new material can be made into even more complex and deformable structures with micron-scale precision. These features could enable a magnetic millibot to move individual features and perform more complex maneuvers.

“We can now make a soft, intricate 3D architecture with components that can move and deform in complex ways within the same microscopic structure,” says study author Carlos Portela, the Robert N. Noyce Career Development Associate Professor of Mechanical Engineering at MIT. “For soft microscopic robotics, or stimuli-responsive matter, that could be a game-changing capability.”

The study’s MIT co-authors include graduate students Rachel Sun and Andrew Chen, along with Yiming Ji and Daryl Yee of EPFL and Eric Stewart of the University of Cincinnati.

In a flash

At MIT, Portela’s group develops new metamaterials — materials engineered with unique, microscopic architectures that give rise to beyond-normal material properties. Portela has fabricated a variety of such metamaterials, including extremely tough and stretchy architectures and designs that can manipulate sound and withstand violent impacts.

Most recently, he’s expanded his research to “programmable” materials, which can be engineered to change their properties in response to stimuli, such as certain chemicals, light, and electric and magnetic fields.

From the team’s perspective, magnetic stimuli stand out from the rest.

“With a magnetically responsive material, we have control at a distance and the response is instantaneous,” says co-lead author Andrew Chen. “We don’t have to wait for a slow chemical reaction or physical process, and we can manipulate the material without touching it.”

For the new study, the team aimed to create a magnetically responsive metamaterial that can be made into structures smaller than a millimeter. Researchers typically fabricate microstructures by using two-photon lithography — a high-resolution 3D printing technique that flashes a laser into a small pool of resin. With repeated flashes, the laser traces a microscopic pattern into the resin, which solidifies into the same pattern, ultimately creating a tiny, three-dimensional structure, layer by layer.

While 3D resin printing produces intricate microstructures, using the same process to print magnetic structures has been a challenge. Researchers have tried to combine the resin with magnetic nanoparticles before printing the mixture. But magnetic particles are essentially bits of metal that inherently scatter light away or agglomerate and sediment unintentionally. Scientists have found that any magnetic particles in the resin can reduce the laser’s power at a given spot and weaken the resulting structure or prevent its printing altogether.

“Directly 3D printing deformable micron-scale structures with a high fraction of magnetic particles is extremely difficult, often involving a tradeoff between magnetic functionality and structural integrity,” says Sun, a co-lead author on the work.

A printed double-dip

The researchers created a new way to fabricate magnetic microstructures, by combining 3D resin printing with a double-dip process. The researchers first applied conventional resin printing to create a microstructure using a typical polymer gel, with no added magnetic particles. Then they dipped the printed gel into a solution containing iron ions, which the gel can absorb. The iron-soaked structure is then dipped again in a second solution of hydroxide ions. The iron ions in the gel bond with the hydroxide ions, creating iron-oxide nanoparticles that are inherently magnetic.

With this new process, the team can print intricate structures smaller than a millimeter, and add magnetic properties to the structures after printing. What’s more, they are able to control how magnetic a structure’s individual features can be. They found that, by tuning the laser’s power as they print certain features, they can set how cross-linked, or “tight” the gel is when printed. The tighter the gel, the fewer magnetic particles it can form. In this way, the researchers can determine how magnetic each tiny feature can be.

“This provides unprecedented design freedom to print multifunctional structures and materials at the microscale,” Sun says.

As a demonstration, the team fabricated ball-and-stick structures resembling tiny lollipops. The structures were less than a millimeter in height, with balls that were smaller than a grain of sand. The researchers printed the lollipops out of polymer gel and infused each ball with different amounts of magnetic particles, giving them various degrees of magnetism. Under a microscope, they observed that when they passed an ordinary refrigerator magnet over the structures, the lollipops pulled toward the magnet in various degrees, in a configuration that mimicked gripping fingers.

“You could imagine a magnetic architecture like this could act as a small robot that you could guide through the body with an external magnet, and it could latch onto something, for instance to take a biopsy,” Portela says. “That is a vision that others can take from this work.”

The team also fabricated a magnetically responsive, “bistable” switch. They first printed a small millimeter-long rectangle of polymer gel and attached to either side four tiny, oar-like magnetic structures. Each oar measured about 8 microns thick — about the size of a red blood cell. When the team applied a magnet on one end of the rectangle, the oars flipped toward the magnet, pulling the rectangle in the same direction and locking it in that position. When the magnet was applied to the other side, the oars flipped again, pulling the rectangle, like a switch, in the opposite direction.

“We think this is a new kind of bistable mechanism that could be used, for instance, in a microfluidic device, as a magnetic valve to open or shut some flow,” Portela says. “For now, we’ve figured out how to fabricate magnetic complex architectures at the microscale and also spatially tune their properties. That opens up a lot of interesting ideas for soft miniature robots going forward.”

This research was supported, in part, by the National Science Foundation and the MathWorks seed grant program.

This work was performed, in part, in the MIT.nano fabrication and characterization facilities.

Robotically assembled building blocks could make construction more efficient and sustainable

New research suggests constructing a simple building from interlocking subunits should be mechanically feasible and have a much smaller carbon footprint.

Robotically assembled building blocks could be a more environmentally friendly method for erecting large-scale structures than some existing construction techniques, according to a new study by MIT researchers.

The team conducted a feasibility study to evaluate the efficiency of constructing a simple building using “voxels,” which are modular 3D subunits that assemble into complex, durable structures.

After studying the performance of multiple voxels, the researchers developed three new designs intended to streamline building construction. They also produced a robotic assembler and a user-friendly interface for generating voxel-based building layouts and feeding instructions to the robots.

Their results indicate this voxel-based robotic assembly system could reduce embodied carbon — all of the carbon emitted during the lifecycle of building materials — by as much as 82 percent, compared with popular techniques like 3D concrete printing, precast modular concrete, and steel framing. The system would also be competitive in terms of cost and construction time. However, the choice of materials used to manufacture the voxels does play a major role in their carbon footprint and cost.

While scalability, durability, long-term robustness, and important considerations like fire resistance remain to be explored before such a system could be widely deployed, the researchers say these initial results highlight the potential of this approach for automated, on-site construction.

“I’m particularly excited about how the robotic assembly of discrete lattices can enable a practical way to apply digital fabrication to the built environment in a way that can let us build much more efficiently and sustainably,” says Miana Smith, a graduate student in the Center for Bits and Atoms (CBA) at MIT and lead author the study.

She is joined on the paper by Paul Richard, a graduate student at École Polytechnique Fédérale de Lausanne in Switzerland and former visiting researcher at MIT; Alfonso Parra Rubio, a CBA graduate student; and senior author Neil Gershenfeld, an MIT professor and the director of the CBA. The research appears in Automation in Construction.

Designing better building blocks

Over the past several years, researchers in the Center for Bits and Atoms have been developing voxels, which are lattice-structured building blocks that can be assembled into objects with high strength and stiffness, like airplane wings, wind turbine blades, and space structures.

“Here, we are taking aerospace principles and applying them to buildings. Why don’t we make buildings as efficiently as we make airplanes?” Gershenfeld says, based on prior work his lab has done on voxel assembly with NASA, Airbus, and Boeing.

To explore the feasibility of voxel-based assembly strategies for buildings, the researchers first evaluated the mechanical performance and sustainability of eight existing voxel designs, including a cuboctahedron made from glass-reinforced nylon and a Kelvin lattice made from steel.

Based on those evaluations, they developed a set of three voxels using a new geometry that could be more easily assembled robotically into a larger structure. The new design, based on a high-strength and high-stiffness octet lattice, mechanically self-aligns into rigid structures.

“The interlocking nature of these voxels means we can get nice mechanical properties without needing to have a lot of connectors in the system, so the construction process can run a lot faster,” Smith says.

To accelerate construction, they designed a robotic assembly system based on inchworm-like robots that crawl across a voxel structure by anchoring and extending their bodies. These Modular Inchworm Lattice Assembler robots, or MILAbots, use grippers on each end to place voxel building blocks and engage the snap-fit connections.

“The robots can assemble the voxels by dropping them into place and then stepping on them to have the pieces interlock. We can do precise maneuvers based on the mechanical relationship between the robots and the voxels,” Smith explains.

The team studied the embodied carbon needed to fabricate their new voxel designs using three materials: plastic, plywood, and steel. Then they evaluated the throughput and cost of using the robotic assembly system to build a simple, one-story building. The researchers compared these estimates with the performance of other construction methods.

Potential environmental benefits

They found that most existing voxels, and especially those made from plastics, performed poorly compared to existing methods in terms of sustainability, but the steel and wood voxels they designed offered significant environmental benefits.

For instance, utilizing their steel voxels would generate only 36 percent of the embodied carbon required for 3D concrete printing and 52 percent of the embodied carbon of precast concrete. The plywood voxels had the lowest carbon footprint, requiring about 17 percent and 24 percent of the embodied carbon needed, respectively.

“There is still a potential viable option for a plastics-based voxel approach, we just have to be a bit more strategic about which types of plastics, infills, and geometries we use,” Smith says.

In addition, projected on-site assembly time for the steel and wood voxel approaches averaged 99 hours, whereas existing construction methods averaged 155 hours.

These speed benefits rely on the distributed nature of voxel-based assembly. While one MILAbot working alone is far slower than existing techniques, with a team of 20 robots working in parallel, the system catches up to or surpasses existing automation methods at a lower cost.

“One benefit of this method is how incremental it is. You can start building, and if it turns out you need a new room, you can just add onto the structure. It is also reversible, so if your use changes, you can dissemble the voxels and change the structure,” Gershenfeld says.

The researchers also developed an interface that enables users to input or hand-design a voxelized structure. The automatic system determines the paths the MILAbots should follow for construction and sends commands to the assemblers.

The next step in this project will be a larger testbed in Bhutan, using the “super fab lab” that CBA helped set up there to replicate the robots to test construction for a planned sustainable city, Gershenfeld says.

Additional areas of future work include studying the stability of voxel structures under lateral loads, improving the design tool to account for the physics of the system, enhancing the MILAbots, and evaluating voxels that have integrated sheeting, insulation, or electrical and plumbing routing.

“Our work helps support why doing this type of distributed robot assembly might be a practical way to bring digital fabrication into building construction,” Smith says.

“This is yet another visionary example from Neil Gershenfeld and his team, of finding ways for buildings to build themselves with the help of tiny robotic machines. I’m now fascinated by how we can harness an idea like this to make it more affordable to make the outsides of buildings more engaging and joyful,” says Thomas Heatherwick, founder of the design and architecture firm Heatherwick Studio, who was not involved with this research.

This work was funded, in part, by the MIT Center for Bits and Atoms Consortia.

Mapping molecular markers of physical fitness

A new study reveals cellular pathways that appear to underlie some differences in physical fitness.

Patterns of molecular activity in the blood may hold clues not only to how fit someone is, but also to the biological processes that support physical performance. Researchers at MIT, GE HealthCare, and the U.S. Military Academy at West Point have developed a computational model that links thousands of these molecular signals to fitness levels, revealing pathways that could inform future studies to improve fitness training and speed injury or disease recovery.

To develop their model, the researchers analyzed more than 50,000 biomarkers in 86 cadets at the U.S. Military Academy who were training for a military competition. Using these data, the researchers were able to identify molecular pathways that appear to contribute to higher levels of physical fitness.

“We had 50,000 measurements, and we wanted to get it down to about 100 where there’s some likelihood that the markers that we’re measuring are mechanistically linked to physical fitness. So, not just a statistical correlation, of which there will be many, but markers where there’s a likelihood that there is a causal relationship,” says Ernest Fraenkel, the Grover M. Hermann Professor in Health Sciences and Technology in MIT’s Department of Biological Engineering.

These biomarkers can be measured by analyzing blood samples, which could offer a simple way to provide an athlete, for example, or perhaps someone with chronic illness or a long-term injury, with additional information about potential areas to focus their efforts to reduce risk of injury, accelerate recovery, or improve their performance ceiling beyond what conventional measures show.

Azar Alizadeh, a principal scientist with GE HealthCare’s Healthcare Technology and Innovation Center, is the paper’s lead author. Fraenkel and Luca Marinelli, a senior principal scientist with GE HealthCare, are the senior authors of the new study, which appears in the journal Communications Biology.

Mapping fitness

To find the genetic basis of a simple trait such as height, scientists can perform large-scale studies known as genome-wide association studies (GWAS), in which genetic markers from thousands of people can be linked with height. However, the picture becomes much more complicated for traits such as physical fitness, which is determined by the interplay of many different genetic, physiological, and environmental factors.

The researchers set out to try to identify some of those factors, working with a group of 86 volunteers at the U.S. Military Academy at West Point who were training for the Sandhurst Military Skills Competition. Alizadeh led the experimental study design and execution, in collaboration with GE HealthCare, GE Research, West Point, and MIT scientists. During the three-month study period, volunteers participated in up to five sessions. At each session, blood samples were taken before and after intense exercise. The researchers also measured other traits such as lean muscle mass and VO₂ max (the maximum rate of oxygen consumption during exercise).

From the blood samples, the researchers were able to measure more than 50,000 biomarkers, which they obtained by analyzing DNA methylation patterns, sequencing messenger RNA transcripts, and analyzing thousands of the proteins and small molecules found in the samples.

From their set of 50,000 biomarkers, the researchers hoped to identify a smaller number that could predict overall physical fitness, as measured by performance on the Army Combat Fitness Test (ACFT). This test includes a 2-mile run, maximum deadlift (the heaviest weight a person can lift for a single repetition up to 340 pounds), and sprint-drag-carry, a test that involves sprinting, dragging a sled, and carrying kettlebells.

One way to do this would be to simply train a computational model to identify correlations between fitness and biomarkers. However, with only 86 subjects in the study, that approach would likely yield correlations that were random and did not actually contribute to physical fitness, Fraenkel says.

To take a more targeted approach, the researchers first created a network model that represents the interactions between the markers, based on existing databases that catalog those interactions. These connections might represent proteins that interact with each other in a signaling pathway, or a transcription factor that turns on a set of genes.

“We built a network that you can think of as a city map. You want to find the places in the city map that are lighting up — not just one light going on, but a whole bunch of houses or street lamps going on in the same neighborhood,” Fraenkel says. “We can find neighborhoods on this enormous molecular map that are active at the same time, in a way that correlates with the phenotype that we measure.”

“We built upon the network bioinformatics from the Fraenkel lab to create an end-to-end predictive modeling framework to discover biological expression circuits that drive groups of physical characteristics predictive of ACFT scores, for example, body composition or exercise physiology metrics like VO₂ max,” Marinelli says.

After feeding the measurements from the study participants into this predictive model, known as PhenoMol, the researchers were able to identify more than 100 biomarkers linked to performance on the ACFT. Fitness predictions based on these biomarkers were much more accurate than those of a model that correlated biomarkers with performance on the ACFT without taking network connections into account. Additionally, PhenoMol performed similarly to a model that predicted participants’ fitness based on measurements of their VO₂ and lean muscle mass.

Cellular pathways

The researchers found that the biomarkers identified by PhenoMol clustered into several different cellular pathways. Those include pathways involved in blood coagulation and the complement cascade — a part of the immune system involved in clearing damaged cells. Those systems likely help with recovery from tissue injury and stress response during exercise, Fraenkel says.

Another prominent cluster involves molecules related to the urea cycle, which is responsible for eliminating the ammonia that results from the breakdown of proteins. The model also identified biomarkers that are linked with the function of mitochondria (the organelles that generate energy within cells).

Fraenkel now hopes to dig deeper into which markers show someone’s current fitness, and which might reveal what their potential fitness levels could be. This could help to reveal potential strengths that might not show up in traditional fitness tests, he says.

That kind of prediction could be useful not only for athletic training, but also for other people who are recovering from an injury or disease, or people experiencing the effects of aging. For example, using this approach in different populations might provide useful information for an elderly person after a stroke, since such events often require months of therapy to regain significant mobility.

“This has relevance for the military and for sports teams, but also in a lot of normal life situations where maybe someone is going through rehabilitation for some injury or disease and they’ve hit a wall,” Fraenkel says. “Or during aging, you may be able to see when somebody’s losing capacity or when they have more capacity than they’ve been able to actualize.”

Molecular markers of fitness could also be used in clinical trials to rigorously test the potential benefits of popular food supplements and fitness programs, he adds.

To make the testing process simpler, the researchers would like to narrow down their pool of biomarkers to a handful that could be easily measured from a blood sample using a single method suitable for widespread use.

The research was developed with funding from the Defense Advanced Research Projects Agency (DARPA), which states that the views, opinions, or findings expressed are those of the authors and should not be interpreted as representing the official views or policies of the U.S. government.

A faster way to estimate AI power consumption

The “EnergAIzer” method generates reliable results in seconds, enabling data center operators to efficiently allocate resources and reduce wasted energy.

Due to the explosive growth of artificial intelligence, it is estimated that data centers will consume up to 12 percent of total U.S. electricity by 2028, according to the Lawrence Berkeley National Laboratory. Improving data center energy efficiency is one way scientists are striving to make AI more sustainable.

Toward that goal, researchers from MIT and the MIT-IBM Watson AI Lab developed a rapid prediction tool that tells data center operators how much power will be consumed by running a particular AI workload on a certain processor or AI accelerator chip.

Their method produces reliable power estimates in a few seconds, unlike traditional modeling techniques that can take hours or even days to yield results. Moreover, their prediction tool can be applied to a wide range of hardware configurations — even emerging designs that haven’t been deployed yet.

Data center operators could use these estimates to effectively allocate limited resources across multiple AI models and processors, improving energy efficiency. In addition, this tool could allow algorithm developers and model providers to assess potential energy consumption of a new model before they deploy it.

“The AI sustainability challenge is a pressing question we have to answer. Because our estimation method is fast, convenient, and provides direct feedback, we hope it makes algorithm developers and data center operators more likely to think about reducing energy consumption,” says Kyungmi Lee, an MIT postdoc and lead author of a paper on this technique.

She is joined on the paper by Zhiye Song, an electrical engineering and computer science (EECS) graduate student; Eun Kyung Lee and Xin Zhang, research managers at IBM Research and the MIT-IBM Watson AI Lab; Tamar Eilam, IBM Fellow, chief scientist of sustainable computing at IBM Research, and a member of the MIT-IBM Watson AI Lab; and senior author Anantha P. Chandrakasan, MIT provost, Vannevar Bush Professor of Electrical Engineering and Computer Science, and a member of the MIT-IBM Watson AI Lab. The research is being presented this week at the IEEE International Symposium on Performance Analysis of Systems and Software.

Expediting energy estimation

Inside a data center, thousands of powerful graphics processing units (GPUs) perform operations to train and deploy AI models. The power consumption of a particular GPU will vary based on its configuration and the workload it is handling.

Many traditional methods used to predict energy consumption involve breaking a workload into individual steps and emulating how each module inside the GPU is being utilized one step at a time. But AI workloads like model training and data preprocessing are extremely large and can take hours or even days to simulate in this manner.

“As an operator, if I want to compare different algorithms or configurations to find the most energy-efficient manner to proceed, if a single emulation is going to take days, that is going to become very impractical,” Lee says.

To speed up the prediction process, the MIT researchers sought to use less-detailed information that could be estimated faster. They found that AI workloads often have many repeatable patterns. They could use these patterns to generate the information needed for reliable but quick power estimation.

In many cases, algorithm developers write programs to run as efficiently as possible on a GPU. For instance, they use well-structured optimizations to distribute the work across parallel processing cores and move chunks of data around in the most efficient manner.

“These optimizations that software developers use create a regular structure, and that is what we are trying to leverage,” explains Lee.

The researchers developed a lightweight estimation model, called EnergAIzer, that captures the power usage pattern of a GPU from those optimizations.

An accurate assessment

But while their estimation was fast, the researchers found that it didn’t take all energy costs into account. For instance, every time a GPU runs a program, there is a fixed energy cost required for setting up and configurating that program. Then each time the GPU runs an operation on a chunk of data, an additional energy cost must be paid.

Due to fluctuations in the hardware or conflicts in accessing or moving data, a GPU might not be able to use all available bandwidth, slowing operations down and drawing more energy over time.

To include these additional costs and variances, the researchers gathered real measurements from GPUs to generate correction terms they applied to their estimation model.

“This way, we can get a fast estimation that is also very accurate,” she says.

In the end, a user can provide their workload information, like the AI model they want to run and the number and length of user inputs to process, and EnergAIzer will output an energy consumption estimation in a matter of seconds.

The user can also change the GPU configuration or adjust the operating speed to see how such design choices impact the overall power consumption.

When the researchers tested EnergAIzer using real AI workload information from actual GPUs, it could estimate the power consumption with only about 8 percent error, which is comparable to traditional methods that can take hours to produce results.

Their method could also be used to predict the power consumption of future GPUs and emerging device configurations, as long as the hardware doesn’t change drastically in a short amount of time.

In the future, the researchers want to test EnergAIzer on the newest GPU configurations and scale the model up so it can be applied to many GPUs that are collaborating to run a workload.

“To really make an impact on sustainability, we need a tool that can provide a fast energy estimation solution across the stack, for hardware designers, data center operators, and algorithm developers, so they can all be more aware of power consumption. With this tool, we’ve taken one step toward that goal,” Lee says.

This research was funded, in part, by the MIT-IBM Watson AI Lab.