Senior Madison Wang, a double major in creative writing and chemistry, developed her passion for writing in middle school. Her interest in chemistry fit nicely alongside her commitment to producing engaging narratives. 

Wang believes that world-building in stories supported by science and research can make for a more immersive reader experience.

“In science and in writing, you have to tell an effective story,” she says. “People respond well to stories.”  

A native of Buffalo, New York, Wang applied early action for admission to MIT and learned quickly that the Institute was where she wanted to be. “It was a really good fit,” she says. “There was positive energy and vibes, and I had a great feeling overall.”

The power of science and good storytelling

“Chemistry is practical, complex, and interesting,” says Wang. “It’s about quantifying natural laws and understanding how reality works.”

Chemistry and writing both help us “see the world’s irregularity,” she continues. Together, they can erase the artificial and arbitrary line separating one from the other and work in concert to tell a more complete story about the world, the ways in which we participate in building it, and how people and objects exist in and move through it. 

“Understanding magnetism, material properties, and believing in the power of magic in a good story … these are why we’re drawn to explore,” she says. “Chemistry describes why things are the way they are, and I use it for world-building in my creative writing.”

Wang lauds MIT’s creative writing program and cites a course she took with Comparative Media Studies/Writing Professor and Pulitzer Prize winner Junot Díaz as an affirmation of her choice. Seeing and understanding the world through the eyes of a scientist — its building blocks, the ways the pieces fit and function together — help explain her passion for chemistry, especially inorganic and physical chemistry.

Wang cites the work of authors like Sam Kean and Knight Science Journalism Program Director Deborah Blum as part of her inspiration to study science. The books “The Disappearing Spoon” by Kean and “The Poisoner’s Handbook” by Blum “both present historical perspectives, opting for a story style to discuss the events and people involved,” she says. “They each put a lot of work into bridging the gap between what can sometimes be sterile science and an effective narrative that gets people to care about why the science matters.”

Genres like fantasy and science fiction are complementary, according to Wang. “Constructing an effective world means ensuring readers understand characters’ motivations — the ‘why’ — and ensuring it makes sense,” she says. “It’s also important to show how actions and their consequences influence and motivate characters.” 

As she explores the world’s building blocks inside and outside the classroom, Wang works to navigate multiple genres in her writing, as with her studies in chemistry. “I like romance and horror, too,” she says. “I have gripes with committing to a single genre, so I just take whatever I like from each and put them in my stories.”

In chemistry, Wang favors an environment in which scientists can regularly test their ideas. “It’s important to ground chemistry in the real world to create connections for students,” she argues. Advancements in the field have occurred, she notes, because scientists could exit the realm of theory and apply ideas practically.

“Fritz Haber’s work on ammonia synthesis revolutionized approaches to food supply chains,” she says, referring to the German chemist and Nobel laureate. “Converting nitrogen and hydrogen gas to ammonia for fertilizer marked a dramatic shift in how farming could work.” This kind of work could only result from the consistent, controlled, practical application of the theories scientists consider in laboratory environments.

A future built on collaboration and cooperation

Watching the world change dramatically and seeing humanity struggle to grapple with the implications of phenomena like climate change, political unrest, and shifting alliances, Wang emphasizes the importance of deconstructing silos in academia and the workplace. Technology can be a tool for harm, she notes, so inviting more people inside previously segregated spaces helps everyone.

Criticism in both chemistry and writing, Wang believes, are valuable tools for continuous improvement. Effective communication, explaining complex concepts, and partnering to develop long-term solutions are invaluable when working at the intersection of history, art, and science. In writing, Wang says, criticism can help define areas to improve writers’ stories and shape interesting ideas.

“We’ve seen the positive results that can occur with effective science writing, which requires rigor and fact-checking,” she says. “MIT’s cross-disciplinary approach to our studies, alongside feedback from teachers and peers, is a great set of tools to carry with us regardless of where we are.”

Wang explores connections between science and stories in her leisure time, too. “I’m a member of MIT’s Anime Club and I enjoy participating in MIT’s Sport Taekwondo Club,” she says. The competitive aspect in tae kwon do allows for her to feed her competitive drive and gets her out of her head. Her participation in DAAMIT (Digital Art and Animation at MIT) creates connections with different groups of people and gives her ideas she can use to tell better stories. “It’s fascinating exploring others’ minds,” she says.

Wang argues that there’s a false divide between science and the humanities and wants the work she does after graduation to bridge that divide. “Writing and learning about science can help,” she asserts. “Fields like conservation and history allow for continued exploration of that intersection.”

Ultimately, Wang believes it’s important to examine narratives carefully and to question notions of science’s inherent superiority over humanities fields. “The humanities and science have equal value,” she says.

Every day, hundreds of chat messages flow between pilots, crew, and controllers of the Air Mobility Command's 618th Air Operations Center (AOC). These controllers direct a thousand-wide fleet of aircraft, juggling variables to determine which routes to fly, how much time fueling or loading supplies will take, or who can fly those missions. Their mission planning allows the U.S. Air Force to quickly respond to national security needs around the globe.

"It takes a lot of work to get a missile defense system across the world, for example, and this coordination used to be done through phone and email. Now, we are using chat, which creates opportunities for artificial intelligence to enhance our workflows," says Colonel Joseph Monaco, the director of strategy at the 618th AOC, which is the Department of Defense's largest air operations center.

The 618th AOC is sponsoring Lincoln Laboratory to develop these artificial intelligence tools, through a project called Conversational AI Technology for Transition (CAITT).

During a visit to Lincoln Laboratory from the 618th AOC's headquarters at Scott Air Force Base in Illinois, Colonel Monaco, Lieutenant Colonel Tim Heaton, and Captain Laura Quitiquit met with laboratory researchers to discuss CAITT. CAITT is a part of a broader effort to transition AI technology into a major Air Force modernization initiative, called the Next Generation Information Technology for Mobility Readiness Enhancement (NITMRE).

The type of AI being used in this project is natural language processing (NLP), which allows models to read and process human language. "We are utilizing NLP to map major trends in chat conversations, retrieve and cite specific information, and identify and contextualize critical decision points," says Courtland VanDam, a researcher in Lincoln Laboratory's AI Technology and Systems Group, which is leading the project. CAITT encompasses a suite of tools leveraging NLP.

One of the most mature tools, topic summarization, extracts trending topics from chat messages and formats those topics in a user-friendly display highlighting critical conversations and emerging issues. For example, a trending topic might read, "Crew members missing Congo visas, potential for delay." The entry shows the number of chats related to the topic and summarizes in bullet points the main points of conversations, linking back to specific chat exchanges.

"Our missions are very time-dependent, so we have to synthesize a lot of information quickly. This feature can really cue us as to where our efforts should be focused," says Monaco.

Another tool in production is semantic search. This tool improves upon the chat service's search engine, which currently returns empty results if chat messages do not contain every word in the query. Using the new tool, users can ask questions in a natural language format, such as why a specific aircraft is delayed, and receive intelligent results. "It incorporates a search model based on neural networks that can understand the user intent of the query and go beyond term matching," says VanDam.

Other tools under development aim to automatically add users to chat conversations deemed relevant to their expertise, predict the amount of ground time needed to unload specific types of cargo from aircraft, and summarize key processes from regulatory documents as a guide to operators as they develop mission plans.

The CAITT project grew out of the DAF–MIT AI Accelerator, a three-pronged effort between MIT, Lincoln Laboratory, and the Department of the Air Force (DAF) to develop and transition AI algorithms and systems to advance both the DAF and society. "Through our involvement in the AI Accelerator via the NITMRE project, we realized we could do something innovative with all of the unstructured chat information in the 618th AOC," says Heaton.

As laboratory researchers advance their prototypes of CAITT tools, they have begun to transition them to the 402nd Software Engineering Group, a software provider for the Department of Defense. That group will implement the tools into the operational software environment in use by the 618th AOC. 

Coordinating complicated interactive systems, whether it’s the different modes of transportation in a city or the various components that must work together to make an effective and efficient robot, is an increasingly important subject for software designers to tackle. Now, researchers at MIT have developed an entirely new way of approaching these complex problems, using simple diagrams as a tool to reveal better approaches to software optimization in deep-learning models.

They say the new method makes addressing these complex tasks so simple that it can be reduced to a drawing that would fit on the back of a napkin.

The new approach is described in the journal Transactions of Machine Learning Research, in a paper by incoming doctoral student Vincent Abbott and Professor Gioele Zardini of MIT’s Laboratory for Information and Decision Systems (LIDS).

“We designed a new language to talk about these new systems,” Zardini says. This new diagram-based “language” is heavily based on something called category theory, he explains.

It all has to do with designing the underlying architecture of computer algorithms — the programs that will actually end up sensing and controlling the various different parts of the system that’s being optimized. “The components are different pieces of an algorithm, and they have to talk to each other, exchange information, but also account for energy usage, memory consumption, and so on.” Such optimizations are notoriously difficult because each change in one part of the system can in turn cause changes in other parts, which can further affect other parts, and so on.

The researchers decided to focus on the particular class of deep-learning algorithms, which are currently a hot topic of research. Deep learning is the basis of the large artificial intelligence models, including large language models such as ChatGPT and image-generation models such as Midjourney. These models manipulate data by a “deep” series of matrix multiplications interspersed with other operations. The numbers within matrices are parameters, and are updated during long training runs, allowing for complex patterns to be found. Models consist of billions of parameters, making computation expensive, and hence improved resource usage and optimization invaluable.

Diagrams can represent details of the parallelized operations that deep-learning models consist of, revealing the relationships between algorithms and the parallelized graphics processing unit (GPU) hardware they run on, supplied by companies such as NVIDIA. “I’m very excited about this,” says Zardini, because “we seem to have found a language that very nicely describes deep learning algorithms, explicitly representing all the important things, which is the operators you use,” for example the energy consumption, the memory allocation, and any other parameter that you’re trying to optimize for.

Much of the progress within deep learning has stemmed from resource efficiency optimizations. The latest DeepSeek model showed that a small team can compete with top models from OpenAI and other major labs by focusing on resource efficiency and the relationship between software and hardware. Typically, in deriving these optimizations, he says, “people need a lot of trial and error to discover new architectures.” For example, a widely used optimization program called FlashAttention took more than four years to develop, he says. But with the new framework they developed, “we can really approach this problem in a more formal way.” And all of this is represented visually in a precisely defined graphical language.

But the methods that have been used to find these improvements “are very limited,” he says. “I think this shows that there’s a major gap, in that we don’t have a formal systematic method of relating an algorithm to either its optimal execution, or even really understanding how many resources it will take to run.” But now, with the new diagram-based method they devised, such a system exists.

Category theory, which underlies this approach, is a way of mathematically describing the different components of a system and how they interact in a generalized, abstract manner. Different perspectives can be related. For example, mathematical formulas can be related to algorithms that implement them and use resources, or descriptions of systems can be related to robust “monoidal string diagrams.” These visualizations allow you to directly play around and experiment with how the different parts connect and interact. What they developed, he says, amounts to “string diagrams on steroids,” which incorporates many more graphical conventions and many more properties.

“Category theory can be thought of as the mathematics of abstraction and composition,” Abbott says. “Any compositional system can be described using category theory, and the relationship between compositional systems can then also be studied.” Algebraic rules that are typically associated with functions can also be represented as diagrams, he says. “Then, a lot of the visual tricks we can do with diagrams, we can relate to algebraic tricks and functions. So, it creates this correspondence between these different systems.”

As a result, he says, “this solves a very important problem, which is that we have these deep-learning algorithms, but they’re not clearly understood as mathematical models.” But by representing them as diagrams, it becomes possible to approach them formally and systematically, he says.

One thing this enables is a clear visual understanding of the way parallel real-world processes can be represented by parallel processing in multicore computer GPUs. “In this way,” Abbott says, “diagrams can both represent a function, and then reveal how to optimally execute it on a GPU.”

The “attention” algorithm is used by deep-learning algorithms that require general, contextual information, and is a key phase of the serialized blocks that constitute large language models such as ChatGPT. FlashAttention is an optimization that took years to develop, but resulted in a sixfold improvement in the speed of attention algorithms.

Applying their method to the well-established FlashAttention algorithm, Zardini says that “here we are able to derive it, literally, on a napkin.” He then adds, “OK, maybe it’s a large napkin.” But to drive home the point about how much their new approach can simplify dealing with these complex algorithms, they titled their formal research paper on the work “FlashAttention on a Napkin.”

This method, Abbott says, “allows for optimization to be really quickly derived, in contrast to prevailing methods.” While they initially applied this approach to the already existing FlashAttention algorithm, thus verifying its effectiveness, “we hope to now use this language to automate the detection of improvements,” says Zardini, who in addition to being a principal investigator in LIDS, is the Rudge and Nancy Allen Assistant Professor of Civil and Environmental Engineering, and an affiliate faculty with the Institute for Data, Systems, and Society.

The plan is that ultimately, he says, they will develop the software to the point that “the researcher uploads their code, and with the new algorithm you automatically detect what can be improved, what can be optimized, and you return an optimized version of the algorithm to the user.”

In addition to automating algorithm optimization, Zardini notes that a robust analysis of how deep-learning algorithms relate to hardware resource usage allows for systematic co-design of hardware and software. This line of work integrates with Zardini’s focus on categorical co-design, which uses the tools of category theory to simultaneously optimize various components of engineered systems.

Abbott says that “this whole field of optimized deep learning models, I believe, is quite critically unaddressed, and that’s why these diagrams are so exciting. They open the doors to a systematic approach to this problem.”

“I’m very impressed by the quality of this research. ... The new approach to diagramming deep-learning algorithms used by this paper could be a very significant step,” says Jeremy Howard, founder and CEO of Answers.ai, who was not associated with this work. “This paper is the first time I’ve seen such a notation used to deeply analyze the performance of a deep-learning algorithm on real-world hardware. ... The next step will be to see whether real-world performance gains can be achieved.”

“This is a beautifully executed piece of theoretical research, which also aims for high accessibility to uninitiated readers — a trait rarely seen in papers of this kind,” says Petar Velickovic, a senior research scientist at Google DeepMind and a lecturer at Cambridge University, who was not associated with this work. These researchers, he says, “are clearly excellent communicators, and I cannot wait to see what they come up with next!”

The new diagram-based language, having been posted online, has already attracted great attention and interest from software developers. A reviewer from Abbott’s prior paper introducing the diagrams noted that “The proposed neural circuit diagrams look great from an artistic standpoint (as far as I am able to judge this).” “It’s technical research, but it’s also flashy!” Zardini says.

Coordinating complicated interactive systems, whether it’s the different modes of transportation in a city or the various components that must work together to make an effective and efficient robot, is an increasingly important subject for software designers to tackle. Now, researchers at MIT have developed an entirely new way of approaching these complex problems, using simple diagrams as a tool to reveal better approaches to software optimization in deep-learning models.

They say the new method makes addressing these complex tasks so simple that it can be reduced to a drawing that would fit on the back of a napkin.

The new approach is described in the journal Transactions of Machine Learning Research, in a paper by incoming doctoral student Vincent Abbott and Professor Gioele Zardini of MIT’s Laboratory for Information and Decision Systems (LIDS).

“We designed a new language to talk about these new systems,” Zardini says. This new diagram-based “language” is heavily based on something called category theory, he explains.

It all has to do with designing the underlying architecture of computer algorithms — the programs that will actually end up sensing and controlling the various different parts of the system that’s being optimized. “The components are different pieces of an algorithm, and they have to talk to each other, exchange information, but also account for energy usage, memory consumption, and so on.” Such optimizations are notoriously difficult because each change in one part of the system can in turn cause changes in other parts, which can further affect other parts, and so on.

The researchers decided to focus on the particular class of deep-learning algorithms, which are currently a hot topic of research. Deep learning is the basis of the large artificial intelligence models, including large language models such as ChatGPT and image-generation models such as Midjourney. These models manipulate data by a “deep” series of matrix multiplications interspersed with other operations. The numbers within matrices are parameters, and are updated during long training runs, allowing for complex patterns to be found. Models consist of billions of parameters, making computation expensive, and hence improved resource usage and optimization invaluable.

Diagrams can represent details of the parallelized operations that deep-learning models consist of, revealing the relationships between algorithms and the parallelized graphics processing unit (GPU) hardware they run on, supplied by companies such as NVIDIA. “I’m very excited about this,” says Zardini, because “we seem to have found a language that very nicely describes deep learning algorithms, explicitly representing all the important things, which is the operators you use,” for example the energy consumption, the memory allocation, and any other parameter that you’re trying to optimize for.

Much of the progress within deep learning has stemmed from resource efficiency optimizations. The latest DeepSeek model showed that a small team can compete with top models from OpenAI and other major labs by focusing on resource efficiency and the relationship between software and hardware. Typically, in deriving these optimizations, he says, “people need a lot of trial and error to discover new architectures.” For example, a widely used optimization program called FlashAttention took more than four years to develop, he says. But with the new framework they developed, “we can really approach this problem in a more formal way.” And all of this is represented visually in a precisely defined graphical language.

But the methods that have been used to find these improvements “are very limited,” he says. “I think this shows that there’s a major gap, in that we don’t have a formal systematic method of relating an algorithm to either its optimal execution, or even really understanding how many resources it will take to run.” But now, with the new diagram-based method they devised, such a system exists.

Category theory, which underlies this approach, is a way of mathematically describing the different components of a system and how they interact in a generalized, abstract manner. Different perspectives can be related. For example, mathematical formulas can be related to algorithms that implement them and use resources, or descriptions of systems can be related to robust “monoidal string diagrams.” These visualizations allow you to directly play around and experiment with how the different parts connect and interact. What they developed, he says, amounts to “string diagrams on steroids,” which incorporates many more graphical conventions and many more properties.

“Category theory can be thought of as the mathematics of abstraction and composition,” Abbott says. “Any compositional system can be described using category theory, and the relationship between compositional systems can then also be studied.” Algebraic rules that are typically associated with functions can also be represented as diagrams, he says. “Then, a lot of the visual tricks we can do with diagrams, we can relate to algebraic tricks and functions. So, it creates this correspondence between these different systems.”

As a result, he says, “this solves a very important problem, which is that we have these deep-learning algorithms, but they’re not clearly understood as mathematical models.” But by representing them as diagrams, it becomes possible to approach them formally and systematically, he says.

One thing this enables is a clear visual understanding of the way parallel real-world processes can be represented by parallel processing in multicore computer GPUs. “In this way,” Abbott says, “diagrams can both represent a function, and then reveal how to optimally execute it on a GPU.”

The “attention” algorithm is used by deep-learning algorithms that require general, contextual information, and is a key phase of the serialized blocks that constitute large language models such as ChatGPT. FlashAttention is an optimization that took years to develop, but resulted in a sixfold improvement in the speed of attention algorithms.

Applying their method to the well-established FlashAttention algorithm, Zardini says that “here we are able to derive it, literally, on a napkin.” He then adds, “OK, maybe it’s a large napkin.” But to drive home the point about how much their new approach can simplify dealing with these complex algorithms, they titled their formal research paper on the work “FlashAttention on a Napkin.”

This method, Abbott says, “allows for optimization to be really quickly derived, in contrast to prevailing methods.” While they initially applied this approach to the already existing FlashAttention algorithm, thus verifying its effectiveness, “we hope to now use this language to automate the detection of improvements,” says Zardini, who in addition to being a principal investigator in LIDS, is the Rudge and Nancy Allen Assistant Professor of Civil and Environmental Engineering, and an affiliate faculty with the Institute for Data, Systems, and Society.

The plan is that ultimately, he says, they will develop the software to the point that “the researcher uploads their code, and with the new algorithm you automatically detect what can be improved, what can be optimized, and you return an optimized version of the algorithm to the user.”

In addition to automating algorithm optimization, Zardini notes that a robust analysis of how deep-learning algorithms relate to hardware resource usage allows for systematic co-design of hardware and software. This line of work integrates with Zardini’s focus on categorical co-design, which uses the tools of category theory to simultaneously optimize various components of engineered systems.

Abbott says that “this whole field of optimized deep learning models, I believe, is quite critically unaddressed, and that’s why these diagrams are so exciting. They open the doors to a systematic approach to this problem.”

“I’m very impressed by the quality of this research. ... The new approach to diagramming deep-learning algorithms used by this paper could be a very significant step,” says Jeremy Howard, founder and CEO of Answers.ai, who was not associated with this work. “This paper is the first time I’ve seen such a notation used to deeply analyze the performance of a deep-learning algorithm on real-world hardware. ... The next step will be to see whether real-world performance gains can be achieved.”

“This is a beautifully executed piece of theoretical research, which also aims for high accessibility to uninitiated readers — a trait rarely seen in papers of this kind,” says Petar Velickovic, a senior research scientist at Google DeepMind and a lecturer at Cambridge University, who was not associated with this work. These researchers, he says, “are clearly excellent communicators, and I cannot wait to see what they come up with next!”

The new diagram-based language, having been posted online, has already attracted great attention and interest from software developers. A reviewer from Abbott’s prior paper introducing the diagrams noted that “The proposed neural circuit diagrams look great from an artistic standpoint (as far as I am able to judge this).” “It’s technical research, but it’s also flashy!” Zardini says.

On March 6, MIT launched its first lunar landing mission since the Apollo era, sending three payloads — the AstroAnt, the RESOURCE 3D camera, and the HUMANS nanowafer — to the moon’s south polar region. The mission was based out of Luna, a mission control space designed by MIT Department of Architecture students and faculty in collaboration with the MIT Space Exploration Initiative, Inploration, and Simpson Gumpertz and Heger. It is installed in the MIT Media Lab ground-floor gallery and is open to the public as part of Artfinity, MIT’s Festival for the Arts. The installation allows visitors to observe payload operators at work and interact with the software used for the mission, thanks to virtual reality.

A central hub for mission operations, the control room is a structural and conceptual achievement, balancing technical challenges with a vision for an immersive experience, and the result of a multidisciplinary approach. “This will be our moon on Earth,” says Mateo Fernandez, a third-year MArch student and 2024 MAD Design Fellow, who designed and fabricated Luna in collaboration with Nebyu Haile, a PhD student in the Building Technology program in the Department of Architecture, and Simon Lesina Debiasi, a research assistant in the SMArchS Computation program and part of the Self-Assembly Lab. “The design was meant for people — for the researchers to be able to see what’s happening at all times, and for the spectators to have a 360 panoramic view of everything that’s going on,” explains Fernandez. “A key vision of the team was to create a control room that broke away from the traditional, closed-off model — one that instead invited the public to observe, ask questions, and engage with the mission,” adds Haile.

For this project, students were advised by Skylar Tibbits, founder and co-director of the Self-Assembly Lab, associate professor of design research, and the Morningside Academy for Design (MAD)’s assistant director for education; J. Roc Jih, associate professor of the practice in architectural design; John Ochsendorf, MIT Class of 1942 Professor with appointments in the departments of Architecture and Civil and Environmental Engineering, and founding director of MAD; and Brandon Clifford, associate professor of architecture. The team worked closely with Cody Paige, director of the Space Exploration Initiative at the Media Lab, and her collaborators, emphasizing that they “tried to keep things very minimal, very simple, because at the end of the day,” explains Fernandez, “we wanted to create a design that allows the researchers to shine and the mission to shine.”

“This project grew out of the Space Architecture class we co-taught with Cody Paige and astronaut and MIT AeroAstro [Department of Aeronautics and Astronautics] faculty member Jeff Hoffman” in the fall semester, explains Tibbits. “Mateo was part of that studio, and from there, Cody invited us to design the mission control project. We then brought Mateo onboard, Simon, Nebyu, and the rest of the project team.” According to Tibbits, “this project represents MIT’s mind-and-hand ethos. We had designers, architects, artists, computational experts, and engineers working together, reflecting the polymath vision — left brain, right brain, the creative and the technical coming together to make this possible.”

Luna was funded and informed by Tibbits and Jih’s Professor Amar G. Bose Research Grant Program. “J. Jih and I had been doing research for the Bose grant around basalt and mono-material construction,” says Tibbits, adding that they “had explored foamed glass materials similar to pumice or foamed basalt, which are also similar to lunar regolith.” “FOAMGLAS is typically used for insulation, but it has diverse applications, including direct ground contact and exterior walls, with strong acoustic and thermal properties,” says Jih. “We helped Mateo understand how the material is used in architecture today, and how it could be applied in this project, aligning with our work on new material palettes and mono-material construction techniques.”

Additional funding came from Inploration, a project run by creative director, author, and curator Lawrence Azerrad, as well as expeditionary artist, curator, and analog astronaut artist Richelle Ellis, and Comcast, a Media Lab member company. It was also supported by the MIT Morningside Academy for Design through Fernandez’s Design Fellowship. Additional support came from industry members such as Owens Corning (construction materials), Bose (communications), as well as MIT Media Lab member companies Dell Technologies (operations hardware) and Steelcase (operations seating). 

A moon on Earth

While the lunar mission ended prematurely, the team says it achieved success in the design and construction of a control room embodying MIT’s design approach and capacity to explore new technologies while maintaining simplicity. Luna looks like variations of the moon, offering different perspectives of the moon’s round or crescent shape, depending on the viewer’s position.

“What’s remarkable is how close the final output is to Mateo’s original sketches and renderings,” Tibbits notes. “That often doesn’t happen — where the final built project aligns so precisely with the initial design intent.”

Luna’s entire structure is built from FOAMGLAS, a durable material composed of glass cells usually used for insulation. “FOAMGLAS is an interesting material,” says Lesina Debiasi, who supported fabrication efforts, ensuring a fast and safe process. “It’s relatively durable and light, but can easily be crumbled with a sharp edge or blade, requiring every step of the fabrication process — cutting, texturing, sealing — to be carefully controlled.”

Fernandez, whose design experience was influenced by the idea that “simple moves” are most powerful, explains: “We’re giving a second life to materials that are not thought of for building construction … and I think that’s an effective idea. Here, you don’t need wood, concrete, rebar — you can build with one material only.” While the interior of the dome-shaped construction is smooth, the exterior was hand textured to evoke the basalt-like surface of the moon.

The lightweight cellular glass produced by Owens Corning, which sponsored part of the material, comes as an unexpected choice for a compression structure — a type of architectural design where stability is achieved through the natural force of compression, usually implying heavy materials. The control room doesn’t use connections or additional supports, and depends upon the precise placement, size, and weight of individual blocks to create a stable form from a succession of arches.

“Traditional compression structures rely on their own weight for stability, but using a material that is more than 10 times lighter than masonry meant we had to rethink everything. It was about finding the perfect balance between design vision and structural integrity,” reflects Haile, who was responsible for the structural calculations for the dome and its support.

Compression relies on gravity, and wouldn’t be a viable construction method on the moon itself. “We’re building using physics, loads, structures, and equilibrium to create this thing that looks like the moon, but depends on Earth’s forces to be built. I think people don’t see that at first, but there’s something cheeky and ironic about it,” confides Fernandez, acknowledging that the project merges historical building methods with contemporary design.

The location and purpose of Luna — both a work space and an installation engaging the public — implied balancing privacy and transparency to achieve functionality. “One of the most important design elements that reflected this vision was the openness of the dome,” says Haile. “We worked closely from the start to find the right balance — adjusting the angle and size of the opening to make the space feel welcoming, while still offering some privacy to those working inside.”

The power of collaboration

With the FOAMGLAS material, the team had to invent a fabrication process that would achieve the initial vision while maintaining structural integrity. Sourcing a material with radically different properties compared to conventional construction implied collaborating closely on the engineering front, the lightweight nature of the cellular glass requiring creative problem-solving: “What appears perfect in digital models doesn’t always translate seamlessly into the real world,” says Haile. “The slope, curves, and overall geometry directly determine whether the dome will stand, requiring Mateo and me to work in sync from the very beginning through the end of construction.” While the engineering was primarily led by Haile and Ochsendorf, the structural design was officially reviewed and approved by Paul Kassabian at Simpson Gumpertz and Heger (SGH), ensuring compliance with engineering standards and building codes.

“None of us had worked with FOAMGLAS before, and we needed to figure out how best to cut, texture, and seal it,” says Lesina Debiasi. “Since each row consists of a distinct block shape and specific angles, ensuring accuracy and repeatability across all the blocks became a major challenge. Since we had to cut each individual block four times before we were able to groove and texture the surface, creating a safe production process and mitigating the distribution of dust was critical,” he explains. “Working inside a tent, wearing personal protective equipment like masks, visors, suits, and gloves made it possible to work for an extended period with this material.”

In addition, manufacturing introduced small margins of error threatening the structural integrity of the dome, prompting hands-on experimentation. “The control room is built from 12 arches,” explains Fernandez. “When one of the arches closes, it becomes stable, and you can move on to the next one … Going from side to side, you meet at the middle and close the arch using a special block — a keystone, which was cut to measure,” he says. “In conversations with our advisors, we decided to account for irregularities in the final keystone of each row. Once this custom keystone sat in place, the forces would stabilize the arch and make it secure,” adds Lesina Debiasi.

“This project exemplified the best practices of engineers and architects working closely together from design inception to completion — something that was historically common but is less typical today,” says Haile. “This collaboration was not just necessary — it ultimately improved the final result.”

Fernandez, who is supported this year by the MAD Design Fellowship, expressed how “the fellowship gave [him] the freedom to explore [his] passions and also keep [his] agency.”

“In a way, this project embodies what design education at MIT should be,” Tibbits reflects. “We’re building at full scale, with real-world constraints, experimenting at the limits of what we know — design, computation, engineering, and science. It’s hands-on, highly experimental, and deeply collaborative, which is exactly what we dream of for MAD, and MIT’s design education more broadly.”

“Luna, our physical lunar mission control, highlights the incredible collaboration across the Media Lab, Architecture, and the School of Engineering to bring our lunar mission to the world. We are democratizing access to space for all,” says Dava Newman, Media Lab director and Apollo Professor of Astronautics.

A full list of contributors and supporters can be found at the Morningside Academy for Design's website.

For a robot, the real world is a lot to take in. Making sense of every data point in a scene can take a huge amount of computational effort and time. Using that information to then decide how to best help a human is an even thornier exercise.

Now, MIT roboticists have a way to cut through the data noise, to help robots focus on the features in a scene that are most relevant for assisting humans.

Their approach, which they aptly dub “Relevance,” enables a robot to use cues in a scene, such as audio and visual information, to determine a human’s objective and then quickly identify the objects that are most likely to be relevant in fulfilling that objective. The robot then carries out a set of maneuvers to safely offer the relevant objects or actions to the human.

The researchers demonstrated the approach with an experiment that simulated a conference breakfast buffet. They set up a table with various fruits, drinks, snacks, and tableware, along with a robotic arm outfitted with a microphone and camera. Applying the new Relevance approach, they showed that the robot was able to correctly identify a human’s objective and appropriately assist them in different scenarios.

In one case, the robot took in visual cues of a human reaching for a can of prepared coffee, and quickly handed the person milk and a stir stick. In another scenario, the robot picked up on a conversation between two people talking about coffee, and offered them a can of coffee and creamer.

Overall, the robot was able to predict a human’s objective with 90 percent accuracy and to identify relevant objects with 96 percent accuracy. The method also improved a robot’s safety, reducing the number of collisions by more than 60 percent, compared to carrying out the same tasks without applying the new method.

“This approach of enabling relevance could make it much easier for a robot to interact with humans,” says Kamal Youcef-Toumi, professor of mechanical engineering at MIT. “A robot wouldn’t have to ask a human so many questions about what they need. It would just actively take information from the scene to figure out how to help.”

Youcef-Toumi’s group is exploring how robots programmed with Relevance can help in smart manufacturing and warehouse settings, where they envision robots working alongside and intuitively assisting humans.

Youcef-Toumi, along with graduate students Xiaotong Zhang and Dingcheng Huang, will present their new method at the IEEE International Conference on Robotics and Automation (ICRA) in May. The work builds on another paper presented at ICRA the previous year.

Finding focus

The team’s approach is inspired by our own ability to gauge what’s relevant in daily life. Humans can filter out distractions and focus on what’s important, thanks to a region of the brain known as the Reticular Activating System (RAS). The RAS is a bundle of neurons in the brainstem that acts subconsciously to prune away unnecessary stimuli, so that a person can consciously perceive the relevant stimuli. The RAS helps to prevent sensory overload, keeping us, for example, from fixating on every single item on a kitchen counter, and instead helping us to focus on pouring a cup of coffee.

“The amazing thing is, these groups of neurons filter everything that is not important, and then it has the brain focus on what is relevant at the time,” Youcef-Toumi explains. “That’s basically what our proposition is.”

He and his team developed a robotic system that broadly mimics the RAS’s ability to selectively process and filter information. The approach consists of four main phases. The first is a watch-and-learn “perception” stage, during which a robot takes in audio and visual cues, for instance from a microphone and camera, that are continuously fed into an AI “toolkit.” This toolkit can include a large language model (LLM) that processes audio conversations to identify keywords and phrases, and various algorithms that detect and classify objects, humans, physical actions, and task objectives. The AI toolkit is designed to run continuously in the background, similarly to the subconscious filtering that the brain’s RAS performs.

The second stage is a “trigger check” phase, which is a periodic check that the system performs to assess if anything important is happening, such as whether a human is present or not. If a human has stepped into the environment, the system’s third phase will kick in. This phase is the heart of the team’s system, which acts to determine the features in the environment that are most likely relevant to assist the human.

To establish relevance, the researchers developed an algorithm that takes in real-time predictions made by the AI toolkit. For instance, the toolkit’s LLM may pick up the keyword “coffee,” and an action-classifying algorithm may label a person reaching for a cup as having the objective of “making coffee.” The team’s Relevance method would factor in this information to first determine the “class” of objects that have the highest probability of being relevant to the objective of “making coffee.” This might automatically filter out classes such as “fruits” and “snacks,” in favor of “cups” and “creamers.” The algorithm would then further filter within the relevant classes to determine the most relevant “elements.” For instance, based on visual cues of the environment, the system may label a cup closest to a person as more relevant — and helpful — than a cup that is farther away.

In the fourth and final phase, the robot would then take the identified relevant objects and plan a path to physically access and offer the objects to the human.

Helper mode

The researchers tested the new system in experiments that simulate a conference breakfast buffet. They chose this scenario based on the publicly available Breakfast Actions Dataset, which comprises videos and images of typical activities that people perform during breakfast time, such as preparing coffee, cooking pancakes, making cereal, and frying eggs. Actions in each video and image are labeled, along with the overall objective (frying eggs, versus making coffee).

Using this dataset, the team tested various algorithms in their AI toolkit, such that, when receiving actions of a person in a new scene, the algorithms could accurately label and classify the human tasks and objectives, and the associated relevant objects.

In their experiments, they set up a robotic arm and gripper and instructed the system to assist humans as they approached a table filled with various drinks, snacks, and tableware. They found that when no humans were present, the robot’s AI toolkit operated continuously in the background, labeling and classifying objects on the table.

When, during a trigger check, the robot detected a human, it snapped to attention, turning on its Relevance phase and quickly identifying objects in the scene that were most likely to be relevant, based on the human’s objective, which was determined by the AI toolkit.

“Relevance can guide the robot to generate seamless, intelligent, safe, and efficient assistance in a highly dynamic environment,” says co-author Zhang.

Going forward, the team hopes to apply the system to scenarios that resemble workplace and warehouse environments, as well as to other tasks and objectives typically performed in household settings.

“I would want to test this system in my home to see, for instance, if I’m reading the paper, maybe it can bring me coffee. If I’m doing laundry, it can bring me a laundry pod. If I’m doing repair, it can bring me a screwdriver,” Zhang says. “Our vision is to enable human-robot interactions that can be much more natural and fluent.”

This research was made possible by the support and partnership of King Abdulaziz City for Science and Technology (KACST) through the Center for Complex Engineering Systems at MIT and KACST.

Nearly 150 years ago, scientists began to imagine how information might flow through the brain based on the shapes of neurons they had seen under the microscopes of the time. With today’s imaging technologies, scientists can zoom in much further, seeing the tiny synapses through which neurons communicate with one another, and even the molecules the cells use to relay their messages. These inside views can spark new ideas about how healthy brains work and reveal important changes that contribute to disease.

This sharper view of biology is not just about the advances that have made microscopes more powerful than ever before. Using methodology developed in the lab of MIT McGovern Institute for Brain Research investigator Edward Boyden, researchers around the world are imaging samples that have been swollen to as much as 20 times their original size so their finest features can be seen more clearly.

“It’s a very different way to do microscopy,” says Boyden, who is also a Howard Hughes Medical Institute (HHMI) investigator, a professor of brain and cognitive sciences and biological engineering, and a member of the Yang Tan Collective at MIT. “In contrast to the last 300 years of bioimaging, where you use a lens to magnify an image of light from an object, we physically magnify objects themselves.” Once a tissue is expanded, Boyden says, researchers can see more even with widely available, conventional microscopy hardware.

Boyden’s team introduced this approach, which they named expansion microscopy (ExM), in 2015. Since then, they have been refining the method and adding to its capabilities, while researchers at MIT and beyond deploy it to learn about life on the smallest of scales.

“It’s spreading very rapidly throughout biology and medicine,” Boyden says. “It’s being applied to kidney disease, the fruit fly brain, plant seeds, the microbiome, Alzheimer’s disease, viruses, and more.”

Origins of ExM 

To develop expansion microscopy, Boyden and his team turned to hydrogel, a material with remarkable water-absorbing properties that had already been put to practical use; it’s layered inside disposable diapers to keep babies dry. Boyden’s lab hypothesized that hydrogels could retain their structure while they absorbed hundreds of times their original weight in water, expanding the space between their chemical components as they swell.

After some experimentation, Boyden’s team settled on four key steps to enlarging tissue samples for better imaging. First, the tissue must be infused with a hydrogel. Components of the tissue, biomolecules, are anchored to the gel’s web-like matrix, linking them directly to the molecules that make up the gel. Then the tissue is chemically softened and water is added. As the hydrogel absorbs the water, it swells and the tissue expands, growing evenly so the relative positions of its components are preserved.

Boyden and graduate students Fei Chen and Paul Tillberg’s first report on expansion microscopy was published in the journal Science in 2015. In it, the team demonstrated that by spreading apart molecules that had been crowded inside cells, features that would have blurred together under a standard light microscope became separate and distinct. Light microscopes can discriminate between objects that are separated by about 300 nanometers — a limit imposed by the laws of physics. With expansion microscopy, Boyden’s group reported an effective resolution of about 70 nanometers, for a fourfold expansion.

Boyden says this is a level of clarity that biologists need. “Biology is fundamentally, in the end, a nanoscale science,” he says. “Biomolecules are nanoscale, and the interactions between biomolecules are over nanoscale distances. Many of the most important problems in biology and medicine involve nanoscale questions.” Several kinds of sophisticated microscopes, each with their own advantages and disadvantages, can bring this kind of detail to light. But those methods are costly and require specialized skills, making them inaccessible for most researchers. “Expansion microscopy democratizes nanoimaging,” Boyden says. “Now, anybody can go look at the building blocks of life and how they relate to each other.”

Empowering scientists

Since Boyden’s team introduced expansion microscopy in 2015, research groups around the world have published hundreds of papers reporting on discoveries they have made using expansion microscopy. For neuroscientists, the technique has lit up the intricacies of elaborate neural circuits, exposed how particular proteins organize themselves at and across synapses to facilitate communication between neurons, and uncovered changes associated with aging and disease.

It has been equally empowering for studies beyond the brain. Sabrina Absalon uses expansion microscopy every week in her lab at Indiana University School of Medicine to study the malaria parasite, a single-celled organism packed with specialized structures that enable it to infect and live inside its hosts. The parasite is so small, most of those structures can’t be seen with ordinary light microscopy. “So as a cell biologist, I’m losing the biggest tool to infer protein function, organelle architecture, morphology, linked to function, and all those things — which is my eye,” she says. With expansion, she can not only see the organelles inside a malaria parasite, she can watch them assemble and follow what happens to them when the parasite divides. Understanding those processes, she says, could help drug developers find new ways to interfere with the parasite’s life cycle.

Absalon adds that the accessibility of expansion microscopy is particularly important in the field of parasitology, where a lot of research is happening in parts of the world where resources are limited. Workshops and training programs in Africa, South America, and Asia are ensuring the technology reaches scientists whose communities are directly impacted by malaria and other parasites. “Now they can get super-resolution imaging without very fancy equipment,” Absalon says.

Always improving

Since 2015, Boyden’s interdisciplinary lab group has found a variety of creative ways to improve expansion microscopy and use it in new ways. Their standard technique today enables better labeling, bigger expansion factors, and higher-resolution imaging. Cellular features less than 20 nanometers from one another can now be separated enough to appear distinct under a light microscope.

They’ve also adapted their protocols to work with a range of important sample types, from entire roundworms (popular among neuroscientists, developmental biologists, and other researchers) to clinical samples. In the latter regard, they’ve shown that expansion can help reveal subtle signs of disease, which could enable earlier or less-costly diagnoses.

Originally, the group optimized its protocol for visualizing proteins inside cells, by labeling proteins of interest and anchoring them to the hydrogel prior to expansion. With a new way of processing samples, users can now re-stain their expanded samples with new labels for multiple rounds of imaging, so they can pinpoint the positions of dozens of different proteins in the same tissue. That means researchers can visualize how molecules are organized with respect to one another and how they might interact, or survey large sets of proteins to see, for example, what changes with disease.

But better views of proteins were just the beginning for expansion microscopy. “We want to see everything,” Boyden says. “We’d love to see every biomolecule there is, with precision down to atomic scale.” They’re not there yet — but with new probes and modified procedures, it’s now possible to see not just proteins, but also RNA and lipids in expanded tissue samples.

Labeling lipids, including those that form the membranes surrounding cells, means researchers can now see clear outlines of cells in expanded tissues. With the enhanced resolution afforded by expansion, even the slender projections of neurons can be traced through an image. Typically, researchers have relied on electron microscopy, which generates exquisitely detailed pictures but requires expensive equipment, to map the brain’s circuitry. “Now, you can get images that look a lot like electron microscopy images, but on regular old light microscopes — the kind that everybody has access to,” Boyden says.

Boyden says expansion can be powerful in combination with other cutting-edge tools. When expanded samples are used with an ultra-fast imaging method developed by Eric Betzig, an HHMI investigator at the University of California at Berkeley, called lattice light-sheet microscopy, the entire brain of a fruit fly can be imaged at high resolution in just a few days.

And when RNA molecules are anchored within a hydrogel network and then sequenced in place, scientists can see exactly where inside cells the instructions for building specific proteins are positioned, which Boyden’s team demonstrated in a collaboration with Harvard University geneticist George Church and then-MIT-professor Aviv Regev. “Expansion basically upgrades many other technologies’ resolutions,” Boyden says. “You’re doing mass-spec imaging, X-ray imaging, or Raman imaging? Expansion just improved your instrument.”

Expanding possibilities

Ten years past the first demonstration of expansion microscopy’s power, Boyden and his team are committed to continuing to make expansion microscopy more powerful. “We want to optimize it for different kinds of problems, and making technologies faster, better, and cheaper is always important,” he says. But the future of expansion microscopy will be propelled by innovators outside the Boyden lab, too. “Expansion is not only easy to do, it’s easy to modify — so lots of other people are improving expansion in collaboration with us, or even on their own,” Boyden says.

Boyden points to a group led by Silvio Rizzoli at the University Medical Center Göttingen in Germany that, collaborating with Boyden, has adapted the expansion protocol to discern the physical shapes of proteins. At the Korea Advanced Institute of Science and Technology, researchers led by Jae-Byum Chang, a former postdoc in Boyden’s group, have worked out how to expand entire bodies of mouse embryos and young zebra fish, collaborating with Boyden to set the stage for examining developmental processes and long-distance neural connections with a new level of detail. And mapping connections within the brain’s dense neural circuits could become easier with light-microscopy based connectomics, an approach developed by Johann Danzl and colleagues at the Institute of Science and Technology in Austria that takes advantage of both the high resolution and molecular information that expansion microscopy can reveal.

“The beauty of expansion is that it lets you see a biological system down to its smallest building blocks,” Boyden says.

His team is intent on pushing the method to its physical limits, and anticipates new opportunities for discovery as they do. “If you can map the brain or any biological system at the level of individual molecules, you might be able to see how they all work together as a network — how life really operates,” he says.

Nearly 150 years ago, scientists began to imagine how information might flow through the brain based on the shapes of neurons they had seen under the microscopes of the time. With today’s imaging technologies, scientists can zoom in much further, seeing the tiny synapses through which neurons communicate with one another, and even the molecules the cells use to relay their messages. These inside views can spark new ideas about how healthy brains work and reveal important changes that contribute to disease.

This sharper view of biology is not just about the advances that have made microscopes more powerful than ever before. Using methodology developed in the lab of MIT McGovern Institute for Brain Research investigator Edward Boyden, researchers around the world are imaging samples that have been swollen to as much as 20 times their original size so their finest features can be seen more clearly.

“It’s a very different way to do microscopy,” says Boyden, who is also a Howard Hughes Medical Institute (HHMI) investigator, a professor of brain and cognitive sciences and biological engineering, and a member of the Yang Tan Collective at MIT. “In contrast to the last 300 years of bioimaging, where you use a lens to magnify an image of light from an object, we physically magnify objects themselves.” Once a tissue is expanded, Boyden says, researchers can see more even with widely available, conventional microscopy hardware.

Boyden’s team introduced this approach, which they named expansion microscopy (ExM), in 2015. Since then, they have been refining the method and adding to its capabilities, while researchers at MIT and beyond deploy it to learn about life on the smallest of scales.

“It’s spreading very rapidly throughout biology and medicine,” Boyden says. “It’s being applied to kidney disease, the fruit fly brain, plant seeds, the microbiome, Alzheimer’s disease, viruses, and more.”

Origins of ExM 

To develop expansion microscopy, Boyden and his team turned to hydrogel, a material with remarkable water-absorbing properties that had already been put to practical use; it’s layered inside disposable diapers to keep babies dry. Boyden’s lab hypothesized that hydrogels could retain their structure while they absorbed hundreds of times their original weight in water, expanding the space between their chemical components as they swell.

After some experimentation, Boyden’s team settled on four key steps to enlarging tissue samples for better imaging. First, the tissue must be infused with a hydrogel. Components of the tissue, biomolecules, are anchored to the gel’s web-like matrix, linking them directly to the molecules that make up the gel. Then the tissue is chemically softened and water is added. As the hydrogel absorbs the water, it swells and the tissue expands, growing evenly so the relative positions of its components are preserved.

Boyden and graduate students Fei Chen and Paul Tillberg’s first report on expansion microscopy was published in the journal Science in 2015. In it, the team demonstrated that by spreading apart molecules that had been crowded inside cells, features that would have blurred together under a standard light microscope became separate and distinct. Light microscopes can discriminate between objects that are separated by about 300 nanometers — a limit imposed by the laws of physics. With expansion microscopy, Boyden’s group reported an effective resolution of about 70 nanometers, for a fourfold expansion.

Boyden says this is a level of clarity that biologists need. “Biology is fundamentally, in the end, a nanoscale science,” he says. “Biomolecules are nanoscale, and the interactions between biomolecules are over nanoscale distances. Many of the most important problems in biology and medicine involve nanoscale questions.” Several kinds of sophisticated microscopes, each with their own advantages and disadvantages, can bring this kind of detail to light. But those methods are costly and require specialized skills, making them inaccessible for most researchers. “Expansion microscopy democratizes nanoimaging,” Boyden says. “Now, anybody can go look at the building blocks of life and how they relate to each other.”

Empowering scientists

Since Boyden’s team introduced expansion microscopy in 2015, research groups around the world have published hundreds of papers reporting on discoveries they have made using expansion microscopy. For neuroscientists, the technique has lit up the intricacies of elaborate neural circuits, exposed how particular proteins organize themselves at and across synapses to facilitate communication between neurons, and uncovered changes associated with aging and disease.

It has been equally empowering for studies beyond the brain. Sabrina Absalon uses expansion microscopy every week in her lab at Indiana University School of Medicine to study the malaria parasite, a single-celled organism packed with specialized structures that enable it to infect and live inside its hosts. The parasite is so small, most of those structures can’t be seen with ordinary light microscopy. “So as a cell biologist, I’m losing the biggest tool to infer protein function, organelle architecture, morphology, linked to function, and all those things — which is my eye,” she says. With expansion, she can not only see the organelles inside a malaria parasite, she can watch them assemble and follow what happens to them when the parasite divides. Understanding those processes, she says, could help drug developers find new ways to interfere with the parasite’s life cycle.

Absalon adds that the accessibility of expansion microscopy is particularly important in the field of parasitology, where a lot of research is happening in parts of the world where resources are limited. Workshops and training programs in Africa, South America, and Asia are ensuring the technology reaches scientists whose communities are directly impacted by malaria and other parasites. “Now they can get super-resolution imaging without very fancy equipment,” Absalon says.

Always improving

Since 2015, Boyden’s interdisciplinary lab group has found a variety of creative ways to improve expansion microscopy and use it in new ways. Their standard technique today enables better labeling, bigger expansion factors, and higher-resolution imaging. Cellular features less than 20 nanometers from one another can now be separated enough to appear distinct under a light microscope.

They’ve also adapted their protocols to work with a range of important sample types, from entire roundworms (popular among neuroscientists, developmental biologists, and other researchers) to clinical samples. In the latter regard, they’ve shown that expansion can help reveal subtle signs of disease, which could enable earlier or less-costly diagnoses.

Originally, the group optimized its protocol for visualizing proteins inside cells, by labeling proteins of interest and anchoring them to the hydrogel prior to expansion. With a new way of processing samples, users can now re-stain their expanded samples with new labels for multiple rounds of imaging, so they can pinpoint the positions of dozens of different proteins in the same tissue. That means researchers can visualize how molecules are organized with respect to one another and how they might interact, or survey large sets of proteins to see, for example, what changes with disease.

But better views of proteins were just the beginning for expansion microscopy. “We want to see everything,” Boyden says. “We’d love to see every biomolecule there is, with precision down to atomic scale.” They’re not there yet — but with new probes and modified procedures, it’s now possible to see not just proteins, but also RNA and lipids in expanded tissue samples.

Labeling lipids, including those that form the membranes surrounding cells, means researchers can now see clear outlines of cells in expanded tissues. With the enhanced resolution afforded by expansion, even the slender projections of neurons can be traced through an image. Typically, researchers have relied on electron microscopy, which generates exquisitely detailed pictures but requires expensive equipment, to map the brain’s circuitry. “Now, you can get images that look a lot like electron microscopy images, but on regular old light microscopes — the kind that everybody has access to,” Boyden says.

Boyden says expansion can be powerful in combination with other cutting-edge tools. When expanded samples are used with an ultra-fast imaging method developed by Eric Betzig, an HHMI investigator at the University of California at Berkeley, called lattice light-sheet microscopy, the entire brain of a fruit fly can be imaged at high resolution in just a few days.

And when RNA molecules are anchored within a hydrogel network and then sequenced in place, scientists can see exactly where inside cells the instructions for building specific proteins are positioned, which Boyden’s team demonstrated in a collaboration with Harvard University geneticist George Church and then-MIT-professor Aviv Regev. “Expansion basically upgrades many other technologies’ resolutions,” Boyden says. “You’re doing mass-spec imaging, X-ray imaging, or Raman imaging? Expansion just improved your instrument.”

Expanding possibilities

Ten years past the first demonstration of expansion microscopy’s power, Boyden and his team are committed to continuing to make expansion microscopy more powerful. “We want to optimize it for different kinds of problems, and making technologies faster, better, and cheaper is always important,” he says. But the future of expansion microscopy will be propelled by innovators outside the Boyden lab, too. “Expansion is not only easy to do, it’s easy to modify — so lots of other people are improving expansion in collaboration with us, or even on their own,” Boyden says.

Boyden points to a group led by Silvio Rizzoli at the University Medical Center Göttingen in Germany that, collaborating with Boyden, has adapted the expansion protocol to discern the physical shapes of proteins. At the Korea Advanced Institute of Science and Technology, researchers led by Jae-Byum Chang, a former postdoc in Boyden’s group, have worked out how to expand entire bodies of mouse embryos and young zebra fish, collaborating with Boyden to set the stage for examining developmental processes and long-distance neural connections with a new level of detail. And mapping connections within the brain’s dense neural circuits could become easier with light-microscopy based connectomics, an approach developed by Johann Danzl and colleagues at the Institute of Science and Technology in Austria that takes advantage of both the high resolution and molecular information that expansion microscopy can reveal.

“The beauty of expansion is that it lets you see a biological system down to its smallest building blocks,” Boyden says.

His team is intent on pushing the method to its physical limits, and anticipates new opportunities for discovery as they do. “If you can map the brain or any biological system at the level of individual molecules, you might be able to see how they all work together as a network — how life really operates,” he says.

MIT engineers have developed a technique to grow and peel ultrathin “skins” of electronic material. The method could pave the way for new classes of electronic devices, such as ultrathin wearable sensors, flexible transistors and computing elements, and highly sensitive and compact imaging devices. 

As a demonstration, the team fabricated a thin membrane of pyroelectric material — a class of heat-sensing material that produces an electric current in response to changes in temperature. The thinner the pyroelectric material, the better it is at sensing subtle thermal variations.

With their new method, the team fabricated the thinnest pyroelectric membrane yet, measuring 10 nanometers thick, and demonstrated that the film is highly sensitive to heat and radiation across the far-infrared spectrum.

The newly developed film could enable lighter, more portable, and highly accurate far-infrared (IR) sensing devices, with potential applications for night-vision eyewear and autonomous driving in foggy conditions. Current state-of-the-art far-IR sensors require bulky cooling elements. In contrast, the new pyroelectric thin film requires no cooling and is sensitive to much smaller changes in temperature. The researchers are exploring ways to incorporate the film into lighter, higher-precision night-vision glasses.

“This film considerably reduces weight and cost, making it lightweight, portable, and easier to integrate,” Xinyuan Zhang, a graduate student in MIT’s Department of Materials Science and Engineering (DMSE). “For example, it could be directly worn on glasses.”

The heat-sensing film could also have applications in environmental and biological sensing, as well as imaging of astrophysical phenomena that emit far-infrared radiation.

What’s more, the new lift-off technique is generalizable beyond pyroelectric materials. The researchers plan to apply the method to make other ultrathin, high-performance semiconducting films.

Their results are reported today in a paper appearing in the journal Nature. The study’s MIT co-authors are first author Xinyuan Zhang, Sangho Lee, Min-Kyu Song, Haihui Lan, Jun Min Suh, Jung-El Ryu, Yanjie Shao, Xudong Zheng, Ne Myo Han, and Jeehwan Kim, associate professor of mechanical engineering and of materials science and engineering, along with researchers at the University Wisconsin at Madison led by Professor Chang-Beom Eom and authors from multiple other institutions.

Chemical peel

Kim’s group at MIT is finding new ways to make smaller, thinner, and more flexible electronics. They envision that such ultrathin computing “skins” can be incorporated into everything from smart contact lenses and wearable sensing fabrics to stretchy solar cells and bendable displays. To realize such devices, Kim and his colleagues have been experimenting with methods to grow, peel, and stack semiconducting elements, to fabricate ultrathin, multifunctional electronic thin-film membranes.

One method that Kim has pioneered is “remote epitaxy” — a technique where semiconducting materials are grown on a single-crystalline substrate, with an ultrathin layer of graphene in between. The substrate’s crystal structure serves as a scaffold along which the new material can grow. The graphene acts as a nonstick layer, similar to Teflon, making it easy for researchers to peel off the new film and transfer it onto flexible and stacked electronic devices. After peeling off the new film, the underlying substrate can be reused to make additional thin films.

Kim has applied remote epitaxy to fabricate thin films with various characteristics. In trying different combinations of semiconducting elements, the researchers happened to notice that a certain pyroelectric material, called PMN-PT, did not require an intermediate layer assist in order to separate from its substrate. Just by growing PMN-PT directly on a single-crystalline substrate, the researchers could then remove the grown film, with no rips or tears to its delicate lattice.

“It worked surprisingly well,” Zhang says. “We found the peeled film is atomically smooth.”

Lattice lift-off

In their new study, the MIT and UW Madison researchers took a closer look at the process and discovered that the key to the material’s easy-peel property was lead. As part of its chemical structure, the team, along with colleagues at the Rensselaer Polytechnic Institute, discovered that the pyroelectric film contains an orderly arrangement of lead atoms that have a large “electron affinity,” meaning that lead attracts electrons and prevents the charge carriers from traveling and connecting to another materials such as an underlying substrate. The lead acts as tiny nonstick units, allowing the material as a whole to peel away, perfectly intact.

The team ran with the realization and fabricated multiple ultrathin films of PMN-PT, each about 10 nanometers thin. They peeled off pyroelectric films and transfered them onto a small chip to form an array of 100 ultrathin heat-sensing pixels, each about 60 square microns (about .006 square centimeters). They exposed the films to ever-slighter changes in temperature and found the pixels were highly sensitive to small changes across the far-infrared spectrum.

The sensitivity of the pyroelectric array is comparable to that of state-of-the-art night-vision devices. These devices are currently based on photodetector materials, in which a change in temperature induces the material’s electrons to jump in energy and briefly cross an energy “band gap,” before settling back into their ground state. This electron jump serves as an electrical signal of the temperature change. However, this signal can be affected by noise in the environment, and to prevent such effects, photodetectors have to also include cooling devices that bring the instruments down to liquid nitrogen temperatures.

Current night-vision goggles and scopes are heavy and bulky. With the group’s new pyroelectric-based approach, NVDs could have the same sensitivity without the cooling weight.

The researchers also found that the films were sensitive beyond the range of current night-vision devices and could respond to wavelengths across the entire infrared spectrum. This suggests that the films could be incorporated into small, lightweight, and portable devices for various applications that require different infrared regions. For instance, when integrated into autonomous vehicle platforms, the films could enable cars to “see” pedestrians and vehicles in complete darkness or in foggy and rainy conditions. 

The film could also be used in gas sensors for real-time and on-site environmental monitoring, helping detect pollutants. In electronics, they could monitor heat changes in semiconductor chips to catch early signs of malfunctioning elements.

The team says the new lift-off method can be generalized to materials that may not themselves contain lead. In those cases, the researchers suspect that they can infuse Teflon-like lead atoms into the underlying substrate to induce a similar peel-off effect. For now, the team is actively working toward incorporating the pyroelectric films into a functional night-vision system.

“We envision that our ultrathin films could be made into high-performance night-vision goggles, considering its broad-spectrum infrared sensitivity at room-temperature, which allows for a lightweight design without a cooling system,” Zhang says. “To turn this into a night-vision system, a functional device array should be integrated with readout circuitry. Furthermore, testing in varied environmental conditions is essential for practical applications.”

This work was supported by the U.S. Air Force Office of Scientific Research.

When chemists design new chemical reactions, one useful piece of information involves the reaction’s transition state — the point of no return from which a reaction must proceed.

This information allows chemists to try to produce the right conditions that will allow the desired reaction to occur. However, current methods for predicting the transition state and the path that a chemical reaction will take are complicated and require a huge amount of computational power.

MIT researchers have now developed a machine-learning model that can make these predictions in less than a second, with high accuracy. Their model could make it easier for chemists to design chemical reactions that could generate a variety of useful compounds, such as pharmaceuticals or fuels.

“We’d like to be able to ultimately design processes to take abundant natural resources and turn them into molecules that we need, such as materials and therapeutic drugs. Computational chemistry is really important for figuring out how to design more sustainable processes to get us from reactants to products,” says Heather Kulik, the Lammot du Pont Professor of Chemical Engineering, a professor of chemistry, and the senior author of the new study.

Former MIT graduate student Chenru Duan PhD ’22, who is now at Deep Principle; former Georgia Tech graduate student Guan-Horng Liu, who is now at Meta; and Cornell University graduate student Yuanqi Du are the lead authors of the paper, which appears today in Nature Machine Intelligence.

Better estimates

For any given chemical reaction to occur, it must go through a transition state, which takes place when it reaches the energy threshold needed for the reaction to proceed. These transition states are so fleeting that they’re nearly impossible to observe experimentally.

As an alternative, researchers can calculate the structures of transition states using techniques based on quantum chemistry. However, that process requires a great deal of computing power and can take hours or days to calculate a single transition state.

“Ideally, we’d like to be able to use computational chemistry to design more sustainable processes, but this computation in itself is a huge use of energy and resources in finding these transition states,” Kulik says.

In 2023, Kulik, Duan, and others reported on a machine-learning strategy that they developed to predict the transition states of reactions. This strategy is faster than using quantum chemistry techniques, but still slower than what would be ideal because it requires the model to generate about 40 structures, then run those predictions through a “confidence model” to predict which states were most likely to occur.

One reason why that model needs to be run so many times is that it uses randomly generated guesses for the starting point of the transition state structure, then performs dozens of calculations until it reaches its final, best guess. These randomly generated starting points may be very far from the actual transition state, which is why so many steps are needed.

The researchers’ new model, React-OT, described in the Nature Machine Intelligence paper, uses a different strategy. In this work, the researchers trained their model to begin from an estimate of the transition state generated by linear interpolation — a technique that estimates each atom’s position by moving it halfway between its position in the reactants and in the products, in three-dimensional space.

“A linear guess is a good starting point for approximating where that transition state will end up,” Kulik says. “What the model’s doing is starting from a much better initial guess than just a completely random guess, as in the prior work.”

Because of this, it takes the model fewer steps and less time to generate a prediction. In the new study, the researchers showed that their model could make predictions with only about five steps, taking about 0.4 seconds. These predictions don’t need to be fed through a confidence model, and they are about 25 percent more accurate than the predictions generated by the previous model.

“That really makes React-OT a practical model that we can directly integrate to the existing computational workflow in high-throughput screening to generate optimal transition state structures,” Duan says.

“A wide array of chemistry”

To create React-OT, the researchers trained it on the same dataset that they used to train their older model. These data contain structures of reactants, products, and transition states, calculated using quantum chemistry methods, for 9,000 different chemical reactions, mostly involving small organic or inorganic molecules.

Once trained, the model performed well on other reactions from this set, which had been held out of the training data. It also performed well on other types of reactions that it hadn’t been trained on, and could make accurate predictions involving reactions with larger reactants, which often have side chains that aren’t directly involved in the reaction.

“This is important because there are a lot of polymerization reactions where you have a big macromolecule, but the reaction is occurring in just one part. Having a model that generalizes across different system sizes means that it can tackle a wide array of chemistry,” Kulik says.

The researchers are now working on training the model so that it can predict transition states for reactions between molecules that include additional elements, including sulfur, phosphorus, chlorine, silicon, and lithium.

“To quickly predict transition state structures is key to all chemical understanding,” says Markus Reiher, a professor of theoretical chemistry at ETH Zurich, who was not involved in the study. “The new approach presented in the paper could very much accelerate our search and optimization processes, bringing us faster to our final result. As a consequence, also less energy will be consumed in these high-performance computing campaigns. Any progress that accelerates this optimization benefits all sorts of computational chemical research.”

The MIT team hopes that other scientists will make use of their approach in designing their own reactions, and have created an app for that purpose.

“Whenever you have a reactant and product, you can put them into the model and it will generate the transition state, from which you can estimate the energy barrier of your intended reaction, and see how likely it is to occur,” Duan says.

The research was funded by the U.S. Army Research Office, the U.S. Department of Defense Basic Research Office, the U.S. Air Force Office of Scientific Research, the National Science Foundation, and the U.S. Office of Naval Research.

By Dr Azhar Ibrahim Alwee, Senior Lecturer from the Dept of Malay Studies, Faculty of Arts and Social Sciences at NUS

• Suria News Online, 21 April 2025

By Assoc Prof Gu Qingyang, from the Lee Kuan Yew School of Public Policy at NUS

• Lianhe Zaobao, 21 April 2025, Opinion, p20

By Dr Qi Dongshou, Senior Researcher, from the East Asian Institute at NUS and Professor Li Xiaodong from the Institute of International Relations at Yunnan University

• Lianhe Zaobao, 21 April 2025, Opinion, p20

• zbSunday, 20 April 2025, News, p8

• The New Paper, 19 April 2025
• The Sunday Times, 20 April 2025, Singapore, pA16

• Tamil Murasu,19 April 2025, p3
• The Sunday Times, 20 April 2025, Singapore, pA16

In metamaterials design, the name of the game has long been “stronger is better.”

Metamaterials are synthetic materials with microscopic structures that give the overall material exceptional properties. A huge focus has been in designing metamaterials that are stronger and stiffer than their conventional counterparts. But there’s a trade-off: The stiffer a material, the less flexible it is.

MIT engineers have now found a way to fabricate a metamaterial that is both strong and stretchy. The base material is typically highly rigid and brittle, but it is printed in precise, intricate patterns that form a structure that is both strong and flexible.

The key to the new material’s dual properties is a combination of stiff microscopic struts and a softer woven architecture. This microscopic “double network,” which is printed using a plexiglass-like polymer, produced a material that could stretch over four times its size without fully breaking. In comparison, the polymer in other forms has little to no stretch and shatters easily once cracked.

The researchers say the new double-network design can be applied to other materials, for instance to fabricate stretchy ceramics, glass, and metals. Such tough yet bendy materials could be made into tear-resistant textiles, flexible semiconductors, electronic chip packaging, and durable yet compliant scaffolds on which to grow cells for tissue repair.

“We are opening up this new territory for metamaterials,” says Carlos Portela, the Robert N. Noyce Career Development Associate Professor at MIT. “You could print a double-network metal or ceramic, and you could get a lot of these benefits, in that it would take more energy to break them, and they would be significantly more stretchable.”

Portela and his colleagues report their findings today in the journal Nature Materials. His MIT co-authors include first author James Utama Surjadi as well as Bastien Aymon and Molly Carton.

Inspired gel

Along with other research groups, Portela and his colleagues have typically designed metamaterials by printing or nanofabricating microscopic lattices using conventional polymers similar to plexiglass and ceramic. The specific pattern, or architecture, that they print can impart exceptional strength and impact resistance to the resulting metamaterial.

Several years ago, Portela was curious whether a metamaterial could be made from an inherently stiff material, but be patterned in a way that would turn it into a much softer, stretchier version.

“We realized that the field of metamaterials has not really tried to make an impact in the soft matter realm,” he says. “So far, we’ve all been looking for the stiffest and strongest materials possible.”

Instead, he looked for a way to synthesize softer, stretchier metamaterials. Rather than printing microscopic struts and trusses, similar to those of conventional lattice-based metamaterials, he and his team made an architecture of interwoven springs, or coils. They found that, while the material they used was itself stiff like plexiglass, the resulting woven metamaterial was soft and springy, like rubber.

“They were stretchy, but too soft and compliant,” Portela recalls.

In looking for ways to bulk up their softer metamaterial, the team found inspiration in an entirely different material: hydrogel. Hydrogels are soft, stretchy, Jell-O-like materials that are composed of mostly water and a bit of polymer structure. Researchers including groups at MIT have devised ways to make hydrogels that are both soft and stretchy, and also tough. They do so by combining polymer networks with very different properties, such as a network of molecules that is naturally stiff,  which gets chemically cross-linked with another molecular network that is inherently soft. Portela and his colleagues wondered whether such a double-network design could be adapted to metamaterials.

“That was our ‘aha’ moment,” Portela says. “We thought: Can we get inspiration from these hydrogels to create a metamaterial with similar stiff and stretchy properties?”

Strut and weave

For their new study, the team fabricated a metamaterial by combining two microscopic architectures. The first is a rigid, grid-like scaffold of struts and trusses. The second is a pattern of coils that weave around each strut and truss. Both networks are made from the same acrylic plastic and are printed in one go, using a high-precision, laser-based printing technique called two-photon lithography.

The researchers printed samples of the new double-network-inspired metamaterial, each measuring in size from several square microns to several square millimeters. They put the material through a series of stress tests, in which they attached either end of the sample to a specialized nanomechanical press and measured the force it took to pull the material apart. They also recorded high-resolution videos to observe the locations and ways in which the material stretched and tore as it was pulled apart.

They found their new double-network design was able stretch three times its own length, which also happened to be 10 times farther compared to a conventional lattice-patterned metamaterial printed with the same acrylic plastic. Portela says the new material’s stretchy resistance comes from the interactions between the material’s rigid struts and the messier, coiled weave as the material is stressed and pulled.

“Think of this woven network as a mess of spaghetti tangled around a lattice. As we break the monolithic lattice network, those broken parts come along for the ride, and now all this spaghetti gets entangled with the lattice pieces,” Portela explains. “That promotes more entanglement between woven fibers, which means you have more friction and more energy dissipation.”

In other words, the softer structure wound throughout the material’s rigid lattice takes on more stress thanks to multiple knots or entanglements promoted by the cracked struts. As this stress spreads unevenly through the material, an initial crack is unlikely to go straight through and quickly tear the material. What’s more, the team found that if they introduced strategic holes, or “defects,” in the metamaterial, they could further dissipate any stress that the material undergoes, making it even stretchier and more resistant to tearing apart.

“You might think this makes the material worse,” says study co-author Surjadi. “But we saw once we started adding defects, we doubled the amount of stretch we were able to do, and tripled the amount of energy that we dissipated. That gives us a material that’s both stiff and tough, which is usually a contradiction.”

The team has developed a computational framework that can help engineers estimate how a metamaterial will perform given the pattern of its stiff and stretchy networks. They envision such a blueprint will be useful in designing tear-proof textiles and fabrics.

“We also want to try this approach on more brittle materials, to give them multifunctionality,” Portela says. “So far we’ve talked of mechanical properties, but what if we could also make them conductive, or responsive to temperature? For that, the two networks could be made from different polymers, that respond to temperature in different ways, so that a fabric can open its pores or become more compliant when it’s warm and can be more rigid when it’s cold. That’s something we can explore now.”

This research was supported, in part, by the U.S. National Science Foundation, and the MIT MechE MathWorks Seed Fund. This work was performed, in part, through the use of MIT.nano’s facilities.

By Dr Georgios Georgiou, Senior Lecturer in the Dept of Economics, Faculty of Arts and Social Sciences at NUS

• The Business Times, 19 April 2025, p21

MIT researchers have created a periodic table that shows how more than 20 classical machine-learning algorithms are connected. The new framework sheds light on how scientists could fuse strategies from different methods to improve existing AI models or come up with new ones.

For instance, the researchers used their framework to combine elements of two different algorithms to create a new image-classification algorithm that performed 8 percent better than current state-of-the-art approaches.

The periodic table stems from one key idea: All these algorithms learn a specific kind of relationship between data points. While each algorithm may accomplish that in a slightly different way, the core mathematics behind each approach is the same.

Building on these insights, the researchers identified a unifying equation that underlies many classical AI algorithms. They used that equation to reframe popular methods and arrange them into a table, categorizing each based on the approximate relationships it learns.

Just like the periodic table of chemical elements, which initially contained blank squares that were later filled in by scientists, the periodic table of machine learning also has empty spaces. These spaces predict where algorithms should exist, but which haven’t been discovered yet.

The table gives researchers a toolkit to design new algorithms without the need to rediscover ideas from prior approaches, says Shaden Alshammari, an MIT graduate student and lead author of a paper on this new framework.

“It’s not just a metaphor,” adds Alshammari. “We’re starting to see machine learning as a system with structure that is a space we can explore rather than just guess our way through.”

She is joined on the paper by John Hershey, a researcher at Google AI Perception; Axel Feldmann, an MIT graduate student; William Freeman, the Thomas and Gerd Perkins Professor of Electrical Engineering and Computer Science and a member of the Computer Science and Artificial Intelligence Laboratory (CSAIL); and senior author Mark Hamilton, an MIT graduate student and senior engineering manager at Microsoft. The research will be presented at the International Conference on Learning Representations.

An accidental equation

The researchers didn’t set out to create a periodic table of machine learning.

After joining the Freeman Lab, Alshammari began studying clustering, a machine-learning technique that classifies images by learning to organize similar images into nearby clusters.

She realized the clustering algorithm she was studying was similar to another classical machine-learning algorithm, called contrastive learning, and began digging deeper into the mathematics. Alshammari found that these two disparate algorithms could be reframed using the same underlying equation.

“We almost got to this unifying equation by accident. Once Shaden discovered that it connects two methods, we just started dreaming up new methods to bring into this framework. Almost every single one we tried could be added in,” Hamilton says.

The framework they created, information contrastive learning (I-Con), shows how a variety of algorithms can be viewed through the lens of this unifying equation. It includes everything from classification algorithms that can detect spam to the deep learning algorithms that power LLMs.

The equation describes how such algorithms find connections between real data points and then approximate those connections internally.

Each algorithm aims to minimize the amount of deviation between the connections it learns to approximate and the real connections in its training data.

They decided to organize I-Con into a periodic table to categorize algorithms based on how points are connected in real datasets and the primary ways algorithms can approximate those connections.

“The work went gradually, but once we had identified the general structure of this equation, it was easier to add more methods to our framework,” Alshammari says.

A tool for discovery

As they arranged the table, the researchers began to see gaps where algorithms could exist, but which hadn’t been invented yet.

The researchers filled in one gap by borrowing ideas from a machine-learning technique called contrastive learning and applying them to image clustering. This resulted in a new algorithm that could classify unlabeled images 8 percent better than another state-of-the-art approach.

They also used I-Con to show how a data debiasing technique developed for contrastive learning could be used to boost the accuracy of clustering algorithms.

In addition, the flexible periodic table allows researchers to add new rows and columns to represent additional types of datapoint connections.

Ultimately, having I-Con as a guide could help machine learning scientists think outside the box, encouraging them to combine ideas in ways they wouldn’t necessarily have thought of otherwise, says Hamilton.

“We’ve shown that just one very elegant equation, rooted in the science of information, gives you rich algorithms spanning 100 years of research in machine learning. This opens up many new avenues for discovery,” he adds.

“Perhaps the most challenging aspect of being a machine-learning researcher these days is the seemingly unlimited number of papers that appear each year. In this context, papers that unify and connect existing algorithms are of great importance, yet they are extremely rare. I-Con provides an excellent example of such a unifying approach and will hopefully inspire others to apply a similar approach to other domains of machine learning,” says Yair Weiss, a professor in the School of Computer Science and Engineering at the Hebrew University of Jerusalem, who was not involved in this research.

This research was funded, in part, by the Air Force Artificial Intelligence Accelerator, the National Science Foundation AI Institute for Artificial Intelligence and Fundamental Interactions, and Quanta Computer.

By Prof Tan Huay Cheem, from the Dept of Medicine, Yong Loo Lin School of Medicine at NUS

• Lianhe Zaobao, 15 April 2025, Fukan, p2

By Dr Chew Han Ei, Senior Research Fellow from the Institute of Policy Studies, Lee Kuan Yew School of Public Policy at NUS, and Assoc Prof Carol Soon, from the Dept of Communications and New Media, Faculty of Arts and Social Sciences at NUS 

• CNA Online, 15 April 2025

Kripa Varanasi, professor of mechanical engineering, was named faculty director of the MIT Deshpande Center for Technological Innovation, effective March 1.

“Kripa is widely recognized for his significant contributions in the field of interfacial science, thermal fluids, electrochemical systems, and advanced materials. It’s remarkable to see the tangible impact Kripa’s ventures have made across such a wide range of fields,” says Anantha P. Chandrakasan, dean of the School of Engineering, chief innovation and strategy officer, and Vannevar Bush Professor of Electrical Engineering and Computer Science. “From energy and water conservation to consumer products and agriculture, his solutions are making a real difference. The Deshpande Center will benefit greatly from both his entrepreneurial expertise and deep technical insight.”

The MIT Deshpande Center for Technological Innovation is an interdepartmental center that empowers MIT students and faculty to make a difference in the world by helping them bring their innovative technologies from the lab to the marketplace in the form of breakthrough products and new companies. The center was established through a gift from philanthropist Guruaj “Desh” Deshpande and his wife, Jaishree.

“Kripa brings an entrepreneurial spirit, innovative thinking, and commitment to mentorship that has always been central to the Deshpande Center’s mission,” says Deshpande. “He is exceptionally well-positioned to help the next generation of MIT innovators turn bold ideas into real-world solutions that make a difference.”

Varanasi has seen the Deshpande Center’s influence on the MIT community since its founding in 2002, when he was a graduate student.

“The Deshpande Center was founded when I was a graduate student, and it truly inspired many of us to think about entrepreneurship and commercialization — with Desh himself being an incredible role model,” says Varanasi. “Over the years, the center has built a storied legacy as a one-of-a-kind institution for propelling university-invented technologies to commercialization. Many amazing companies have come out of this program, shaping industries and making a real impact.”

A member of the MIT faculty since 2009, Varanasi leads the interdisciplinary Varanasi Research Group, which focuses on understanding physico-chemical and biological phenomena at the interfaces of matter. His group develops novel surfaces, materials, and technologies that improve efficiency and performance across industries, including energy, decarbonization, life sciences, water, agriculture, transportation, and consumer products.

In addition to his academic work, Varanasi is a prolific entrepreneur who has co-founded six companies, including AgZen, Alsym Energy, CoFlo Medical, Dropwise, Infinite Cooling, and LiquiGlide, which was a Deshpande Center grantee in 2009. These ventures aim to translate research breakthroughs into products with global reach.

His companies have been widely recognized for driving innovation across a range of industries. LiquiGlide, which produces frictionless liquid coatings, was named one of Time and Forbes’ “Best Inventions of the Year” in 2012. Infinite Cooling, which offers a technology to capture and recycle power plant water vapor, has won the U.S. Department of Energy’s National Cleantech University Prize and top prizes at MassChallenge and the MIT $100K competition. It is also a participating company at this year’s IdeaStream: Next Gen event, hosted by the Deshpande Center.

Another company that Varanasi co-founded, AgZen, is pioneering feedback optimization for agrochemical application that allows farmers to use 30-90 percent less pesticides and fertilizers while achieving 1-10 percent more yield. Meanwhile, Alsym Energy is advancing nonflammable, high-performance batteries for energy storage solutions that are lithium-free and capable of a wide range of storage durations. 

Throughout his career, Varanasi has been recognized for both research excellence and mentorship. His honors include the National Science Foundation CAREER Award, DARPA Young Faculty Award, SME Outstanding Young Manufacturing Engineer Award, ASME’s Bergles-Rohsenow Heat Transfer Award and Gustus L. Larson Memorial Award, Boston Business Journal’s 40 Under 40, and MIT’s Frank E. Perkins Award for Excellence in Graduate Advising​.

Varanasi earned his undergraduate degree in mechanical engineering from the Indian Institute of Technology Madras, and his master’s degree and PhD from MIT. Prior to joining the Institute’s faculty, he served as lead researcher and project leader at the GE Global Research Center, where he received multiple internal awards for innovation and technical excellence​.

"It’s an honor to lead the Deshpande Center, and in collaboration with the MIT community, I look forward to building on its incredible foundation — fostering bold ideas, driving real-world impact from cutting-edge innovations, and making it a powerhouse for commercialization,” adds Varanasi.

As faculty director, Varanasi will work closely with Deshpande Center executive director Rana Gupta to guide the center’s support of MIT faculty and students developing technology-based ventures.

“With Kripa’s depth and background, we will capitalize on the initiatives started with Angela Koehler. Kripa shares our vision to grow and expand the center’s capabilities to serve more of MIT,” adds Gupta.

Varanasi succeeds Angela Koehler, associate professor of biological engineering, who served as faculty director from July 2023 through March 2025.

“Angela brought fresh vision and energy to the center,” he says. “She expanded its reach, introduced new funding priorities in climate and life sciences, and re-imagined the annual IdeaStream event as a more robust launchpad for innovation. We’re deeply grateful for her leadership.”

Koehler, who was recently appointed faculty lead of the MIT Health and Life Sciences Collaborative, will continue to play a key role in the Institute’s innovation and entrepreneurship ecosystem​.

Essential for many industries ranging from Hollywood computer-generated imagery to product design, 3D modeling tools often use text or image prompts to dictate different aspects of visual appearance, like color and form. As much as this makes sense as a first point of contact, these systems are still limited in their realism due to their neglect of something central to the human experience: touch.

Fundamental to the uniqueness of physical objects are their tactile properties, such as roughness, bumpiness, or the feel of materials like wood or stone. Existing modeling methods often require advanced computer-aided design expertise and rarely support tactile feedback that can be crucial for how we perceive and interact with the physical world.

With that in mind, researchers at MIT’s Computer Science and Artificial Intelligence Laboratory (CSAIL) have created a new system for stylizing 3D models using image prompts, effectively replicating both visual appearance and tactile properties.

The CSAIL team’s “TactStyle” tool allows creators to stylize 3D models based on images while also incorporating the expected tactile properties of the textures. TactStyle separates visual and geometric stylization, enabling the replication of both visual and tactile properties from a single image input.

PhD student Faraz Faruqi, lead author of a new paper on the project, says that TactStyle could have far-reaching applications, extending from home decor and personal accessories to tactile learning tools. TactStyle enables users to download a base design — such as a headphone stand from Thingiverse — and customize it with the styles and textures they desire. In education, learners can explore diverse textures from around the world without leaving the classroom, while in product design, rapid prototyping becomes easier as designers quickly print multiple iterations to refine tactile qualities.

“You could imagine using this sort of system for common objects, such as phone stands and earbud cases, to enable more complex textures and enhance tactile feedback in a variety of ways,” says Faruqi, who co-wrote the paper alongside MIT Associate Professor Stefanie Mueller, leader of the Human-Computer Interaction (HCI) Engineering Group at CSAIL. “You can create tactile educational tools to demonstrate a range of different concepts in fields such as biology, geometry, and topography.”

Traditional methods for replicating textures involve using specialized tactile sensors — such as GelSight, developed at MIT — that physically touch an object to capture its surface microgeometry as a “heightfield.” But this requires having a physical object or its recorded surface for replication. TactStyle allows users to replicate the surface microgeometry by leveraging generative AI to generate a heightfield directly from an image of the texture.

On top of that, for platforms like the 3D printing repository Thingiverse, it’s difficult to take individual designs and customize them. Indeed, if a user lacks sufficient technical background, changing a design manually runs the risk of actually “breaking” it so that it can’t be printed anymore. All of these factors spurred Faruqi to wonder about building a tool that enables customization of downloadable models on a high level, but that also preserves functionality.

In experiments, TactStyle showed significant improvements over traditional stylization methods by generating accurate correlations between a texture’s visual image and its heightfield. This enables the replication of tactile properties directly from an image. One psychophysical experiment showed that users perceive TactStyle’s generated textures as similar to both the expected tactile properties from visual input and the tactile features of the original texture, leading to a unified tactile and visual experience.

TactStyle leverages a preexisting method, called “Style2Fab,” to modify the model’s color channels to match the input image’s visual style. Users first provide an image of the desired texture, and then a fine-tuned variational autoencoder is used to translate the input image into a corresponding heightfield. This heightfield is then applied to modify the model’s geometry to create the tactile properties.

The color and geometry stylization modules work in tandem, stylizing both the visual and tactile properties of the 3D model from a single image input. Faruqi says that the core innovation lies in the geometry stylization module, which uses a fine-tuned diffusion model to generate heightfields from texture images — something previous stylization frameworks do not accurately replicate.

Looking ahead, Faruqi says the team aims to extend TactStyle to generate novel 3D models using generative AI with embedded textures. This requires exploring exactly the sort of pipeline needed to replicate both the form and function of the 3D models being fabricated. They also plan to investigate “visuo-haptic mismatches” to create novel experiences with materials that defy conventional expectations, like something that appears to be made of marble but feels like it’s made of wood.

Faruqi and Mueller co-authored the new paper alongside PhD students Maxine Perroni-Scharf and Yunyi Zhu, visiting undergraduate student Jaskaran Singh Walia, visiting masters student Shuyue Feng, and assistant professor Donald Degraen of the Human Interface Technology (HIT) Lab NZ in New Zealand.

What happens when a fashion legend taps into the transformative power of artificial intelligence? For more than five decades, fashion designer and entrepreneur Norma Kamali has pioneered bold industry shifts, creating iconic silhouettes worn by celebrities including Whitney Houston and Jessica Biel. Now, she is embracing a new frontier — one that merges creativity with algorithms and AI to redefine the future of her industry.

Through MIT Professional Education’s online “Applied Generative AI for Digital Transformation” course, which she completed in 2023, Kamali explored AI’s potential to serve as creative partner and ensure the longevity and evolution of her brand.

Kamali’s introduction to AI began with a meeting in Abu Dhabi, where industry experts, inspired by her Walmart collection, suggested developing an AI-driven fashion platform. Intrigued by the idea, but wary of the concept of “downloading her brain,” Kamali instead envisioned a system that could expand upon her 57-year archive — a closed-loop AI tool trained solely on her work. “I thought, AI could be my Karl Lagerfeld,” she says, referencing the designer’s reverence for archival inspiration.

To bring this vision to life, Kamali sought a deeper understanding of generative AI — so she headed to MIT Professional Education, an arm of MIT that has taught and inspired global professionals for more than 75 years. “I wasn’t sure how much I could actually do,” she recalls. “I had all these preconceived notions, but the more I learned, the more ideas I had.” Initially intimidated by the technical aspects of AI, she persevered, diving into prompts and training data, and exploring its creative potential. “I was determined,” she says. “And then suddenly, I was playing.”

Experimenting with her proprietary AI model, created by Maison Meta, Kamali used AI to reinterpret one of her signature styles — black garments adorned with silver studs. By prompting AI with iterations of her existing silhouettes, she witnessed unexpected and thrilling results. “It was magic,” she says. “Art, technology, and fashion colliding in ways I never imagined.” Even AI’s so-called “hallucinations” — distortions often seen as errors — became a source of inspiration. “Some of the best editorial fashion is absurd,” she notes. “AI-generated anomalies created entirely new forms of art.”

Kamali’s approach to AI reflects a broader shift across industries, where technology is not just a tool but a catalyst for reinvention. Bhaskar Pant, executive director of MIT Professional Education, underscores this transformation. “While everyone is speculating about the impact of AI, we are committed to advancing AI’s role in helping industries and leaders achieve breakthroughs, higher levels of productivity, and, as in this case, unleash creativity. Professionals must be empowered to harness AI’s potential in ways that not only enhance their work, but redefine what’s possible. Norma’s journey is a testament to the power of lifelong learning — demonstrating that innovation is ageless, fueled by curiosity and ambition.”

The experience also deepened Kamali’s perspective on AI’s role in the creative process. “AI doesn’t have a heartbeat,” she asserts. “It can’t replace human passion. But it can enhance creativity in ways we’re only beginning to understand.” Kamali also addressed industry fears about job displacement, arguing that the technology is already reshaping fashion’s labor landscape. “Sewing talent is harder to find. Designers need new tools to adapt.”

Beyond its creative applications, Kamali sees AI as a vehicle for sustainability. A longtime advocate for reducing dry cleaning — a practice linked to chemical exposure — she envisions AI streamlining fabric selection, minimizing waste, and enabling on-demand production. “Imagine a system where you design your wedding dress online, and a robot constructs it, one garment at a time,” she says. “The possibilities are endless.”

Abel Sanchez, MIT research scientist and lead instructor for MIT Professional Education’s Applied Generative AI for Digital Transformation course, emphasizes the transformative potential of AI across industries. “AI is a force reshaping the foundations of every sector, including fashion. Generative AI is unlocking unprecedented digital transformation opportunities, enabling organizations to rethink processes, design, and customer engagement. Norma is at the forefront of this shift, exploring how AI can propel the fashion industry forward, spark new creative frontiers, and redefine how designers interact with technology.”

Kamali’s experience in the course sparked an ongoing exchange of ideas with Sanchez, further fueling her curiosity. “AI is evolving so fast, I know I’ll need to go back,” she says. “MIT gave me the foundation, but this is just the beginning.” For those hesitant to embrace AI, she offers a striking analogy: “Imagine landing in a small town, in a foreign country, where you don’t speak the language, don’t recognize the food, and feel completely lost. That’s what it will be like if you don’t learn AI. The train has left the station — it’s time to get on board.”

With her AI-generated designs now featured on her website alongside her traditional collections, Kamali is proving that technology and creativity aren’t at odds — they’re collaborators. And as she continues to push the boundaries of both, she remains steadfast in her belief: “Learning is the adventure of life. Why stop now?”

Globally, and especially in low- and middle-income countries (LMICs), a significant portion of the population lacks access to essential health-care services. Although there are many contributing factors that create barriers to access, in many LMICs failing or obsolete equipment plays a significant role.

“Those of us who have investigated health-care systems in LMICs are familiar with so-called ‘equipment graveyards,’” says Nevan Hanumara SM ’06, PhD ’12, a research scientist in MIT’s Department of Mechanical Engineering, describing piles of broken, imported equipment, often bearing stickers indicating their origins from donor organizations.

“Looking at the root causes of medical equipment failing and falling out of service in LMICs, we find that the local biomedical engineers truly can’t do the maintenance, due to a cascade of challenges,” he says.

Among these challenges are: design weaknesses — systems designed for temperate, air-conditioned hospitals and stabilized power don’t fare well in areas with inconsistent power supply, dust, high heat and humidity, and continuous utilization; lack of supply chain — parts ordered in the U.S. can arrive in days, where parts ordered to East Africa may take months; and limited access to knowledgeable professionals — outside of major metropolitan areas, biomedical engineers are scarce.

Hanumara, Leroy Sibanda SM ’24, a recent graduate with a dual degree in management and electrical engineering and computer science (EECS), and Anthony Pennes ’16, a technical instructor in EECS, began to ponder what could be changed if local biomedical engineers were actually involved with the design of the equipment that they’re charged with maintaining.

Pennes, who staffs class 2.75/6.4861 (Medical Device Design), among other courses, developed hands-on biosensing and mechatronics exercises as class activities several years ago. Hanumara became interested in expanding that curriculum to produce something that could have a larger impact.

Working as a team, and with support from MIT International Science and Technology Initiatives (MISTI), the MIT Jameel World Education Lab, and the Priscilla King Gray Public Service Center, the trio created a hands-on course, exercises, and curriculum, supported by what they’ve now dubbed a “Biomed Lab in a Box” kit.

Sibanda, who hails from Bulawayo, Zimbabwe, brings additional lived experience to the project. He says friends up and down the continent speak about great practical primary and secondary education, and a tertiary education that provides a heavy emphasis on theory. The consequence, he says, is a plethora of graduates who are absolutely brilliant at the theory, but less experienced in advanced practical concepts.

“Anyone who has ever had to build systems that need to stand up to real-world conditions understands the chasm between knowing how to calculate the theoretically perfect ‘x’ and being capable of implementing a real-world solution with the materials available,” says Sibanda.

Hanumara and Sibanda traveled to Nairobi, Kenya, and Mbarara, Uganda, in late 2024 to test their kit and their theory, teaching three-day long biomedical innovation mini-courses at both Kenyatta University and Mbarara University of Science and Technology (MUST), with Pennes providing remote support from MIT’s campus.

With a curriculum based off of 2.75, labs were designed to connect the theoretical to the physical, increasing in complexity and confronting students with the real challenges of biomedical hardware and sensing, such as weak signals, ambient noise, motion artifacts, debugging, and precision assembly.

Pennes says the goal for the mini-courses was to shape the project around the real-world experiences of the region’s biomedical engineering students. “One of the problems that they experience in this region is not simply a lack of equipment, but the lack of ability to maintain it,” he says. “Some organization will come in and donate thousands of dollars of surgical lighting; then a power supply will burn out, and the organization will never come back to fix it.”

But that’s just the beginning of the problem, he adds. Engineers often find that the design isn’t open, and there’s no manual, making it impossible to find a circuit design for what’s inside the donated, proprietary system. “You have to poke and prod around the disassembled gear to see if you can discern the makers’ original goals in wiring it, and figure out a fix,” says Pennes.

In one example, he recalls seeing a donated screen for viewing X-rays — the lightbox kind, used to backlight film so that technicians can read the image — with a burned-out bulb. “The screen is lit by a proprietary bulb, so when it burned out, they could not replace it,” he recounts.

Local biomedical engineers ultimately realized that they could take a number of off-the-shelf fluorescent bulbs and angle them to fit inside the box. “Then they sort of MacGyver’d the wiring to make them all work. You get the medical technology to work however you can.”

It’s this hands-on, imaginative approach to problem-solving that the team hopes to promote — and it’s one that’s very familiar at MIT. “We’re not just ideas people, where we write a paper and we’re done with it — we want to see it applied,” says Hanumara. “It’s why so many startups come out of MIT.”

Course modules presented at Kenyatta and MUST included “Breadboarding an optical LED – photodetector pulse detector,” “Soldering a PCB and testing a 3-lead EKG,” and “Assembling and programming a syringe pump.” Each module is designed to be a self-contained learning experience, and the kit is accompanied by a USB flash drive with a 96-page lab manual written by Sibanda, and all the needed software, which is important to have when internet access is unreliable. The third exercise, relating to the syringe pump, is available via open access from the journal Biomedical Engineering Education.

“Our mission was to expose eager, young biomedical engineers to the hands-on, ‘mens-et-manus’ (‘mind-and-hand’) culture which is the cornerstone of MIT, and encourage them to develop their talents and aspirations as engineers and innovators,” says Hanumara. “We wanted to help empower them to participate in developing high-quality, contextually appropriate, technologies that improve health-care delivery in their own region.”

A LinkedIn post written by Hanumara shared reflections from students on their experiences with the material. “Every lab — from pulse oximetry and EKGs to syringe pump prototyping — brought classroom concepts to life, showing me the real-world applications of what we study,” wrote Muthoni Muriithi, a student at Kenyatta University. “Using breadboards, coding microcontrollers, soldering components, and analyzing biological data in real time helped me grasp how much careful design and precision go into creating reliable health-care tools.”

Feedback provided by students at both institutions is already helping to inform updates to the materials and future pilot programs.

Sibanda says another key thing the team is tracking what happens beyond the sessions, after the instructors leave. “It’s not just about offering the resource,” he says. “It’s important to understand what students find to be the most valuable, especially on their own.”

Hanumara concurs. “[Pennes] designed the core board that we’re using to be multifunctional. We didn’t touch any of the functions he built in — we want to see what the students will do with them. We also want to see what they can do with the mental framework,” he says, adding that this approach is important to empower students to explore, invent, and eventually scale up their own ideas.

Further, the project addresses another challenge the team identified early on: supply chain issues. In keeping with the mission of local capacity building, the entire kit was assembled in Nairobi by Gearbox Europlacer, which operates the only automated circuit board line in East Africa and is licensed to produce Raspberry Pi’s microcontrollers. “We did not tell the students anything,” says Hanumara, “but left it to them to notice that their circuit boards and microcontrollers said ‘Made in Kenya.’”

“The insistence on local manufacturing keeps us from falling into the trap that so much equipment donated into East Africa creates — you have one of these items, and if some part of it breaks you can never replace it,” says Pennes. “Having locally sourced items instead means that if you need another component, or devise an interesting side project, you have a shopping list and you can go get whatever you need.”

“Building off our ‘Biomed Lab in a Box’ experiment,” says Hanumara, “we aim to work with our colleagues in East Africa to further explore what can be designed and built with the eager, young talent and capabilities in the region.”

Hanumara’s LinkedIn post also thanked collaborating professors June Madete and Dean Johnes Obungoloch, from Kenyatta and MUST, respectively, and Latiff Cherono, managing director of Gearbox. The team hopes to eventually release the whole course in open-source format. 

MIT astronomers have discovered a planet some 140 light-years from Earth that is rapidly crumbling to pieces.

The disintegrating world is about the mass of Mercury, although it circles about 20 times closer to its star than Mercury does to the sun, completing an orbit every 30.5 hours. At such close proximity to its star, the planet is likely covered in magma that is boiling off into space. As the roasting planet whizzes around its star, it is shedding an enormous amount of surface minerals and effectively evaporating away.

The astronomers spotted the planet using NASA’s Transiting Exoplanet Survey Satellite (TESS), an MIT-led mission that monitors the nearest stars for transits, or periodic dips in starlight that could be signs of orbiting exoplanets. The signal that tipped the astronomers off was a peculiar transit, with a dip that fluctuated in depth every orbit.

The scientists confirmed that the signal is of a tightly orbiting rocky planet that is trailing a long, comet-like tail of debris.

“The extent of the tail is gargantuan, stretching up to 9 million kilometers long, or roughly half of the planet’s entire orbit,” says Marc Hon, a postdoc in MIT’s Kavli Institute for Astrophysics and Space Research.

It appears that the planet is disintegrating at a dramatic rate, shedding an amount of material equivalent to one Mount Everest each time it orbits its star. At this pace, given its small mass, the researchers predict that the planet may completely disintegrate in about 1 million to 2 million years.

“We got lucky with catching it exactly when it’s really going away,” says Avi Shporer, a collaborator on the discovery who is also at the TESS Science Office. “It’s like on its last breath.”

Hon and Shporer, along with their colleagues, have published their results today in the Astrophysical Journal Letters. Their MIT co-authors include Saul Rappaport, Andrew Vanderburg, Jeroen Audenaert, William Fong, Jack Haviland, Katharine Hesse, Daniel Muthukrishna, Glen Petitpas, Ellie Schmelzer, Sara Seager, and George Ricker, along with collaborators from multiple other institutions.

Roasting away

The new planet, which scientists have tagged as BD+05 4868 Ab, was detected almost by happenstance.

“We weren’t looking for this kind of planet,” Hon says. “We were doing the typical planet vetting, and I happened to spot this signal that appeared very unusual.”

The typical signal of an orbiting exoplanet looks like a brief dip in a light curve, which repeats regularly, indicating that a compact body such as a planet is briefly passing in front of, and temporarily blocking, the light from its host star.

This typical pattern was unlike what Hon and his colleagues detected from the host star BD+05 4868 A, located in the constellation of Pegasus. Though a transit appeared every 30.5 hours, the brightness took much longer to return to normal, suggesting a long trailing structure still blocking starlight. Even more intriguing, the depth of the dip changed with each orbit, suggesting that whatever was passing in front of the star wasn’t always the same shape or blocking the same amount of light.

“The shape of the transit is typical of a comet with a long tail,” Hon explains. “Except that it’s unlikely that this tail contains volatile gases and ice as expected from a real comet — these would not survive long at such close proximity to the host star. Mineral grains evaporated from the planetary surface, however, can linger long enough to present such a distinctive tail.”

Given its proximity to its star, the team estimates that the planet is roasting at around 1,600 degrees Celsius, or close to 3,000 degrees Fahrenheit. As the star roasts the planet, any minerals on its surface are likely boiling away and escaping into space, where they cool into a long and dusty tail.

The dramatic demise of this planet is a consequence of its low mass, which is between that of Mercury and the moon. More massive terrestrial planets like the Earth have a stronger gravitational pull and therefore can hold onto their atmospheres. For BD+05 4868 Ab, the researchers suspect there is very little gravity to hold the planet together.

“This is a very tiny object, with very weak gravity, so it easily loses a lot of mass, which then further weakens its gravity, so it loses even more mass,” Shporer explains. “It’s a runaway process, and it’s only getting worse and worse for the planet.”

Mineral trail

Of the nearly 6,000 planets that astronomers have discovered to date, scientists know of only three other disintegrating planets beyond our solar system. Each of these crumbling worlds were spotted over 10 years ago using data from NASA’s Kepler Space Telescope. All three planets were spotted with similar comet-like tails. BD+05 4868 Ab has the longest tail and the deepest transits out of the four known disintegrating planets to date.

“That implies that its evaporation is the most catastrophic, and it will disappear much faster than the other planets,” Hon explains.

The planet’s host star is relatively close, and thus brighter than the stars hosting the other three disintegrating planets, making this system ideal for further observations using NASA’s James Webb Space Telescope (JWST), which can help determine the mineral makeup of the dust tail by identifying which colors of infrared light it absorbs.

This summer, Hon and graduate student Nicholas Tusay from Penn State University will lead observations of BD+05 4868 Ab using JWST. “This will be a unique opportunity to directly measure the interior composition of a rocky planet, which may tell us a lot about the diversity and potential habitability of terrestrial planets outside our solar system,” Hon says.

The researchers also will look through TESS data for signs of other disintegrating worlds.

“Sometimes with the food comes the appetite, and we are now trying to initiate the search for exactly these kinds of objects,” Shporer says. “These are weird objects, and the shape of the signal changes over time, which is something that’s difficult for us to find. But it’s something we’re actively working on.”

This work was supported, in part, by NASA.

In 2000, Patrick J. McGovern ’59 and Lore Harp McGovern made an extraordinary gift to establish the McGovern Institute for Brain Research at MIT, driven by their deep curiosity about the human mind and their belief in the power of science to change lives. Their $350 million pledge began with a simple yet audacious vision: to understand the human brain in all its complexity, and to leverage that understanding for the betterment of humanity.
 
Twenty-five years later, the McGovern Institute stands as a testament to the power of interdisciplinary collaboration, continuing to shape our understanding of the brain and improve the quality of life for people worldwide.

In the beginning

“This is, by any measure, a truly historic moment for MIT,” said MIT’s 15th president, Charles M. Vest, during his opening remarks at an event in 2000 to celebrate the McGovern gift agreement. “The creation of the McGovern Institute will launch one of the most profound and important scientific ventures of this century in what surely will be a cornerstone of MIT scientific contributions from the decades ahead.”
 
Vest tapped Phillip A. Sharp, MIT Institute professor emeritus of biology and Nobel laureate, to lead the institute, and appointed six MIT professors — Emilio Bizzi, Martha Constantine-Paton, Ann Graybiel PhD ’71, H. Robert Horvitz ’68, Nancy Kanwisher ’80, PhD ’86, and Tomaso Poggio — to represent its founding faculty.  Construction began in 2003 on Building 46, a 376,000 square foot research complex at the northeastern edge of campus. MIT’s new “gateway from the north” would eventually house the McGovern Institute, the Picower Institute for Learning and Memory, and MIT’s Department of Brain and Cognitive Sciences.

Robert Desimone, the Doris and Don Berkey Professor of Neuroscience at MIT, succeeded Sharp as director of the McGovern Institute in 2005, and assembled a distinguished roster of 22 faculty members, including a Nobel laureate, a Breakthrough Prize winner, two National Medal of Science/Technology awardees, and 15 members of the American Academy of Arts and Sciences.
 
A quarter century of innovation

On April 11, 2025, the McGovern Institute celebrated its 25th anniversary with a half-day symposium featuring presentations by MIT Institute Professor Robert Langer, alumni speakers from various McGovern labs, and Desimone, who is in his 20th year as director of the institute.

Desimone highlighted the institute’s recent discoveries, including the development of the CRISPR genome-editing system, which has culminated in the world’s first CRISPR gene therapy approved for humans — a remarkable achievement that is ushering in a new era of transformative medicine. In other milestones, McGovern researchers developed the first prosthetic limb fully controlled by the body’s nervous system; a flexible probe that taps into gut-brain communication; an expansion microscopy technique that paves the way for biology labs around the world to perform nanoscale imaging; and advanced computational models that demonstrate how we see, hear, use language, and even think about what others are thinking. Equally transformative has been the McGovern Institute’s work in neuroimaging, uncovering the architecture of human thought and establishing markers that signal the early emergence of mental illness, before symptoms even appear.

Synergy and open science
 
“I am often asked what makes us different from other neuroscience institutes and programs around the world,” says Desimone. “My answer is simple. At the McGovern Institute, the whole is greater than the sum of its parts.”
 
Many discoveries at the McGovern Institute have depended on collaborations across multiple labs, ranging from biological engineering to human brain imaging and artificial intelligence. In modern brain research, significant advances often require the joint expertise of people working in neurophysiology, behavior, computational analysis, neuroanatomy, and molecular biology. More than a dozen different MIT departments are represented by McGovern faculty and graduate students, and this synergy has led to insights and innovations that are far greater than what any single discipline could achieve alone.
 
Also baked into the McGovern ethos is a spirit of open science, where newly developed technologies are shared with colleagues around the world. Through hospital partnerships for example, McGovern researchers are testing their tools and therapeutic interventions in clinical settings, accelerating their discoveries into real-world solutions.

The McGovern legacy  

Hundreds of scientific papers have emerged from McGovern labs over the past 25 years, but most faculty would argue that it’s the people — the young researchers — that truly define the McGovern Institute. Award-winning faculty often attract the brightest young minds, but many McGovern faculty also serve as mentors, creating a diverse and vibrant scientific community that is setting the global standard for brain research and its applications. Kanwisher, for example, has guided more than 70 doctoral students and postdocs who have gone on to become leading scientists around the world. Three of her former students, Evelina Fedorenko PhD ’07, Josh McDermott PhD ’06, and Rebecca Saxe PhD ’03, the John W. Jarve (1978) Professor of Brain and Cognitive Sciences, are now her colleagues at the McGovern Institute. Other McGovern alumni shared stories of mentorship, science, and real-world impact at the 25th anniversary symposium.

Looking to the future, the McGovern community is more committed than ever to unraveling the mysteries of the brain and making a meaningful difference in lives of individuals at a global scale.
 
“By promoting team science, open communication, and cross-discipline partnerships,” says institute co-founder Lore Harp McGovern, “our culture demonstrates how individual expertise can be amplified through collective effort. I am honored to be the co-founder of this incredible institution — onward to the next 25 years!”

One of the most exciting developments in cancer treatment is a wave of new cell therapies that train a patient’s immune system to attack cancer cells. Such therapies have saved the lives of patients with certain aggressive cancers and few other options. Most of these therapies work by teaching immune cells to recognize and attack specific proteins on the surface of cancer cells.

Unfortunately, most proteins found on cancer cells aren’t unique to tumors. They’re also often present on healthy cells, making it difficult to target cancer aggressively without triggering dangerous attacks on other tissue. The problem has limited the application of cell therapies to a small subset of cancers.

Now Senti Bio is working to create smarter cell therapies using synthetic biology. The company, which was founded by former MIT faculty member and current MIT Research Associate Tim Lu ’03, MEng ’03, PhD ’08 and Professor James Collins, is equipping cells with gene circuits that allow the cells to sense and respond to their environments.

Lu, who studied computer science as an undergraduate at MIT, describes Senti’s approach as programming living cells to behave more like computers — responding to specific biological cues with “if/then” logic, just like computer code.

“We have innovated a cell therapy that says, ‘Kill anything displaying the cancer target, but spare anything that has this healthy target,’” Lu explains. “Despite the promise of certain cancer targets, problems can arise when they are expressed on healthy cells that we want to protect. Our logic gating technology was designed to recognize and avoid killing those healthy cells, which introduces a whole spectrum of additional cancers that don’t have a single clean target that we can now potentially address. That’s the power of embedding these cells with logic.”

The company’s lead drug candidate aims to help patients with acute myeloid leukemia (AML) who have experienced a relapse or are unresponsive to other therapies. The prognosis for such patients is poor, but early data from the company’s first clinical trial showed that two of the first three patients Senti treated experienced complete remission, where subsequent bone marrow tests couldn’t detect a single cancer cell.

“It’s essentially one of the best responses you can get in this disease, so we were really excited to see that,” says Lu, who served on MIT’s faculty until leaving to lead Senti in 2022.

Senti is expecting to release more patient data at the upcoming American Association for Cancer Research (AACR) meeting at the end of April.

“Our groundbreaking work at Senti is showing that one can harness synthetic biology technologies to create programmable, smart medicines for treating patients with cancer,” says Collins, who is currently MIT’s Termeer Professor of Medical Engineering and Science. “This is tremendously exciting and demonstrates how one can utilize synthetic biological circuits, in this case logic gates, to design highly effective, next-generation living therapeutics.”

From computer science to cancer care

Lu was inspired as an undergraduate studying electrical engineering and computer science by the Human Genome Project, an international race to sequence the human genome. Later, he entered the Harvard-MIT Health Sciences and Technology (HST) program, through which he earned a PhD from MIT in electrical and biomedical imaging and an MD from Harvard. During that time, he worked in the lab of his eventual Senti co-founder James Collins, a synthetic biology pioneer.

In 2010, Lu joined MIT as an assistant professor with a joint appointment in the departments of Biological Engineering and of Electrical Engineering and Computer Science. Over the course of the next 14 years, Lu led the Synthetic Biology Group at MIT and started several biotech companies, including Engine Biosciences and Tango Therapeutics, which are also developing precision cancer treatments.

In 2015, a group of researchers including Lu and MIT Institute Professor Phillip Sharp published research showing they could use gene circuits to get immune cells to selectively respond to tumor cells in their environment.

“One of the first things we published focused on the idea of logic gates in living cells,” Lu says. “A computer has ‘and’ gates, ‘or’ gates, and ‘not’ gates that allow it to perform computations, and we started publishing gene circuits that implement logic into living cells. These allow cells to detect signals and then make logical decisions like, ‘Should we switch on or off?’”

Around that time, the first cell therapies and cancer immunotherapies began to be approved by the Food and Drug Administration, and the founders saw their technology as a way to take those approaches to the next level. They officially founded Senti Bio in 2016, with Lu taking a sabbatical from MIT to serve as CEO.

The company licensed technology from MIT and subsequently advanced the cellular logic gates so they could work with multiple types of engineered immune cells, including T cells and “natural killer” cells. Senti’s cells can respond to specific proteins that exist on the surface of both cancer and healthy cells to increase selectivity.

“We can now create a cell therapy where the cell makes a decision as to whether to kill a cancer cell or spare a healthy cell even when those cells are right next to each other,” Lu says. “If you can’t distinguish between cancerous and healthy cells, you get unwanted side effects, or you may not be able to hit the cancer as hard as you’d like. But once you can do that, there’s a lot of ways to maximize your firepower against the cancer cells.”

Hope for patients

Senti’s lead clinical trial is focusing on patients with relapsed or refractory blood cancers, including AML.

“Obviously the most important thing is getting a good response for patients,” Lu says. “But we’re also doing additional scientific work to confirm that the logic gates are working the way we expect them to in humans. Based on that information, we can then deploy logic gates into additional therapeutic indications such as solid tumors, where you have a lot of the same problems with finding a target.”

Another company that has partnered with Senti to use some of Senti’s technology also has an early clinical trial underway in liver cancer. Senti is also partnering with other companies to apply its gene circuit technology in areas like regenerative medicine and neuroscience.

“I think this is broader than just cell therapies,” Lu says. “We believe if we can prove this out in AML, it will lead to a fundamentally new way of diagnosing and treating cancer, where we’re able to definitively identify and target cancer cells and spare healthy cells. We hope it will become a whole new class of medicines moving forward.”

Programmers can now use large language models (LLMs) to generate computer code more quickly. However, this only makes programmers’ lives easier if that code follows the rules of the programming language and doesn’t cause a computer to crash.

Some methods exist for ensuring LLMs conform to the rules of whatever language they are generating text in, but many of these methods either distort the model’s intended meaning or are too time-consuming to be feasible for complex tasks.

A new approach developed by researchers at MIT and elsewhere automatically guides an LLM to generate text that adheres to the rules of the relevant language, such as a particular programming language, and is also error-free. Their method allows an LLM to allocate efforts toward outputs that are most likely to be valid and accurate, while discarding unpromising outputs early in the process. This probabilistic approach boosts computational efficiency.

Due to these efficiency gains, the researchers’ architecture enabled small LLMs to outperform much larger models in generating accurate, properly structured outputs for several real-world use cases, including molecular biology and robotics.

In the long run, this new architecture could help nonexperts control AI-generated content. For instance, it could allow businesspeople to write complex queries in SQL, a language for database manipulation, using only natural language prompts.

“This work has implications beyond research. It could improve programming assistants, AI-powered data analysis, and scientific discovery tools by ensuring that AI-generated outputs remain both useful and correct,” says João Loula, an MIT graduate student and co-lead author of a paper on this framework.

Loula is joined on the paper by co-lead authors Benjamin LeBrun, a research assistant at the Mila-Quebec Artificial Intelligence Institute, and Li Du, a graduate student at John Hopkins University; co-senior authors Vikash Mansinghka ’05, MEng ’09, PhD ’09, a principal research scientist and leader of the Probabilistic Computing Project in the MIT Department of Brain and Cognitive Sciences; Alexander K. Lew SM ’20, an assistant professor at Yale University; Tim Vieira, a postdoc at ETH Zurich; and Timothy J. O’Donnell, an associate professor at McGill University and a Canada CIFAR AI Chair at Mila, who led the international team; as well as several others. The research will be presented at the International Conference on Learning Representations.

Enforcing structure and meaning

One common approach for controlling the structured text generated by LLMs involves checking an entire output, like a block of computer code, to make sure it is valid and will run error-free. If not, the user must start again, racking up computational resources.

On the other hand, a programmer could stop to check the output along the way. While this can ensure the code adheres to the programming language and is structurally valid, incrementally correcting the code may cause it to drift from the meaning the user intended, hurting its accuracy in the long run.

“It is much easier to enforce structure than meaning. We can quickly check whether something is in the right programming language, but to check its meaning you have to execute the code. Our work is also about dealing with these different types of information,” Loula says.

The researchers’ approach involves engineering knowledge into the LLM to steer it toward the most promising outputs. These outputs are more likely to follow the structural constraints defined by a user, and to have the meaning the user intends.

“We are not trying to train an LLM to do this. Instead, we are engineering some knowledge that an expert would have and combining it with the LLM’s knowledge, which offers a very different approach to scaling than you see in deep learning,” Mansinghka adds.

They accomplish this using a technique called sequential Monte Carlo, which enables parallel generation from an LLM to compete with each other. The model dynamically allocates resources to different threads of parallel computation based on how promising their output appears.

Each output is given a weight that represents how likely it is to be structurally valid and semantically accurate. At each step in the computation, the model focuses on those with higher weights and throws out the rest.

In a sense, it is like the LLM has an expert looking over its shoulder to ensure it makes the right choices at each step, while keeping it focused on the overall goal. The user specifies their desired structure and meaning, as well as how to check the output, then the researchers’ architecture guides the LLM to do the rest.

“We’ve worked out the hard math so that, for any kinds of constraints you’d like to incorporate, you are going to get the proper weights. In the end, you get the right answer,” Loula says.

Boosting small models

To test their approach, they applied the framework to LLMs tasked with generating four types of outputs: Python code, SQL database queries, molecular structures, and plans for a robot to follow.

When compared to existing approaches, the researchers’ method performed more accurately while requiring less computation.

In Python code generation, for instance, the researchers’ architecture enabled a small, open-source model to outperform a specialized, commercial closed-source model that is more than double its size.

“We are very excited that we can allow these small models to punch way above their weight,” Loula says.

Moving forward, the researchers want to use their technique to control larger chunks of generated text, rather than working one small piece at a time. They also want to combine their method with learning, so that as they control the outputs a model generates, it learns to be more accurate.

In the long run, this project could have broader applications for non-technical users. For instance, it could be combined with systems for automated data modeling, and querying generative models of databases.

The approach could also enable machine-assisted data analysis systems, where the user can converse with software that accurately models the meaning of the data and the questions asked by the user, adds Mansinghka.

“One of the fundamental questions of linguistics is how the meaning of words, phrases, and sentences can be grounded in models of the world, accounting for uncertainty and vagueness in meaning and reference. LLMs, predicting likely token sequences, don’t address this problem. Our paper shows that, in narrow symbolic domains, it is technically possible to map from words to distributions on grounded meanings. It’s a small step towards deeper questions in cognitive science, linguistics, and artificial intelligence needed to understand how machines can communicate about the world like we do,” says O’Donnell.

This research is funded and supported, in part, by the Canada CIFAR AI Chairs Program, the MIT Quest for Intelligence, and Convergent Research. 

The following is part of a series of short interviews from the Department of Electrical Engineering and Computer Science (EECS). Each spotlight features a student answering questions about themselves and life at MIT. Today’s interviewee, YongYan (Crystal) Liang, is a senior majoring in EECS with a particular interest in bioengineering and medical devices — which led her to join the Living Machines track as part of New Engineering Education Transformation (NEET) at MIT. An Advanced Undergraduate Research Opportunities Program (SuperUROP) scholar, Liang was supported by the Nadar Foundation Undergraduate Research and Innovation Scholar award for her project, which focused on steering systems for intravascular drug delivery devices. A world traveler, Liang has also taught robotics to students in MISTI Global Teaching Labs (GTL) programs in Korea and Germany — and is involved with the Terrascope and MedLinks communities. 

Q: Do you have a bucket list? If so, share one or two of the items on it.

A: I’d like to be proficient in at least five languages in a conversational sense (though probably not at a working proficiency level). Currently, I’m fluent in English, and can speak Cantonese and Mandarin. I also have a 1,600-plus day Duolingo streak where I’m trying to learn the foundations of a few languages, including German, Korean, Japanese, and Russian. 

Another bucket list item I have is to try every martial art/combat sport there is, even if it’s just an introduction class. So far, I’ve practiced taekwondo for a few years, taken a few lessons in boxing/kickboxing, and dabbled in beginners’ classes for karate, Krav Maga, and Brazilian jiujitsu. I’ll probably try to take judo, aikido, and other classes this upcoming year! It would also be pretty epic to be a fourth dan black belt one day, though that may take a decade or two.

Q: If you had to teach a really in-depth class about one niche topic, what would you pick?

A: Personally, I think artificial organs are pretty awesome! I would probably talk about the fusion of engineering with our bodies, and organ enhancement. This might include adding functionalities and possible organ regeneration, so that those waiting for organ donations can be helped without being morally conflicted by waiting for another person’s downfall. I’ve previously done research in several BioEECS-related labs that I’d love to talk about as well. This includes the Traverso Lab at Pappalardo, briefly in the Edelman Lab at the [Institute for Medical Engineering and Science], the Langer Lab at the Koch Institute of Integrative Cancer Research, as well as in the MIT Media Lab with the Conformable Decoders and BioMechatronics group. I also contributed to a recently published paper related to gastrointestinal devices: OSIRIS.  

Q: If you suddenly won the lottery, what would you spend some of the money on? 

A: I would make sure my mom got most of the money. The first thing we’d do is probably go house shopping around the world and buy properties in great travel destinations — then go around and live in said properties. We would do this on rotation with our friends until we ran out of money, then put the properties up for rent and use the money to open a restaurant with my mom’s recipes as the menu. Then I’d get to eat her food forever.

Q: What do you believe is an underrated invention or technology?

A: I feel like many people wear glasses or put on contacts nowadays and don’t really think twice about it, glossing over how cool it is that we can fix bad sight and how critical sight is for our survival. If a zombie apocalypse happened and my glasses broke, it would be over for me. And don’t get me started about the invention of the indoor toilet and plumbing systems!

Q: Are you a re-reader or a re-watcher? If so, what are your comfort books, shows, or movies? 

A: I’m both a re-reader and a re-watcher! I have a lot of fun binging webtoons and dramas. I’m also a huge Marvel fan, although recently, it’s been a hit or miss. Action and romcoms are my kinda vibes, and occasionally I do watch some anime. If I’m bored I usually re-watch some [Marvel Cinematic Universe] movies, or Fairy Tail, or read some Isekai genre stories. 

Q: It’s time to get on the shuttle to the first Mars colony, and you can only bring one personal item. What are you going to bring along with you?

A: My first thought was my phone, but I feel like that may be too standard of an answer. If we were talking about the fantasy realm, I might ask Stephen Strange to borrow his sling ring to open more portals to link the Earth and Mars. As to why he wouldn’t have just come with us in the first place, I don’t know; maybe he’s too busy fighting aliens, or something?

Q: What are you looking forward to about life after graduation? What do you think you’ll miss about MIT? 

A: I won’t be missing dining hall food very much, that’s for sure — except for the amazing oatmeal from one of the Maseeh dining hall chefs, Sum! I am, however, excited to live the nine-to-five life for a few years and have my weekends back. I’ll miss my friends dearly, since everyone will be so spread out across the States and abroad. I’ll miss the nights we spent watching movies, playing games, cooking, eating, and yapping away. I’m excited to see everyone grow and take another step closer to their dreams. It will be fun visiting them and being able to explore the world at the same time! For more immediate plans, I’ll be going back to Apple this summer to intern again, and will finish my MEng with the 6A program at Cadence. Afterwards, I shall see where life takes me!

It is clear that humankind needs increasingly more resources, from computing power to steel and concrete, to meet the growing demands associated with data centers, infrastructure, and other mainstays of society. New, cost-effective approaches for producing the advanced materials key to that growth were the focus of a two-day workshop at MIT on March 11 and 12.

A theme throughout the event was the importance of collaboration between and within universities and industries. The goal is to “develop concepts that everybody can use together, instead of everybody doing something different and then trying to sort it out later at great cost,” said Lionel Kimerling, the Thomas Lord Professor of Materials Science and Engineering at MIT.

The workshop was produced by MIT’s Materials Research Laboratory (MRL), which has an industry collegium, and MIT’s Industrial Liaison Program. 

The program included an address by Javier Sanfelix, lead of the Advanced Materials Team for the European Union. Sanfelix gave an overview of the EU’s strategy to developing advanced materials, which he said are “key enablers of the green and digital transition for European industry.”

That strategy has already led to several initiatives. These include a material commons, or shared digital infrastructure for the design and development of advanced materials, and an advanced materials academy for educating new innovators and designers. Sanfelix also described an Advanced Materials Act for 2026 that aims to put in place a legislative framework that supports the entire innovation cycle.

Sanfelix was visiting MIT to learn more about how the Institute is approaching the future of advanced materials. “We see MIT as a leader worldwide in technology, especially on materials, and there is a lot to learn about [your] industry collaborations and technology transfer with industry,” he said.

Innovations in steel and concrete

The workshop began with talks about innovations involving two of the most common human-made materials in the world: steel and cement. We’ll need more of both but must reckon with the huge amounts of energy required to produce them and their impact on the environment due to greenhouse-gas emissions during that production.

One way to address our need for more steel is to reuse what we have, said C. Cem Tasan, the POSCO Associate Professor of Metallurgy in the Department of Materials Science and Engineering (DMSE) and director of the Materials Research Laboratory.

But most of the existing approaches to recycling scrap steel involve melting the metal. “And whenever you are dealing with molten metal, everything goes up, from energy use to carbon-dioxide emissions. Life is more difficult,” Tasan said.

The question he and his team asked is whether they could reuse scrap steel without melting it. Could they consolidate solid scraps, then roll them together using existing equipment to create new sheet metal? From the materials-science perspective, Tasan said, that shouldn’t work, for several reasons.

But it does. “We’ve demonstrated the potential in two papers and two patent applications already,” he said. Tasan noted that the approach focuses on high-quality manufacturing scrap. “This is not junkyard scrap,” he said.

Tasan went on to explain how and why the new process works from a materials-science perspective, then gave examples of how the recycled steel could be used. “My favorite example is the stainless-steel countertops in restaurants. Do you really need the mechanical performance of stainless steel there?” You could use the recycled steel instead.

Hessam Azarijafari addressed another common, indispensable material: concrete. This year marks the 16th anniversary of the MIT Concrete Sustainability Hub (CSHub), which began when a set of industry leaders and politicians reached out to MIT to learn more about the benefits and environmental impacts of concrete.

The hub’s work now centers around three main themes: working toward a carbon-neutral concrete industry; the development of a sustainable infrastructure, with a focus on pavement; and how to make our cities more resilient to natural hazards through investment in stronger, cooler construction.

Azarijafari, the deputy director of the CSHub, went on to give several examples of research results that have come out of the CSHub. These include many models to identify different pathways to decarbonize the cement and concrete sector. Other work involves pavements, which the general public thinks of as inert, Azarijafari said. “But we have [created] a state-of-the-art model that can assess interactions between pavement and vehicles.” It turns out that pavement surface characteristics and structural performance “can influence excess fuel consumption by inducing an additional rolling resistance.”

Azarijafari emphasized  the importance of working closely with policymakers and industry. That engagement is key “to sharing the lessons that we have learned so far.”

Toward a resource-efficient microchip industry

Consider the following: In 2020 the number of cell phones, GPS units, and other devices connected to the “cloud,” or large data centers, exceeded 50 billion. And data-center traffic in turn is scaling by 1,000 times every 10 years.

But all of that computation takes energy. And “all of it has to happen at a constant cost of energy, because the gross domestic product isn’t changing at that rate,” said Kimerling. The solution is to either produce much more energy, or make information technology much more energy-efficient. Several speakers at the workshop focused on the materials and components behind the latter.

Key to everything they discussed: adding photonics, or using light to carry information, to the well-established electronics behind today’s microchips. “The bottom line is that integrating photonics with electronics in the same package is the transistor for the 21st century. If we can’t figure out how to do that, then we’re not going to be able to scale forward,” said Kimerling, who is director of the MIT Microphotonics Center.

MIT has long been a leader in the integration of photonics with electronics. For example, Kimerling described the Integrated Photonics System Roadmap – International (IPSR-I), a global network of more than 400 industrial and R&D partners working together to define and create photonic integrated circuit technology. IPSR-I is led by the MIT Microphotonics Center and PhotonDelta. Kimerling began the organization in 1997.

Last year IPSR-I released its latest roadmap for photonics-electronics integration, “which  outlines a clear way forward and specifies an innovative learning curve for scaling performance and applications for the next 15 years,” Kimerling said.

Another major MIT program focused on the future of the microchip industry is FUTUR-IC, a new global alliance for sustainable microchip manufacturing. Begun last year, FUTUR-IC is funded by the National Science Foundation.

“Our goal is to build a resource-efficient microchip industry value chain,” said Anuradha Murthy Agarwal, a principal research scientist at the MRL and leader of FUTUR-IC. That includes all of the elements that go into manufacturing future microchips, including workforce education and techniques to mitigate potential environmental effects.

FUTUR-IC is also focused on electronic-photonic integration. “My mantra is to use electronics for computation, [and] shift to photonics for communication to bring this energy crisis in control,” Agarwal said.

But integrating electronic chips with photonic chips is not easy. To that end, Agarwal described some of the challenges involved. For example, currently it is difficult to connect the optical fibers carrying communications to a microchip. That’s because the alignment between the two must be almost perfect or the light will disperse. And the dimensions involved are minuscule. An optical fiber has a diameter of only millionths of a meter. As a result, today each connection must be actively tested with a laser to ensure that the light will come through.

That said, Agarwal went on to describe a new coupler between the fiber and chip that could solve the problem and allow robots to passively assemble the chips (no laser needed). The work, which was conducted by researchers including MIT graduate student Drew Wenninger, Agarwal, and Kimerling, has been patented, and is reported in two papers. A second recent breakthrough in this area involving a printed micro-reflector was described by Juejun “JJ” Hu, John F. Elliott Professor of Materials Science and Engineering.

FUTUR-IC is also leading educational efforts for training a future workforce, as well as techniques for detecting — and potentially destroying — the perfluroalkyls (PFAS, or “forever chemicals”) released during microchip manufacturing. FUTUR-IC educational efforts, including virtual reality and game-based learning, were described by Sajan Saini, education director for FUTUR-IC. PFAS detection and remediation were discussed by Aristide Gumyusenge, an assistant professor in DMSE, and Jesus Castro Esteban, a postdoc in the Department of Chemistry.

Other presenters at the workshop included Antoine Allanore, the Heather N. Lechtman Professor of Materials Science and Engineering; Katrin Daehn, a postdoc in the Allanore lab; Xuanhe Zhao, the Uncas (1923) and Helen Whitaker Professor in the Department of Mechanical Engineering; Richard Otte, CEO of Promex; and Carl Thompson, the Stavros V. Salapatas Professor in Materials Science and Engineering.

As technology rapidly propels society forward, MIT is rethinking how it prepares students to face the world and its greatest challenges. Generations of educators have shared knowledge at MIT by connecting lessons to practical applications, but what does the Institute’s motto “mens et manus” (“mind and hand”), referring to hands-on learning, look like in the future?

This was the guiding question of the annual Festival of Learning, co-hosted by MIT Open Learning and the Office of the Vice Chancellor. MIT faculty, instructors, students, and staff engaged in meaningful discussions about teaching and learning as the Institute critically revisits its undergraduate academic program.

“Because the world is changing, we owe it to our students to reflect these realities in our academic experiences,” said Daniel E. Hastings, Cecil and Ida Green Education Professor of Aeronautics and Astronautics and then-interim vice chancellor. “It’s in our DNA to try new things at MIT.”

Fostering a greater sense of purpose

MIT emphasizes hands-on learning much like many engineering schools. What deeply concerned panelists like Susan Silbey, the Leon and Anne Goldberg Professor of Humanities, Sociology, and Anthropology, is that students are not engaging in enough intellectual thinking via significant reading, textual interpretation, or involvement with uncertain questions.

Christopher Capozzola, senior associate dean for open learning, echoed this, saying, “We have designed a world in which [students] feel enormous pressure to maximize their career outcomes at the end” of their undergraduate education.

Students move in systems of explicit incentives, he said, such as grades and the General Institute Requirements, but also respond to unwritten incentives, like extracurriculars, internships, and prestige. “That’s our fault, not theirs,” Capozzola said, and identified this as an opportunity to improve the MIT curriculum.

How can educators encourage students to connect more with course material, instead of treating it as a means to an end? Adam Martin, professor of biology, always asks his students to challenge the status quo by incorporating test questions with data arguing against the models from the textbook.

“I want them to think,” Martin said. “I want them to challenge what we think is the frontier of the field.”

Considering context

One of the most significant topics of discussion was the importance of context in education. For example, class 7.102 (Introduction to Molecular Biology Techniques) uses story-based problem-solving to show students how the curriculum fits into real-world contexts.

The fictional premise driving 7.102 is that a child fell into the Charles River and caught an antibiotic-resistant bacterial infection. To save the child, students must characterize the bacteria and identify phages that could kill it.

“It really shows the students not only basic techniques, but what it’s like to be in a team and in a discovery situation,” said Martin.

This hands-on approach — collecting water, isolating the phages within, and comparing to more reliable sources — unlocks students’ imaginations, Martin said. In an environment intentionally designed to give students room to fail, the narrative incentivizes students to persist with repeated experimentation.

But Silbey, who is also a professor of behavioral and policy sciences at MIT Sloan School of Management, has noticed the reluctance of students to engage with nontechnical contexts. Students, she concluded, “have minimal understanding of how the action of any individual becomes part of something larger, durable, consequential through invisible but powerful mechanisms of aggregation.”

Educators agreed that contextual understanding was equally important to a STEM curriculum as technical instruction. “Teaching and thinking at that interface between technology and society is really crucial for making technologists feel responsible for the things that they create and the things that they use,” added Capozzola.

Amitava Mitra, founding executive director of MIT New Engineering Education Transformation (NEET), highlighted an example where students developed an effective technical solution to decarbonize homes in Ulaanbaatar, Mongolia. Or so they thought.

“Once we saw what was on the ground and understood the context — the social model, the social processes — we realized we had no clue,” the students told Mitra.

One way MIT is trying to bridge these gaps is through the Social and Ethical Responsibilities of Computing program. This curriculum integrates ethical considerations alongside computing courses to help students envision the social and moral consequences of their actions.

In one technical machinery lecture, Silbey’s students had trouble envisioning the negative impacts of autonomous vehicles. But after she shared the history of the regulation of dangerous products, she said many students became more open to examining potential ripple effects.

Creating interdisciplinary opportunities

The panelists viewed interdisciplinary education as critical preparation for the complexities of the real world.

“Whether it’s tackling climate change, creating sustainable infrastructure, creating cutting-edge technologies in life sciences or robotics, we need our engineers, social scientists, and scientists to work in teams cutting across disciplines to create solutions today,” said Mitra.

To expand opportunities for undergraduates to collaborate across academic departments and other campus units, NEET was launched in 2017. NEET is a project-based experiential learning curriculum that requires technical and social expertise. One student group, for example, is designing, building, and installing a solar-powered charging station at MIT Open Space. To introduce a project like this into MIT’s infrastructure, students must coordinate with a variety of Institute offices — such as Campus Planning, Engineering & Energy Management, and Insurance — and city groups, like the Cambridge Fire Department.

“It's an eye-opener for them,” said Mitra.

Capozzola noted how “para-curricular” activities like NEET, MIT Undergraduate Research Opportunities Program, MISTI, D-Lab, and others prove that effective hands-on education doesn’t have to be a formal credit-bearing program.

“Students put in enormous amounts of time and effort for things that shape them, that speak to their passion and this deep engagement,” Capozzola said. “This is a special area where I think MIT particularly excels.”

Moving forward together

In a panel featuring both MIT instructors and students, educators recognized that designing an effective curriculum requires balancing content across subjects or core topics while organizing materials on Canvas — MIT’s learning management system — in a way that’s intuitive for students. Instructors collaborated directly with students and staff via MIT’s Canvas Innovation Fund to make these improvements.

“There are things that the novice students see in what I’m teaching that I don’t see,” said Sean Robinson, lecturer in physics and associate director of the Helena Foundation Junior Laboratory. “Our class is aimed at taking people who think of themselves as physics students and getting them to think of themselves as physicists. I want junior colleagues.”

The biggest takeaway from student panelists was the importance of minimizing logistical struggles by structuring Canvas to guide students toward learning objectives. Cory Romanov ’24, technical instructor of physics, and McKenzie Dinesen, a senior in aerospace engineering and Russian and Eurasian studies, emphasized that explaining learning goals and organizing course content with clear deadlines were simple improvements that went a long way to enhance the student experience.

Emphasizing the benefit of feedback like this, Capozzola said, “It’s important to give people at MIT — students, staff, and others who are often closed out of conversations — a more democratic voice so that we can be a model for the university that we want to be in 25 years.”

As MIT continues to enhance its educational approach, the insights from the Festival of Learning highlight a crucial evolution in how students engage with knowledge. From rethinking course structures to integrating interdisciplinary and experiential learning, the panelists underscored the need for a curriculum that balances technical expertise with a deep understanding of social and ethical contexts.

“It’s important to equip students on the ‘mens’ side with the kinds of civic knowledge that they need to go out into the world,” said Capozzola, “but also the ‘manus,’ to be able to do the everyday work of getting your hands dirty and building democratic institutions.” 

MIT political scientist Adam Berinsky has been named to the 2025 class of Andrew Carnegie Fellows, a high-profile honor for scholars pursuing research in the social sciences and humanities.

The fellowship is provided by The Carnegie Corp. of New York. Berinsky, the Mitsui Professor of Political Science, and 25 other fellows were selected from more than 300 applicants. They will each receive stipends of $200,000 for research that seeks to understand how and why our society has become so polarized, and how we can strengthen the forces of cohesion to fortify our democracy.

“Through these fellowships Carnegie is harnessing the unrivaled brainpower of our universities to help us to understand how our society has become so polarized,” says Carnegie President Louise Richardson. “Our future grant-making will be informed by what we learn from these scholars as we seek to mitigate the pernicious effects of political polarization.”

Berinsky said he is “incredibly honored to be named an Andrew Carnegie Fellow for the coming year. This fellowship will allow me to work on critical issues in the current political moment.”

During his year as a Carnegie Fellow, Berinsky will be working on a project, “Fostering an Accurate Information Ecosystem to Mitigate Polarization in the United States.”

“For a functioning democracy, it is essential that citizens share a baseline of common facts,” says Berinsky. “However, in today’s politically polarized climate, ‘alternative facts,’ and other forms of misinformation — from political rumors to conspiracy theories — distort how people see reality, and damage our social fabric.”

“I’ve spent the last 15 years investigating why individuals accept misinformation and how to counter misperceptions. But there is still a lot of work to be done. My project aims to tackle the serious problem of misinformation in the United States by bringing together existing approaches in new, more powerful combinations. I’m hoping that the whole can be more than the sum of its parts.”

Berinsky has been a member of the MIT faculty since 2003. He is the author of “Political Rumors: Why We Accept Misinformation and How to Fight It” (Princeton University Press, 2023).

Other MIT faculty who have received the Carnegie Fellowship in recent years include economists David Autor and Daron Acemoglu and political scientists Fotini Christia, Taylor Fravel, Richard Nielsen, and Charles Stewart.

Cleft lip and cleft palate are among the most common birth defects, occurring in about one in 1,050 births in the United States. These defects, which appear when the tissues that form the lip or the roof of the mouth do not join completely, are believed to be caused by a mix of genetic and environmental factors.

In a new study, MIT biologists have discovered how a genetic variant often found in people with these facial malformations leads to the development of cleft lip and cleft palate.

Their findings suggest that the variant diminishes cells’ supply of transfer RNA, a molecule that is critical for assembling proteins. When this happens, embryonic face cells are unable to fuse to form the lip and roof of the mouth.

“Until now, no one had made the connection that we made. This particular gene was known to be part of the complex involved in the splicing of transfer RNA, but it wasn’t clear that it played such a crucial role for this process and for facial development. Without the gene, known as DDX1, certain transfer RNA can no longer bring amino acids to the ribosome to make new proteins. If the cells can’t process these tRNAs properly, then the ribosomes can’t make protein anymore,” says Michaela Bartusel, an MIT research scientist and the lead author of the study.

Eliezer Calo, an associate professor of biology at MIT, is the senior author of the paper, which appears today in the American Journal of Human Genetics.

Genetic variants

Cleft lip and cleft palate, also known as orofacial clefts, can be caused by genetic mutations, but in many cases, there is no known genetic cause.

“The mechanism for the development of these orofacial clefts is unclear, mostly because they are known to be impacted by both genetic and environmental factors,” Calo says. “Trying to pinpoint what might be affected has been very challenging in this context.”

To discover genetic factors that influence a particular disease, scientists often perform genome-wide association studies (GWAS), which can reveal variants that are found more often in people who have a particular disease than in people who don’t.

For orofacial clefts, some of the genetic variants that have regularly turned up in GWAS appeared to be in a region of DNA that doesn’t code for proteins. In this study, the MIT team set out to figure out how variants in this region might influence the development of facial malformations.

Their studies revealed that these variants are located in an enhancer region called e2p24.2. Enhancers are segments of DNA that interact with protein-coding genes, helping to activate them by binding to transcription factors that turn on gene expression.

The researchers found that this region is in close proximity to three genes, suggesting that it may control the expression of those genes. One of those genes had already been ruled out as contributing to facial malformations, and another had already been shown to have a connection. In this study, the researchers focused on the third gene, which is known as DDX1.

DDX1, it turned out, is necessary for splicing transfer RNA (tRNA) molecules, which play a critical role in protein synthesis. Each transfer RNA molecule transports a specific amino acid to the ribosome — a cell structure that strings amino acids together to form proteins, based on the instructions carried by messenger RNA.

While there are about 400 different tRNAs found in the human genome, only a fraction of those tRNAs require splicing, and those are the tRNAs most affected by the loss of DDX1. These tRNAs transport four different amino acids, and the researchers hypothesize that these four amino acids may be particularly abundant in proteins that embryonic cells that form the face need to develop properly.

When the ribosomes need one of those four amino acids, but none of them are available, the ribosome can stall, and the protein doesn’t get made.

The researchers are now exploring which proteins might be most affected by the loss of those amino acids. They also plan to investigate what happens inside cells when the ribosomes stall, in hopes of identifying a stress signal that could potentially be blocked and help cells survive.

Malfunctioning tRNA

While this is the first study to link tRNA to craniofacial malformations, previous studies have shown that mutations that impair ribosome formation can also lead to similar defects. Studies have also shown that disruptions of tRNA synthesis — caused by mutations in the enzymes that attach amino acids to tRNA, or in proteins involved in an earlier step in tRNA splicing — can lead to neurodevelopmental disorders.

“Defects in other components of the tRNA pathway have been shown to be associated with neurodevelopmental disease,” Calo says. “One interesting parallel between these two is that the cells that form the face are coming from the same place as the cells that form the neurons, so it seems that these particular cells are very susceptible to tRNA defects.”

The researchers now hope to explore whether environmental factors linked to orofacial birth defects also influence tRNA function. Some of their preliminary work has found that oxidative stress — a buildup of harmful free radicals — can lead to fragmentation of tRNA molecules. Oxidative stress can occur in embryonic cells upon exposure to ethanol, as in fetal alcohol syndrome, or if the mother develops gestational diabetes.

“I think it is worth looking for mutations that might be causing this on the genetic side of things, but then also in the future, we would expand this into which environmental factors have the same effects on tRNA function, and then see which precautions might be able to prevent any effects on tRNAs,” Bartusel says.

The research was funded by the National Science Foundation Graduate Research Program, the National Cancer Institute, the National Institute of General Medical Sciences, and the Pew Charitable Trusts.

Anders Sejr Hansen, Class of 1943 Career Development Professor in the Department of Biological Engineering, has been named as the recipient of the 2024-25 Harold E. Edgerton Faculty Achievement Award.

The annual award was established in fall 1982 as a permanent tribute to Institute Professor Emeritus Harold E. Edgerton for his great and enduring support for younger faculty members over the years. The purpose of the award is to recognize exceptional distinction in teaching, in research, and in service.

Hansen is the principal investigator of the Hansen Lab, which develops new methods to resolve 3D genome structure at high spatiotemporal resolution to understand how DNA looping and 3D folding regulates gene expression in health and disease. His areas of research include cancer biology, computational systems biology, instrumentation and measurement, and synthetic biology.

“My research focuses on how the expression of our genes is regulated,” says Hansen. “All the cells in our body have the same DNA and the same genes. Thus, the software or applications to each cell are the same. What’s different between a neuron and a blood cell is what genes they choose to express. My research focuses on understanding how this regulation takes place.”

Those who nominated Anders for the award emphasized his remarkable productivity, mentioning his two “highly cited, paradigm-shifting research articles in Science and Nature Genetics,” and his research presentations at 50 invited talks, including two keynotes, at universities and conferences worldwide. They also highlighted his passion for mentorship and career development for the 20 current members of his laboratory.

“Anders is an outstanding role model and ambassador of biological engineering, combining a powerful research program, run as a caring mentor, and innovative undergraduate education,” says Christopher Voigt, the Daniel I.C. Wang Professor in Biological Engineering and head of the Department of Biological Engineering.

Adds Laurie Boyer, a professor of biology and biological engineering, “His work reveals new insights into how we think about the dynamics of gene regulation that would not otherwise be possible. The Hansen Lab’s work provides a unified framework rapidly adopted by the field to learn how conserved regulators provide exquisite spatial and temporal control of gene expression in the context of 3D genome architecture.”

During the nomination process, students praised Hansen’s passion for his work, along with his ability to prepare them to apply their education outside the classroom.

“He always strives to guide each lab member towards both short-term scientific success and long-term career planning through regular one-on-one meetings, facilitating collaborations and access to scientific resources, and sharing his own experiences,” says Jin Yang, a graduate student in biological engineering and member of the Hansen Lab.

“Dr. Hansen's infectious excitement for the course material made it very enjoyable to come to class and envision potential applications of the fundamental topics he taught,” adds another one of his students. “Excellent lecturer!”

Hansen obtained his undergraduate and master’s degree in chemistry at Oxford University. He received his PhD in chemistry and chemical biology from Harvard University, where he applied systems biology approaches to understand how cells can encode and transmit information in the dynamics of transcription factor activation. For his postdoc at the University of California at Berkeley, Hansen developed new imaging approaches for dissecting the dynamics of architectural proteins with single-molecule resolution in living cells. Hansen joined MIT as an assistant professor of biological engineering in early 2020.

His recognitions include an NIH K99 Pathway to Independence Award (2019), NIH Director’s New Innovator Award (2020), a Pew-Stewart Scholar for Cancer Research Award (2021), an NSF CAREER Award (2024), and an NIH Director’s Transformative Research Award (2024).

Hansen has served on several committees at MIT, including the MIT Biological Engineering Graduate Program Admissions Committee, the MIT Computational and Systems Biology Graduate Admissions Committee, and the MIT Biological Engineering Graduate Recruiting Committee, of which he has been chair since 2023.

“I have known about the Edgerton Award since I started at MIT, and I think the broad focus on both research, teaching, and service really captures what makes MIT such a unique and wonderful place,” says Hansen. “I was therefore absolutely thrilled to receive the news that I would receive the Edgerton Award this year, and I am very grateful to all the wonderful colleagues here at MIT who have supported me over the years, and all the exceptional people in my lab whose work is being recognized.”

A new website led by University of Melbourne academics and designed for peopleundergoing IVF treatment will help them navigate and decide on optional extras or so-called add-ons during their treatment.

At Tembusu College, students are not just learning about society—they are learning with it. Through carefully designed, socially responsive learning experiences, the College’s Communities and Engagement (C&E) Senior Seminars invite students to step beyond their disciplinary boundaries and confront real-world complexity head-on.

Engaging directly with community partners, students co-create knowledge and build mutual understanding with individuals such as elderly residents, migrant workers and public health officers. For these students, such conversations become the curriculum—they are not only taught to think critically, but also to act meaningfully.

Each Academic Year, over 220 students from diverse academic backgrounds participate in small, interdisciplinary seminars that extend beyond classroom walls. Rather than relying solely on textbooks, they immerse themselves in complex social environments, engaging in participatory research and reflection to cultivate both intellectual rigour and emotional intelligence.

“As part of our diverse, humanistic, interdisciplinary, inquiry-oriented programmes that broadens students’ perspectives, we connect theory with practice, and transform ideas into impact,” explained Dr Connor Graham, Director of Studies at Tembusu College. “People-centred learning allows students to develop an awareness of others and their worldviews, reflexivity and adaptability in addition to the more academic outcomes of thinking critically and questioning with relevance”.

Building empathy through action

Tembusu College’s people-centred pedagogical model fosters openness across disciplines—from sociology and biomedical science to engineering and the arts—while uniting students under a shared ethos: Helping People and Species in Need, Engaging the World, Developing Creativity, and Promoting Interdisciplinarity.

In small-class settings and interdisciplinary teams, students apply methods such as ethnographic field notes, interviews, and participatory observation to tackle pressing social issues. These are not short-term projects. Many are rooted in longstanding partnerships with community organisations, allowing successive cohorts to build on previous work and deepen engagement over time.

One such course, Health and the Community in Singapore, is part of an ongoing partnership with the Health Promotion Board. It aims to bridge the STEM–non-STEM divide and understand gaps in public health literacy.

In April 2024, students conducted research into the health needs of youths using community-based participatory methods. One group designed a low-sodium dietary intervention tailored for NUS undergraduates, featuring a visually intuitive labelling system to nudge the making of healthier food choices. Their project received a grant from NUS Health & Wellbeing and is under plans for a collaborative pilot study with Associate Professor Alberto Salvo from the NUS Department of Economics.

In another course, Technologies and Ageing in Singapore, students explored the ageing process and the lived experiences of elderly citizens through interviews, fieldwork, and person-centred therapy frameworks. Between February and April 2024, one group developed an assistive tool to facilitate end-of-life planning conversations with seniors, which was piloted at the Evergreen Circle Active Ageing Centre and later adapted for use at St Luke’s Eldercare (Ayer Rajah).

Another group created Dig Deeper, a reflective card game designed to prompt family conversations about end-of-life matters. One parent who participated in the exercise said, “As a family, we’ve never really talked about end-of-life topics. We’re not that old yet, but this game really made us reflect on our beliefs and values, and the legacy we want to leave behind.” The student participant added, “It’s funny how I haven’t felt this close to my parents in such a long time. Sometimes we’re so busy doing our own things that we forget how important it is to connect.”

Understanding happiness through dialogue

The Happiness by Design course also exemplifies Tembusu College’s people-centred approach. Partnering with social enterprises such as Happiness Initiative Singapore, students explore the multifaceted nature of well-being and life satisfaction through interviews, focus groups, and thematic analysis.

Over the semester, students develop the interpersonal and professional competencies needed for working in real-world contexts. Their conversations with members of the public helped to ground abstract academic concepts in lived realities, often challenging assumptions and leaving a lasting emotional impact. Through these interactions, students expand their conceptual vocabulary and come to understand well-being as a dynamic, deeply personal process that includes resilience, self-transcendence and growth.

Vu Bao Lien Hoa, a Year 2 student from the NUS Business School, who read the course, said, “The class discussions not only deepened my intellectual curiosity but also motivated me to communicate effectively, helping me express ideas more clearly while actively listening to others’ insights. I also became more open-minded, learning to navigate differing viewpoints with empathy and respect.”

Mr Sherman Ho, the co-founder of Happiness Initiative noted that the students’ project findings, which were shared at the course’s community engagement event, helped to illuminate the different well-being needs of various age groups in Singapore to the community participants. He appreciated how the dialogues opened up space for honest conversations about what it means to thrive today. Beyond surfacing key themes and generational shifts, many felt reassured knowing they were not alone in their challenges and aspirations—and were part of a larger, caring community.

Growing a community of learners and changemakers

With momentum building, Tembusu College is expanding its course offerings. A new seminar, Migrant Workers, Rhetoric, and Performance, marks the first NUS course centred on migrant workers. Students collaborate with Kaugnay, a migrant worker group under the auspices of the Humanitarian Organisation for Migration Economics, also known as HOME, to co-create inclusive and empowering activities and performances to engage migrant workers.

Associate Professor Ho Chee Kong, Tembusu College Master said, “Tembusu College’s C&E courses offer a diverse variety of channels for dialogue between the students and the communities they engage with. Arising from these dialogues are meaningful constructs for deeper understanding, compassion and gratitude towards one another in a more responsive societal development.”

By connecting classrooms with communities, Tembusu College is redefining what it means to teach and learn—where critical thinking meets meaningful action, and conversation becomes the heart of the curriculum.

At any given time, about 100,000 people in the U.S. are waiting to become kidney transplant recipients. Roughly one-fifth of those get a new kidney each year, but others die while waiting. In short, the demand for kidneys makes it important to think about how we use the limited supply.

A study co-authored by an MIT economist brings new data to this issue, providing nuanced estimates of the lifespan-lengthening effect of kidney transplants. That can be hard to measure well, but the study is the first to account for some of the complexities involved, including the decisions patients make when accepting kidney transplants, and some of their pre-existing health factors.

The research concludes the system in use produces an additional 9.29 life-years from transplantation (LYFT) for kidney recipients. (LYFT is the difference in median survival for those with and without transplants.) If the organs were assigned randomly to patients, the study finds, that LYFT average would only be 7.54 overall. From that perspective, the current transplant system is a net positive for patients. However, the study also finds that the LYFT figure could potentially be raised as high as 14.08, depending on how the matching system is structured.

In any case, more precise estimates about the benefits of kidney transplants can help inform policymakers about the dynamics of the matching system in use.

“There’s always this question about how to take the scarce number of organs being donated and place them efficiently, and place them well,” says MIT economist Nikhil Agarwal, co-author of a newly published paper detailing the study’s results. As he emphasizes, the point of the paper is to inform the ongoing refinement of the matching system, rather than advocate one viewpoint or another.

The paper, “Choices and Outcomes in Assignment Mechanisms: The Allocation of Deceased Donor Kidneys,” is published in the latest issue of Econometrica. The authors are Agarwal, who is a professor in MIT’s Department of Economics; Charles Hodgson, an assistant professor of economics at Yale University; and Paulo Somaini, an associate professor of economics in Stanford University’s Graduate School of Business.

After people die, there is a period lasting up to 48 hours when they could be viable organ donors. Potential kidney recipients are prioritized by time spent on wait-lists as well as tissue-type similarity, and can accept or reject any given transplant offer.

Over the last decade-plus, Agarwal has conducted significant empirical research on matching systems for organ donations, especially kidney transplants. To conduct this study, the researchers used comprehensive data about patients on the kidney wait-list from 2000-2010, made available by the Organ Procurement and Transplantation Network, the national U.S. registry. This allowed the scholars to analyze both the matching system and the health effects of transplants; they track patient survival until February 2020.

The work is the first quasiexperimental study of kidney transplants; by carefully examining the decision-making tendencies of kidney recipients, along with many other health factors, the scholars are able to evaluate the effects of a transplant, other things being equal. Recipients are more likely to select kidney offers from donors who are younger, lacked hypertension, died of head trauma (suggesting their internal organs were healthy), and with whom they have perfect tissue-type matches.

“The [previous] methodology of estimating what are the life-years benefits was not incorporating this selection issue,” Agarwal says.

Additionally, overall, a key empirical feature of kidney transplants is that recipients who are healthier overall tend to have the largest realized life-years benefits from a transplant, meaning that the greatest increase in LYFT is not found in the set of patients with the worst health.

“You might think people who are the sickest and who are most likely to die without an organ are going to benefit the most from it [in added life-years],” Agarwal says. “But there might be some other comorbidity or factor that made them sick, and their body’s going to take a toll on the new organ, so the benefits might not be as large.”

With this in mind, the maximal LYFT number of 14.08 in the study comes from, broadly, a hypothetical scenario in which an increased number of otherwise healthy people receive transplants. Again, the current system tends to prioritize time spent on a wait-list. And some observers might advocate for a system that prioritizes those who are sickest. With all that in mind, the policymaking process for kidney transplants may still involve recognition that the biggest gains in patient life-years are not necessarily aligned with other prioritization factors.

“Our results indicate … a dilemma rooted in the tension between these two goals,” the authors write in the paper.

To be clear, Agarwal is not advocating for any one system over another, but conducting data-driven research so that policy officials can make more fully informed decisions in the ongoing, long-term process of trying to refine valuable transplant networks.

“I don’t necessarily think it’s my comparative advantage to make the ethical decisions, but we can at least think about and quantify what some of the tradeoffs are,” Agarwal adds.

Support for the research was provided in part by the National Science Foundation and by the Alfred P. Sloan Foundation. 

• CNA Online, 14 April 2025

By Prof Johan Sulaeman, Director of the Sustainable and Green Finance Institute (SGFIN) at NUS, and Dean’s Chair and Professor at the Dept of Finance, NUS Business School; with Mr Desmond Tay, Research Associate, and Mr Michael Alexander, Research Analyst, at SGFIN

• The Business Times, 14 April 2025, p44

By Dr Azhar Ibrahim Alwee, Senior Lecturer at the Department of Malay Studies, Faculty of Arts and Social Sciences at NUS

• Suria News Online, 13 April 2025

Anders Serj Hansen, Class of 1943 Career Development Professor in the Department of Biological Engineering, has been named as the recipient of the 2024-25 Harold E. Edgerton Faculty Achievement Award.

The annual award was established in fall 1982 as a permanent tribute to Institute Professor Emeritus Harold E. Edgerton for his great and enduring support for younger faculty members over the years. The purpose of the award is to recognize exceptional distinction in teaching, in research, and in service.

Hansen is the principal investigator of the Hansen Lab, which develops new methods to resolve 3D genome structure at high spatiotemporal resolution to understand how DNA looping and 3D folding regulates gene expression in health and disease. His areas of research include cancer biology, computational systems biology, instrumentation and measurement, and synthetic biology.

“My research focuses on how the expression of our genes is regulated,” says Hansen. “All the cells in our body have the same DNA and the same genes. Thus, the software or applications to each cell are the same. What’s different between a neuron and a blood cell is what genes they choose to express. My research focuses on understanding how this regulation takes place.”

Those who nominated Anders for the award emphasized his remarkable productivity, mentioning his two “highly cited, paradigm-shifting research articles in Science and Nature Genetics,” and his research presentations at 50 invited talks, including two keynotes, at universities and conferences worldwide. They also highlighted his passion for mentorship and career development for the 20 current members of his laboratory.

“Anders is an outstanding role model and ambassador of biological engineering, combining a powerful research program, run as a caring mentor, and innovative undergraduate education,” says Christopher Voigt, the Daniel I.C. Wang Professor in Biological Engineering and head of the Department of Biological Engineering.

Adds Laurie Boyer, a professor of biology and biological engineering, “His work reveals new insights into how we think about the dynamics of gene regulation that would not otherwise be possible. The Hansen Lab’s work provides a unified framework rapidly adopted by the field to learn how conserved regulators provide exquisite spatial and temporal control of gene expression in the context of 3D genome architecture.”

During the nomination process, students praised Hansen’s passion for his work, along with his ability to prepare them to apply their education outside the classroom.

“He always strives to guide each lab member towards both short-term scientific success and long-term career planning through regular one-on-one meetings, facilitating collaborations and access to scientific resources, and sharing his own experiences,” says Jin Yang, a graduate student in biological engineering and member of the Hansen Lab.

“Dr. Hansen's infectious excitement for the course material made it very enjoyable to come to class and envision potential applications of the fundamental topics he taught,” adds another one of his students. “Excellent lecturer!”

Hansen obtained his undergraduate and master’s degree in chemistry at Oxford University. He received his PhD in chemistry and chemical biology from Harvard University, where he applied systems biology approaches to understand how cells can encode and transmit information in the dynamics of transcription factor activation. For his postdoc at the University of California at Berkeley, Hansen developed new imaging approaches for dissecting the dynamics of architectural proteins with single-molecule resolution in living cells. Hansen joined MIT as an assistant professor of biological engineering in early 2020.

His recognitions include an NIH K99 Pathway to Independence Award (2019), NIH Director’s New Innovator Award (2020), a Pew-Stewart Scholar for Cancer Research Award (2021), an NSF CAREER Award (2024), and an NIH Director’s Transformative Research Award (2024).

Hansen has served on several committees at MIT, including the MIT Biological Engineering Graduate Program Admissions Committee, the MIT Computational and Systems Biology Graduate Admissions Committee, and the MIT Biological Engineering Graduate Recruiting Committee, of which he has been chair since 2023.

“I have known about the Edgerton Award since I started at MIT, and I think the broad focus on both research, teaching, and service really captures what makes MIT such a unique and wonderful place,” says Hansen. “I was therefore absolutely thrilled to receive the news that I would receive the Edgerton Award this year, and I am very grateful to all the wonderful colleagues here at MIT who have supported me over the years, and all the exceptional people in my lab whose work is being recognized.”

Johann Wolfgang von Goethe (1749-1832), the German polymath whose life and work embodied the connections between the arts and sciences, is said to have described architecture as “frozen music.” 

When the new Edward and Joyce Linde Music Building at MIT had its public opening earlier this year, the temperature outside may have been below freezing but the performances inside were a warm-up for the inaugural concert that took place in the evening. During the afternoon, visitors were invited to workshops in Balinese gamelan and Senegalese drumming, alongside performances by the MIT Chamber Music Society, MIT Festival Jazz Ensemble, and the MIT Laptop Ensemble (FaMLE), demonstrating the synergy between global music traditions and contemporary innovation in music technology. The building was filled with visitors from the MIT community and the Boston area, keen to be among the first to enter the new building and discover what MIT Music had planned for the opening occasion.

The evening’s landmark concert, Sonic Jubilance, celebrated the building’s completion and the pivotal role of MIT Music and Theater Arts (MTA) at the center of life on campus. The program was distinguished by five world premieres by MIT composers: “Summit and Mates,” by assistant professor in jazz Miguel Zenón; “Grace,” by senior lecturer in music Charles Shadle; “Two Noble Kinsmen,” by professor emeritus in music John Harbison; and “Madrigal,” by Keeril Makan, the Michael (1949) and Sonja Koerner Music Composition Professor. 

The premieres were interwoven through the program with performances by MIT ensembles demonstrating the breadth and depth of the conservatory-level music program — from the European classical tradition to Brazilian beats to Boston jazz (the full list of participating ensembles can be found below). 

Each performance demonstrated the different ways the space could be used to create new relationships between musicians and audiences. Designed in the round by the architecture firm SANAA, the Thomas Tull Concert Hall allows sound to resonate from the circular stage or from the aisles above the tiered seating; performers might be positioned below, above, or even in the midst of the audience.

“Music has been a part of MIT's curriculum and culture from the beginning,” said Chancellor Melissa Nobles in her opening address. “Arriving at this magnificent space has taken the collective efforts of past presidents, provosts, deans, faculty, alumni, and students, all working to get us here this evening.” 

Jay Scheib, the Class of 1949 Professor and MIT MTA section head, emphasized the vital role of Music at MIT as a source of cohesion and creativity for students, faculty, and the wider MIT community. 

“The new building is an extraordinary home for us. As a destination to convene communities around world musics and cultures, to engage in emerging music technologies, and to experience concerts and premieres featuring our extraordinary students and our internationally renowned faculty — the Edward and Joyce Linde Music Building is truly a transformational thing." 

The concert was also the launch event of Artfinity, MIT’s largest public festival of the arts since 2011, featuring more than 80 free performing and visual arts events. The concert hall will host performances throughout the spring, ranging from classical to jazz to rap, and more.

Institute Professor Marcus Thompson — the faculty co-lead for Artfinity alongside Azra Akšamija, director and associate professor of the Art, Culture, and Technology Program (ACT) at MIT — shared thoughts on the Edward and Joyce Linde Music Building as a point of orientation for the festival. 

“Our building offers the opportunity to point to the presence and importance of other art forms, media, practices, and experiences that can bring us together as practitioners and audiences, lifting our spirits and our sights,” Thompson reflected. “An ensemble of any kind is a community as well as a metaphor for what connects us, applying different talents to create more than we can do alone.”

The new compositions by the four faculty members were a case in point. The program opened with “Summit,” a brass fanfare projected from the top of the hall with ceremonial zeal. “The piece was specifically written as an opener for the concert,” Zenón explained. “My aim was to compose something that would make a statement straight away, while also using the idea of the ‘groove’ as a driving force. The title has two meanings. The first is a mountaintop, or the top of a structure — which is where the ensemble will be placed for the performance. The second is a gathering of great minds and great leaders, which is what MIT feels like for me.” Later in the program, Zenón premiered a jazz contrafact, “Mates,” playing on Benny Golson’s Stablemates, a tribute to Herb Pomeroy, founder of MIT’s jazz program. “The idea here is to use something connected to the jazz tradition — and to Boston’s history — and approach it from a more personal perspective,” said Zenón.

“Two Noble Kinsmen,” by Harbison, was composed as a benediction for the new home of MIT Music. “In choosing to set Shakespeare’s final words in this new piece for choir and strings, I wanted to convey the sense of an invocation, an introduction, an address to unseen forces,” said Harbison. “In this case, I wanted to leave the musical structure as plain as possible so that we understand why these words are chosen. I hoped to capture the stoic balance of these lines — they are in themselves a kind of verbal music.”

In setting the words of the poem “Grace,” by the Chickasaw poet Linda Hogan, Shadle — a composer of Choctaw heritage — envisioned a “sonic extension” of the MIT Land Acknowledgement. “‘Grace’ intended to speak to the Indigenous presence at the Institute and to open the new building with a reminder of the balm music that can bring to a troubled world,” said Shadle. “I hope that I have composed music that links Indigenous and Western traditions in ways that are compelling and thoughtful and that, while recognizing the ‘pieces of hurt,’ still makes a place for grace.”

Before the concert’s euphoric finale — a performance by Rambax Senegalese Drum Ensemble directed by Lamine Touré — “Madrigal” (the evening’s fourth world premiere) served to demonstrate the spatial dimensions of sound made possible by the design of the concert hall. 

Makan’s composition was performed by four student violinists positioned at the top of each aisle and a fifth, Professor Natalie Lin Douglas, at the center of the stage, simultaneously showcasing the geometry of the hall and referencing the ever-shifting perspectives of the sculpture that stands at the north entrance of the building — “Madrigal (2024),” by Sanford Biggers.

“My piece aims to capture the multifaceted quality of Sanford Biggers’ sculpture. From whichever vantage point we might look at it, we see the same patterns in new relationships with one another. In other words, there is no one point of view that is privileged over another.”

As faculty lead for the building project, Makan developed a friendship with Joyce Linde, who provided the principal gift that led to the building. “Joyce and I were on the selection committee to choose an artist to create a site-specific sculpture outside the building. She was very excited about the process, and very engaged with Sanford,” said Makan. “Joyce passed away before she was able to see the building’s completion, and I wanted to honor her legacy by writing an original piece of music in her memory.”

That sense of relationship, pattern-making, and new beginnings was articulated by Frederick Harris, director and senior lecturer in music and the co-producer of the concert, alongside Andy Wilds, program manager in music. “The hall is an instrument; we’re communing with this incredible space and getting to know it,” said Harris. “It’s a relationship. The circular form of the hall is very welcoming, not only to immersive experiences but also to shared experiences.”

The role of music in cultivating community will ensure that the building will become an integral part of MIT life. The work taking place in rehearsal rooms matches the innovation of the Institute’s labs — proving that the arts are a necessary counterpart to science and technology, continuous with the human instinct to express and invent. Sonic Jubilance sets the tone of what’s to come. 

MIT Music ensembles (in order of concert appearance):

• MIT Concert Choir

• MIT Chamber Chorus

• MIT Chamber Music Society

• MIT Vocal Jazz Ensemble

• MIT Jazz Advanced Music Performance Ensemble

• MIT Axiom Ensemble

• MIT Wind Ensemble

• MIT Gamelan Galak Tika

• Rambax MIT
   

When Michael Benjamin, principal research scientist in the MIT Center for Ocean Engineering, arrived at MIT 25 years ago, only professors and postdocs were allowed to touch the department’s underwater vehicles. The vehicles were expensive, he explains, and required extensive training to operate.

“People were scared to death about losing or damaging them, [and] there was no education pipeline to teach students,” he says, adding that the introduction of class 2.680 (Marine Autonomy, Sensing, and Communication) changed this a lot, by creating a class where undergraduate and graduate students could learn to write autonomy code, and run their software on robots on the Charles River. The addition of class 2.S01 (Introduction to Autonomous Underwater Vehicles) last year took the hands-on learning opportunities even further.

“2.S01 is a return to our roots: underwater vehicles. We wanted to create a learning environment where every student handles a robot, and no one is afraid about losing one,” he says. Each student is sent home with an electronics kit, which Benjamin calls the heart of the robot. “They can experiment all they want in their dorm room, and we’ll give them another kit if they break it.”

The AUVs and student test kits in 2.S01 were designed and built by Supun Randeni, a research scientist in mechanical engineering and the primary lecturer and content creator of 2.S01, and Captain Michael Sacarny from MIT Sea Grant. “Dr. Randeni and Captain Sacarny are the geniuses behind the class,” says Benjamin. Together, Randeni and Sacarny run the hands-on lab instruction.

The goal is to expand education and research opportunities to include a larger and younger group of students. “It’s the exact opposite of 25 years ago, when only a privileged few people were allowed to get inside the robot,” says Benjamin. “Student growth and interest is directly related to the degree they have ‘ownership’ of their robot. Physical possession, but also responsibility for its safe operation and return.”

2.S01 provides students with an in-depth insight into autonomous underwater vehicles (AUVs), by introducing theoretical and practical aspects of the AUV design process. This includes fundamentals of naval architecture, electrical systems design, mechanical design, and software design. Students assemble their own AUVs by using a kit of parts and guidance from instructors, beginning with core electronics and building out a full vehicle for deployment in the Charles River on the MIT campus in the final weeks.

Among the activities, students engage in waterproofing vacuum tests, pre-launch sub-system tests, and dockside tests for ballasting, all followed by in-water low-level control tuning runs. Students also construct autonomy missions — first in simulation, followed by in-water autonomous missions to conduct an environmental survey in the Charles River. The course’s final labs include group competitions involving in-water challenges. For the second iteration of the course, which starts in late March, the instructors plan to add more labs that allow the students to explore the intricacies of the electronic, more simulations options, and more water time.

Adowyn Bryne, a second-year mechanical engineering (MechE) student, took the course last year as a member of the first cohort, but this wasn’t her first experience with underwater vehicles. She’d participated in a SeaPerch program in high school. “I chose 2.S01 because I wanted to learn about more complex underwater vehicles,” says Bryne. “I didn’t find out until later in the semester that SeaPerch was actually started at MIT Sea Grant!”

Benjamin says he hopes there are a few things that first-year students take away from participating in 2.S01: first, an understanding that marine robotics is a very cross-disciplinary effort, involving mechanical engineering, electrical engineering, control theory, computer science and ocean science; and, second, the opportunity to view the effort as a gateway to exploring and understanding the ocean. Students says it’s that, and so much more.

Isabella Yeung, a third-year Course 12 student, took the class during her sophomore year after participating in an MIT Undergraduate Research Opportunities Program (UROP) in the MIT Sea Grant Bio Lab with Carolina Bastidas. Bastidas is a research scientist in MIT Sea Grant's Marine Advisory Services group.

“While UROP-ing, I’d seen many AUVs and other projects being developed at MIT Sea Grant,” Yeung says. “I was curious to learn more and have a deeper insight into what they were doing. This class was a prime opportunity to jump into the world of marine robotics without having any background in Course 2.”

She called the course “easily one of the most hands-on (and downright fun) classes” she’s ever taken, adding that she appreciated having the opportunity to assemble and deploy the AUV.

“As someone who enjoys tinkering, I appreciated the opportunity to get my hands dirty — quite literally, with grease and Charles [River] water,” says Yeung. “I looked forward to all of the classes, especially the deployment sessions. Nothing quite matched the sheer rush of launching our program, rushing to drop the AUV into the Charles, and engaging in a boat chase, hoping it hadn’t gone rogue.”

Bryne advises students considering the course to not worry too much if the class lines up with a particular career path they’re considering. “Your first year is about exploring. If you’re interested in the class, take it! You might find a new area of interest. Regardless of whether you want to keep learning about AUVs, you’ll get valuable transferable skills and have a lot of fun.”

Bryne, herself, says the experience is helping to set the stage for exploring future interests and opportunities. “Every time I’ve gotten to do something with robots, I’ve loved it,” she says, “but I’m also very passionate about women’s health. I want to design medical technology specifically for women, but I definitely think there’s room to incorporate robotics into that. It’s great that MechE is such a broad field, and that the curriculum at MIT allows me to explore so many potential areas of study.”

Assistant Professor Wang Xinchao from the Department of Electrical and Computer Engineering under the College of Design and Engineering at NUS has been named one of the 2024 recipients of the prestigious IEEE AI’s 10 to Watch. 

As an NUS Presidential Young Professor and the only award recipient from Singapore, Asst Prof Wang is recognised for his impactful research in Efficient Machine Learning. His work focuses on developing Artificial Intelligence (AI) models that are more compact, faster, and less dependent on large datasets — a direction that promises to make AI more accessible, environmentally friendly, and widely applicable across industries. 

Presented every two years by the IEEE Computer Society, the award honours rising stars who are making significant contributions to the field of AI. In addition to celebrating exceptional talent, it also highlights pioneering innovation and visionary thinking that are shaping the future of AI. 

See more
 

Like a job interview, the university admissions process is a two-way street. While the university is processing documents from prospective students and assessing them through interviews and auditions, applicants are also gathering information, often from their school seniors and the Internet, to make their final decisions.

Applicants to the Yong Siew Toh Conservatory of Music (YST) who are shortlisted for admission will now add first-hand experience to their considerations, after the school revamped its admissions process in 2024. Moving away from traditional auditions as the primary means of assessment, YST has created a full-day immersion experience to give prospective students a taste of life in NUS and YST while enabling faculty members to assess them holistically as they participate in lessons and collaborative sessions.

Candidates must first undergo a pre-screening process, with specific requirements varying based on the programme they are applying for. Screened candidates are then invited to take part in the immersion experience, where they will be further assessed before they are considered for an admissions offer.

“As our reputation climbs, so does our competition with top conservatories in the world to recruit the best music students internationally,” said Mr Tan Wei Boon, Deputy Director of Student Life at YST. The new immersion experience is designed to evaluate candidates more effectively and encourage higher acceptance of admissions offers so as to ensure a high-quality student cohort each year, he added.

Early results since the revamped process was implemented are promising. Students who were admitted through the first edition in 2024 thrived in their first semester of studies, achieving an unprecedented 100% pass rate in all their courses.

Said Professor Peter Tornquist, Dean of YST: “YST Immersion has truly been a game-changing initiative, energising and modernising our admissions process. It gives applicants a fuller reflection of our pedagogical approach, allows us to achieve a better student fit, and resonates with the multi-faceted capacities that young music professionals need in today’s industry.”

The second edition of the immersion experience was conducted from January to March this year and hosted 89 prospective students hailing from 12 countries including Singapore, China, Uzbekistan, Russia, Indonesia, and Australia. Since applicants may now need to travel farther than before to complete the in-person admissions process, financial aid is offered in special cases to ensure that deserving students are not excluded due to financial difficulties.

Enhanced interactions for better assessments

The old admissions process followed the approach used by most music schools, which assesses applicants primarily through auditions before a faculty panel. These auditions would take place at YST for applicants based in Singapore or at one of YST’s regional audition centres, usually in China, Taiwan, Hong Kong, or South Korea, for applicants based elsewhere.

This approach was not ideal as it did not leave much time for the students and the panel to interact on a deeper level beyond a 20 to 30-minute audition performance, said Mr Tan.

The new immersive experience enhances the assessment process by giving candidates a glimpse into a day in the life of a YST undergraduate. While the candidates learn about YST’s pedagogical approach and interact with their potential future classmates through various activities, the faculty members teaching and observing the sessions get to conduct a more holistic assessment of each prospective student across various areas and competencies.

Typical activities include individual and chamber music lessons with a faculty member from the relevant major, as well as a collaborative music-making session with peers. Activities are also tailored for the various majors. Applicants for performance majors such as strings, winds, and brass are given the opportunity to rehearse in groups with a chamber coach, while applicants for the Audio Arts & Sciences major complete a hands-on practicum where they learn the basics of setting up a recording session. Meanwhile, Composition applicants attend a seminar where they take part in discussions and debates on contemporary composers in the field.

Additional information sessions are incorporated throughout, such as a tour of YST’s state-of-the-art facilities and the NUS campus led by student ambassadors, and a dialogue with the YST Student Life Office where students can ask questions about topics ranging from scholarships and bursaries to campus housing and educational opportunities, such as pursuing minors or second majors in other NUS schools and faculties.

Sneak peek into YST life

Current YST students who were admitted through the revamped admissions process said it gave them an accurate preview of what YST classes would be like and enabled them to evaluate whether the school culture and teaching style would suit them.

First-year student Lucius Quek, who is majoring in Audio Arts & Sciences, said that going through the immersion experience and getting to engage in conversation with YST faculty and staff helped him to feel confident in his decision to accept YST’s offer when it was extended. The teaching methodology and materials used in the immersion matched his actual student experience, enabling him to quickly find his footing in YST.

Christian Daniel Ragay Borres, a first-year Percussion student from the Philippines, was pleased to learn that YST would not be putting him through the traditional audition process used by most conservatories, including the Australian music schools he applied to.

He appreciated the two-way exchange of information facilitated by the lesson format, noting: “It wasn’t just my skills being presented to them, but also their teaching styles and plans being experienced by me.”

The collaborative music-making session in the immersion experience for Music & Society / Music, Collaboration & Production (MS/MCP) candidates was especially memorable for first-year student Cervone Seah, who recalls working with musicians from diverse backgrounds to create a new piece based on improvisation. The exercise highlighted the importance of teamwork and creativity, both of which she has found to be integral to her MS/MCP studies.

“The immersion was an excellent preview of the dynamic and interdisciplinary approach that defines the MS/MCP programme,” Cervone said. “Experiencing YST’s culture first-hand reassured me that this was the right place for my musical and academic growth.”

By Assoc Prof Thang Leng Leng, Deputy Head of the Dept of Japanese Studies, Faculty of Arts and Social Sciences at NUS

• zbSunday, 13 April 2025, Views, p17

By Dr Azhar Ibrahim Alwie, Senior Lecturer from the Dept of Malay Studies, Faculty of Arts and Social Sciences at NUS

• Berita Minggu, 13 April 2025, p13

• Money 89.3FM Online, 11 April 2025
• Lianhe Zaobao, 12 April 2025, Singapore, p7

When some commuter trains arrive at the end of the line, they must travel to a switching platform to be turned around so they can depart the station later, often from a different platform than the one at which they arrived.

Engineers use software programs called algorithmic solvers to plan these movements, but at a station with thousands of weekly arrivals and departures, the problem becomes too complex for a traditional solver to unravel all at once.

Using machine learning, MIT researchers have developed an improved planning system that reduces the solve time by up to 50 percent and produces a solution that better meets a user’s objective, such as on-time train departures. The new method could also be used for efficiently solving other complex logistical problems, such as scheduling hospital staff, assigning airline crews, or allotting tasks to factory machines.

Engineers often break these kinds of problems down into a sequence of overlapping subproblems that can each be solved in a feasible amount of time. But the overlaps cause many decisions to be needlessly recomputed, so it takes the solver much longer to reach an optimal solution.

The new, artificial intelligence-enhanced approach learns which parts of each subproblem should remain unchanged, freezing those variables to avoid redundant computations. Then a traditional algorithmic solver tackles the remaining variables.

“Often, a dedicated team could spend months or even years designing an algorithm to solve just one of these combinatorial problems. Modern deep learning gives us an opportunity to use new advances to help streamline the design of these algorithms. We can take what we know works well, and use AI to accelerate it,” says Cathy Wu, the Thomas D. and Virginia W. Cabot Career Development Associate Professor in Civil and Environmental Engineering (CEE) and the Institute for Data, Systems, and Society (IDSS) at MIT, and a member of the Laboratory for Information and Decision Systems (LIDS).

She is joined on the paper by lead author Sirui Li, an IDSS graduate student; Wenbin Ouyang, a CEE graduate student; and Yining Ma, a LIDS postdoc. The research will be presented at the International Conference on Learning Representations.

Eliminating redundance

One motivation for this research is a practical problem identified by a master’s student Devin Camille Wilkins in Wu’s entry-level transportation course. The student wanted to apply reinforcement learning to a real train-dispatch problem at Boston’s North Station. The transit organization needs to assign many trains to a limited number of platforms where they can be turned around well in advance of their arrival at the station.

This turns out to be a very complex combinatorial scheduling problem — the exact type of problem Wu’s lab has spent the past few years working on.

When faced with a long-term problem that involves assigning a limited set of resources, like factory tasks, to a group of machines, planners often frame the problem as Flexible Job Shop Scheduling.

In Flexible Job Shop Scheduling, each task needs a different amount of time to complete, but tasks can be assigned to any machine. At the same time, each task is composed of operations that must be performed in the correct order.

Such problems quickly become too large and unwieldy for traditional solvers, so users can employ rolling horizon optimization (RHO) to break the problem into manageable chunks that can be solved faster.

With RHO, a user assigns an initial few tasks to machines in a fixed planning horizon, perhaps a four-hour time window. Then, they execute the first task in that sequence and shift the four-hour planning horizon forward to add the next task, repeating the process until the entire problem is solved and the final schedule of task-machine assignments is created.

A planning horizon should be longer than any one task’s duration, since the solution will be better if the algorithm also considers tasks that will be coming up.

But when the planning horizon advances, this creates some overlap with operations in the previous planning horizon. The algorithm already came up with preliminary solutions to these overlapping operations.

“Maybe these preliminary solutions are good and don’t need to be computed again, but maybe they aren’t good. This is where machine learning comes in,” Wu explains.

For their technique, which they call learning-guided rolling horizon optimization (L-RHO), the researchers teach a machine-learning model to predict which operations, or variables, should be recomputed when the planning horizon rolls forward.

L-RHO requires data to train the model, so the researchers solve a set of subproblems using a classical algorithmic solver. They took the best solutions — the ones with the most operations that don’t need to be recomputed — and used these as training data.

Once trained, the machine-learning model receives a new subproblem it hasn’t seen before and predicts which operations should not be recomputed. The remaining operations are fed back into the algorithmic solver, which executes the task, recomputes these operations, and moves the planning horizon forward. Then the loop starts all over again.

“If, in hindsight, we didn’t need to reoptimize them, then we can remove those variables from the problem. Because these problems grow exponentially in size, it can be quite advantageous if we can drop some of those variables,” she adds.

An adaptable, scalable approach

To test their approach, the researchers compared L-RHO to several base algorithmic solvers, specialized solvers, and approaches that only use machine learning. It outperformed them all, reducing solve time by 54 percent and improving solution quality by up to 21 percent.

In addition, their method continued to outperform all baselines when they tested it on more complex variants of the problem, such as when factory machines break down or when there is extra train congestion. It even outperformed additional baselines the researchers created to challenge their solver.

“Our approach can be applied without modification to all these different variants, which is really what we set out to do with this line of research,” she says.

L-RHO can also adapt if the objectives change, automatically generating a new algorithm to solve the problem — all it needs is a new training dataset.

In the future, the researchers want to better understand the logic behind their model’s decision to freeze some variables, but not others. They also want to integrate their approach into other types of complex optimization problems like inventory management or vehicle routing.

This work was supported, in part, by the National Science Foundation, MIT’s Research Support Committee, an Amazon Robotics PhD Fellowship, and MathWorks.

• CNA Online, 11 April 2025

• Suria News Online, 11 April 2025

In a 2014 essay on mentorship in The Chronicle of Higher Education, American scholar Leonard Cassuto wrote: “In Greek myth, Mentor was a wise man who earned the trust of Odysseus, who selected him to educate his son, Telemachus. The word has a legacy: ‘Mentor’ is a title that should be earned.” 

Earlier this year, it was announced that two MIT affiliates — Kimberly “Kim” Benard, associate dean and director of distinguished fellowships and academic excellence at MIT Career Advising and Professional Development (CAPD), and Leigh Estabrooks, longtime invention education officer with the Lemelson-MIT Program — had been honored by the Joe Biden administration with the Presidential Award for Excellence in Science, Mathematics and Engineering Mentoring (PAESMEM). The award, administered by the National Science Foundation on behalf of the White House Office of Science and Technology Policy, celebrates those who’ve made “significant contributions to mentoring and thereby support the future productivity” of the nation’s science, technology, engineering, and mathematics workforce.

While this award marks Benard’s and Estabrooks’ decades of service and leadership at the Institute, it also spotlights the ways MIT staff and other community members provide essential mentorship.

“Too often, notions of good mentorship focus on faculty advisors and overlook the vital work done by others,” notes Anjali Tripathi ’09, one of Benard’s nominators. To her, the PAESMEM awards recognize the dedicated effort behind cultivating effective mentoring relationships across higher education, which can inspire other unsung heroes as they show up for their mentees.

Kim Benard: Growing a garden

When Tripathi, now a scientist at NASA’s Jet Propulsion Laboratory, established a mentoring program while earning her advanced degrees at Harvard University, she took inspiration from a transformative mentoring experience from her senior year at MIT: working on graduate fellowship applications with Benard. Throughout the process, Benard’s power to motivate, listen, and give to her students left a mark on Tripathi, who later wrote, “I have had many ‘mentors’ in the form of advisers, but in my life only two mentors, as Cassuto would have it. Kim is one of them.”

In Benard’s 18 years at MIT, she has personally mentored over 2,000 students from all backgrounds as they tackle the question of what comes next after MIT and explore post-graduate opportunities such as the Rhodes, Marshall, and Fulbright scholarships. To help students through the competitive application process, Benard established MIT’s Distinguished Fellowships program, which sits within CAPD.  

“Someone once said to me that mentoring is like growing a garden. You plant a seed and hope that it grows and bears fruit,” Benard notes. “Some produce fruit quickly, and others take a long time to finally see the result. Being nominated by a former student for this award, and seeing so many others celebrate it, means that I have hopefully allowed these students to bear fruit.”

As Nancy Kanwisher, the Walter A. Rosenblith Professor in the Department of Brain and Cognitive Sciences, sees it, “Kim is remarkable in so many ways.” Alongside one-on-one consultations and mock interviews, Benard gets to know dozens of applicants individually, then synthesizes their information into recommendation letters each summer. “She works extremely hard, accomplishing with her small team a job done by much larger teams at our competitor institutions.”

While her work has left a remarkable impact on students exploring fellowships, much of that garden-tending happens in the tasks described above — the quiet consistency that doesn’t regularly make the news. Thomas Levenson, professor of science writing and co-chair of the Distinguished Fellowships Committee, describes Benard as “one of the hidden heroes who sustain MIT.” He adds: “Every university needs someone like Kim. We’re very lucky to have the original.”

Benard sees this recognition as an opportunity to illuminate the tangible ways MIT community members can — and do — make an impact on the next generation. “Mentoring and advising is valuable work, but often unseen,” she explains. “This recognition demonstrates the effort MIT mentors and advisors put into budding scientists and demonstrates that these are important and vital tasks for the success of research.”

She designs her work around two main tenets of good mentoring: adaptive practices and deep, or active, listening. As Benard has noticed in her countless sessions, each person’s unique needs require a different kind of guided self-reflection. Throughout the process, she employs active listening. It’s not always an easy conversation, so a caring approach is essential.

“She has guided 20 years’ worth of students through an intense process of self-examination and reflection with her extraordinarily successful combination of tough, often existential questioning and unconditionally caring moral support every step of the way,” Will Broadhead, associate professor of history and MacVicar Faculty Fellow, says. “Her students love her, and it’s easy to see why!”

In fact, the cohort of Benard’s advisees — both fellowship winners and non-winners — who proudly call themselves “Kim’s kids” can speak to all the large and small ways that mentorship plays out. Years after they graduate, “Kim’s kids” are still in touch with her, and many volunteer their time to help current fellowship applicants. Keen to emulate Bernard’s mentorship and pay it forward, alumni such as Tripathi become a part of the MIT community “of people eager to help, support, and lift each other up.”

After all, Tripathi observes, “Kim is the human heart of MIT.”

Leigh Estabrooks: Inspiring mentees to pay it forward

As the Invention Education Officer for the Lemelson-MIT (LMIT) program for 18 years until her retirement from MIT last December, Leigh Estabrooks played a pivotal role in mentoring thousands of students and educators through LMIT’s High School InvenTeam grant initiative and other programs.

Stephanie Couch, LMIT’s executive director, notes, “Leigh created a network of exceptional educators devoted to helping students discover their full potential. Her research led to the development of new curriculum and program offerings for all ages and grade levels, fueling the growth of invention education across the U.S. We are so grateful for her time with the Lemelson-MIT program.”

“Receiving this award has been a humbling and emotional experience,” Estabrooks shares. “I never set out to be an honored mentor; I simply set out to help others build confidence in and understanding of what it means to invent technological solutions to improve the world. I will be forever grateful for the sustained mentoring opportunities with K-14 students and teachers while at LMIT.”

Michael Cima, MIT faculty director for LMIT, underscores the depth of Estabrooks’ impact: “Leigh is a tireless champion of the value of invention education. Her efforts have helped untold numbers of students and teachers over the years. We still cross paths with students from decades ago who tell us about the difference Dr. Estabrooks made in their lives.”

Estabrooks emphasizes that mentoring is a long-term commitment rather than a one-time event. For students, it can begin in middle or high school and continue into college and professional careers. For teachers, mentoring starts even before they apply for grants and remains integral throughout their careers and educational advancements. “[Mentoring] doesn’t take place within one school year; it is informal with no end date,” Estabrooks says. “The ongoing act of mentoring forges strong bonds and builds relationships that endure for decades.”

“Leigh has been able to alter the trajectory of students’ lives through invention education,” Cima adds. “Many students who had never even considered college, let alone engineering or science as a career path, ended up attending college — some even at MIT.”

Believing that every student and teacher deserves a caring mentor, Estabrooks encourages others to take on mentorship roles, noting how vital mentors were in shaping her own personal and professional journey. “Students and teachers may not directly ask, ‘Will you be my mentor?’ However, you can become a mentor simply by being available,” She says.

One former student shared that Estabrooks naturally assumed a mentor role during their time working together. As a 10th grader, this student wouldn’t have thought to ask, yet Estabrooks became a mentor and has remained one for over half of the student’s life.

One of the most remarkable aspects of mentorship, according to Estabrooks, is its ripple effect. Many of Estabrooks’ mentees have gone on to become mentors themselves, fostering a culture of support and guidance that spans generations. As one mentee put it, “One hallmark of a great mentor is their ability to inspire their mentees to pay it forward, increasing their impact exponentially.” Katelyn Sweeney ’18, for example, whom Estabrooks has mentored since Sweeney was in 10th grade, now mentors middle and high school inventors and roboticists and serves as an educational counselor for MIT.

Doug Scott, an LMIT Invention Education Fellow who nominated Estabrooks for the PAESMEM award, affirms her influence: “Leigh is the genuine article and a mentor in every sense of the word. She has developed inventors both young and old through her knowledge and kindness. Over the years, I have seen her help every person she has encountered.”

Instilling the importance of mentorship in her mentees, Estabrooks encourages them to reflect on how guidance has helped them navigate key decision points in their schooling and careers. She hopes they will extend this generosity of spirit to others who may not initially see themselves in STEM or know how to pursue college and career opportunities.

Today, Estabrooks continues to collaborate with LMIT. “Mentoring, to me, includes the gift of time to listen, provide opportunities, make connections, and offer gentle guidance — all while genuinely caring about mentees,” she says.

The legacies of both Estabrooks and Benard will continue to shape future generations of scientists, engineers, inventors, educators, and more, ensuring that the cycle of mentorship remains unbroken.

On board Intuitive Machines’ Athena spacecraft, which made a moon landing on March 6, were cutting-edge MIT payloads: a depth-mapping camera and a mini-rover called “AstroAnt.” Also on that craft were the words and voices of people from around the world speaking in dozens of languages. These were etched on a 2-inch silicon wafer computationally designed by Professor Craig Carter of the MIT Department of Materials Science and Engineering and mounted on the mission’s Lunar Outpost MAPP Rover.

Dubbed the Humanity United with MIT Art and Nanotechnology in Space (HUMANS), the project is a collaboration of art and science, bringing together experts from across MIT — with technical expertise from the departments of Aeronautics and Astronautics, Mechanical Engineering, and Electrical Engineering and Computer Science; nano-etching and testing from MIT.nano; audio processing from the MIT Media Lab’s Opera of the Future and the Music and Theater Arts Section; and lunar mission support from the Media Lab’s Space Exploration Initiative.

While a 6-inch HUMANS wafer flew on the Axiom-2 mission to the International Space Station in 2023, the 2-inch wafer was a part of the IM-2 mission to the lunar south polar region, linked to the MIT Media Lab’s To the Moon to Stay program, which reimagines humankind’s return to the moon. IM-2 ended prematurely after the Athena spacecraft tipped onto its side shortly after landing in March, but the HUMANS wafer fulfilled its mission by successfully reaching the lunar surface.

“If you ask a person on the street: ‘What does MIT do?’ Well, that person might say they’re a bunch of STEM nerds who make devices and create apps. But that’s not the entire MIT. It’s more multifaceted than that,” Carter says. “This project embodies that. It says, ‘We’re not just one-trick ponies.’”

A message etched in silicon

The HUMANS project, initially conceived of by MIT students, was inspired by the Golden Record, a pair of gold-plated phonograph records launched in 1977 aboard the Voyager 1 and 2 spacecraft, with human voices, music, and images. Designed to explore the outer solar system, the Voyagers have since traveled into interstellar space, beyond the sun’s heliosphere. But while the earlier project was intended to introduce humanity to an extraterrestrial audience, the HUMANS message is directed at fellow human beings — reminding us that space belongs to all.

Maya Nasr PhD ’23, now a researcher at Harvard University, has led the project since 2020, when she was a graduate student in the MIT Department of Aeronautics and Astronautics. She co-founded it with Lihui Zhang SM ’21, from the MIT Technology and Policy Program. The team invited people to share what space means to them, in writing or audio, to create a “symbol of unity that promotes global representation in space.”

When Nasr and Zhang sought an expert to translate their vision into a physical artifact, they turned to Carter, who had previously created the designs and algorithms for many art projects and, most recently, for One.MIT, a series of mosaics composed of the names of MIT faculty, students, and staff. Carter quickly agreed.

“I love figuring out how to turn equations into code, into artifacts,” Carter says. “Whether they’re art or not is a difficult question. They’re definitely artful. They’re definitely artisanal.”

Carter played a pivotal role in the computational design and fabrication of the silicon wafer now on the surface of the moon. He first translated the submitted phrases, in 64 languages, into numerical representations that could be turned into fonts. He also reverse-engineered a typesetting language to “kern” the text — adjusting the spacing between letters for visual clarity.

“Kerning is important for the aesthetics of written text. You’d want a Y to be not-too-close to a neighboring T, but farther from a W,” Carter said. “All of the phrases were sequences of words like D-O-G, and it’s not as simple as, put a D, put an O, put a G. It’s put a D, figure out where the O should be, put the O, figure out where the G should be, put the G.”

After refining the text placement, Carter designed an algorithm that geometrically transformed both the text and the audio messages’ digital waveforms — graphical representations of sound — into spirals on the wafer. The design pays homage to the Voyagers’ Golden Records, which featured spiral grooves, much like a vinyl record.

In the center of the disc is an image of a globe, or map projection — Carter found publicly available geospatial coordinates and mapped them into the design.

“I took those coordinates and then created something like an image from the coordinates. It had to be geometry, not pixels,” he says.

Once the spirals and globe imagery were in place, Carter handed the data for the design to MIT.nano, which has specialized instruments for high-precision etching and fabrication.

Human voices, lunar surface

“I hope people on Earth feel a deep sense of connection and belonging — that their voices, stories, and dreams are now part of this new chapter in lunar exploration,” Nasr says. “When we look at the moon, we can feel an even deeper connection, knowing that our words — in all their diversity — are now part of its surface, carrying the spirit of humanity forward.”

For Carter, the project conveys the human capacity for wonder and a shared sense of what’s possible. “In many cases, looking outward forces you to look inward at the same time to put the wonder in some kind of personal context,” Carter says. “So if this project somehow conveys that we are all wondering about this marvelous universe together in all of our languages, I would consider that a victory.”

The project’s link to the Golden Record — an artifact launched nearly 50 years ago and now traveling beyond the solar system — strikes another chord with Carter.

“It’s unimaginably far away, and so the notion that we can connect to something in time and space, to something that’s out there, I think it is just a wonderful connection.” 

In 1949, the U.S. Air Force called upon MIT with an urgent need. Soviet aircraft carrying atomic bombs were capable of reaching the U.S. homeland, and the nation was defenseless. A dedicated center — MIT Lincoln Laboratory — was established. The brightest minds from MIT came together in service to the nation, making scientific and engineering leaps to prototype the first real-time air defense system. The commercial sector and the U.S. Department of Defense (DoD) then produced and deployed the system, called SAGE, continent-wide.

The SAGE story still describes MIT Lincoln Laboratory’s approach to national security innovation today. The laboratory works with DoD agencies to identify challenging national security gaps, determines if technology can contribute to a solution, and then executes an R&D program to advance critical technologies. The principal products of these programs are advanced technology prototypes, which are often rapidly fabricated and demonstrated through test and evaluation.

Throughout this process, the laboratory closely coordinates with the DoD and other federal agency sponsors, and then transfers the technology in many forms to industry for manufacturing at scale to meet national needs. For nearly 75 years, these technologies have saved lives, responded to emergencies, fueled the nation’s economy, and impacted the daily life of Americans and our allies. 

"Lincoln Laboratory accelerates the pace of national security technology development, in partnership with the government, private industry, and the broader national security ecosystem," says Melissa Choi, director of MIT Lincoln Laboratory. "We integrate high-performance teams with advanced facilities and the best technology available to bring novel prototypes to life, providing lasting benefits to the United States."

The Air Force and MIT recently renewed their contract for the continued operation of Lincoln Laboratory. The contract was awarded by the Air Force Lifecycle Management Center Strategic Services Division on Hanscom Air Force Base for a term of five years, with an option for an additional five years. Since Lincoln Laboratory’s founding, MIT has operated the laboratory in the national interest for no fee and strictly on a cost-reimbursement basis. The contract award is indicative of the DoD’s continuing recognition of the long-term value of, and necessity for, cutting-edge R&D in service of national security.

Critical contributions to national security

MIT Lincoln Laboratory is the DoD’s largest federally funded research and development center R&D laboratory. Sponsored by the under secretary of defense for research and engineering, it contributes to a broad range of national security missions and domains.

Among the most critical domains are air and missile defense. Laboratory researchers pioneer advanced radar systems and algorithms crucial for detecting, tracking, and targeting ballistic missiles and aircraft, and serve as scientific advisors to the Reagan Test Site. They also conduct comprehensive studies on missile defense needs, such as the recent National Defense Authorization Act–directed study on the defense of Guam, and provide actionable insights to Congress.  

MIT Lincoln Laboratory is also at the forefront of space systems and technologies, enabling the military to monitor space activities and communicate at very high bandwidths. Laboratory engineers developed the innovatively curved detector within the Space Surveillance Telescope that allows the U.S. Space Force to track tiny space objects. It also operates the world's highest-resolution long-range radar for imaging satellites. Recently, the laboratory worked closely with NASA to demonstrate laser communications systems in space, setting a record for the fastest satellite downlink and farthest lasercom link ever achieved. These breakthroughs are heralding a new era in satellite communications for defense and civil missions.

Perhaps most importantly, MIT Lincoln Laboratory is asked to rapidly prototype solutions to urgent and emerging threats. These solutions are both transferred to industry for production and fielded directly to war-fighters, saving lives. To combat improvised explosive devices in Iraq and Afghanistan, the laboratory quickly and iteratively developed several novel systems to detect and defeat explosive devices and insurgent networks. When insurgents were attacking forward-operating bases at night, the laboratory developed an advanced infrared camera system to prevent the attacks. Like other multi-use technologies developed at the laboratory, that system led to a successful commercial startup, which was recently acquired by Anduril.

Responding to domestic crises is also a key part of the laboratory’s mission. After the attacks of 9/11/2001, the laboratory quickly integrated a system to defend the airspace around critical locations in the capital region. More recently, the laboratory’s application of AI to video forensics and physical screening has resulted in commercialized systems deployed in airports and mass transit settings. Over the last decade, the laboratory has adapted its technology for many other homeland security needs, including responses to natural disasters. As one example, researchers repurposed a world-class lidar system first used by the military for terrain mapping to quickly quantify damage after hurricanes.

For all of these efforts, the laboratory exercises responsible stewardship of taxpayer funds, identifying multiple uses for the technologies it develops and introducing disruptive approaches to reduce costs for the government. Sometimes, the system architecture or design results in cost savings, as is the case with the U.S. Air Force's SensorSat; the laboratory’s unique sensor design enabled a satellite 10 times smaller and cheaper than those typically used for space surveillance. Another approach is by creating novel systems from low-cost components. For instance, laboratory researchers discovered a way to make phased-array radars using cell phone electronics instead of traditional expensive components, greatly reducing the cost of deploying the radars for weather and aircraft surveillance.

The laboratory also pursues emerging technology to bring about transformative solutions. In the 1960s, such vision brought semiconductor lasers into the world, and in the 1990s shrunk transistors more than industry imagined possible. Today, laboratory staff are pursuing other new realms: making imagers reconfigurable at the pixel level, designing quantum sensors to transform navigation technology, and developing superconducting electronics to improve computing efficiency.

A long, beneficial relationship between MIT and the DoD

"Lincoln Laboratory has created a deep understanding and knowledge base in core national security missions and associated technologies. We look forward to continuing to work closely with government sponsors, industry, and academia through our trusted, collaborative relationships to address current and future national security challenges and ensure technological superiority," says Scott Anderson, assistant director for operations at MIT Lincoln Laboratory.

"MIT has always been proud to support the nation through its operation of Lincoln Laboratory. The long-standing relationship between MIT and the Department of Defense through this storied laboratory has been a difference-maker for the safety, economy, and industrial power of the United States, and we look forward to seeing the innovations ahead of us," notes Ian Waitz, MIT vice president for research.

Under the terms of the renewed contract, MIT will ensure that Lincoln Laboratory remains ready to meet R&D challenges that are critical to national security.

When visual information enters the brain, it travels through two pathways that process different aspects of the input. For decades, scientists have hypothesized that one of these pathways, the ventral visual stream, is responsible for recognizing objects, and that it might have been optimized by evolution to do just that.

Consistent with this, in the past decade, MIT scientists have found that when computational models of the anatomy of the ventral stream are optimized to solve the task of object recognition, they are remarkably good predictors of the neural activities in the ventral stream.

However, in a new study, MIT researchers have shown that when they train these types of models on spatial tasks instead, the resulting models are also quite good predictors of the ventral stream’s neural activities. This suggests that the ventral stream may not be exclusively optimized for object recognition.

“This leaves wide open the question about what the ventral stream is being optimized for. I think the dominant perspective a lot of people in our field believe is that the ventral stream is optimized for object recognition, but this study provides a new perspective that the ventral stream could be optimized for spatial tasks as well,” says MIT graduate student Yudi Xie.

Xie is the lead author of the study, which will be presented at the International Conference on Learning Representations. Other authors of the paper include Weichen Huang, a visiting student through MIT’s Research Science Institute program; Esther Alter, a software engineer at the MIT Quest for Intelligence; Jeremy Schwartz, a sponsored research technical staff member; Joshua Tenenbaum, a professor of brain and cognitive sciences; and James DiCarlo, the Peter de Florez Professor of Brain and Cognitive Sciences, director of the Quest for Intelligence, and a member of the McGovern Institute for Brain Research at MIT.

Beyond object recognition

When we look at an object, our visual system can not only identify the object, but also determine other features such as its location, its distance from us, and its orientation in space. Since the early 1980s, neuroscientists have hypothesized that the primate visual system is divided into two pathways: the ventral stream, which performs object-recognition tasks, and the dorsal stream, which processes features related to spatial location.

Over the past decade, researchers have worked to model the ventral stream using a type of deep-learning model known as a convolutional neural network (CNN). Researchers can train these models to perform object-recognition tasks by feeding them datasets containing thousands of images along with category labels describing the images.

The state-of-the-art versions of these CNNs have high success rates at categorizing images. Additionally, researchers have found that the internal activations of the models are very similar to the activities of neurons that process visual information in the ventral stream. Furthermore, the more similar these models are to the ventral stream, the better they perform at object-recognition tasks. This has led many researchers to hypothesize that the dominant function of the ventral stream is recognizing objects.

However, experimental studies, especially a study from the DiCarlo lab in 2016, have found that the ventral stream appears to encode spatial features as well. These features include the object’s size, its orientation (how much it is rotated), and its location within the field of view. Based on these studies, the MIT team aimed to investigate whether the ventral stream might serve additional functions beyond object recognition.

“Our central question in this project was, is it possible that we can think about the ventral stream as being optimized for doing these spatial tasks instead of just categorization tasks?” Xie says.

To test this hypothesis, the researchers set out to train a CNN to identify one or more spatial features of an object, including rotation, location, and distance. To train the models, they created a new dataset of synthetic images. These images show objects such as tea kettles or calculators superimposed on different backgrounds, in locations and orientations that are labeled to help the model learn them.

The researchers found that CNNs that were trained on just one of these spatial tasks showed a high level of “neuro-alignment” with the ventral stream — very similar to the levels seen in CNN models trained on object recognition.

The researchers measure neuro-alignment using a technique that DiCarlo’s lab has developed, which involves asking the models, once trained, to predict the neural activity that a particular image would generate in the brain. The researchers found that the better the models performed on the spatial task they had been trained on, the more neuro-alignment they showed.

“I think we cannot assume that the ventral stream is just doing object categorization, because many of these other functions, such as spatial tasks, also can lead to this strong correlation between models’ neuro-alignment and their performance,” Xie says. “Our conclusion is that you can optimize either through categorization or doing these spatial tasks, and they both give you a ventral-stream-like model, based on our current metrics to evaluate neuro-alignment.”

Comparing models

The researchers then investigated why these two approaches — training for object recognition and training for spatial features — led to similar degrees of neuro-alignment. To do that, they performed an analysis known as centered kernel alignment (CKA), which allows them to measure the degree of similarity between representations in different CNNs. This analysis showed that in the early to middle layers of the models, the representations that the models learn are nearly indistinguishable.

“In these early layers, essentially you cannot tell these models apart by just looking at their representations,” Xie says. “It seems like they learn some very similar or unified representation in the early to middle layers, and in the later stages they diverge to support different tasks.”

The researchers hypothesize that even when models are trained to analyze just one feature, they also take into account “non-target” features — those that they are not trained on. When objects have greater variability in non-target features, the models tend to learn representations more similar to those learned by models trained on other tasks. This suggests that the models are using all of the information available to them, which may result in different models coming up with similar representations, the researchers say.

“More non-target variability actually helps the model learn a better representation, instead of learning a representation that’s ignorant of them,” Xie says. “It’s possible that the models, although they’re trained on one target, are simultaneously learning other things due to the variability of these non-target features.”

In future work, the researchers hope to develop new ways to compare different models, in hopes of learning more about how each one develops internal representations of objects based on differences in training tasks and training data.

“There could be still slight differences between these models, even though our current way of measuring how similar these models are to the brain tells us they’re on a very similar level. That suggests maybe there’s still some work to be done to improve upon how we can compare the model to the brain, so that we can better understand what exactly the ventral stream is optimized for,” Xie says.

The research was funded by the Semiconductor Research Corporation and the U.S. Defense Advanced Research Projects Agency.

By Prof Lawrence Loh, Director and Ms Ang Hui Min, Senior Manager; both from the Centre for Governance and Sustainability at NUS Business School

• The Straits Times, 11 April 2025, Opinion, pB4

• The Business Times, 11 April 2015, p7
• Lianhe Zaobao, 11 April 2025, Business, p22