If you think of a single atom as a grain of sand, then a wavelength of visible light — which is a thousand times larger than the atom’s width — is comparable to an ocean wave. The light wave can dwarf an atom, missing it entirely as it passes by. This gulf in size has long made it impossible for scientists to see and resolve individual atoms using optical microscopes alone.

Only recently have scientists found ways to break this “diffraction limit,” to see features that are smaller than the wavelength of light. With new techniques known as super-resolution microscopy, scientists can see down to the scale of a single molecule.

And yet, individual atoms have still been too small for optical microscopes — which are much simpler and less expensive than super-resolution techniques — to distinguish, until now.

In an open-access paper appearing today in Nature Communications, MIT scientists present a new computational method that enables optical microscopes to resolve individual atoms and zero in on their exact locations in a crystal structure.

The team’s new “discrete grid imaging technique,” or DIGIT, is a computational imaging approach that scientists can apply to optical data to calculate the most probable location of individual atoms based on a very important clue: the material’s known atomic configuration. As long as scientists have an idea of what a material’s physical atomic layout should be, they can use this layout as a sort of map to determine where specific atoms or features must be located.

“It’s like you know there’s a seating chart,” says lead author Yuqin “Sophia” Duan, a graduate student in MIT’s Department of Electrical Engineering and Computer Science (EECS). “Previous methods could tell you what section an atom is in. But now we can take this seating chart as prior knowledge, and can pinpoint exactly which seat the atom is in.”

With DIGIT, the team can now pinpoint individual atoms with a resolution of 0.178 angstroms. (One angstrom is one-tenth of a nanometer, which is less than half the width of a single atom). The technique enables optical microscopes to localize atomic-scale features in any material that has a known atomic pattern, such as crystalline materials or certain proteins with repeating molecular chains.

The team says the method could help guide the design of quantum devices, which often require placing individual atoms precisely within a crystal. Beyond quantum technologies, DIGIT can also provide new insights into how defects and impurities shape the behavior of advanced materials — from semiconductors to superconductors.

Duan’s co-authors at MIT are Qiushi Gu, Hanfeng Wang, Yong Hu, Kevin Chen, Matthew Trusheim, and EECS Professor Dirk Englund.

Grid support

Scientists can image features smaller than a nanometer, and sometimes as small as a single atom, but not with optical microscopes. In these cases, they use transmission or scanning electron microscopes, which send high-energy beams of electrons into a sample to generate an image based on the pattern in which the electrons scatter. These electron-based methods produce highly detailed, near-atomic-scale images, but they require imaging in a vacuum and at high energies, and only work in ultrathin, synthetic, or solid-state materials. Electron-based imaging methods are too harsh for more delicate living specimens.

In contrast, optical microscopes work at lower energies, in ambient conditions, and are safe to apply to biological samples. But they cannot discern features past the diffraction limit. Essentially, a microscope is unable to see features that are smaller than half the wavelength of visible light (about 200 to 300 nanometers) that a microscope sends in to probe a sample. Atoms, then, have long eluded optical microscopes.

In 2014, however, the Nobel Prize in Chemistry was awarded to developers of a technique to overcome the diffraction limit. Super-resolution microscopy works by shining laser light on a sample at a specific frequency that is known to resonate with a feature of interest, such as a certain molecule. When that molecule resonates, it effectively announces its presence in the material. With this optical manipulation, scientists can visualize features as small as 10 nanometers, on the scale of a single molecule.

Duan and Englund looked to resolve even smaller features by combining super-resolution techniques with statistical analysis and knowledge of materials that has often been overlooked.

“One thing that gets ignored in imaging optical systems is the physical configuration of your system,” Duan says. “For example, if you want to visualize defects in a diamond system, these defects can only be at certain positions, since they have to follow the grid of the atomic diamond structure. In proteins, there are some structures that grow in an organized grid, and their location must be somewhere along that physical grid.”

The researchers suspected that if they had a reasonably accurate map of a material’s atomic structure (imagine the ball-and-stick models of molecules in a chemistry classroom), they might use such maps as a template and try out many different orientations and rotation angles to find the closest match to whatever features are initially visualized using super-resolution microscopy.

“No one has ever done this before, to include the physical constraints or system information into the resolution technique,” Duan says.

Blurriness, collapsed

To test their idea, the researchers worked with a sample of diamond — a crystal whose microstructure is well-understood and resembles an organized grid, or lattice, of repeating carbon atoms. The researchers blindly knocked out some carbon atoms in the lattice and replaced them with silicon atoms using facilities at MIT.nano. Their goal was to identify and determine the precise locations of the errant silicon atoms.

To do so, they first used established techniques of super-resolution microscopy to probe the diamond sample, using lasers set to specific wavelengths at frequencies known to resonate with the silicon atoms but not the carbon atoms. With this technique, researchers produced images that depicted the silicon atoms, but only as a uniform blur.

The team then applied DIGIT to further resolve the picture. Knowing that diamond in general has a grid-like configuration of carbon atoms, the researchers took this configuration as a map, or seating chart of sorts, and assumed that any silicon atoms that took the place of a carbon atom must sit within the grid, which has a known spacing between atoms.

“Because the silicon atoms are substituting carbon atoms in the lattice, that means they must obey some integer multiple of the atomic spacing of the crystal lattice, separating any two silicon atoms,” Englund says. “That prior knowledge makes the localization different than if you add a purely amorphous material.”

The researchers essentially simulated many possibilities of orientations and rotation angles of the diamond lattice, superimposed on the blurry image of atoms that the super-resolution microscopy technique produced.

“The trick is that, in certain materials, atoms aren’t spread out randomly — they sit on a grid inside a crystal,” Duan explains. “We used that prior knowledge to sharpen the microscope’s picture. Once we factored in that ‘atomic grid,’ the blurriness collapsed, and we could pinpoint exact positions.”

In the end, they found the technique could pinpoint the location of individual silicon atoms within the diamond lattice, with a precision of 0.178 angstroms — the sharpest resolution of any optical-based imaging technique. The team has made the DIGIT code available on GitHub for anyone to apply to their optical measurements, provided their sample of interest has a well-understood atomic structure. Then, they hope that scientists will start to see much finer and detailed features and processes using light.

“It’s a big step — it takes optical microscopes into the realm of atomic scale, something people thought only electron microscopes or X-rays could do,” Duan says. “That opens up a whole new way of studying materials and biology.”

The degree to which someone trusts the information depicted in a chart can depend on their assumptions about who made the data visualization, according to a pair of studies by MIT researchers.

For instance, if someone infers that a graph about a controversial topic like gun violence was produced by an organization they feel is in opposition with their beliefs or political views, they may discredit the information or dismiss the visualization all together.

The researchers found that even the clearest visualizations often communicate more than the data they explicitly depict, and can elicit strong judgments from viewers about the social contexts, identities, and characteristics of those who made the chart.

Readers make these assessments about the social context of a visualization primarily from its design features, like the color palette or the way information is arranged, rather than the underlying data. Often, these inferences are unintended by the designers.

Qualitative and quantitative studies revealed that these social inferences aren’t restricted to certain subgroups, nor are they caused by limited data literacy.

The researchers consolidate their findings into a framework that scientists and communicators can use to think critically about how design choices might affect these social assumptions. Ultimately, they hope this work leads to better strategies for scientific communication.

“If you are scrolling through social media and you see a chart, and you immediately dismiss it as something an influencer has produced just to get attention, that shapes your entire experience with the chart before you even dig into the data. We’ve shown in these papers that visualizations do more than just communicate the data they are depicting — they also communicate other social signals,” says Arvind Satyanarayan, an associate professor in the MIT Department of Electrical Engineering and Computer Science (EECS) and member of the Computer Science and Artificial Intelligence Laboratory (CSAIL) and co-senior author of this research.

He is joined on the paper by co-lead authors Amy Rae Fox, a former CSAIL postdoc, and Michelle Morgenstern, a current postdoc in MIT’s anthropology program; and co-senior author Graham M. Jones, professor of anthropology. Two related papers on this research will be presented at the IEEE Visualization Conference.

Charts as social artifacts

During the height of the Covid-19 pandemic, social media was awash in charts from organizations like the World Health Organization and Centers for Disease Control and Prevention, which were designed to convey information about the spread of disease.

The MIT researchers studied how these visualizations were being used to discuss the pandemic. They found that some citizen scientists were using the underlying data to make visualizations of their own, challenging the findings of mainstream science.

“This was an unexpected discovery as, previously, citizen scientists were typically aligned with mainstream scientists. It took us a few years to figure out how to study this phenomenon more deeply,” Satyanarayan says.

Most research into data visualization studies how charts communicate data. Instead, the researchers wanted to explore visualizations from a social and linguistic perspective to assess the information they convey beyond the data.

Linguistic anthropologists have found that, while language allows people to communicate ideas, it also holds social meaning beyond the words people use. For instance, an accent or dialect can indicate that someone is part of a particular community.

By “pointing” to certain social meanings, identities, and characteristics, language serves what is known as a socio-indexical function.

“We wanted to see if things in the visual language of data communication might point to certain institutions, or the kinds of people in those institutions, that carry a meaning that could be unintended by the makers of the visualization,” Jones says.

To do this, the researchers conducted an initial, qualitative study of users on the social media platform Tumblr. During one-on-one interviews, the researchers showed users a variety of real visualizations from online sources, as well as modified visualizations where they removed the textual information, like titles and axes labels.

Stripping out the textual information was particularly important, since it mimics the way people often interact with online visualizations.

“Our engagement with social media is a few quick seconds. People aren’t taking the time to read the title of a chart or look at the data very carefully,” Satyanarayan says.

The interviews revealed that users made detailed inferences about the people or organizations who created the visualizations based on what they called “vibes,” design elements, like colors or the use of certain graphics. These inferences in turn impacted their trust in the data.

For instance, after seeing a chart with the flags of Georgia and Texas and a graph with two lines in red and black, but no text, one user said, “This kind of looks like something a Texas Republican (legislator) would put on Twitter or on their website, or as part of a campaign presentation.”

A quantitative approach

Building on this initial work, the researchers used the same methodology in three quantitative studies involving surveys sent to larger groups of people from a variety of backgrounds.

They found the same phenomenon: People make inferences about the social context of a visualization based on its design, which can lead to misunderstandings about, and mistrust in, the data it depicts.

For instance, users felt some visualizations were so neatly arranged they believed them to be advertisements, and therefore not trustworthy. In another example, one user dismissed a chart by a Pulitzer-prize winning designer because they felt the hand-drawn graphical style indicated it was made by “some female Instagram influencer who is just trying to look for attention.”

“If that is the first reaction someone has to a chart, it is going to massively impact the degree to which they trust it,” Satyanarayan says.

Moreover, when the researchers reintroduced text in the visualizations from which it had been removed, users still made these social inferences.

Typically, in data visualization, the solution to such a problem would be to create clearer charts or educate people about data literacy. But this research points to a completely different kind of data literacy, Jones says.

“It is not erroneous for people to be drawing these inferences. It requires a lot of cultural knowledge about where visualizations come from, how they are made, and how they circulate. Drawing these inferences is a feature, not a bug, of the way we use signs,” he says.

From these results, they created a classification framework to organize the social inferences users made and the design elements that contributed to them. They hope the typology serves as a tool designers can use to develop more effective visualizations, as well as a starting point for additional studies.

Moving forward, the researchers want to continue exploring the role of data visualizations as social artifacts, perhaps by drilling down on each design feature they identified in the typology. They also want to expand the scope of their study to include visualizations in research papers and scientific journals.

“Part of the value of this work is a methodological contribution to render a set of phenomena amenable to experimental study. But this work is also important because it showcases an interdisciplinary cross-pollination that is powerful and unique to MIT,” Jones says.

This work was supported, in part, by MIT METEOR and PFPFEE fellowships, an Amar G. Bose Fellowship, an Alfred P. Sloan Fellowship, and the National Science Foundation.

Coming from a small high school in rural South Dakota that didn’t offer advanced placement (AP) classes, Titus Roesler ’25 didn’t have the easiest start at MIT. But when his efforts to catch up academically to his peers led to a job as a teaching assistant, it changed everything.

Roesler, who graduated last spring with a bachelor’s degree in electrical engineering and is now working on a master’s, has built a reputation for himself as a student-teacher at MIT. Since discovering his affinity for teaching and mentoring, he’s been a teaching assistant for four different classes and designed two seminars from scratch.

Through teaching, Roesler has not only helped other students, but also improved his own grasp of complex subjects. That includes signal processing, which involves manipulating signals, such as radio waves, to make them more useful for applications like wireless communications. He has become fascinated by the topic and hopes to continue working in the field.

Roesler lights up when talking about teaching, but he didn’t always think it was in the cards.

“I don't know that anyone who knew me pre-MIT would believe that I do things like give recitations to crowded rooms, because I think everyone thought, ‘Titus is that quiet kid, he never talked at all.’”

Learning through teaching

Growing up in Marion, South Dakota, a town with a population around 800, Roesler didn’t have MIT on his radar, but he knew he liked math. His high school capstone project involved helping his classmates on the math section of the ACT, and he tutored a few of his classmates. His teacher let him teach trigonometry one day, and he toured local colleges with the plan of becoming a high school math teacher.

But that changed after he self-studied calculus through MIT’s OpenCourseWare offerings and set his sights on the Institute.

Roesler worked overtime during his first year at MIT to catch up with what his peers had learned back in high school. On his first physics exam, he answered only one question correctly — a multiple-choice question he had guessed on. But MIT’s Experimental Study Group (ESG) kept him afloat during his first year, and it quickly led to more opportunities.

When, in the spring of his first year, his multivariable calculus instructor asked him to stay after class one day, Roesler was sure he was in trouble. She actually wanted to see if he could TA for her next year.

“I was flattered because there was still a month left in the class. Plenty of time for me to fail,” Roesler jokes.

He loved the job. During a Friday night office hour session, he stayed for extra hours to help a student whom he saw a lot of himself in — someone who was also from a rural background and had also entered MIT without a strong mathematics background. He went on to become the student’s tutor. The position gave him the opportunity to be the teacher he’d always wanted to have.

As a TA, “I wasn't coming at things from the perspective of ‘Everyone already knows A, B, C’ before I explained. I would always try to start from the ground up and give my perspective on it,” Roesler says.

From his mentorship and teaching work, he received the Undergraduate Teaching Award from the Department of Electrical Engineering and Computer Science and the Outstanding Associate Advisor Award from the Office of the First Year. After joining ESG during his first year, Roesler stayed on as an associate advisor in the learning community for the next three years. His work earned him the Fiekowsky Award for Excellence in Teaching and the Fiekowsky Award for Community Service.

The right blend

Signal processing, the focus of his graduate work, “is where calculus, geometry, linear algebra, probability, statistics, algorithms, and numerical analysis all come into play on practical problems of real-world interest,” Roesler says. “For me, it’s the right blend of theory and application.”

Due to the field’s wide scope, Roesler notices potential applications for signal processing everywhere, and how different fields intersect within the discipline. “Everything comes together in just the right way,” he says.

He is especially interested in signal-processing problems such as source separation, which aims to recover a set of source signals from a set of mixed signals. During his senior year, he spent two semesters on a project where he wrote a Python program to separate harmonies in Bach chorales.

For his master’s degree, following a summer research internship at MIT Lincoln Laboratory, Roesler has stayed at the laboratory, this time venturing into high-frequency radio communications. He’s currently working on a research project that applies the theory of compressed sensing (which states that, under certain conditions, it is possible to reconstruct signals from very few measurements) to communications.

What fascinates Roesler are “something-from-nothing” problems.

“The kind of problems I’m interested in are underdetermined, inverse problems,” he says. For example, imagine trying to reconstruct a full image from only a handful of pixels. While on the surface this seems impossible, researchers have recovered quality images by applying the techniques of compressed sensing.

Running and serving

Roesler has also spent extensive time running, a sport he’s loved since fifth grade. In 2023, he raced a marathon in 2 hours and 46 minutes and went on to run the Boston Marathon in both 2024 and 2025. To prepare, he spent a lot of time reading up on the psychology of running, which he says was the first time he used the scientific method. Now, he just runs for fun and uses it as a way to focus and collect this thoughts.

He has also served on the executive team of the Undergraduate Mathematics Association, as a resident peer mentor at Baker House, and a tutor for two classes. At the PKG Center, he’s been a program lead and counselor for its pre-orientation program.

Roesler still gets excited about seeing the impact of his teaching. At the end of one semester teaching a tutorial, he took his class on a picnic. They surprised him with a card and a bag of goodies. 

Recalling the moment, he says: “I thought, How does it get better? It was wonderful.”

Nervous system functions, from motion to perception to cognition, depend on the active zones of neural circuit connections, or “synapses,” sending out the right amount of their chemical signals at the right times. By tracking how synaptic active zones form and mature in fruit flies, researchers at The Picower Institute for Learning and Memory at MIT have revealed a fundamental model for how neural activity during development builds properly working connections.

Understanding how that happens is important, not only for advancing fundamental knowledge about how nervous systems develop, but also because many disorders such as epilepsy, autism, or intellectual disability can arise from aberrations of synaptic transmission, says senior author Troy Littleton, the Menicon Professor in The Picower Institute and MIT’s Department of Biology. The new findings, funded in part by a 2021 grant from the National Institutes of Health, provide insights into how active zones develop the ability to send neurotransmitters across synapses to their circuit targets. It’s not instant or predestined, the study shows. It can take days to fully mature, and that is regulated by neural activity.

If scientists can fully understand the process, Littleton says, then they can develop molecular strategies to intervene to tweak synaptic transmission when it’s happening too much or too little in disease.

“We’d like to have the levers to push to make synapses stronger or weaker, that’s for sure,” Littleton says. “And so knowing the full range of levers we can tug on to potentially change output would be exciting.”

Littleton Lab research scientist Yuliya Akbergenova led the study published Oct. 14 in the Journal of Neuroscience.

How newborn synapses grow up

In the study, the researchers examined neurons that send the neurotransmitter glutamate across synapses to control muscles in the fly larvae. To study how the active zones in the animals matured, the scientists needed to keep track of their age. That hasn’t been possible before, but Akbergenova overcame the barrier by cleverly engineering the fluorescent protein mMaple, which changes its glow from green to red when zapped with 15 seconds of ultraviolet light, into a component of the glutamate receptors on the receiving side of the synapse. Then, whenever she wanted, she could shine light and all the synapses already formed before that time would glow red, and any new ones that formed subsequently would glow green.

With the ability to track each active zone’s birthday, the authors could then document how active zones developed their ability to increase output over the course of days after birth. The researchers actually watched as synapses were built over many hours by tagging each of eight kinds of proteins that make up an active zone. At first, the active zones couldn’t transmit anything. Then, as some essential early proteins accumulated, they could send out glutamate spontaneously, but not if evoked by electrical stimulation of their host neuron (simulating how that neuron might be signaled naturally in a circuit). Only after several more proteins arrived did active zones possess the mature structure for calcium ions to trigger the fusion of glutamate vesicles to the cell membrane for evoked release across the synapse.

Activity matters

Of course, construction does not go on forever. At some point, the fly larva stops building one synapse and then builds new ones further down the line as the neuronal axon expands to keep up with growing muscles. The researchers wondered whether neural activity had a role in driving that process of finishing up one active zone and moving on to build the next.

To find out, they employed two different interventions to block active zones from being able to release glutamate, thereby preventing synaptic activity. Notably, one of the methods they chose was blocking the action of a protein called Synaptotagmin 1. That’s important because mutations that disrupt the protein in humans are associated with severe intellectual disability and autism. Moreover, the researchers tailored the activity-blocking interventions to just one neuron in each larva because blocking activity in all their neurons would have proved lethal.

In neurons where the researchers blocked activity, they observed two consequences: the neurons stopped building new active zones and instead kept making existing active zones larger and larger. It was as if the neuron could tell the active zone wasn’t releasing glutamate and tried to make it work by giving it more protein material to work with. That effort came at the expense of starting construction on new active zones.

“I think that what it’s trying to do is compensate for the loss of activity,” Littleton says.

Testing indicated that the enlarged active zones the neurons built in hopes of restarting activity were functional (or would have been if the researchers weren’t artificially blocking them). This suggested that the way the neuron sensed that glutamate wasn’t being released was therefore likely to be a feedback signal from the muscle side of the synapse. To test that, the scientists knocked out a glutamate receptor component in the muscle, and when they did, they found that the neurons no longer made their active zones larger.

Littleton says the lab is already looking into the new questions the discoveries raise. In particular: What are the molecular pathways that initiate synapse formation in the first place, and what are the signals that tell an active zone it has finished growing? Finding those answers will bring researchers closer to understanding how to intervene when synaptic active zones aren’t developing properly.

In addition to Littleton and Akbergenova, the paper’s other authors are Jessica Matthias and Sofya Makeyeva.

In addition to the National Institutes of Health, The Freedom Together Foundation provided funding for the study.

When it comes to artificial intelligence, MIT and IBM were there at the beginning: laying foundational work and creating some of the first programs — AI predecessors — and theorizing how machine “intelligence” might come to be.

Today, collaborations like the MIT-IBM Watson AI Lab, which launched eight years ago, are continuing to deliver expertise for the promise of tomorrow’s AI technology. This is critical for industries and the labor force that stand to benefit, particularly in the short term: from $3-4 trillion of forecast global economic benefits and 80 percent productivity gains for knowledge workers and creative tasks, to significant incorporations of generative AI into business processes (80 percent) and software applications (70 percent) in the next three years.

While industry has seen a boom in notable models, chiefly in the past year, academia continues to drive the innovation, contributing most of the highly cited research. At the MIT-IBM Watson AI Lab, success takes the form of 54 patent disclosures, an excess of 128,000 citations with an h-index of 162, and more than 50 industry-driven use cases. Some of the lab’s many achievements include improved stent placement with AI imaging techniques, slashing computational overhead, shrinking models while maintaining performance, and modeling of interatomic potential for silicate chemistry.

“The lab is uniquely positioned to identify the ‘right’ problems to solve, setting us apart from other entities,” says Aude Oliva, lab MIT director and director of strategic industry engagement in the MIT Schwarzman College of Computing. “Further, the experience our students gain from working on these challenges for enterprise AI translates to their competitiveness in the job market and the promotion of a competitive industry.”

“The MIT-IBM Watson AI Lab has had tremendous impact by bringing together a rich set of collaborations between IBM and MIT’s researchers and students,” says Provost Anantha Chandrakasan, who is the lab’s MIT co-chair and the Vannevar Bush Professor of Electrical Engineering and Computer Science. “By supporting cross-cutting research at the intersection of AI and many other disciplines, the lab is advancing foundational work and accelerating the development of transformative solutions for our nation and the world.”

Long-horizon work

As AI continues to garner interest, many organizations struggle to channel the technology into meaningful outcomes. A 2024 Gartner study finds that, “at least 30% of generative AI projects will be abandoned after proof of concept by the end of 2025,” demonstrating ambition and widespread hunger for AI, but a lack of knowledge for how to develop and apply it to create immediate value.

Here, the lab shines, bridging research and deployment. The majority of the lab’s current-year research portfolio is aligned to use and develop new features, capacities, or products for IBM, the lab’s corporate members, or real-world applications. The last of these comprise large language models, AI hardware, and foundation models, including multi-modal, bio-medical, and geo-spatial ones. Inquiry-driven students and interns are invaluable in this pursuit, offering enthusiasm and new perspectives while accumulating domain knowledge to help derive and engineer advancements in the field, as well as opening up new frontiers for exploration with AI as a tool.

Findings from the AAAI 2025 Presidential panel on the Future of AI Research support the need for contributions from academia-industry collaborations like the lab in the AI arena: “Academics have a role to play in providing independent advice and interpretations of these results [from industry] and their consequences. The private sector focuses more on the short term, and universities and society more on a longer-term perspective.”

Bringing these strengths together, along with the push for open sourcing and open science, can spark innovation that neither could achieve alone. History shows that embracing these principles, and sharing code and making research accessible, has long-term benefits for both the sector and society. In line with IBM and MIT’s missions, the lab contributes technologies, findings, governance, and standards to the public sphere through this collaboration, thereby enhancing transparency, accelerating reproducibility, and ensuring trustworthy advances.

The lab was created to merge MIT’s deep research expertise with IBM’s industrial R&D capacity, aiming for breakthroughs in core AI methods and hardware, as well as new applications in areas like health care, chemistry, finance, cybersecurity, and robust planning and decision-making for business.

Bigger isn't always better

Today, large foundation models are giving way to smaller, more task-specific models yielding better performance. Contributions from lab members like Song Han, associate professor in the MIT Department of Electrical Engineering and Computer Science (EECS), and IBM Research’s Chuang Gan help make this possible, through work such as once-for-all and AWQ. Innovations such as these improve efficiency with better architectures, algorithm shrinking, and activation-aware weight quantization, letting models like language processing run on edge devices at faster speeds and reduced latency.

Consequently, foundation, vision, multimodal, and large language models have seen benefits, allowing for the lab research groups of Oliva, MIT EECS Associate Professor Yoon Kim, and IBM Research members Rameswar Panda, Yang Zhang, and Rogerio Feris to build on the work. This includes techniques to imbue models with external knowledge and the development of linear attention transformer methods for higher throughput, compared to other state-of-the-art systems. 

Understanding and reasoning in vision and multimodal systems has also seen a boon. Works like “Task2Sim” and “AdaFuse” demonstrate improved vision model performance if pre-training takes place on synthetic data, and how video action recognition can be boosted by fusing channels from past and current feature maps.

As part of a commitment to leaner AI, the lab teams of Gregory Wornell, the MIT EECS Sumitomo Electric Industries Professor in Engineering, IBM Research’s Chuang Gan, and David Cox, VP for foundational AI at IBM Research and the lab’s IBM director, have shown that model adaptability and data efficiency can go hand in hand. Two approaches, EvoScale and Chain-of-Action-Thought reasoning (COAT), enable language models to make the most of limited data and computation by improving on prior generation attempts through structured iteration, narrowing in on a better response. COAT uses a meta-action framework and reinforcement learning to tackle reasoning-intensive tasks via self-correction, while EvoScale brings a similar philosophy to code generation, evolving high-quality candidate solutions. These techniques help to enable resource-conscious, targeted, real-world deployment.

“The impact of MIT-IBM research on our large language model development efforts cannot be overstated,” says Cox. “We’re seeing that smaller, more specialized models and tools are having an outsized impact, especially when they are combined. Innovations from the MIT-IBM Watson AI Lab help shape these technical directions and influence the strategy we are taking in the market through platforms like watsonx.”

For example, numerous lab projects have contributed features, capabilities, and uses to IBM’s Granite Vision, which provides impressive computer vision designed for document understanding, despite its compact size. This comes at a time when there’s a growing need for extraction, interpretation, and trustworthy summarization of information and data contained in long formats for enterprise purposes.

Other achievements that extend beyond direct research on AI and across disciplines are not only beneficial, but necessary for advancing the technology and lifting up society, concludes the 2025 AAAI panel.

Work from the lab’s Caroline Uhler and Devavrat Shah — both Andrew (1956) and Erna Viterbi Professors in EECS and the Institute for Data, Systems, and Society (IDSS) — along with IBM Research’s Kristjan Greenewald, transcends specializations. They are developing causal discovery methods to uncover how interventions affect outcomes, and identify which ones achieve desired results. The studies include developing a framework that can both elucidate how “treatments” for different sub-populations may play out, like on an ecommerce platform or mobility restrictions on morbidity outcomes. Findings from this body of work could influence the fields of marketing and medicine to education and risk management.

“Advances in AI and other areas of computing are influencing how people formulate and tackle challenges in nearly every discipline. At the MIT-IBM Watson AI Lab, researchers recognize this cross-cutting nature of their work and its impact, interrogating problems from multiple viewpoints and bringing real-world problems from industry, in order to develop novel solutions,” says Dan Huttenlocher, MIT lab co-chair, dean of the MIT Schwarzman College of Computing, and the Henry Ellis Warren (1894) Professor of Electrical Engineering and Computer Science.

A significant piece of what makes this research ecosystem thrive is the steady influx of student talent and their contributions through MIT’s Undergraduate Research Opportunities Program (UROP), MIT EECS 6A Program, and the new MIT-IBM Watson AI Lab Internship Program. Altogether, more than 70 young researchers have not only accelerated their technical skill development, but, through guidance and support by the lab’s mentors, gained knowledge in AI domains to become emerging practitioners themselves. This is why the lab continually seeks to identify promising students at all stages in their exploration of AI’s potential.

“In order to unlock the full economic and societal potential of AI, we need to foster ‘useful and efficient intelligence,’” says Sriram Raghavan, IBM Research VP for AI and IBM chair of the lab. “To translate AI promise into progress, it’s crucial that we continue to focus on innovations to develop efficient, optimized, and fit-for-purpose models that can easily be adapted to specific domains and use cases. Academic-industry collaborations, such as the MIT-IBM Watson AI Lab, help drive the breakthroughs that make this possible.”

Recently, more than 1,000 MIT students stepped into the shoes of global decision-makers by trying out En-ROADS, a simulation tool developed to test climate policies, explore solutions, and envision a cleaner and safer environmental future.

MIT is committed to climate action, and this year’s new student orientation showcased that commitment. For the first time ever, incoming Leaders for Global Operations (LGO), Executive MBA, Sloan Fellow MBA, MBA, and undergraduate students all explored the capabilities of En-ROADS.

“The goal is for MIT to become one of the world’s most prolific, collaborative, and interdisciplinary sources of technological, behavioral, and policy solutions for the global climate challenge over the next decade,” MIT Provost Anantha P. Chandrakasan told an audience of about 300 undergraduates from the Class of 2029. “It is something we need to do urgently, and today is your opportunity to play a role in that bold mission.”

Connecting passion with science for change

In group workshop sessions, students collaborated to create a world in which global warming stays well below 2 degrees Celsius above preindustrial levels — the goal of the 2015 Paris Agreement. Backed by the latest science, the En-ROADS simulator let them explore firsthand how policies like carbon pricing and clean energy investments affect our climate, economy, and health. Over 450 incoming MBA students even role-played as delegates at a global climate summit conference, tasked with negotiating a global agreement to address the harm caused by climate change.

For first-year MBA student Allison Somuk, who played the role of President Xi Jinping of China, the workshop was not only eye-opening about climate, but also altered how she plans to approach her future work and advocacy.

“Before the simulation, I didn’t have data on climate change, so I was surprised to see how close we are to catastrophic temperature increases. What surprised me most was how difficult it was to slow that trajectory. It required significant action and compromise from nearly every sector, not just a few. As someone passionate about improving maternal health care in developing nations, my view of contributing factors has broadened. I now see how maternal health may be affected by a larger system where climate policy decisions directly affect women’s health outcomes.”

MIT Sloan Research Affiliate Andrew Jones, who is also executive director and co-founder of Climate Interactive and co-creator of the En-ROADS tool, presented several sessions during orientation. Looking back on the week, he found the experience personally rewarding.  

“Engaging with hundreds of students, I was inspired by the powerful alignment between their passion for climate action and MIT’s increased commitment to delivering on climate goals. This is a pivotal moment for breakthroughs on our campus.”

Other presenters included Jennifer Graham, MIT Sustainability Initiative senior associate director; Jason Jay, MIT Sustainability Initiative director; Krystal Noiseux, MIT Climate Pathways Project associate director; Bethany Patten, MIT Climate Policy Center executive director; and John Sterman, Jay W. Forrester Professor of Management, professor in the MIT Institute for Data, Systems, and Society, and director of the MIT System Dynamics Group.

Chris Rabe, the MIT Climate Project’s Education Program director, was impressed, but not surprised, by how much students learned so quickly as they worked together to solve the problem with En-ROADS.

“By integrating reflection, emotional dynamics, multi-generational perspectives, group work, and inquiry, the En-ROADS simulation provides an ideal foundation for first-year students to explore the breadth of climate and sustainability opportunities at MIT. In the process, students came to recognize the many levers and multi-solving approaches required to address the complex challenges of climate change.”

Inspiring climate leaders

The En-ROADS workshops were a true team effort, made possible with the help of senior staff at MIT Sloan School of Management and the MBA program office, and members of the MIT Sloan Sustainability Initiative, Climate Pathways Project, Climate Policy Center, the Climate Project, Office of the First Year, and entire undergraduate Orientation team.

“Altogether, over a thousand of the newest members of the MIT community have now had a chance to learn for themselves about the climate crisis,” says Sterman, “and what we can do to create a healthier, safer, more prosperous, and more sustainable world — and how they can get involved to bring that world into being, even as first-year undergrads and MBAs.” 

By the end of the workshops, the students’ spirits were buoyed. They all had successfully found ways to keep global warming to below 2 C.  When asked, “What would you love about being part of this new future you’ve created?,”  a more positive, optimistic word cloud came into view. Answers included:

• breathing cleaner air;
• giving my children a better and safer environment;
• my hometown would not be flooded constantly;
• rich marine life and protection of reefs;
• exciting, new clean industries;
• increased socioeconomic equality; and
• proof that we as a global community can work together to save ourselves. 

First-year MBA student Ruby Eisenbud sums up the sentiment many new MIT students came away with after their workshop.

“Coming to Sloan, one of the questions on my mind was: How can we, as future leaders, make a positive impact related to climate change? While En-ROADS is a simulation, it felt like we experienced, in the smallest way, what it could be like to be a leader navigating the diverging interests of all stakeholders involved in mitigating the impacts of the climate crisis. While the simulation prompted us to face the difficult reality of climate change, it also reinforced my motivation to emphasize climate in my work at Sloan and beyond.”

On any given day, MIT’s famed 825-foot Infinite Corridor serves as a busy, buzzing pedestrian highway, offering campus commuters a quick, if congested, route from point A to B. With the possible exception of MIT Henge twice a year, it doesn’t exactly invite lingering.

Thanks to a recent renovation on the first floor of Building 11, the former location of Student Financial Services, there’s now a compelling reason for students to step off the busy throughfare and pause for conversation or respite.

Dubbed by one onlooker as “the spaceport,” the area has been transformed into an airy, multi-functional hub. Nestled inside is the Undergraduate Advising Center (UAC), which launched in 2023 to provide holistic support for students’ personal and academic growth by providing individualized advising for all four years, offering guidance about and connections to MIT resources, and partnering with faculty and departments to ensure a comprehensive advising experience.

Students can now find another key service conveniently located close by: Career Advising and Professional Development has moved into renovated office suites just down the hall, in Building 7.

“It’s just stunning!” marvels Diep Luu, senior associate dean and director of the UAC. “You can’t help but notice the contrast between the historic architecture and the contemporary design. The space is filled with natural light thanks to the floor-to-ceiling windows, and it makes the environment both energizing and comfortable.”

Designed by Merge Architects, the 5,000 square-foot space opens off the Infinite with several informal public spaces for students and community members. These include a series of soaring, vaulted booths with a variety of tables and seating to support multiple kinds of socialization and/or work, a cozy lounge lined with pi wallpaper (carried out to 10,638 digits after 3.14), and the “social stairs” for informal gatherings and workshops. Beyond that, glass doors lead to the UAC office space, which features open workstations, private advising rooms, and conference rooms with Zoom capability.

“We wanted to incorporate as many different kinds of spaces to accommodate as many different kinds of interactions as we could,” explains Kate Trimble, senior associate dean and chief of staff of the Division of Graduate and Undergraduate Education (GUE), who helped guide the renovation project. “After all, the UAC will support all undergraduate students for their entire four-year MIT journey, through a wide variety of experiences, challenges, and celebrations.”

Homing in on the  “Boardwalk or Park Place of MIT real estate”

The vision for the new district began to percolate in 2022. At the time, GUE (then known as the Office of the Vice Chancellor, or OVC) was focusing on two separate, key priorities: reconfiguring office space in a post-pandemic, flex-work world; and creating a new undergraduate advising center, in accordance with one of the Task Force 2021 recommendations.

A faculty and staff working group gathered information and ideas from offices and programs that had already implemented “flex-space” strategies, such as Human Resources, IS&T, and the MIT Innovation Headquarters. In thinking about an advising center of the size and scope envisioned, Trimble notes, “we quickly zeroed in on the Building 11 space. It’s such a prominent location. Former Vice Chancellor (and current Vice President for Research) Ian A. Waitz referred to it as the “Boardwalk or Park Place of MIT real estate. And if you’re thinking about a center that’s going to serve all undergraduates, you really want it to be convenient and centrally located — and boy, that’s a perfect space.”

As plans were made to relocate Student Financial Services to a new home in Building E17, the renovation team engaged undergraduate students and advising staff in the design process through a series of charrette-style workshops and focus groups. Students shared feedback about spaces on campus where they felt most comfortable, as well as those they disliked. From staff, the team learned which design elements would make the space as functional as possible, allowing for the variety of interactions they typically have with students.

The team selected Merge Architects for the project, Trimble says, because “they understood that we were not looking to build something that was an architectural temple, but rather a functional and fun space that meets the needs of our students and staff. They’ve been creative and responsive partners.” She also credits the MIT Campus Construction group and the Office of Campus Planning for their crucial role in the renovation. “I can’t say enough good things about them. They’ve been superb guides through a long and complicated process.”

A more student-centric Infinite Corridor

Construction wrapped up in late summer, and the UAC held an open house for students on Registration Day, Sept. 3. It buzzed with activity as students admired the space, chatted with UAC staff, took photos, and met the office mascot, Winni, a friendly chocolate Labrador retriever.

“Students have been amazed by the transformation,” says Luu. “We wanted a space that encourages community and collaboration, one that feels alive and dynamic, and the early feedback suggests that’s exactly what’s happening,” Luu explains. “It also gives us a chance to better connect students not only with what the UAC offers, but also with support across the Institute.

“Last year, the UAC offices were behind these two wooden doors in the Infinite Corridor and you had to know that they were there to get to them,” says junior Caleb Mathewos, who has been a UAC orientation leader and captain over the past two years. “The space is very inviting now. I’ve seen people sitting there and working, or just relaxing between classes. I see my friends every now and then, and I’ll stop by and chat with them. Because it’s so much more open, it makes the UAC feel a lot more accessible to students.”

Senior Calvin Macatantan, who’s been involved with the UAC’s First Generation/Low Income Program since his first year and served as an associate advisor and orientation leader, thinks the new space will make it easier for students — especially first years — to find what they need to navigate at MIT. “Before, resources felt scattered across different parts of the Infinite, even though they had similar missions of advising and supporting students. It's nice that there’s a central, welcoming space where those supports connect, and I think that will make a big difference in how students experience MIT.”

The transformation adds significantly to a trend toward creating more student-centric spaces along the Infinite. In the past few years, MIT has added two new study lounges in Building 3, the DEN and the LODGE, and the Department of Materials Science and Engineering built the DMSE Breakerspace in Building 4. This fall, another office suite along the Infinite will be remodeled into a new tutoring hub.

"It’s wonderful to see the UAC space and the whole advising ‘neighborhood,’ if you will, come to fruition,” says Vice Chancellor for Graduate and Undergraduate Education David L. Darmofal. “The need to strengthen undergraduate advising and the opportunity to do so through an Institute advising hub was an outcome of the Task Force 2021 effort, and it’s taken years of thoughtful reflection by many stakeholders to lay the foundation for such a significant sea change in advising. This space is a tangible, visible commitment to putting students first.”

Commercial shipping accounts for 3 percent of all greenhouse gas emissions globally. As the sector sets climate goals and chases a carbon-free future, nuclear power — long used as a source for military vessels — presents an enticing solution. To date, however, there has been no clear, unified public document available to guide design safety for certain components of civilian nuclear ships. A new “Nuclear Ship Safety Handbook” by the MIT Maritime Consortium aims to change that and set the standard for safe maritime nuclear propulsion.

“This handbook is a critical tool in efforts to support the adoption of nuclear in the maritime industry,” explains Themis Sapsis, the William I. Koch Professor of Mechanical Engineering at MIT, director of the MIT Center for Ocean Engineering, and co-director of the MIT Maritime Consortium. “The goal is to provide a strong basis for initial safety on key areas that require nuclear and maritime regulatory research and development in the coming years to prepare for nuclear propulsion in the maritime industry.”

Using research data and standards, combined with operational experiences during civilian maritime nuclear operations, the handbook provides unique insights into potential issues and resolutions in the design efficacy of maritime nuclear operations, a topic of growing importance on the national and international stage. 

“Right now, the nuclear-maritime policies that exist are outdated and often tied only to specific technologies, like pressurized water reactors,” says Jose Izurieta, a graduate student in the Department of Mechanical Engineering (MechE) Naval Construction and Engineering (2N) Program, and one of the handbook authors. “With the recent U.K.-U.S. Technology Prosperity Deal now including civil maritime nuclear applications, I hope the handbook can serve as a foundation for creating a clear, modern regulatory framework for nuclear-powered commercial ships.”

The recent memorandum of understanding signed by the U.S. and U.K calls for the exploration of “novel applications of advanced nuclear energy, including civil maritime applications,” and for the parties to play “a leading role informing the establishment of international standards, potential establishment of a maritime shipping corridor between the Participants’ territories, and strengthening energy resilience for the Participants’ defense facilities.”

“The U.S.-U.K. nuclear shipping corridor offers a great opportunity to collaborate with legislators on establishing the critical framework that will enable the United States to invest on nuclear-powered merchant vessels — an achievement that will reestablish America in the shipbuilding space,” says Fotini Christia, the Ford International Professor of the Social Sciences, director of the Institute for Data, Systems, and Society (IDSS), director of the MIT Sociotechnical Systems Research Center, and co-director of the MIT Maritime Consortium.

“With over 30 nations now building or planning their first reactors, nuclear energy’s global acceptance is unprecedented — and that momentum is key to aligning safety rules across borders for nuclear-powered ships and the respective ports,” says Koroush Shirvan, the Atlantic Richfield Career Development Professor in Energy Studies at MIT and director of the Reactor Technology Course for Utility Executives.

The handbook, which is divided into chapters in areas involving the overlapping nuclear and maritime safety design decisions that will be encountered by engineers, is careful to balance technical and practical guidance with policy considerations.

Commander Christopher MacLean, MIT associate professor of the practice in mechanical engineering, naval construction, and engineering, says the handbook will significantly benefit the entire maritime community, specifically naval architects and marine engineers, by providing standardized guidelines for design and operation specific to nuclear powered commercial vessels.

“This will assist in enhancing safety protocols, improve risk assessments, and ensure consistent compliance with international regulations,” MacLean says. “This will also help foster collaboration amongst engineers and regulators. Overall, this will further strengthen the reliability, sustainability, and public trust in nuclear-powered maritime systems.”

Anthony Valiaveedu, the handbook’s lead author, and co-author Nat Edmonds, are both students in the MIT Master’s Program in Technology and Policy (TPP) within the IDSS. The pair are also co-authors of a paper published in Science Policy Review earlier this year that offered structured advice on the development of nuclear regulatory policies.

“It is important for safety and technology to go hand-in-hand,” Valiaveedu explains. “What we have done is provide a risk-informed process to begin these discussions for engineers and policymakers.”

“Ultimately, I hope this framework can be used to build strong bilateral agreements between nations that will allow nuclear propulsion to thrive,” says fellow co-author Izurieta.

Impact on industry

“Maritime designers needed a source of information to improve their ability to understand and design the reactor primary components, and development of the 'Nuclear Ship Safety Handbook' was a good step to bridge this knowledge gap,” says Christopher J. Wiernicki, American Bureau of Shipping (ABS) chair and CEO. “For this reason, it is an important document for the industry.”

The ABS, which is the American classification society for the maritime industry, develops criteria and provides safety certification for all ocean-going vessels. ABS is among the founding members of the MIT Maritime Consortium. Capital Clean Energy Carriers Corp., HD Korea Shipbuilding and Offshore Engineering, and Delos Navigation Ltd. are also consortium founding members. Innovation members are Foresight-Group, Navios Maritime Partners L.P., Singapore Maritime Institute, and Dorian LPG.

“As we consider a net-zero framework for the shipping industry, nuclear propulsion represents a potential solution. Careful investigation remains the priority, with safety and regulatory standards at the forefront,” says Jerry Kalogiratos, CEO of Capital Clean Energy Carriers Corp. “As first movers, we are exploring all options. This handbook lays the technical foundation for the development of nuclear-powered commercial vessels.”

Sangmin Park, senior vice president at HD Korea Shipbuilding and Offshore Engineering, says “The 'Nuclear Ship Safety Handbook' marks a groundbreaking milestone that bridges shipbuilding excellence and nuclear safety. It drives global collaboration between industry and academia, and paves the way for the safe advancement of the nuclear maritime era.”

Maritime at MIT

MIT has been a leading center of ship research and design for over a century, with work at the Institute today representing significant advancements in fluid mechanics and hydrodynamics, acoustics, offshore mechanics, marine robotics and sensors, and ocean sensing and forecasting. Maritime Consortium projects, including the handbook, reflect national priorities aimed at revitalizing the U.S. shipbuilding and commercial maritime industries.

The MIT Maritime Consortium, which launched in 2024, brings together MIT and maritime industry leaders to explore data-powered strategies to reduce harmful emissions, optimize vessel operations, and support economic priorities.

“One of our most important efforts is the development of technologies, policies, and regulations to make nuclear propulsion for commercial ships a reality,” says Sapsis. “Over the last year, we have put together an interdisciplinary team with faculty and students from across the Institute. One of the outcomes of this effort is this very detailed document providing detailed guidance on how such effort should be implemented safely.”

Handbook contributors come from multiple disciplines and MIT departments, labs, and research centers, including the Center for Ocean Engineering, IDSS, MechE’s Course 2N Program, the MIT Technology and Policy Program, and the Department of Nuclear Science and Engineering.

MIT faculty members and research advisors on the project include Sapsis; Christia; Shirvan; MacLean; Jacopo Buongiorno, the Battelle Energy Alliance Professor in Nuclear Science and Engineering, director, Center for Advanced Nuclear Energy Systems, and director of science and technology for the Nuclear Reactor Laboratory; and Captain Andrew Gillespy, professor of the practice and director of the Naval Construction and Engineering (2N) Program.

“Proving the viability of nuclear propulsion for civilian ships will entail getting the technologies, the economics and the regulations right,” says Buongiorno. “This handbook is a meaningful initial contribution to the development of a sound regulatory framework.”

“We were lucky to have a team of students and knowledgeable professors from so many fields,” says Edmonds. “Before even beginning the outline of the handbook, we did significant archival and history research to understand the existing regulations and overarching story of nuclear ships. Some of the most relevant documents we found were written before 1975, and many of them were stored in the bellows of the NS Savannah.”

The NS Savannah, which was built in the late 1950s as a demonstration project for the potential peacetime uses of nuclear energy, was the first nuclear-powered merchant ship. The Savannah was first launched on July 21, 1959, two years after the first nuclear-powered civilian vessel, the Soviet ice-breaker Lenin, and was retired in 1971.

Historical context for this project is important, because the reactor technologies envisioned for maritime propulsion today are quite different from the traditional pressurized water reactors used by the U.S. Navy. These new reactors are being developed not just in the maritime context, but also to power ports and data centers on land; they all use low-enriched uranium and are passively cooled. For the maritime industry, Sapsis says, “the technology is there, it’s safe, and it’s ready.”

“The Nuclear Ship Safety Handbook” is publicly available on the MIT Maritime Consortium website and from the MIT Libraries. 

The inaugural PITCH.nano competition, hosted by MIT.nano’s hard technology accelerator START.nano, provided a platform for early-stage startups to present their innovations to MIT and Boston’s hard-tech startup ecosystem.

The grand prize winner was Active Surfaces, a startup that is generating renewable energy exactly where it is going to be used through lightweight, flexible solar cells. Active Surfaces says its ultralight, peel-and-stick panels will reimagine how we deploy photovoltaics in the built environment.

Shiv Bhakta MBA ’24, SM ’24, CEO and co-founder, delivered the winning presentation to an audience of entrepreneurs, investors, startup incubators, and industry partners at PITCH.nano on Sept. 30. Active Surfaces received the grand prize of 25,000 nanoBucks — equivalent to $25,000 that can be spent at MIT.nano facilities.

Why has MIT.nano chosen to embrace startup activity as much as we do? asked Vladimir Bulović, MIT.nano faculty director, at the start of PITCH.nano. “We need to make sure that entrepreneurs can be born out of MIT and can take the next technical ideas developed in the lab out into the market, so they can make the next millions of jobs that the world needs.”

The journey of a hard-tech entrepreneur takes at least 10 years and 100 million dollars, explained Bulović. By linking open tool facilities to startup needs, MIT.nano can make those first few years a little bit easier, bringing more startups to the scale-up stage.

“Getting VCs [venture capitalists] to invest in hard tech is challenging,” explained Joyce Wu SM ’00, PhD ’07, START.nano program manager. “Through START.nano, we provide discounted access to MIT.nano’s cleanrooms, characterization tools, and laboratories for startups to build their prototypes and attract investment earlier and with reduced spend. Our goal is to support the translation of fundamental research to real-world solutions in hard tech.”

In addition to discounted access to tools, START.nano helps early-stage companies become part of the MIT and Cambridge innovation network. PITCH.nano, inspired by the MIT 100K Competition, was launched as a new opportunity this year to introduce these hard-tech ventures to the investor and industry community. Twelve startups delivered presentations that were evaluated by a panel of four judges who are, themselves, venture capitalists and startup founders.

“It is amazing to see the quality, diversity, and ingenuity of this inspiring group of startups,” said judge Brendan Smith PhD ’18, CEO of SiTration, a company that was part of the inaugural START.nano cohort. “Together, these founders are demonstrating the power of fundamental hard-tech innovation to solve the world’s greatest challenges, in a way that is both scalable and profitable.”

Startups who presented at PITCH.nano spanned a wide range of focus areas. In the fields of climate, energy, and materials, the audience heard from Addis Energy, Copernic Catalysts, Daqus Energy, VioNano Innovations, Active Surfaces, and Metal Fuels; in life sciences, Acorn Genetics, Advanced Silicon Group, and BioSens8; and in quantum and photonics, Qunett, nOhm Devices, and Brightlight Photonics. The common thread for these companies: They are all using MIT.nano to advance their innovations.

“MIT.nano has been instrumental in compressing our time to market, especially as a company building a novel, physical product,” said Bhakta. “Access to world-class characterization tools — normally out of reach for startups — lets us validate scale-up much faster. The START.nano community accelerates problem-solving, and the nanoBucks award is directly supporting the development of our next prototypes headed to pilot.”

In addition to the grand prize, a 5,000 nanoBucks audience choice award went to Advanced Silicon Group, a startup that is developing a next-generation biosensor to improve testing in pharma and health tech.

Now in its fifth year, START.nano has supported 40 companies spanning a diverse set of market areas — life sciences, clean tech, semiconductors, photonics, quantum, materials, and software. Fourteen START.nano companies have graduated from the program, proving that START.nano is indeed succeeding in its mission to help early-stage ventures advance from prototype to manufacturing. “I believe MIT.nano has a fantastic opportunity here,” said judge Davide Marini, PhD ’03, co-founder and CEO of Inkbit, “to create the leading incubator for hard tech entrepreneurs worldwide.”

START.nano accepts applications on a monthly basis. The program is made possible through the generous support of FEMSA.

Since launching in 2008, the MIT Global Seed Funds (GSF) program has awarded roughly $30 million to more than 1,300 high-impact faculty research projects across the world, spurring consequential collaborations on topics that include swine-fever vaccines, deforestation of the Amazon, the impact of “coral mucus” on the Japanese island of Okinawa, and the creation of an AI-driven STEM-education lab within Nigeria’s oldest university.

Administered by the MIT Center for International Studies (CIS) and open to MIT faculty and principal investigators, GSF boasts a unique funding structure consisting of both a general fund for unrestricted geographical use and more than 20 different specific funds for individual universities, regions, and countries.

GSF projects often tackle critical challenges that require international solutions, culminating in patents, policy changes, and published papers in journals such as Nature and Science. Some faculty-led projects from this year include Professor Hugh Herr’s modular crutches for people with disabilities in Sierra Leone, Research Scientist Paolo Santi’s large-language models to predict energy consumption in grocery stores, and Professor Ernest Fraenkel’s development of mRNA therapies for the neurodegenerative disease amyotrophic lateral sclerosis (ALS).

GSF Assistant Director Justin Leahey, who is managing director of the MIT-Germany and MIT-Switzerland programs, says that GSF has expanded exponentially over the years, including most recently into the Czech Republic, Norway, Slovakia, and — starting in fall 2025 — Hungary. This year there were a grand total of roughly 300 research proposals submitted for consideration, with many of the accepted proposals including the active participation of students at both the graduate and undergraduate level.

Central to GSF’s work is “reciprocal exchange” — the concept of collaborators in and out of MIT sharing their work and exchanging ideas in an egalitarian way, rather than bringing a one-sided approach to different research challenges. Frequent collaborator Raffaella Gozzelino, a neurology researcher and principal investigator at NOVA Medical School in Portugal who works closely with Jacquin Niles, an MIT professor of biological engineering, says that research is more impactful “when specialized knowledge integrates local realities and reveals potential solutions to national challenges,” and views the spirit of reciprocal exchange as something that revolves around “sharing knowledge and co-creating solutions that empower one another and build bridges across borders.”

For Cindy Xie ’24, MCP ’25, her master’s thesis emerged from the first-ever GSF-supported research internship in Cape Verde, where she worked with Niles and Gozzelino to explore the impact of climate change on anemia in the country of 500,000 people, focusing specifically on its largest island of Santiago. Xie says that she was struck by the intertwined intersectional nature of the issues of nutrition, climate, and infection in Santiago, home to the nation’s capital city of Praia. For example, Xie and Gozzelino’s team found that respondents perceived a rise in costs of fresh produce over time, exacerbated by drought and unpredictable agricultural conditions, which in turn impacted existing nutritional deficiencies and increased residents’ susceptibility to mosquito-borne diseases.

“Though this multidisciplinary research lens is challenging in terms of actual project implementation, it was meaningful in that it generated insights and connections across fields that allow our research to be better contextualized within the experiences of the communities that it impacts,” Xie says.

Gozzelino says that it has been meaningful to witness how scientific research can transcend academic boundaries and generate real impact. She says that, by examining the effects of climate change on infectious diseases and nutrition in Cape Verde, the team will be able to build a framework that can directly inform public policy.

“Contributing to a project that underscores the importance of integrating scientific knowledge into decision-making will safeguard vulnerable populations and make them feel included in the society they belong,” Gozzelino says. “This collaboration has revealed the enormous potential of international partnerships to strengthen local research capacity and address global challenges.”

During her time in Cape Verde working with Xie and Gozzelino, Amulya Aluru ’23, MEng ’24 got to meet with 20 local officials and connect with new people in a wide range of roles across the country, helping her “recognize the power of interpersonal relationships and collaboration” in public health research. She says that the structure of the GSF grant gave her the unique experience of having mentors and coworkers in three different countries, spanning Cape Verde, the United States, and Portugal.

Aluru says that this kind of cross-pollination “enabled me to strengthen my research with different perspectives and challenged me to approach my work in a way that I’d never done before, with a more global mindset.”

Xie similarly expresses her deep appreciation for the long-term relationships she has built through the project and the linkages between Santiago and Boston, which itself is home to one of the world’s largest Cape Verdean diasporas. “As a student, this was a valuable experience to inform the approaches to collaboration that I would like to implement in my own future work,” Xie says.

More broadly, Gozzelino sees GSF grants like the Cape Verde one as being not simply a vehicle for financial support, but “a catalyst for turning partnerships into long-term impactful collaborations, demonstrating how global networks can aid the development of human capital.”

GSF’s long history of reaching across departments and borders has led to multiple meaningful academic collaborations that have since come to span continents — and decades. In 2015, Professor Jörn Dunkel — an applied mathematician at MIT — kicked off work on a data-sharing repository for bacterial biofilms with the interdisciplinary German microbiologist Knut Drescher, then a professor of biophysics at Philipps-Universität Marburg in Germany. Dunkel and Drescher have since co-authored more than 15 papers together in publications like Nature Physics and Science Advances alongside their teams of graduate students and postdocs, even with Drescher having moved locations and crossed country lines to Switzerland as a faculty member at the University of Basel’s Biozentrum Center for Molecular Life Sciences.

“Our collaboration often creates great synergy by combining my team’s experiments with the theory from Jörn’s team,” says Drescher. “It is a great joy to see his perspective on the experimental systems we are working on. He is able to really understand and engage with experimental biological data, identifying patterns in seemingly distant biological systems.”

In explaining the CIS initiative’s success, Leahey points to the synergistic, academically eclectic, cross-disciplinary nature of the program. “[GSF] is a research fund that doesn’t ‘fund research’ in the conventional sense,” he says. “It seeds early-stage collaboration and lets people explore.”

The MIT Global Seed Funds applications are now open, with a deadline of Dec. 16.

Alan Robert Whitney ’66, SM ’67, PhD ’74, a longtime research scientist at the MIT Haystack Observatory who also served its associate director and interim director, died on Sept. 28 at age 81.

Whitney was a key contributor to the accomplishments and reputation of Haystack Observatory, having led the development of innovative technologies to advance the powerful radio science technique of very long baseline interferometry (VLBI). He ascended to the rank of MIT principal research scientist, served for many years as associate director of the observatory, and in 2007–08 took the reins as interim director. In 2011, he was awarded an MIT Excellence award.

From an early age, Whitney displayed extraordinary talent. Raised in Wyoming, as a high schooler he won the state science fair in 1962 by building a satellite telemetry receiver, which he designed and built from transistors and other discrete components in a barn on his family’s dairy farm. He enrolled at MIT and completed a five-year master’s degree via a cooperative internship program with Bell Laboratories, subsequently earning his PhD in electrical engineering.

Haystack Director Phil Erickson says, “Alan’s personality and enthusiasm were infectious, and his work represented the best ideals of the Haystack and MIT research enterprise — innovative, curious, and exploring the frontiers of basic and applied science and technology.”

In the late 1960s, as part of his PhD work, he was heavily involved in the pioneering development of VLBI, an extraordinary technique that yielded direct measurements of continental drift and information on distant radio sources at unprecedented angular resolution. A landmark paper led by Whitney demonstrated the presence of apparent superluminal (faster than light) motion of radio sources, which was explained as highly relativistic motion aligned toward the Earth. He spent the rest of his long and productive career at Haystack, pushing forward VLBI technology to ever-greater heights and ever-more impactful scientific capabilities.

“Alan was a technology pillar, a stalwart builder and worldwide ambassador of Haystack, and a leading figure of the VLBI geodetic community who inspired generations of scientists and engineers,” says Pedro Elosegui, leader of the Haystack geodesy group. “He contributed fundamentally to the vision and design of the VLBI Geodetic Observing System, outlining a path to a next-generation VLBI system with unprecedented new capabilities to address emerging space geodesy science needs such as global sea-level rise.”

The early days of VLBI demanded heroic and grueling efforts, traveling the world with exotic devices in hand-carried luggage, mounting and dismounting thousands of magnetic tapes every couple of minutes for hours on end, troubleshooting complex and sensitive instrumentation, and writing highly specialized software for the mainframe computers of the day. Whitney was fully engaged on all these fronts. By the early 1980s, the Mark III recording and correlation systems, whose development was led by Whitney, were established as the state of the art in VLBI technology, and a standard around which the global VLBI community coalesced.

Whitney later led the transition to VLBI disk-based recording. Specialized and robust Mark V systems optimized for shipping logistics and handling were transferred to industry for commercialization, leading once again to widespread global adoption of Haystack-developed VLBI technology. Consistently across all these developments, Whitney identified and exploited the most relevant and practical emerging technologies for the Haystack VLBI mission in hardware, software, and computing infrastructure.

In the latter part of his career, Whitney continued to innovate, pushing the technical boundaries of VLBI. A key advance was the Mark 6 (Mk6) recording system, capable of yet faster recording, higher sensitivity, and more robustness. The Mk6 recorders’ essential capability allowed the creation of the Event Horizon Telescope, which famously yielded the first image of the shadow of a black hole. Mk6 recorders are now used to routinely record data roughly 100,000 times faster than the computer tapes used at the start of his career.

As a senior technical and scientific leader, Whitney provided broad leadership and consultation to Haystack, and worked on a number of projects outside of the VLBI world. He served as interim Haystack director from January 2007 until a permanent director was appointed in September 2008. He also engaged with the development project for the international Murchison Widefield Array (MWA) in Australia, focused on frontier research studying early universe development. Whitney assumed the role of MWA project director from 2008 until groups in Australia took over the construction phase of the project a few years later. Until his full retirement in 2012, Whitney continued to provide invaluable technical insights and support at Haystack, and was a trusted and wise counsel to the Haystack Director’s Office. In 2020, Whitney was a co-recipient of the 2020 Breakthrough Prize in Fundamental Physics awarded to the Event Horizon Telescope Collaboration.

Alan Whitney was a top-notch technologist with a broad perspective that allowed him to guide Haystack to decades of influential leadership in the development and refinement of the VLBI technique. His dedication at MIT to the observatory, its people, and its mission were a source of inspiration to many at Haystack and well beyond. He was widely admired for the clarity of his thought, the sharpness of his intellect, and his genial and friendly nature. His numerous local, national, and global colleagues will feel his absence.

The MIT School of Engineering welcomes new faculty members across six of its academic units. This new cohort of faculty members, who have recently started their roles at MIT, conduct research across a diverse range of disciplines.

“We are thrilled to welcome these accomplished scholars to the School of Engineering,” says Maria C. Yang, interim dean of engineering and William E. Leonhard (1940) Professor in the Department of Mechanical Engineering. “Each brings unique expertise across a wide range of fields and is advancing knowledge with real-world impact. They all share a deep commitment to research excellence and a passion for teaching and mentorship.”

Faculty with appointments in the Department of Electrical Engineering and Computer Science (EECS) and the Institute for Data, Systems, and Society (IDSS) report into both the School of Engineering and the MIT Stephen A. Schwarzman College of Computing.

The new engineering faculty include:

Masha Folk joined the Department of Aeronautics and Astronautics as an assistant professor in July 2024 and is currently the Charles Stark Draper Career Development Professor. Her research focuses on sustainable aerospace technology driven by a deep desire to accelerate carbon-neutral aviation. She previously worked as an aerodynamics specialist for Rolls-Royce. Folk received her BS in aerospace engineering from Ohio State University, her MS in aerospace engineering from Purdue University, and her PhD in energy, fluids, and turbomachinery from the University of Cambridge.

Sophia Henneberg joined the Department of Nuclear Science and Engineering (NSE) as an assistant professor in September. Her research focuses on developing, utilizing, and extending optimization tools to identify new, promising stellarator designs, which are a promising path toward fusion energy. Previously, she was the principal investigator of EUROfusion’s Stellarator Optimization Theory, Simulation, Validation, and Verification group. Henneberg received a BS in physics at the Goethe-Universität, an MA in physics at the University of Wisconsin at Madison, and a PhD in physics at the University of York.

Omar Khattab joined the Department of Electrical Engineering and Computer Science as an assistant professor in July. He is also affiliated with the Computer Science and Artificial Intelligence Laboratory (CSAIL). His research develops new algorithms and abstractions for declarative AI programming and for composing retrieval and reasoning. Khattab previously worked as a research scientist at Databricks. He received a BS in computer science from Carnegie Mellon University and a PhD in computer science from Stanford University.

Tania Lopez-Silva joined the Department of Materials Science and Engineering as an assistant professor in July. Her research focuses on supramolecular hydrogels — soft materials made from self-assembling molecules, primarily peptides. Previously, she served as a postdoc at the National Cancer Institute. Lopez-Silva earned her BS in chemistry from Tecnológico de Monterrey and her MA and PhD in chemistry from Rice University.

Ethan Peterson ’13 joined the Department of Nuclear Science and Engineering as an assistant professor in July 2024. His research focuses on improving radiation transport and transmutation methods for the design of fusion technologies, as well as whole-facility modeling for fusion power plants. Previously, he worked as a research scientist at MIT’s Plasma Science and Fusion Center. Peterson received his BS in nuclear engineering and physics from MIT and his PhD in plasma physics from the University of Wisconsin at Madison.

Dean Price joined the Department of Nuclear Science and Engineering as the Atlantic Richfield Career Development Professor in Energy Studies and an assistant professor in September. His work focuses on the simulation and control of advanced reactors, with expertise in uncertainty quantification, scientific machine learning, and artificial intelligence for nuclear applications. Previously, he was the Russell L. Heath Distinguished Postdoctoral Fellow at Idaho National Laboratory. He earned his BS in nuclear engineering from the University of Illinois and his PhD in nuclear engineering from the University of Michigan.

Daniel Varon joined the Department of Aeronautics and Astronautics as the Boeing Assistant Professor, holding an MIT Schwarzman College of Computing shared position with IDSS, in July. Varon’s research focuses on using satellite observations of atmospheric composition to better understand human impacts on the environment and identify opportunities to reduce them. Previously, he held a visiting postdoctoral fellowship at the Princeton School of Public and International Affairs. Varon earned a BS in physics and a BA in English literature from McGill University, and an MS in applied mathematics and PhD in atmospheric chemistry from Harvard University.

Raphael Zufferey joined the Department of Mechanical Engineering as an assistant professor in January. He studies bioinspired methods and unconventional designs to solve seamless aerial and aquatic locomotion for applications in ocean sciences. Zufferey previously worked as a Marie Curie postdoc at the École Polytechnique Fédérale de Lausanne (EPFL). He received his BA in micro-engineering and MS in robotics from EPFL and a PhD in robotics and aeronautics from Imperial College London.

The School of Engineering is also welcoming a number of faculty in the Department of EECS and the IDSS who hold shared positions with the MIT Schwarzman College of Computing and other departments. These include: Bailey Flanigan, Brian Hedden, Yunha Hwang, Benjamin Lindquist, Paris Smaragdis, Pu “Paul" Liang, Mariana Popescu, and Daniel Varon. For more information about these faculty members, read the Schwarzman College of Computing’s recent article.

Additionally, the School of Engineering has adopted the shared faculty search model to hire its first shared faculty member: Mark Rau. For more information, read the School of Humanities, Arts, and Social Sciences recent article.

The MIT Schwarzman College of Computing welcomes 11 new faculty members in core computing and shared positions to the MIT community. They bring varied backgrounds and expertise spanning sustainable design, satellite remote sensing, decision theory, and the development of new algorithms for declarative artificial intelligence programming, among others.

“I warmly welcome this talented group of new faculty members. Their work lies at the forefront of computing and its broader impact in the world,” says Dan Huttenlocher, dean of the MIT Schwarzman College of Computing and the Henry Ellis Warren Professor of Electrical Engineering and Computer Science.

College faculty include those with appointments in the Department of Electrical Engineering and Computer Science (EECS) or in the Institute for Data, Systems, and Society (IDSS), which report into both the MIT Schwarzman College of Computing and the School of Engineering. There are also several new faculty members in shared positions between the college and other MIT departments and sections, including Political Science, Linguistics and Philosophy, History, and Architecture.

“Thanks to another successful year of collaborative searches, we have hired six additional faculty in shared positions, bringing the total to 20,” says Huttenlocher.

The new shared faculty include:

Bailey Flanigan is an assistant professor in the Department of Political Science, holding an MIT Schwarzman College of Computing shared position with EECS. Her research combines tools from social choice theory, game theory, algorithms, statistics, and survey methods to advance political methodology and strengthen democratic participation. She is interested in sampling algorithms, opinion measurement, and the design of democratic innovations like deliberative minipublics and participatory budgeting. Flanigan was a postdoc at Harvard University’s Data Science Initiative, and she earned her PhD in computer science from Carnegie Mellon University.

Brian Hedden PhD ’12 is a professor in the Department of Linguistics and Philosophy, holding an MIT Schwarzman College of Computing shared position with EECS. His research focuses on how we ought to form beliefs and make decisions. His works span epistemology, decision theory, and ethics, including ethics of AI. He is the author of “Reasons without Persons: Rationality, Identity, and Time” (Oxford University Press, 2015) and articles on topics such as collective action problems, legal standards of proof, algorithmic fairness, and political polarization. Prior to joining MIT, he was a faculty member at the Australian National University and the University of Sydney, and a junior research fellow at Oxford University. He received his BA from Princeton University and his PhD from MIT, both in philosophy.

Yunha Hwang is an assistant professor in the Department of Biology, holding an MIT Schwarzman College of Computing shared position with EECS. She is also a member of the Laboratory for Information and Decision Systems. Her research interests span machine learning for sustainable biomanufacturing, microbial evolution, and open science. She serves as the co-founder and chief scientist at Tatta Bio, a scientific nonprofit dedicated to advancing genomic AI for biological discovery. She holds a BS in computer science from Stanford University and a PhD in biology from Harvard University.

Ben Lindquist is an assistant professor in the History Section, holding an MIT Schwarzman College of Computing shared position with EECS. Through a historical lens, his work observes the ways that computing has circulated with ideas of religion, emotion, and divergent thinking. His book, “The Feeling Machine” (University of Chicago Press, forthcoming), follows the history of synthetic speech to examine how emotion became a subject of computer science. He was a postdoc in the Science in Human Culture Program at Northwestern University and earned his PhD in history from Princeton University.

Mariana Popescu is an assistant professor in the Department of Architecture, holding an MIT Schwarzman College of Computing shared position with EECS. She is also a member of the Computer Science and Artificial Intelligence Laboratory (CSAIL). A computational architect and structural designer, Popescu has a strong interest and experience in innovative ways of approaching the fabrication process and use of materials in construction. Her area of expertise is computational and parametric design, with a focus on digital fabrication and sustainable design. Popescu earned her doctorate at ETH Zurich.

Paris Smaragdis SM ’97, PhD ’01 is a professor in the Music and Theater Arts Section, holding an MIT Schwarzman College of Computing shared position with EECS. His research focus lies at the intersection of signal processing and machine learning, especially as it relates to sound and music. Prior to coming to MIT, he worked as a research scientist at Mitsubishi Electric Research Labs, a senior research scientist at Adobe Research, and an Amazon Scholar with Amazon’s AWS. He spent 15 years as a professor at the University of Illinois Urbana Champaign in the Computer Science Department, where he spearheaded the design of the CS+Music program, and served as an associate director of the School of Computer and Data Science. He holds a BMus from Berklee College of Music and earned his PhD in perceptual computing from MIT.

Daniel Varon is an assistant professor in the Department of Aeronautics and Astronautics, holding an MIT Schwarzman College of Computing shared position with IDSS. His work focuses on using satellite observations of atmospheric composition to better understand human impacts on the environment and identify opportunities to reduce them. An atmospheric scientist, Varon is particularly interested in greenhouse gasses, air pollution, and satellite remote sensing. He holds an MS in applied mathematics and a PhD in atmospheric chemistry, both from Harvard University.

In addition, the School of Engineering has adopted the shared faculty search model to hire its first shared faculty member:

Mark Rau is an assistant professor in the Music and Theater Arts Section, holding a School of Engineering shared position with EECS. He is involved in developing graduate programming focused on music technology. He has an interest in musical acoustics, vibration and acoustic measurement, audio signal processing, and physical modeling synthesis. His work focuses on musical instruments and creative audio effects. He holds an MA in music, science, and technology from Stanford, as well as a BS in physics and BMus in jazz from McGill University. He earned his PhD at Stanford’s Center for Computer Research in Music and Acoustics.

The new core faculty are:

Mitchell Gordon is an assistant professor in EECS. He is also a member of CSAIL. In his research, Gordon designs interactive systems and evaluation approaches that bridge principles of human-computer interaction with the realities of machine learning. His work has won awards at conferences in human-computer interaction and artificial intelligence, including a best paper award at CHI and an Oral at NeurIPS. Gordon received a BS from the University of Rochester, and MS and PhD from Stanford University, all in computer science.

Omar Khattab is an assistant professor in EECS. He is also a member of CSAIL. His work focuses on natural language processing, information retrieval, and AI systems. His research includes developing new algorithms and abstractions for declarative AI programming and for composing retrieval and reasoning. He received his BS from Carnegie Mellon University and his PhD from Stanford University, both in computer science.

Rachit Nigam will join EECS as an assistant professor in January 2026. He will also be a member of CSAIL and the Microsystems Technology Laboratories. He works on programming languages and computer architecture to address the design, verification, and usability challenges of specialized hardware. He was previously a visiting scholar at MIT. Nigam earned an MS and PhD in computer science from Cornell University.

For centuries, humans have sought to study the stars and celestial bodies, whether through observations made by naked eye or by telescopes on the ground and in space that can view the universe across nearly the entire electromagnetic spectrum. Each view unlocks new information about the denizens of space — X-ray pulsars, gamma-ray bursts — but one is still missing: the low-frequency radio sky.

Researchers from MIT Lincoln Laboratory, the MIT Haystack Observatory, and Lowell Observatory are working on a NASA-funded concept study called the Great Observatory for Long Wavelengths, or GO-LoW, that outlines a method to view the universe at as-of-yet unseen low frequencies using a constellation of thousands of small satellites. The wavelengths of these frequencies are 15 meters to several kilometers in length, which means they require a very big telescope in order to see clearly.

"GO-LoW will be a new kind of telescope, made up of many thousands of spacecraft that work together semi-autonomously, with limited input from Earth," says Mary Knapp, the principal investigator for GO-LoW at the MIT Haystack Observatory. "GO-LoW will allow humans to see the universe in a new light, opening up one of the very last frontiers in the electromagnetic spectrum."

The difficulty in viewing the low-frequency radio sky comes from Earth's ionosphere, a layer of the atmosphere that contains charged particles that prevent very low-frequency radio waves from passing through. Therefore, a space-based instrument is required to observe these wavelengths. Another challenge is that long-wavelength observations require correspondingly large telescopes, which would need to be many kilometers in length if built using traditional dish antenna designs. GO-LoW will use interferometry — a technique that combines signals from many spatially separated receivers that, when put together, will function as one large telescope — to obtain highly detailed data from exoplanets and other sources in space. A similar technique was used to make the first image of a black hole and, more recently, an image of the first known extrasolar radiation belts.

Melodie Kao, a member of the team from Lowell Observatory, says the data could reveal details about an exoplanet's makeup and potential for life. "[The radio wave aurora around an exoplanet] carries important information, such as whether or not the planet has a magnetic field, how strong it is, how fast the planet is rotating, and even hints about what's inside," she says. "Studying exoplanet radio aurorae and the magnetic fields that they trace is an important piece of the habitability puzzle, and it's a key science goal for GO-LoW."

Several recent trends and technology developments will make GO-LoW possible in the near future, such as the declining cost of mass-produced small satellites, the rise of mega-constellations, and the return of large, high-capacity launch vehicles like NASA's Space Launch System. Go-LoW would be the first mega-constellation that uses interferometry for scientific purposes.

The GO-LoW constellation will be built through several successive launches, each containing thousands of spacecraft. Once they reach low-Earth orbit, the spacecraft will be refueled before journeying on to their final destination — an Earth-sun Lagrange point where they will then be deployed. Lagrange points are regions in space where the gravitational forces of two large celestial bodies (like the sun and Earth) are in equilibrium, such that a spacecraft requires minimal fuel to maintain its position relative to the two larger bodies.  At this long distance from Earth (1 astronomical unit, or approximately 93 million miles), there will also be much less radio-frequency interference that would otherwise obscure GO-LoW’s sensitive measurements.

"GO-LoW will have a hierarchical architecture consisting of thousands of small listener nodes and a smaller number of larger communication and computation nodes (CCNs)," says Kat Kononov, a team member from Lincoln Laboratory's Applied Space Systems Group, who has been working with MIT Haystack staff since 2020, with Knapp serving as her mentor during graduate school. A node refers to an individual small satellite within the constellation. "The listener nodes are small, relatively simple 3U CubeSats — about the size of a loaf of bread — that collect data with their low-frequency antennas, store it in memory, and periodically send it to their communication and computation node via a radio link." In comparison, the CCNs are about the size of a mini-fridge.

The CCN will keep track of the positions of the listener nodes in their neighborhood; collect and reduce the data from their respective listener nodes (around 100 of them); and then transmit that data back to Earth, where more intensive data processing can be performed.

At full strength, with approximately 100,000 listener nodes, the GO-LoW constellation should be able to see exoplanets with magnetic fields in the solar neighborhood — within 5 to 10 parsecs — many for the very first time.

The GO-LoW research team recently published the results of their findings from Phase I of the study, which identified a type of advanced antenna called a vector sensor as the best type for this application. In 2024, Lincoln Laboratory designed a compact deployable version of the sensor suitable for use in space.

The team is now working on Phase II of the program, which is to build a multi-agent simulation of constellation operations.

"What we learned during the Phase I study is that the hard part for GO-LoW is not any specific technology … the hard part is the system: the system engineering and the autonomy to run the system," says Knapp. "So, how do we build this constellation such that it's a tractable problem? That's what we’re exploring in this next part of the study."

GO-LoW is one of many civil space programs at Lincoln Laboratory that aim to harness advanced technologies originally developed for national security to enable new space missions that support science and society. "By adapting these capabilities to serve new stakeholders, the laboratory helps open novel frontiers of discovery while building resilient, cost-effective systems that benefit the nation and the world," says Laura Kennedy, who is the deputy lead of Lincoln Laboratory's Civil Space Systems and Technology Office.

"Like landing on the moon in 1969, or launching Hubble in the 1990s, GO-LoW is envisioned to let us see something we've never seen before and generate scientific breakthroughs," says Kononov.

Go-LoW is a collaboration between Lincoln Laboratory, Haystack Observatory, and Lowell University, as well as Lenny Paritsky from LeafLabs and Jacob Turner from Cornell University.

It’s hard to keep up with the ever-changing trends of the fashion world. What’s “in” one minute is often out of style the next season, potentially causing you to re-evaluate your wardrobe.

Staying current with the latest fashion styles can be wasteful and expensive, though. Roughly 92 million tons of textile waste are produced annually, including the clothes we discard when they go out of style or no longer fit. But what if we could simply reassemble our clothes into whatever outfits we wanted, adapting to trends and the ways our bodies change?

A team of researchers at MIT’s Computer Science and Artificial Intelligence Laboratory (CSAIL) and Adobe are attempting to bring eco-friendly, versatile garments to life. Their new “Refashion” software system breaks down fashion design into modules — essentially, smaller building blocks — by allowing users to draw, plan, and visualize each element of a clothing item. The tool turns fashion ideas into a blueprint that outlines how to assemble each component into reconfigurable clothing, such as a pair of pants that can be transformed into a dress.

With Refashion, users simply draw shapes and place them together to develop an outline for adaptable fashion pieces. It’s a visual diagram that shows how to cut garments, providing a straightforward way to design things like a shirt with an attachable hood for rainy days. One could also create a skirt that can then be reconfigured into a dress for a formal dinner, or maternity wear that fits during different stages of pregnancy.

“We wanted to create garments that consider reuse from the start,” says Rebecca Lin, MIT Department of Electrical Engineering and Computer Science (EECS) PhD student, CSAIL and Media Lab researcher, and lead author on a paper presenting the project. “Most clothes you buy today are static, and are discarded when you no longer want them. Refashion instead makes the most of our garments by helping us design items that can be easily resized, repaired, or restyled into different outfits.”

Modules à la mode

The researchers conducted a preliminary user study where both designers and novices explored Refashion and were able to create garment prototypes. Participants assembled pieces such as an asymmetric top that could be extended into a jumpsuit, or remade into a formal dress, often within 30 minutes. These results suggest that Refashion has the potential to make prototyping garments more approachable and efficient. But what features might contribute to this ease of use?

Its interface first presents a simple grid in its “Pattern Editor” mode, where users can connect dots to outline the boundaries of a clothing item. It’s essentially drawing rectangular panels and specifying how different modules will connect to each other.

Users can customize the shape of each component, create a straight design for garments (which might be useful for less form-fitting items, like chinos) or perhaps tinkering with one of Refashion’s templates. A user can edit pre-designed blueprints for things like a T-shirt, fitted blouse, or trousers.

Another, more creative route is to change the design of individual modules. One can choose the “pleat” feature to fold a garment over itself, similar to an accordion, for starters. It’s a useful way to design something like a maxi dress. The “gather” option adds an artsy flourish, where a garment is crumpled together to create puffy skirts or sleeves. A user might even go with the “dart” module, which removes a triangular piece from the fabric. It allows for shaping a garment at the waist (perhaps for a pencil skirt) or tailor to the upper body (fitted shirts, for instance).

While it might seem that each of these components needs to be sewn together, Refashion enables users to connect garments through more flexible, efficient means. Edges can be seamed together via double-sided connectors such as metal snaps (like the buttons used to close a denim jacket) or Velcro dots. A user could also fasten them in pins called brads, which have a pointed side that they stick through a hole and split into two “legs” to attach to another surface; it’s a handy way to secure, say, a picture on a poster board. Both connective methods make it easy to reconfigure modules, should they be damaged or a “fit check” calls for a new look.

As a user designs their clothing piece, the system automatically creates a simplified diagram of how it can be assembled. The pattern is divided into numbered blocks, which is dragged onto different parts of a 2D mannequin to specify the position of each component. The user can then simulate how their sustainable clothing will look on 3D models of a range of body types (one can also upload a model).

Finally, a digital blueprint for sustainable clothing can extend, shorten, or combine with other pieces. Thanks to Refashion, a new piece could be emblematic of a potential shift in fashion: Instead of buying new clothes every time we want a new outfit, we can simply reconfigure existing ones. Yesterday’s scarf could be today’s hat, and today’s T-shirt could be tomorrow’s jacket.

“Rebecca’s work is at an exciting intersection between computation and art, craft, and design,” says MIT EECS professor and CSAIL principal investigator Erik Demaine, who advises Lin. “I’m excited to see how Refashion can make custom fashion design accessible to the wearer, while also making clothes more reusable and sustainable.”

Constant change

While Refashion presents a greener vision for the future of fashion, the researchers note that they’re actively improving the system. They intend to revise the interface to support more durable items, stepping beyond standard prototyping fabrics. Refashion may soon support other modules, like curved panels, as well. The CSAIL-Adobe team may also evaluate whether their system can use as few materials as possible to minimize waste, and whether it can help “remix” old store-bought outfits.

Lin also plans to develop new computational tools that help designers create unique, personalized outfits using colors and textures. She’s exploring how to design clothing by patchwork — essentially, cutting out small pieces from materials like decorative fabrics, recycled denim, and crochet blocks and assembling them into a larger item.

“This is a great example of how computer-aided design can also be key in supporting more sustainable practices in the fashion industry,” says Adrien Bousseau, a senior researcher at Inria Centre at Université Côte d'Azur who wasn’t involved in the paper. “By promoting garment alteration from the ground up, they developed a novel design interface and accompanying optimization algorithm that helps designers create garments that can undergo a longer lifetime through reconfiguration. While sustainability often imposes additional constraints on industrial production, I am confident that research like the one by Lin and her colleagues will empower designers in innovating despite these constraints.”

Lin wrote the paper with Adobe Research scientists Michal Lukáč and Mackenzie Leake, who is the paper’s senior author and a former CSAIL postdoc. Their work was supported, in part, by the MIT Morningside Academy for Design, an MIT MAKE Design-2-Making Mini-Grant, and the Natural Sciences and Engineering Research Council of Canada. The researchers presented their work recently at the ACM Symposium on User Interface Software and Technology.

Before cells can divide, they first need to replicate all of their chromosomes, so that each of the daughter cells can receive a full set of genetic material. Until now, scientists had believed that as division occurs, the genome loses the distinctive 3D internal structure that it typically forms.

Once division is complete, it was thought, the genome gradually regains that complex, globular structure, which plays an essential role in controlling which genes are turned on in a given cell.

However, a new study from MIT shows that in fact, this picture is not fully accurate. Using a higher-resolution genome mapping technique, the research team discovered that small 3D loops connecting regulatory elements and genes persist in the genome during cell division, or mitosis.

“This study really helps to clarify how we should think about mitosis. In the past, mitosis was thought of as a blank slate, with no transcription and no structure related to gene activity. And we now know that that’s not quite the case,” says Anders Sejr Hansen, an associate professor of biological engineering at MIT. “What we see is that there’s always structure. It never goes away.”

The researchers also discovered that these regulatory loops appear to strengthen when chromosomes become more compact in preparation for cell division. This compaction brings genetic regulatory elements closer together and encourages them to stick together. This may help cells “remember” interactions present in one cell cycle and carry it to the next one.

“The findings help to bridge the structure of the genome to its function in managing how genes are turned on and off, which has been an outstanding challenge in the field for decades,” says Viraat Goel PhD ’25, the lead author of the study.

Hansen and Edward Banigan, a research scientist in MIT’s Institute for Medical Engineering and Science, are the senior authors of the paper, which appears today in Nature Structural and Molecular Biology. Leonid Mirny, a professor in MIT’s Institute for Medical Engineering and Science and the Department of Physics, and Gerd Blobel, a professor at the Perelman School of Medicine at the University of Pennsylvania, are also authors of the study.

A surprising finding

Over the past 20 years, scientists have discovered that inside the cell nucleus, DNA organizes itself into 3D loops. While many loops enable interactions between genes and regulatory regions that may be millions of base pairs away from each other, others are formed during cell division to compact chromosomes. Much of the mapping of these 3D structures has been done using a technique called Hi-C, originally developed by a team that included MIT researchers and was led by Job Dekker at the University of Massachusetts Chan Medical School. To perform Hi-C, researchers use enzymes to chop the genome into many small pieces and biochemically link pieces that are near each other in 3D space within the cell’s nucleus. They then determine the identities of the interacting pieces by sequencing them.

However, that technique doesn’t have high enough resolution to pick out all specific interactions between genes and regulatory elements such as enhancers. Enhancers are short sequences of DNA that can help to activate the transcription of a gene by binding to the gene’s promoter — the site where transcription begins.

In 2023, Hansen and others developed a new technique that allows them to analyze 3D genome structures with 100 to 1,000 times greater resolution than was previously possible. This technique, known as Region-Capture Micro-C (RC-MC), uses a different enzyme that cuts the genome into small fragments of similar size. It also focuses on a smaller segment of the genome, allowing for high-resolution 3-D mapping of a targeted genome region.

Using this technique, the researchers were able to identify a new kind of genome structure that hadn’t been seen before, which they called “microcompartments.” These are tiny highly connected loops that form when enhancers and promoters located near each other stick together.

In that paper, experiments revealed that these loops were not formed by the same mechanisms that form other genome structures, but the researchers were unable to determine exactly how they do form. In hopes of answering that question, the team set out to study cells as they undergo cell division. During mitosis, chromosomes become much more compact, so that they can be duplicated, sorted, and divvied up between two daughter cells. As this happens, larger genome structures called A/B compartments and topologically associating domains (TADs) disappear completely.

The researchers believed that the microcompartments they had discovered would also disappear during mitosis. By tracking cells through the entire cell division process, they hoped to learn how the microcompartments appear after mitosis is completed.

“During mitosis, it has been thought that almost all gene transcription is shut off. And before our paper, it was also thought that all 3D structure related to gene regulation was lost and replaced by compaction. It’s a complete reset every cell cycle,” Hansen says.

However, to their surprise, the researchers found that microcompartments could still be seen during mitosis, and in fact they become more prominent as the cell goes through cell division.

“We went into this study thinking, well, the one thing we know for sure is that there’s no regulatory structure in mitosis, and then we accidentally found structure in mitosis,” Hansen says.

Using their technique, the researchers also confirmed that larger structures such as A/B compartments and TADs do disappear during mitosis, as had been seen before.

“This study leverages the unprecedented genomic resolution of the RC-MC assay to reveal new and surprising aspects of mitotic chromatin organization, which we have overlooked in the past using traditional 3C-based assays. The authors reveal that, contrary to the well-described dramatic loss of TADs and compartmentalization during mitosis, fine-scale “microcompartments” — nested interactions between active regulatory elements — are maintained or even transiently strengthened,” says Effie Apostolou, an associate professor of molecular biology in medicine at Weill Cornell Medicine, who was not involved in the study.

A spike in transcription

The findings may offer an explanation for a spike in gene transcription that usually occurs near the end of mitosis, the researchers say. Since the 1960s, it had been thought that transcription ceased completely during mitosis, but in 2016 and 2017, a few studies showed that cells undergo a brief spike of transcription, which is quickly suppressed until the cell finishes dividing.

In their new study, the MIT team found that during mitosis, microcompartments are more likely to be found near the genes that spike during cell division. They also discovered that these loops appear to form as a result of the genome compaction that occurs during mitosis. This compaction brings enhancers and promoters closer together, allowing them to stick together to form microcompartments.

Once formed, the loops that constitute microcompartments may activate gene transcription somewhat by accident, which is then shut off by the cell. When the cell finishes dividing, entering a state known as G1, many of these small loops become weaker or disappear.

“It almost seems like this transcriptional spiking in mitosis is an undesirable accident that arises from generating a uniquely favorable environment for microcompartments to form during mitosis,” Hansen says. “Then, the cell quickly prunes and filters many of those loops out when it enters G1.”

Because chromosome compaction can also be influenced by a cell’s size and shape, the researchers are now exploring how variations in those features affect the structure of the genome and in turn, gene regulation.

“We are thinking about some natural biological settings where cells change shape and size, and whether we can perhaps explain some 3D genome changes that previously lack an explanation,” Hansen says. “Another key question is how does the cell then pick what are the microcompartments to keep and what are the microcompartments to remove when you enter G1, to ensure fidelity of gene expression?”

The research was funded in part by the National Institutes of Health, a National Science Foundation CAREER Award, the Gene Regulation Observatory of the Broad Institute, a Pew-Steward Scholar Award for Cancer Research, the Mathers Foundation, the MIT Westaway Fund, the Bridge Project of the Koch Institute and Dana-Farber/Harvard Cancer Center, and the Koch Institute Support (core) Grant from the National Cancer Institute.

Two leading experts in the field of carbon capture and sequestration (CCS) — Howard J. Herzog, a senior research engineer in the MIT Energy Initiative, and Niall Mac Dowell, a professor in energy systems engineering at Imperial College London — explore methods for removing carbon dioxide already in the atmosphere in their new book, “Carbon Removal.” Published in October, the book is part of the Essential Knowledge series from the MIT Press, which consists of volumes “synthesizing specialized subject matter for nonspecialists” and includes Herzog’s 2018 book, “Carbon Capture.”

Burning fossil fuels, as well as other human activities, cause the release of carbon dioxide (CO2) into the atmosphere, where it acts like a blanket that warms the Earth, resulting in climate change. Much attention has focused on mitigation technologies that reduce emissions, but in their book, Herzog and Mac Dowell have turned their attention to “carbon dioxide removal” (CDR), an approach that removes carbon already present in the atmosphere.

In this new volume, the authors explain how CO2 naturally moves into and out of the atmosphere and present a brief history of carbon removal as a concept for dealing with climate change. They also describe the full range of “pathways” that have been proposed for removing CO2 from the atmosphere. Those pathways include engineered systems designed for “direct air capture” (DAC), as well as various “nature-based” approaches that call for planting trees or taking steps to enhance removal by biomass or the oceans. The book offers easily accessible explanations of the fundamental science and engineering behind each approach.

The authors compare the “quality” of the different pathways based on the following metrics:

Accounting. For public acceptance of any carbon-removal strategy, the authors note, the developers need to get the accounting right — and that’s not always easy. “If you’re going to spend money to get CO2 out of the atmosphere, you want to get paid for doing it,” notes Herzog. It can be tricky to measure how much you have removed, because there’s a lot of CO2 going in and out of the atmosphere all the time. Also, if your approach involves, say, burning fossil fuels, you must subtract the amount of CO2 that’s emitted from the total amount you claim to have removed. Then there’s the timing of the removal. With a DAC device, the removal happens right now, and the removed CO2 can be measured. “But if I plant a tree, it’s going to remove CO2 for decades. Is that equivalent to removing it right now?” Herzog queries. How to take that factor into account hasn’t yet been resolved.

Permanence. Different approaches keep the CO2 out of the atmosphere for different durations of time. How long is long enough? As the authors explain, this is one of the biggest issues, especially with nature-based solutions, where events such as wildfires or pestilence or land-use changes can release the stored CO2 back into the atmosphere. How do we deal with that?

Cost. Cost is another key factor. Using a DAC device to remove CO2 costs far more than planting trees, but it yields immediate removal of a measurable amount of CO2 that can then be locked away forever. How does one monetize that trade-off?

Additionality. “You’re doing this project, but would what you’re doing have been done anyway?” asks Herzog. “Is your effort additional to business as usual?” This question comes into play with many of the nature-based approaches involving trees, soils, and so on.

Permitting and governance. These issues are especially important — and complicated — with approaches that involve doing things in the ocean. In addition, Herzog points out that some CCS projects could also achieve carbon removal, but they would have a hard time getting permits to build the pipelines and other needed infrastructure.

The authors conclude that none of the CDR strategies now being proposed is a clear winner on all the metrics. However, they stress that carbon removal has the potential to play an important role in meeting our climate change goals — not by replacing our emissions-reduction efforts, but rather by supplementing them. However, as Herzog and Mac Dowell make clear in their book, many challenges must be addressed to move CDR from today’s speculation to deployment at scale, and the book supports the wider discussion about how to move forward. Indeed, the authors have fulfilled their stated goal: “to provide an objective analysis of the opportunities and challenges for CDR and to separate myth from reality.”

At an age when many kids prefer to play games on their phones, 11-year-old Vivan Mirchandani wanted to explore physics videos. Little did he know that MIT Open Learning’s free online resources would change the course of his life. 

Now, at 16, Mirchandani is well on his way to a career as a physics scholar — all because he forged his own unconventional educational journey.

Nontraditional education has granted Mirchandani the freedom to pursue topics he’s personally interested in. This year, he wrote a paper on cosmology that proposes a new framework for understanding Einstein’s general theory of relativity. Other projects include expanding on fluid dynamics laws for cats, training an AI model to resemble the consciousness of his late grandmother, and creating his own digital twin. That’s in addition to his regular studies, regional science fairs, Model United Nations delegation, and a TEDEd Talk.

Mirchandani started down this path between the ages of 10 and 12, when he decided to read books and find online content about physics during the early Covid-19 lockdown in India. He was shocked to find that MIT Open Learning offers free course videos, lecture notes, exams, and other resources from the Institute on sites like MIT OpenCourseWare and the newly launched MIT Learn.

“My first course was 8.01 (Classical Mechanics), and it completely changed how I saw physics,” Mirchandani says. “Physics sounded like elegance. It’s the closest we’ve ever come to have a theory of everything.”

Experiencing “real learning”

Mirchandani discovered MIT Open Learning through OpenCourseWare, which offers free, online, open educational resources from MIT undergraduate and graduate courses. He says MIT Open Learning’s “academically rigorous” content prepares learners to ask questions and think like a scientist.

“Instead of rote memorization, I finally experienced real learning,” Mirchandani says. “OpenCourseWare was a holy grail. Without it, I would still be stuck on the basic concepts.”

Wanting to follow in the footsteps of physicists like Sir Isaac Newton, Albert Einstein, and Stephen Hawking, Mirchandani decided at age 12 he would sacrifice his grade point average to pursue a nontraditional educational path that gave him hands-on experience in science.

“The education system doesn’t prepare you for actual scientific research, it prepares you for exams,” Mirchandani says. “What draws me to MIT Open Learning and OpenCourseWare is it breaks the old model of education. It’s not about sitting in a lecture hall, it’s about access and experimentation.”

With guidance from his physics teacher, Mirchandani built his own curriculum using educational materials on MIT OpenCourseWare to progress from classical physics to computer science to quantum physics. He has completed more than 27 online MIT courses to date.

“The best part of OpenCourseWare is you get to study from the greatest institution in the world, and you don’t have to pay for it,” he says.

Innovating in the real world

6.0001 (Introduction to Computer Science and Programming Using Python) and slides from 2.06 (Fluid Dynamics) gave Mirchandani the foundation to help with the family business, Dynamech Engineers, which sells machinery for commercial snack production. Some of the recent innovations he has assisted with include a zero-oil frying technology that cuts 300 calories per kilogram, a gas-based heat exchange system, and a simplified, singular machine combining the processes of two separate machines. Using the modeling techniques he learned through MIT OpenCourseWare, Mirchandani designed how these products would work without losing efficiency.

But when you ask Mirchandani which achievement he is most proud of, he’ll say it’s being one of 35 students accepted for the inaugural RSI-India cohort, an academic program for high school students modeled after the Research Science Institute program co-sponsored by MIT and the Center for Excellence in Education. Competing against other Indian students who had perfect scores on their board exams and SATs, he didn’t expect to get in, but the program valued the practical research experience he was able to pursue thanks to the knowledge he gained from his external studies.

“None of it would have happened without MIT OpenCourseWare,” he says. “It’s basically letting curiosity get the better of us. If everybody does that, we’d have a better scientific community.”

Say a person takes their French Bulldog, Bowser, to the dog park. Identifying Bowser as he plays among the other canines is easy for the dog-owner to do while onsite.

But if someone wants to use a generative AI model like GPT-5 to monitor their pet while they are at work, the model could fail at this basic task. Vision-language models like GPT-5 often excel at recognizing general objects, like a dog, but they perform poorly at locating personalized objects, like Bowser the French Bulldog.    

To address this shortcoming, researchers from MIT and the MIT-IBM Watson AI Lab have introduced a new training method that teaches vision-language models to localize personalized objects in a scene.

Their method uses carefully prepared video-tracking data in which the same object is tracked across multiple frames. They designed the dataset so the model must focus on contextual clues to identify the personalized object, rather than relying on knowledge it previously memorized.

When given a few example images showing a personalized object, like someone’s pet, the retrained model is better able to identify the location of that same pet in a new image.

Models retrained with their method outperformed state-of-the-art systems at this task. Importantly, their technique leaves the rest of the model’s general abilities intact.

This new approach could help future AI systems track specific objects across time, like a child’s backpack, or localize objects of interest, such as a species of animal in ecological monitoring. It could also aid in the development of AI-driven assistive technologies that help visually impaired users find certain items in a room.

“Ultimately, we want these models to be able to learn from context, just like humans do. If a model can do this well, rather than retraining it for each new task, we could just provide a few examples and it would infer how to perform the task from that context. This is a very powerful ability,” says Jehanzeb Mirza, an MIT postdoc and senior author of a paper on this technique.

Mirza is joined on the paper by co-lead authors Sivan Doveh, a graduate student at Weizmann Institute of Science; and Nimrod Shabtay, a researcher at IBM Research; James Glass, a senior research scientist and the head of the Spoken Language Systems Group in the MIT Computer Science and Artificial Intelligence Laboratory (CSAIL); and others. The work will be presented at the International Conference on Computer Vision.

An unexpected shortcoming

Researchers have found that large language models (LLMs) can excel at learning from context. If they feed an LLM a few examples of a task, like addition problems, it can learn to answer new addition problems based on the context that has been provided.

A vision-language model (VLM) is essentially an LLM with a visual component connected to it, so the MIT researchers thought it would inherit the LLM’s in-context learning capabilities. But this is not the case.

“The research community has not been able to find a black-and-white answer to this particular problem yet. The bottleneck could arise from the fact that some visual information is lost in the process of merging the two components together, but we just don’t know,” Mirza says.

The researchers set out to improve VLMs abilities to do in-context localization, which involves finding a specific object in a new image. They focused on the data used to retrain existing VLMs for a new task, a process called fine-tuning.

Typical fine-tuning data are gathered from random sources and depict collections of everyday objects. One image might contain cars parked on a street, while another includes a bouquet of flowers.

“There is no real coherence in these data, so the model never learns to recognize the same object in multiple images,” he says.

To fix this problem, the researchers developed a new dataset by curating samples from existing video-tracking data. These data are video clips showing the same object moving through a scene, like a tiger walking across a grassland.

They cut frames from these videos and structured the dataset so each input would consist of multiple images showing the same object in different contexts, with example questions and answers about its location.

“By using multiple images of the same object in different contexts, we encourage the model to consistently localize that object of interest by focusing on the context,” Mirza explains.

Forcing the focus

But the researchers found that VLMs tend to cheat. Instead of answering based on context clues, they will identify the object using knowledge gained during pretraining.

For instance, since the model already learned that an image of a tiger and the label “tiger” are correlated, it could identify the tiger crossing the grassland based on this pretrained knowledge, instead of inferring from context.

To solve this problem, the researchers used pseudo-names rather than actual object category names in the dataset. In this case, they changed the name of the tiger to “Charlie.”

“It took us a while to figure out how to prevent the model from cheating. But we changed the game for the model. The model does not know that ‘Charlie’ can be a tiger, so it is forced to look at the context,” he says.

The researchers also faced challenges in finding the best way to prepare the data. If the frames are too close together, the background would not change enough to provide data diversity.

In the end, finetuning VLMs with this new dataset improved accuracy at personalized localization by about 12 percent on average. When they included the dataset with pseudo-names, the performance gains reached 21 percent.

As model size increases, their technique leads to greater performance gains.

In the future, the researchers want to study possible reasons VLMs don’t inherit in-context learning capabilities from their base LLMs. In addition, they plan to explore additional mechanisms to improve the performance of a VLM without the need to retrain it with new data.

“This work reframes few-shot personalized object localization — adapting on the fly to the same object across new scenes — as an instruction-tuning problem and uses video-tracking sequences to teach VLMs to localize based on visual context rather than class priors. It also introduces the first benchmark for this setting with solid gains across open and proprietary VLMs. Given the immense significance of quick, instance-specific grounding — often without finetuning — for users of real-world workflows (such as robotics, augmented reality assistants, creative tools, etc.), the practical, data-centric recipe offered by this work can help enhance the widespread adoption of vision-language foundation models,” says Saurav Jha, a postdoc at the Mila-Quebec Artificial Intelligence Institute, who was not involved with this work.

Additional co-authors are Wei Lin, a research associate at Johannes Kepler University; Eli Schwartz, a research scientist at IBM Research; Hilde Kuehne, professor of computer science at Tuebingen AI Center and an affiliated professor at the MIT-IBM Watson AI Lab; Raja Giryes, an associate professor at Tel Aviv University; Rogerio Feris, a principal scientist and manager at the MIT-IBM Watson AI Lab; Leonid Karlinsky, a principal research scientist at IBM Research; Assaf Arbelle, a senior research scientist at IBM Research; and Shimon Ullman, the Samy and Ruth Cohn Professor of Computer Science at the Weizmann Institute of Science.

This research was funded, in part, by the MIT-IBM Watson AI Lab.

A decade-plus collaboration between MIT’s AgeLab and the Toyota Motor Corporation is recognized as a key contributor to advancements in automotive safety and human-machine interaction. Through the AgeLab at the MIT Center for Transportation and Logistics (CTL), researchers have collected and analyzed vast real-world driving datasets that have helped inform Toyota’s vehicle design and safety systems.

Toyota recently marked the completion of its 100th project through the Collaborative Safety Research Center (CSRC), celebrating MIT’s role in shaping technologies that enhance driver-assistance features and continue to forge the path for automated mobility. A key foundation for the 100th project is CSRC’s ongoing support for MIT CTL’s Advanced Vehicle Technology (AVT) Consortium.

Real-world data, real-world impact

“AVT was conceptualized over a decade ago as an academic-industry partnership to promote shared investment in real-world, naturalistic data collection, analysis, and collaboration — efforts aimed at advancing safer, more convenient, and more comfortable automobility,” says Bryan Reimer, founder and co-director of AVT. “Since its founding, AVT has drawn together over 25 organizations — including vehicle manufacturers, suppliers, insurers, and consumer research groups — to invest in understanding how automotive technologies function, how they influence driver behavior, and where further innovation is needed. This work has enabled stakeholders like Toyota to make more-informed decisions in product development and deployment.”

“CSRC’s 100th project marks a significant milestone in our collaboration,” Reimer adds. “We deeply value CSRC’s sustained investment, and commend the organization’s commitment to global industry impact and the open dissemination of research to advance societal benefit.”

“Toyota, through its Collaborative Safety Research Center, is proud to be a founding member of the AVT Consortium,” says Jason Hallman, senior manager of Toyota CSRC. “Since 2011, CSRC has collaborated with researchers such as AVT and MIT AgeLab on projects that help inform future products and policy, and to promote a future safe mobility society for all. The AVT specifically has helped us to study the real-world use of several vehicle technologies now available.”

Among these technologies are lane-centering assistance and adaptive cruise control — widely-used technologies that benefit from an understanding of how drivers interact with automation. “AVT uniquely combines vehicle and driver data to help inform future products and highlight the interplay between the performance of these features and the drivers using them,” says Josh Domeyer, principal scientist at CSRC.

Influencing global standards and Olympic-scale innovation

Insights from MIT’s pedestrian-driver interaction research with CSRC also helped shape Toyota’s automated vehicle communication systems. “These data helped develop our foundational understanding that drivers and pedestrians use their movements to communicate during routine traffic encounters,” said Domeyer. “This concept informed the deployment of Toyota’s e-Palette at the Tokyo Olympics, and it has been captured as a best practice in an ISO standard for automated driving system communication.”

The AVT Consortium's naturalistic driving datasets continue to serve as a foundation for behavioral safety strategies. From identifying moments of distraction to understanding how drivers multitask behind the wheel, the work is guiding subtle but impactful design considerations.

“By studying the natural behaviors of drivers and their contexts in the AVT datasets, we hope to identify new ways to encourage safe habits that align with customer preferences,” Domeyer says. “These can include subtle nudges, or modifications to existing vehicle features, or even communication and education partnerships outside of Toyota that reinforce these safe driving habits.”

Professor Yossi Sheffi, director of MIT CTL, comments, “This partnership exemplifies the impact of MIT collaborative research on industry to make real, practical innovation possible.” 

A model for industry-academic collaboration

Founded in 2015, the AVT Consortium brings together automotive manufacturers, suppliers, and insurers to accelerate research in driver behavior, safety, and the transition toward automated systems. The consortium’s interdisciplinary approach — integrating engineering, human factors, and data science — has helped generate one of the world’s most unique and actionable real-world driving datasets.

As Toyota celebrates its research milestone, MIT reflects on a partnership that exemplifies the power of industry-academic collaboration to shape safer, smarter mobility.