Yu Deng ’11 and Hong Wang PhD ’19 awarded Fields Medal

MIT alumni Yu Deng ’11 and Hong Wang PhD ’19 were among the four young mathematicians awarded Fields Medals on July 23 at the 2026 International Congress of Mathematicians (ICM). The other two honorees were John Pardon and Jacob Tsimerman.

The Fields Medal is awarded once every four years at the ICM, and is regarded as one of the highest honors a mathematician can receive. 

Yu Deng received his BS in mathematics at MIT in 2011, and was a Putnam Fellow in 2010. He earned his Fields Medal for his work in partial differential equations (PDE), including the rigorous derivation of the Boltzmann equation from hard-sphere dynamics for rarefied gases, the derivation of wave kinetic equations from nonlinear dispersive systems, and probabilistic approaches to nonlinear Schrödinger dynamics. His first published paper (in Analysis & PDE) was on the latter topic, and stemmed from summer research conducted at MIT on a problem suggested by Gigliola Staffilani. Deng is currently a professor at the University of Chicago.

Hong Wang received her PhD at MIT in 2019 under the supervision of Larry Guth PhD ’05. She is awarded the Fields Medal for her work in harmonic analysis and geometric measure theory, including applications of multiscale and decoupling techniques to the local smoothing conjecture for the planar wave equation, and other major advances such as the solution of the Kakeya problem in three dimensions (with Joshua Zahl). As a student in the department, she was a graduate mentor in the Summer Program in Undergraduate Research (SPUR) and, alongside her mentee, was awarded the Hartley Rogers Jr. SPUR Prize, presented to the best student-mentor team. Wang, a Silver Professor of Mathematics at New York University and a professor at the Institut des Hautes Études Scientifiques in Paris, is the third woman ever to win a Fields Medal.

“The achievements of Yu Deng and Hong Wang are truly monumental, and we are all elated that they were awarded Fields Medals,” department head and RSA Professor of Mathematics Michel Goemans says. “Their success is a testimony of the amazing mathematical talent we have at all levels at MIT, and the top-quality education, mentorship, and research opportunities we provide to both our large pool of math majors and our PhD students, during their lifelong mathematical journey.” 

Goemans adds, “MIT is a unique and exciting place to learn mathematics, and I am sure we have more future Fields medalists among our students and junior members of the department.”

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Making robots faster by helping them think ahead

A new method developed by MIT researchers makes robots better at thinking ahead while they are acting, leading to smoother motions and quicker reactions.

This technique enables the artificial intelligence model that plans a robot’s motion to forecast its future position. The model uses this prediction to seamlessly transition current movements into the next actions.

Many existing methods cause a robot to stop and think about what it needs to do next, leading to slow and jerky motions. By basing its calculations on the future state of the robot, rather than its current position, the MIT method helps robots operate much faster.

Importantly, the technique does not add any computational overhead to the planning process and can be applied to varied robotic hardware.

This new method doubled the speed of robots performing activities like pick-and-place tasks, while significantly reducing lag time between motions. It also boosted the performance of robotic arms in highly dynamic activities, such as playing table tennis and Whack-a-Mole.

The system could be especially useful for robots that perform fast and agile maneuvers in challenging real-world environments, like emergency response or search-and-rescue. It could also allow robots to react more quickly when recovering from mistakes.

“This work sets up a good foundation for efficient, fast, accelerated, and low-cost robotics applications. We look forward to expanding our work into the latest world action models, so it has even stronger capabilities as we keep pushing to make physical AI faster,” says Song Han, an associate professor in the MIT Department of Electrical Engineering and Computer Science (EECS), member of the Research Laboratory of Electronics, and lead author of a paper on this method.

Han is joined on the paper by co-lead authors Jiaming Tang, an MIT EECS graduate student, and Yufei Sun, a student at Tsinghua University; as well as others at Nvidia, the University of California at Berkeley, the University of California at San Diego, and Caltech. The research will be presented at the Intelligent Robots and Systems Conference. 

Forecasting the future 

In state-of-the-art robotics applications, generative AI systems called vision-language-action (VLA) models act as the brain of a robot, planning its next moves and executing those actions. 

A VLA model takes environmental observations from the robot’s camera and instructions about its task, outputs the next few motions as one chunk of actions, then executes those actions on the robotic hardware.

But VLA inference — the real-time procedure during which the model processes visual inputs, reasons about the task, and outputs actions — is computationally demanding, so the robot can experience substantial pauses while planning its next actions. These pauses disrupt the fluidity of its motions and make it slower to react to changes in the environment. 

“Our motivation was to overlap the thinking process with the execution process to make the reaction speed faster,” Tang says.

The MIT researchers developed a new system called VLASH that enables a VLA to predict the future state of the robot and its environment. It uses this information to plan the next set of motions while the robot is completing the current action chunk.

This solves a major hurdle faced by many other methods, which use the current state of the robot to predict its next moves.

“Since the environment will change after the robot moves, if we plan based on stale observations of the current environment, there will be a misalignment that causes very unstable control,” Tang explains.

VLASH avoids this misalignment due to a key insight by the researchers. Although the model doesn’t know exactly what the environment will look like in the future, it does know the robot’s current position and how it will move to perform the actions it is about to take. 

The framework uses this information to predict the state of the robot after it completes its current chunk of actions. It uses that estimation to plan the next motions.

“In this way, we give the robot awareness of its future state,” Tang says.

Augmenting acceleration

On its own, this technique speeds up the robot’s motions by eliminating lag time that usually occurs between action chunks, accelerating reaction speeds more than 30-fold. 

But to make their approach even faster, the MIT researchers generate coarser chunks of actions, so the robot executes a few larger steps that follow the same trajectory. This technique is called action quantization.

While action quantization led to a slight dip in accuracy, it enables a robot to complete the overall task two to three times faster.

However, the researchers found that simply feeding future robot states to the VLA during deployment is not enough to enable accurate and stable control of the robot. 

They developed a training-augmentation method that groups training data in such a way that the VLA learns to use future state information instead of current observations. 

By reusing some training data, this fine-tuning method accelerated training fivefold with no additional computational overhead. 

“Even though there is a very large model working in the background, VLASH lets the robot react and execute its actions very fast, much more like a human would. This could help to make robots for all sorts of dynamic tasks more effective,” Tang says.

When compared with baseline methods in simulation, VLASH consistently performed faster while maintaining the accuracy of robotic maneuvers. The system also outpaced these methods on real hardware in pick-and-place, stacking, and sorting tasks.

For instance, VLASH placed cubes in a box while sorting them by color twice as fast as these methods, while achieving the same 90 percent accuracy as the best baseline. The system can also perform highly dynamic tasks like playing ping-pong and whack-a-mole.

In the future, the researchers want to combine VLASH with more powerful generative AI systems called world models that can predict the robot’s actual environmental observations, in an effort to boost performance and open new applications.

This work is supported, in part, by the MIT-IBM Computing Research Lab, Amazon, the National Science Foundation, and Nvidia.

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MIT engineers design recyclable elastic yarn

After a closet cleanout, what options are there for recyling our old threads? Not many. Apart from bringing used clothes to a donation center, there is no process for recycling textiles like there is for bottles and cans. And, the average American throws out around 81 pounds of clothing each year. That amounts to more than 11 million tons of textiles that end up in the landfill or incinerator. 

But MIT engineers hope to cut down on the growing mountain of textile waste, with a new, recyclable yarn. 

The team has designed a yarn made from a form of plastic that is commonly used in milk bottles and grocery bags. The new yarn, which has a feel similar to traditional sewing thread, can be woven into stretchy, lightweight clothing. The researchers say that at the end of its use, a yarn-spun garment could be melted down and redrawn into new yarn, and then woven into new clothing or even cast into buttons, belt buckles, and other plastic accessories.

To demonstrate the yarn’s recyclability, the researchers spun a spool of yarn, melted the yarn down, and respun it into new yarn, multiple times. They found that even after 10 cycles, the yarn was as strong and flexible as conventional thread. 

They envision the new yarn could be an alternative to elastic spandex-polyester or spandex-nylon yarns, which are spun from a combination of fibers that cannot be recycled together. The team’s new yarn, in contrast, is made from a specific combination of plastic materials that mimics the tough and stretchy properties of spandex yarns, while also being easily recycled. 

“Eighty percent of textiles on the U.S. market currently contain some amount of spandex, which makes them nonrecyclable,” says Svetlana Boriskina, a research scientist in MIT’s Department of Mechanical Engineering. “There’s no widely adopted technology now that recycles textiles into textiles. With our new yarn, we hope to change that.”

Boriskina and her colleagues have published the details of the new yarn in a study published in the journal ACS Materials Letters. MIT co-authors include first author SeongHyeon Kim, Duo Xu, Volodymyr Korolovych, Domingo Flores-Hernandez, Kaniz Moriam, and Daniel Braconnier.

The core of the problem

Spandex is a polyurethane-based synthetic fiber that is springy but not very strong. A thread of an elastic yarn is made from two parts: a spandex-based core, surrounded by a sheath of tough polyester or nylon. The combination of these materials gives elastic yarns their unique stretch and strength. 

But this same material mixture makes elastic yarns nearly impossible to recycle. Yarns would first have to be chemically treated to separate the polyester sheath from the spandex core. The polyester-based sheath material could then be melted down and reused. But there is no way to recycle the yarn as a whole, without chemical separation.

“Even though chemical separation technologies exist, they add extra cost and complexity, and usually require toxic chemicals that are harmful to the environment,” Boriskina says. “That’s why most stretchy garments go to the dump.”

In 2021, Boriskina’s group developed a new type of yarn made from polyethylene. Polyethylene is the most common type of plastic in the world, used to make everything from grocery bags, water bottles, trash bins, and toys to industrial pipes and plastic sheeting. Polyethylene is a thermoplastic, meaning that it can be melted down and remade, and thus recycled. 

And yet, polyethylene had never really been considered as a textile. In their previous work, Boriskina and her colleagues showed they could spin yarn out of polyethylene, which they then wove into various garments. In those experiments, they focused on the yarn’s moisture wicking, stain-resisting, and cooling properties. 

Spaghetti yarn

In their new study, the group aimed to tailor polyethylene yarn to mimic the strength and flexibility of spandex; they also sought to demonstrate the yarn’s recyclability. 

They first looked for formulations of stretchy, polyethylene-based copolymers that resemble a spandex elastic core. Separately, they engineered polyethylene yarns that can act as the sturdier sheath. Looking through the scientific literature and combing through industrial reports, the team evaluated many chemical variations of polyethylene.

“The chemical structure of polyethylene is like Christmas garland — a backbone of carbon, carbon, carbon, and also these dangling ‘decorations’ of hydrogen atoms or short branches with the same structure as a backbone,” Boriskina explains. “How these chains are arranged can change the properties of the whole structure.”

“Polyethylene can give us a wide range of properties, depending on how you make it,” adds first author SeongHyeon Kim.

For the yarn’s core, the team used one polyethylene-based resin that results in a more stretchy fiber. They chose a second, stiffer resin as the basis for the yarn’s sheath. The researchers obtained pellets of each resin from a chemical manufacturer, and then put each type of pellet through a process of fiber fabrication, first pouring them into a hopper, then heating the pellets to about 350 degrees Fahrenheit, past their melting temperature. The melted polyethylene was then drawn through small extruders to make hair-thin fibers. 

“You just melt it in a barrel with a heater, and then you extrude and spin it into fibers,” Kim says. “It’s like a spaghetti machine.”

The team used an industrial yarn spinner to wind the sheath fibers around a core fiber to make the final, elastic yarn. 

Because both the yarn’s core and sheath come from the same chemical family of polyethylene, Boriskina says the materials do not have to be separated before recycling, in contrast to spandex-based elastic yarns. The new yarn can be melted as is, and reformed into new yarn or other plastic products.

“Because they are exactly the same chemistry, they play nicely together,” she says. “That’s what makes this yarn very recyclable.”

As a demonstration, the team twisted an elastic core-sheath yarn, then melted it down and re-spun it, 10 times. Each time, they tested the yarn’s mechanical properties by precisely stretching a thread and measuring the pulling force at which the thread eventually broke. From these tests, they found that the yarn’s recycled versions were just as strong as the original sheath yarn. These recycled yarns can now be used to make new stetchy yarns by twisting them around a newly spun elastic core.

“Now we have something that can be knitted and woven,” Boriskina says. “That is the next stage.”

The team says their new recipe for polyethylene yarn can be scaled up into industrial-sized spools. Just like conventional spandex fibers, it would take kilometers of yarn to weave a single textile. But once woven and used, the team envisions that a polyethylene garment could conceivably be dropped in a recycling bin and sent to a facility to be melted down and respun, enabling a more sustainable, circular fashion and textile economy. 

“Hopefully it will prevent the need for making more and more textile materials, because you can keep recycling a large portion of it,” Boriskina says. 

This work was supported in part by the DEVCOM Soldier Center through the U.S. Army Research Office, the Office of Naval Research Global via Tecnologico de Monterrey, and the MIT Portugal Program.

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Looking beyond research

In Professor Anna-Christina Eilers’ research group, mentorship happens through small, meaningful gestures: thoughtful feedback on a draft, a check-in after a rough week, and a readiness to help when things get tough. For her students, these everyday moments have become a defining feature of her approach.

An observational astrophysicist, Eilers studies how the universe evolved from its earliest beginnings. Her research investigates the formation and growth of black holes across cosmic time, particularly during the “cosmic dawn,” when the first stars, galaxies, and quasars illuminated the young universe.

Working alongside her in this field, graduate students describe a mentor who pairs high expectations with genuine attentiveness, encouraging both scientific independence and a strong sense of community. This approach has earned Eilers recognition through MITs Committed to Caring initiative — a student-driven program honoring exemplary mentorship within the graduate community.

Showing up in the everyday moments

Students say one of Eilers’ defining qualities is her consistency. No matter how busy her schedule, they know they can count on thoughtful feedback, productive meetings, and regular conversations about both research and broader career development. While those practices may sound routine, her mentees emphasize that they are anything but guaranteed within many academic spaces.

“As Christina's advisees,” two students wrote in their joint nomination, “we are both extremely grateful for the professional and emotional support we constantly receive. She always keeps an eye out for us.”

Eilers’ support takes many forms. Students describe an advisor who carefully reads every draft, provides timely and detailed feedback, and creates space for conversations that extend beyond immediate research questions. 

Students also reflect on the manner in which Eilers celebrates their wins alongside them. “She brings our favorite desserts to group meetings when we publish a paper,” shared one nominator. 

Her attentiveness becomes especially meaningful when challenges arise. Students note that she regularly checks in on them and does not hesitate to step in when research collaborations become difficult or obstacles threaten to slow their progress. Rather than leaving them to navigate those situations alone, she helps identify solutions before small problems become larger ones.

For Eilers, building a successful research group means cultivating connections among its members as well as producing strong science.

One of the group’s traditions takes place whenever a member returns from a conference or research visit. The traveler brings back a small treat — cookies, chocolates, or another local specialty — to share during the next group meeting. Along with the snacks comes a conversation about the talks they attended, the researchers they met, and the ideas they brought home.

The tradition transforms an individual trip into a shared opportunity for learning, with new perspectives becoming part of the group’s collective conversation. These exchanges work to not only reinforce a sense of community, but also to expose students to research and ideas beyond their own projects.

Through moments like these, students develop both as researchers and as colleagues who celebrate one another’s successes and learn from one another's discoveries. 

Remembering the person behind the researcher

One of Eilers’ most consistent pieces of advice has little to do with coursework or research.

“I always recommend to incoming graduate students to find a hobby outside of work that they enjoy, and ideally where they interact with people they don’t work with,” she says.

She believes maintaining interests beyond the lab helps students sustain both their curiosity and their perspective. “Graduate school can be all-consuming,” she says, reflecting on her own experiences. “It's easy to let your research become your entire identity.”

This same philosophy shapes her mentorship: successful researchers are also people with lives, relationships, and interests beyond their work. Making space for those parts of life helps students build careers that are both ambitious and sustainable.

Eilers traces her approach to the advisors who shaped her own career.

“I was very fortunate to have had — and continue to have — several mentors who have challenged me scientifically and supported me along the way," she says. “They modeled how to pursue excellent research without losing sight of the importance of personal connection and integrity.”

Her students see these values reflected within the group environment. They are encouraged to tackle ambitious questions while developing the confidence to think independently, but they know that guidance is available when they need it. 

In their nominations of Eilers, students describe an advisor who is present in both the ordinary and the difficult moments — someone who notices when support is needed, advocates for her students, celebrates their successes, and builds a community where students consistently feel seen. 

Through this steady commitment, Eilers demonstrates that care is not separate from academic excellence. Rather, it creates the conditions that allow excellence to flourish.

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MIT projects selected for funding under US Department of Energy’s Genesis Mission

MIT researchers are set to contribute to the U.S. Department of Energy’s (DOE) Genesis Mission, with 15 collaborative projects among those selected for funding under Genesis Phase I, DOE announced Wednesday.

The Genesis Mission, a national initiative, intends to build “the world’s most powerful integrated science discovery platform” by incentivizing cross-sector collaborations that leverage AI, supercomputing, quantum systems, and advanced scientific instruments to accelerate breakthroughs in energy, scientific discovery, and national security.

“MIT researchers are proud to be leading and contributing to projects under the Genesis Mission, in vital areas of research that support national priorities,” says Ian A. Waitz, MIT’s vice president for research. “The Genesis Mission represents a fantastic opportunity to catalyze the power of universities, industry, and the U.S. national laboratories to advance science, technology, and innovation for the benefit of the nation and the world.”

The DOE announced the initial projects during its Genesis Summit in Washington on Wednesday. The research funding to MIT is pending completion of negotiations toward an award agreement for each project. In phase I, funded project teams will work to demonstrate research workflows that integrate AI with scientific investigation, and to rigorously evaluate the scientific merit of their approach.

Projects under the Genesis Mission are collaborative by design; teams must draw on the expertise of researchers from academia, industry, and/or the national laboratories. Among the selected phase I projects with MIT involvement are those that aim to develop powerful quantum sensors to help explain fundamental questions about the universe; advance knowledge of chemical-free methods to extract rare earth elements; model the behavior of plasma in fusion tokamaks and future fusion reactors; develop digital twins for fusion magnet systems; exploit the self-assembly of biomolecules to design materials with targeted properties; generatively design rotating blades for machinery systems; and more. Phase I projects that identify promising pathways toward transformative capabilities at scale may be considered by DOE for further Genesis Mission funding.

Six of the selected projects are to be led by MIT principal investigators (PIs):

• AI-Driven Discovery of Electrochemical Separation Methods for Rare Earth Elements
  MIT lead: Martin Bazant (Department of Chemical Engineering, ChemE), Chevron Professor in Chemical Engineering and professor of mathematics
   
• AI for Learning Missing Constitutive Structure in Fracture Models
  MIT lead: Laurent Demanet (Department of Earth, Atmospheric and Planetary Sciences), professor of applied mathematics and co-director of the MIT Center for Computational Science and Engineering
   
• AI-Driven Quantum Sensing for Precision Tests of Fundamental Physics
  MIT lead: Ronald Garcia Ruiz (Laboratory for Nuclear Science, LNS), associate professor of physics and Thomas A. Frank (1977) Career Development Professor
   
• Multi-Agent Inverse Design of Block Polypeptoids Into Hierarchical Nanostructures
  MIT lead: Bradley Olsen (ChemE), Alexander and I. Michael Kasser (1960) Professor
   
• CATALYST: Core Accelerated Trajectories with Augmented Learning bY Sim-to-experiment Transfer
  MIT lead: Cristina Rea (Plasma Science and Fusion Center), principal research scientist and division head for data science
   
• Multi-Modal and Multi-Facility Application of the FM4NPP Foundation Model: Silicon Trackers and Electron Colliders
  MIT lead: Gunther Roland (LNS), professor of physics and division head for experimental nuclear and particle physics

MIT researchers are expected to participate in another nine selected projects led by other institutions, companies, and labs:

• Framework for Optimized Rotating Blade Design Using Generative Engineering (FORGE)
  Project lead: GE Vernova Advanced Research Center
  MIT lead: Faez Ahmed (Department of Mechanical Engineering), associate professor of mechanical engineering and the Esther and Harold E. Edgerton Career Development Professor
   
• Superconducting Polychronous Computation Near Criticality
  Project lead: Argonne National Laboratory
  MIT lead: Karl Berggren (Research Laboratory of Electronics), the Julius A. Stratton Professor in Electrical Engineering and Physics
   
• Scalable Agentic Digital Twins for Autonomous Precision Facilities
  Project lead: Texas A&M University
  MIT lead: Ronald Garcia Ruiz (LNS)
   
• Agentic AI for Real-Time Expedited Discovery from High-Complexity EIC Data Streams
  Project lead: Purdue University
  MIT lead: Philip Harris (LNS), associate professor of physics
   
• Self-Driving Discovery and Co-Design of MXene Memristors for 3D Compute-in-Memory Systems
  Project lead: Northeastern University
  MIT lead: Ju Li (Department of Nuclear Science and Engineering, NSE), the Carl Richard Soderberg Professor in Power Engineering and professor of materials science and engineering
   
• A-WILD: AI-driven Workflows for Intelligent Lab Discovery
  Project lead: Lawrence Berkeley National Laboratory (LBNL)
  MIT lead: Ju Li (NSE)
   
• A Foundational Generative AI Framework to Advance Water-Energy Security
  Project lead: LBNL
  MIT lead: Haruko Wainwright (NSE), Atlantic Richfield Career Development Professor in Energy Studies, assistant professor of nuclear science and engineering, and assistant professor of civil and environmental engineering
   
• An AI-Driven Platform for HLW Repository Design and Analysis with Digital Twins, GIS Data Integration, and Surrogate Models
  Project lead: LBNL
  MIT lead: Haruko Wainwright (NSE)
   
• Toward Physics-Informed Digital Twins for Fusion Magnet Systems
  Project lead: LBNL
  MIT lead: Holger Witte (LNS), associate director of MIT’s Bates Research and Engineering Center.

“The extraordinary response to this Genesis Mission application process demonstrates that America’s scientific community is ready to reimagine how discovery happens,” said DOE Under Secretary Darío Gil SM ’00 PhD ’03, in the DOE’s announcement. “Through the Genesis Mission, we are bringing together the nation’s leading researchers, institutions, and technology partners to build the next generation of scientific capability. We look forward to seeing these teams demonstrate new research workflows that accelerate discovery and reveal what is possible when AI and science advance together.”

A complete list of the first Genesis Mission projects selected for award negotiations is available from the U.S. Department of Energy.

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Professor Emeritus Dimitri Bertsekas, influential computer scientist and prolific author, dies at 83

Dimitri Bertsekas PhD ’71, the Jerry McAfee (1940) Emeritus Professor in Engineering in the Department of Electrical Engineering and Computer Science (EECS), a principal investigator in the Laboratory for Information and Decision Systems (LIDS), and the Fulton Professor of Computational Decision Making at Arizona State University, died on June 3 at his home in Belmont, Massachusetts. He was 83 years old. 

Over the course of his career, Bertsekas’ research spanned, and had a definitive influence upon, several fields, including optimization, control, large-scale computation, reinforcement learning, and artificial intelligence. He served as a consultant to various private companies; an editor for several scientific journals; the founder of a publishing company, Athena Scientific; and chief scientific advisor of Bayforest Technologies, a London-based quantitative investment company. However, his most lasting impact may have come through his prolific authorship and co-authorship of over 20 highly influential books, monographs, and textbooks, and through his vast network of students, mentees, friends, and collaborators.

Bertsekas earned his undergraduate degree at the National Technical University of Athens, Greece, before obtaining his MS in electrical engineering at George Washington University in 1969, and his PhD in system science at MIT in 1971. He began his faculty career at Stanford University, where he spent three years, and the University of Illinois at Urbana-Champaign, where he spent five more before returning to MIT in 1979. He would stay with MIT’s Department of EECS until 2019, at which point he became a full-time faculty member at Arizona State University at Tempe. Along the way, Bertsekas taught, advised, and mentored students who would eventually become his colleagues at all four institutions. 

“Dimitri played a defining role in my career,” says Asu Ozdaglar, department head of EECS at MIT. “I decided to change my research focus after taking his nonlinear optimization class. The conceptual clarity and the mathematical rigor he has brought to every topic, combined with his ability to connect theory to important problems established a foundation that has continued to inform my scholarly work in the years to follow.” Another former MIT student, Jinane Abounadi, now executive director of the MIT Sandbox Innovation Fund Program, still remembers Bertsekas’ tutelage as a highlight of her time as a student at MIT: “I feel so fortunate to have had Dimitri as my professor and advisor. I had the opportunity to learn about optimization, dynamic programming, and neuro-dynamic programming from a true master.” 

A former student at the University of Illinois, Steven E. Shreve remembers being impressed by Bertsekas’ course on nonlinear optimization and asking if Bertsekas would consider becoming his PhD advisor. “Rather than answering my question directly, Dimitri gave me a preliminary draft of his manuscript, which eventually became his book 'Dynamic Programming and Stochastic Control,' and asked me to proofread it,” remembers Shreve, now Orion Hoch University Professor Emeritus in the Department of Mathematical Sciences at Carnegie Mellon University. “From this manuscript, I learned the theory of dynamic programming and mastered many important special cases. Talking with Dimitri as I read, I received one-on-one instruction. When the book finally appeared, Dimitri generously acknowledged my participation, as if I had done him a favor, rather than the other way around.” The gambit was typical of Bertsekas’ understated approach to mentorship; after the first successful collaboration, Bertsekas arranged a research fellowship for Shreve and challenged him to solve a fundamental question in dynamic programming. “I needed to learn a good deal of set theory to even think about the question he asked,” remembers Shreve, whose work on the problem was combined with Bertsekas’ notes to create their co-authored book “Stochastic Optimal Control: The Discrete Time Case.” 

“Working with Dimitri on [that book] is how I learned to write,” says Shreve. “I learned from Dimitri that if you want to be recognized for your research, you must present it so others want to read it, and I learned how to do that.” 

The clarity and elegance of Bertsekas’ explanatory style would become his educational hallmark. “Everyone recognized Dimitri’s great talents as a writer, but he went far beyond that, organizing entire subjects into something that was understandable and a well-organized totality,” says Robert Gallager, professor emeritus of electrical engineering at MIT, who co-authored a 1987 book with Bertsekas entitled “Data Networks.” “The field was changing rapidly then, with a factor-of-two decrease every two years in computation costs, and with optical fiber on the horizon for transmission. Dimitri and I each understood only parts of this field, with the rest a fast-moving learning experience. Dimitri was the ideal partner in this, able to quickly translate hard concepts into simple but accurate explanations and able to combine my knowledge with his into an understandable whole.” 

Bertsekas’ close colleague in LIDS, Munther Dahleh, remembers, “what always struck me was that, through his writing, one could almost hear Dimitri speaking directly to the reader. His intuition, clarity of thought, and distinctive perspective come through beautifully in his books. They reflect not only his profound technical contributions, but also his passion for teaching and his desire to help others understand the subject at a deep level. … In particular, his joint book with John Tsitsiklis on neuro-dynamic programming is a tour de force. It anticipated and helped define many of the ideas that later became central to reinforcement learning and approximate dynamic programming.” 

Tsitsiklis himself remembers the co-writing process with Bertsekas fondly: “For Dimitri, research was a creative form, combining craftsmanship and the creativity that we usually call art.” The definition of art and its practice was a subject of great fascination for Bertsekas, and one that he explored at length in his 2025 essay, “Academia, Art, and Life,” an attempt to meaningfully categorize creative work into three broadly descriptive roles — technician, craftsman, and artist — and to explore the overlaps between the three types of practice. Beyond his clear and lucid writing, Bertsekas was known for his strong graphic eye, a talent which he put to good use not only developing illustrations for all his textbooks, but in taking memorable and artistically inspired photographs of his worldwide travels. 

Longtime collaborator and friend David Castañón, now a professor of electrical and computer engineering at Boston University, remembers Bertsekas as a true Renaissance man who drew inspiration from countless sources: “Dimitri had an insatiable curiosity for algorithmic ideas, both theory and practice. Many of these ideas were inspired by new technologies (parallel computers, reinforcement learning, chess-playing algorithms) ... Whenever we met, Dimitri would introduce new concepts of interest; we would work out theoretical details, design and conduct numerical experiments, and generate results. Then, Dimitri’s artistic talents would take over: designing graphics to illustrate concepts, typesetting text and figures for the papers to be completed. He had a rare gift for generating concise explanations of complex concepts. These talents led to his publishing company Athena Scientific, where Dimitri and his coauthors generated elegant pedagogical volumes with broad appeal.” Tsitsiklis agrees, noting, “for Dimitri, [research] was about discovering meaning, to uncover the 'right' way to view a subject, enrich it, and convey it in a crystal-clear manner through his prolific writings.” 

Many of the 20-plus books either authored or co-authored by Bertsekas were adopted for use as textbooks at MIT in subjects including data networks, nonlinear programming, dynamic programming, network optimization, parallel and distributed computation, neuro-dynamic programming, convex analysis and optimization, probability, and reinforcement learning. Stephen Boyd, Samsung Professor in the School of Engineering at Stanford, testifies to the great impact of Bertsekas’ collected works: “generations of researchers in optimization, control, and many related areas learned these topics from Dimitri’s exquisitely clear and beautifully written text books. I was one of them; indeed, I went into these fields in no small part because of Dimitri’s books, and his influence has been with me the whole time.”

That influence can be measured by the sheer number of awards and honors Bertsekas accumulated over the course of his career, including the INFORMS 1997 Prize for Research Excellence in the Interface Between Operations Research and Computer Science for Neuro-Dynamic Programming, the 2001 ACC John R. Ragazzini Education Award, the 2009 INFORMS Expository Writing Award, the 2014 ACC Richard E. Bellman Control Heritage Award for “contributions to the foundations of deterministic and stochastic optimization-based methods in systems and control,” the 2014 Khachiyan Prize for Life-Time Accomplishments in Optimization, the SIAM/MOS 2015 George B. Dantzig Prize, and the 2022 IEEE Control Systems Award. Together with his coauthor John Tsitsiklis, he was awarded the 2018 INFORMS John von Neumann Theory Prize for the contributions of the research monographs “Parallel and Distributed Computation” and “Neuro-Dynamic Programming.” In 2001, Bertsekas was elected to the U.S. National Academy of Engineering for “pioneering contributions to fundamental research, practice and education of optimization/control theory.”

However, a more personal measure of Bertsekas’ impact can be taken by the warmth and affection with which his friends, co-workers, and former students uniformly remember him. Co-author John Tsitsiklis wrote, “I was most fortunate to be one of his apprentices, and to have lived his warmth and friendship.” His former student at MIT, Angelia Nedich, later became Bertsekas’ colleague at Arizona State University. She remembers: “Dimitri was an exceptional mind, a gifted soul that shed light for us seekers, but at the same time he was very humble as he enjoyed simple moments of life, a sip of good coffee, a bite of flavorful food, or a glass of spicy margarita on our road trips in Southwest. That is how I love to remember him.”

Former student Benjamin Van Roy, now a professor at Stanford, wrote about the transformation of Bertsekas from authority figure to friend (and the subject of friendly teasing). “I recall the intimidating comments of more senior PhD students as I began my own PhD journey in LIDS. Some referred to Dimitri as an “immortal.” Another comment I recall fondly — and often reminded Dimitri about — was: “Professor Bertsekas is a very handsome man!” Their bond continued long after Van Roy’s graduation. “Dimitri was a treasure to humanity: one of the great scholars of our time, a Renaissance man, and a phenomenal role model. I was privileged to be among the many he mentored, and even more privileged to count him as a longtime friend.” Yuchao Li, a postdoc mentored by Bertsekas at Arizona State University, remembers his mentor as an almost inexhaustible source of both inspiration and support: “For me, Professor Bertsekas was like a loving father, full of infinite wisdom. … He seemed to know everything, yet he remained deeply humble and open-minded. He was always eager to help, even at the slightest sign of difficulty in my life. He instilled in me a lasting faith in the very best qualities of human beings, and I will strive to carry that faith forward.”

Bertsekas was preceded in death by his son Costas. He is survived by his wife Joanna Bertsekas (née Palashas); his son Telis Bertsekas and his wife Wendy Bertsekas; and three grandchildren, Melina, Alexandros, and Leonidas. 

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Diffuse puffs of “missing” matter surround most galaxies

Stars and galaxies make up much of the universe’s ordinary, observable matter. But for decades, scientists have wrestled with a cosmic conflict: There should be much more. 

Physicists have good estimates of how much matter was present in the early universe. Shortly after the Big Bang, roughly 83 percent of all matter in the universe was composed of invisible dark matter, with ordinary matter making up the rest. And yet, these estimates exceed the amount of ordinary matter seen in stars and galaxies today. Where, then, did all the missing ordinary matter go? 

Now MIT scientists, as part of the CHIME/FRB Collaboration, are using far-off radio signals to reveal missing matter in the vast space between galaxies. The team has developed a new method to search out missing matter by combining locations of galaxies with detections of fast radio bursts. 

A fast radio burst, or FRB, is an ultrabright, millisecond flash of radio waves emitted by extremely energetic phenomena in the distant universe. As it travels through space, the signal from a fast radio burst gets stretched, or “smeared,” in time. The more missing matter that it passes through, the more smeared the signal becomes. 

The MIT-led team measured the degree of smearing experienced by thousands of FRB signals detected on Earth. Then they compared each FRB smear with locations of galaxies across the universe to determine how much of an FRB’s smearing was due to galaxy matter versus other, missing matter. 

The new method revealed not only whether missing matter was present, but also where. Specifically, the researchers discovered that it exists in very diffuse clouds surrounding groups of galaxies. These clouds extend out from the galaxies, to much further distances than scientists had predicted. 

“We find that, overall, where there are more galaxies, there tends to be more missing matter around them,” says Haochen Wang, a graduate student in MIT’s Kavli Institute for Astrophysics and Space Research.

The results, reported today in the journal Physical Review Letters, support the idea that matter is flung outside a galaxy through black hole jets, exploding stars, and other highly energetic processes within a galaxy. What’s more, the findings suggest that such processes are more energetic than scientists had thought. 

“We’re finding missing matter that is pushed out to larger scales,” says Kiyoshi Masui, associate professor of physics at MIT. “These measurements indicate that star activity, and activity from black holes, is stronger and much more violent than predicted.”

Masui and Wang are co-authors of the new study, which includes Shion Andrew, Adam Lanman, Kenzie Nimmo, and Ryan Raikman from MIT, and collaborators from multiple other institutions as part of the CHIME/FRB Collaboration. 

The shape of matter

The vast majority of ordinary, observable matter in the universe is built from baryons — a type of subatomic particle that includes protons and neutrons, and that makes up most of an atom’s mass. Scientists estimate that just 17 percent of the early universe was made from this “baryonic” matter, shortly after the Big Bang. 

Some of that early matter was forged into every substantial thing we see today, from planets, stars, and galaxies, to our own bodies. But as scientists have realized, this matter doesn’t quite add up. The total mass of all the stars, galaxies, and galactic clouds is about a tenth of the baryonic matter that existed in the early universe. There must be more matter, likely in the spaces between galaxies. But the universe is vast. Any leftover matter likely exists at extremely low densities, of around a single proton per cubic meter, making it extremely challenging to detect.  

Recently, however, Masui and others have found that such missing matter could be sussed out using fast radio bursts. FRBs were first discovered in 2007, and since then astronomers have detected several thousand of the mysterious, ultrashort signals from distant galaxies, billions of light years away. 

“What makes FRBs good to probe missing matter is that they have a special property,” Wang says. “They start out as a very quick flash, and as they pass through matter, they smear out in time. And we can measure that smearing very precisely, which is directly proportional to how much missing matter the FRB passed through.”

Researchers have previously taken advantage of this smearing property of FRBs to detect missing matter around galaxies. These efforts have confirmed that tenous clouds exist in the vast spaces between galaxies. Masui and Wang wanted to go a step further. 

“We’re not just probing if the gas is with the galaxy or not, but we are seeing the shape of the missing matter that’s around the galaxies,” Wang says. “By mapping the shape of missing matter, we can understand how galaxies form and how they interact with their environment.”

Galactic fountains

For their new study, the team mapped the shape of missing matter around galaxies by cross-correlating thousands of FRB measurements with locations of millions of galaxies. They used data from two sources: the Canadian Hydrogen Intensity Mapping Experiment (CHIME) and the Dark Energy Spectroscopic Instrument (DESI) survey. 

CHIME is a large radio telescope located in British Columbia, Canada, that is designed to scan the entire northern sky for incoming radio waves. The telescope is sensitive to ultrashort, ultrabright radio signals, and since it began observing, CHIME has detected about 4,000 fast radio bursts across the sky. 

DESI is an instrument that is mounted on the Mayall Telescope at Kitt Peak National Observatory, near Tucson, Arizona. The instrument makes detailed measurements of the light coming from over 30 million galaxies, to provide estimates of dark energy — the mysterious force that drives the expansion of the universe. 

From CHIME’s catalog of detections, members of the CHIME/FRB collaboration analyzed 2,870 FRB signals. Each signal is a burst of radio waves, at multiple wavelengths, from highest to lowest energy. The higher-energy “blue” waves typically are less affected by any missing matter they travel through, and therefore should arrive at a detector before lower-energy “red” wavelengths, which are more delayed, or “smeared,” in time. 

The team measured the smearing of each FRB’s various wavelengths, which they could then directly relate to the amount of matter that the FRB must have traveled through before reaching CHIME’s detectors. Masui and Wang then correlated these measurements with the locations of over 6 million galaxies provided by DESI data. In this way, they could look for an association between the missing matter and the galaxies, and measure where one is in relation to the other. 

Their analysis revealed a pattern: Missing baryonic matter tended to be found around galaxies and galaxy clusters. But rather than gathering close to galaxies in a dense ball, missing matter was scattered across a large radius, similar to a diffuse puff. 

“A galaxy is maybe a few 100,000 light years across, and we found missing matter out to about 4 million light years,” Masui says. “That’s further than the simulations predict, by quite a bit.”

“We are finding that the activity in galaxies is messier than we thought,” Wang says. “They’re more like fountains, and really push out gas to very large distances.”

The new results show that fast radio bursts can be a reliable method by which to search for missing matter. As CHIME continues to detect more FRBs, the team says its method can only improve.

“We got it to work for the first time, and will get it to work even more precisely as data gets better,” Masui says. 

CHIME and CHIME/FRB are supported by the Canada Foundation for Innovation, the Natural Sciences and Engineering Research Council of Canada and, the provinces of British Columbia, Québec, and Ontario. This study was supported in part by the U.S. National Science Foundation.

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