Self-organizing “pencil beam” laser could help scientists design brain-targeted therapies

MIT researchers discovered a paradoxical phenomenon in optical physics that could enable a new bioimaging method that’s faster and higher-resolution than existing technology.

They discovered that, under the right conditions, a chaotic mess of laser light can spontaneously self-organize into a highly focused “pencil beam.”

Using this self-organized pencil beam, the researchers captured 3D images of the human blood-brain barrier 25 times faster than the gold-standard method, while maintaining comparable resolution.

By showing individual cells absorbing drugs in real-time, this technology could help scientists test whether new drugs for neurodegenerative disease like Alzheimer’s or ALS reach their targets in the brain, with greater speed and resolution.

“The common belief in the field is that if you crank up the power in this type of laser, the light will inevitably become chaotic. But we proved that this is not the case. We followed the evidence, embraced the uncertainty, and found a way to let the light organize itself into a novel solution for bioimaging,” says Sixian You, assistant professor in the MIT Department of Electrical Engineering and Computer Science (EECS), a member of the Research Laboratory for Electronics, and senior author of a paper on this imaging technique.

She is joined on the paper by lead author Honghao Cao, an EECS graduate student; EECS graduate students Li-Yu Yu and Kunzan Liu; postdocs Sarah Spitz, Francesca Michela Pramotton, and Federico Presutti; Zhengyu Zhang PhD ’24; Subhash Kulkarni, an assistant professor at Harvard University and the Beth Israel Deaconess Medical Center; and Roger Kamm, the Cecil and Ida Green Distinguished Professor of Biological and Mechanical Engineering at MIT. The paper appears today in Nature Methods.

A surprising finding

The discovery began with an observation that initially puzzled the researchers.

The team previously developed a precise fiber shaper, a device that enables them to carefully tune the laser light shining through a multimode optical fiber. This type of optical fiber can carry a significant amount of power.

Cao was pushing the multimode fiber toward its limit to see how much power it could take.

Typically, the more power one pumps into the laser, the more disordered and scattered the beam of light becomes due to imperfections in the fiber.

But Cao observed that, as he increased the power almost to the point where it would burn the fiber, the light did the opposite of what was expected: It collapsed into a single, needle-sharp beam.

“Disorder is intrinsic to these fibers. The light engineering you typically need to do to overcome that disorder, especially at high power, is a longstanding hassle. But with this self-organization, you can get a stable, ultrafast pencil beam without the need for custom beam-shaping components,” You says.

To replicate this phenomenon, the researchers found they had to satisfy two simple, but precise conditions.

First, the laser must enter the fiber at a perfect, zero-degree angle. This is a more rigorous requirement than is usually used for these types of fibers. Second, the power must be dialed up until the light begins to interact with the glass of the fiber itself.

“At this critical power, the nonlinearity can counter the intrinsic disorder, creating a balance that transforms the input beam into a self-organized pencil beam,” Cao explains.

Typically, researchers conduct these experiments at much lower power levels for fear of destroying the fiber, in which case they wouldn’t see this self-organization. In addition, such precise on-axis alignment isn’t typically necessary since a multimode fiber can carry so much power.

But taken together, these two techniques can generate a stable pencil-beam without any complicated light engineering methods.

“That is the charm of this method — you could do this with a normal, optical setup and without much domain expertise,” You says.

A better beam

When the researchers performed characterization experiments of this pencil beam, it was more stable and high-resolution than many similar beams. Other beams often suffer from “sidelobes” — blurry halos of light that can distort images.

Their beam was more pristine and tightly focused.

Building on those experiments, the researchers demonstrated the use of this pencil-beam in biomedical imaging of the human blood-brain barrier.

This barrier is a tightly packed layer of cells that protects the brain from toxins, but it also blocks many medicines. Scientists and clinicians often want to see how drugs flow inside the vasculature of the blood-brain barrier and whether they reach their targets within the brain.

But with standard optical settings, the best one can do is capture one 2D section of the vasculature at a time, and then repeat the process multiple times to generate a fuller image, You explains.

Using this new technique, the researchers created an ultrafast, high-precision pencil beam that enabled them to dynamically track how cells absorb proteins in real-time.

“The pharmaceutical industry is especially interested in using human-based models to screen for drugs that effectively cross the barrier, as animal models often fail to predict what happens in humans. That this new method doesn’t require the cells to have a fluorescent tag is a game-changer. For the first time, we can now visualize the time-dependent entry of drugs into the brain and even identify the rate at which specific cell types internalize the drug,” says Kamm.

“Importantly, however, this approach is not limited to the blood-brain barrier but enables time-resolved tracking of diverse compounds and molecular targets across engineered tissue models, providing a powerful tool for biological engineering,” Spitz adds.

The team captured cellular-level 3D images that were higher quality than with other methods, and generated these images about 25 times faster.

“Usually, you have a tradeoff between image resolution and depth of focus — you can only probe so far at a time. But with our method, we can overcome this tradeoff by creating a pencil-beam with both high resolution and a large depth of focus,” You says.

In the future, the researchers want to better understand the fundamental physics of the pencil-beam and the mechanisms behind its self-organization. They also plan to apply the technique to other scenarios, such as imaging neurons in the brain, and work toward commercializing the technology.

“You’s group realized this beam that concentrates energy in time and space could be valuable for microscopy techniques that depend on the intensity of the light that illuminates the sample. They demonstrated just that and found advantages over ordinary laser beams for imaging. It will be scientifically interesting to fully understand the creation of the new pencil beams, which could find use in a variety of imaging applications,” says Frank Wise, the Samuel B. Eckert Professor of Engineering Emeritus at Cornell University, who was not involved with this work.

This work was funded, in part, by MIT startup funds, the National Science Foundation (NSF), the Silicon Valley Community Foundation, Diacomp Foundation, the Harvard Digestive Disease Core, a MathWorks Fellowship, and the Claude E. Shannon Award.

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A faster way to estimate AI power consumption

Due to the explosive growth of artificial intelligence, it is estimated that data centers will consume up to 12 percent of total U.S. electricity by 2028, according to the Lawrence Berkeley National Laboratory. Improving data center energy efficiency is one way scientists are striving to make AI more sustainable.

Toward that goal, researchers from MIT and the MIT-IBM Watson AI Lab developed a rapid prediction tool that tells data center operators how much power will be consumed by running a particular AI workload on a certain processor or AI accelerator chip.

Their method produces reliable power estimates in a few seconds, unlike traditional modeling techniques that can take hours or even days to yield results. Moreover, their prediction tool can be applied to a wide range of hardware configurations — even emerging designs that haven’t been deployed yet.

Data center operators could use these estimates to effectively allocate limited resources across multiple AI models and processors, improving energy efficiency. In addition, this tool could allow algorithm developers and model providers to assess potential energy consumption of a new model before they deploy it.

“The AI sustainability challenge is a pressing question we have to answer. Because our estimation method is fast, convenient, and provides direct feedback, we hope it makes algorithm developers and data center operators more likely to think about reducing energy consumption,” says Kyungmi Lee, an MIT postdoc and lead author of a paper on this technique.

She is joined on the paper by Zhiye Song, an electrical engineering and computer science (EECS) graduate student; Eun Kyung Lee and Xin Zhang, research managers at IBM Research and the MIT-IBM Watson AI Lab; Tamar Eilam, IBM Fellow, chief scientist of sustainable computing at IBM Research, and a member of the MIT-IBM Watson AI Lab; and senior author Anantha P. Chandrakasan, MIT provost, Vannevar Bush Professor of Electrical Engineering and Computer Science, and a member of the MIT-IBM Watson AI Lab. The research is being presented this week at the IEEE International Symposium on Performance Analysis of Systems and Software.

Expediting energy estimation

Inside a data center, thousands of powerful graphics processing units (GPUs) perform operations to train and deploy AI models. The power consumption of a particular GPU will vary based on its configuration and the workload it is handling.

Many traditional methods used to predict energy consumption involve breaking a workload into individual steps and emulating how each module inside the GPU is being utilized one step at a time. But AI workloads like model training and data preprocessing are extremely large and can take hours or even days to simulate in this manner.

“As an operator, if I want to compare different algorithms or configurations to find the most energy-efficient manner to proceed, if a single emulation is going to take days, that is going to become very impractical,” Lee says.

To speed up the prediction process, the MIT researchers sought to use less-detailed information that could be estimated faster. They found that AI workloads often have many repeatable patterns. They could use these patterns to generate the information needed for reliable but quick power estimation.

In many cases, algorithm developers write programs to run as efficiently as possible on a GPU. For instance, they use well-structured optimizations to distribute the work across parallel processing cores and move chunks of data around in the most efficient manner.

“These optimizations that software developers use create a regular structure, and that is what we are trying to leverage,” explains Lee.

The researchers developed a lightweight estimation model, called EnergAIzer, that captures the power usage pattern of a GPU from those optimizations.

An accurate assessment

But while their estimation was fast, the researchers found that it didn’t take all energy costs into account. For instance, every time a GPU runs a program, there is a fixed energy cost required for setting up and configurating that program. Then each time the GPU runs an operation on a chunk of data, an additional energy cost must be paid.

Due to fluctuations in the hardware or conflicts in accessing or moving data, a GPU might not be able to use all available bandwidth, slowing operations down and drawing more energy over time.

To include these additional costs and variances, the researchers gathered real measurements from GPUs to generate correction terms they applied to their estimation model.

“This way, we can get a fast estimation that is also very accurate,” she says.

In the end, a user can provide their workload information, like the AI model they want to run and the number and length of user inputs to process, and EnergAIzer will output an energy consumption estimation in a matter of seconds.

The user can also change the GPU configuration or adjust the operating speed to see how such design choices impact the overall power consumption.

When the researchers tested EnergAIzer using real AI workload information from actual GPUs, it could estimate the power consumption with only about 8 percent error, which is comparable to traditional methods that can take hours to produce results.

Their method could also be used to predict the power consumption of future GPUs and emerging device configurations, as long as the hardware doesn’t change drastically in a short amount of time.

In the future, the researchers want to test EnergAIzer on the newest GPU configurations and scale the model up so it can be applied to many GPUs that are collaborating to run a workload.

“To really make an impact on sustainability, we need a tool that can provide a fast energy estimation solution across the stack, for hardware designers, data center operators, and algorithm developers, so they can all be more aware of power consumption. With this tool, we’ve taken one step toward that goal,” Lee says.

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

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The power of “and” in energy and climate entrepreneurship

A supportive ecosystem is a cornerstone in entrepreneurship, according to Georgina Campbell Flatter, the CEO of Greentown Labs. “If we really want to be driving the most transformational technologies to scale at a speed in which we need them to happen for our planet, we need to be thinking about the ecosystem that we build around it.” During a seminar titled MITEI Presents: Advancing the Energy Transition, Campbell Flatter spoke of “the power of ‘and’” — the importance of multiple people, companies, and solutions collaborating to advance energy and climate solutions — and how that underpins Greentown Labs’ mission. “Innovation is a team sport. No one can go alone,” she said.

Creating these ecosystems is paramount at Greentown Labs, the world’s largest energy and climate incubator. “Through the lens of Greentown, we think about the power of ‘and’ through how we can work together better in the ecosystems where we have physical presence, but also how we can connect better across ecosystems,” said Campbell Flatter. The concept of "and" also exists in energy and climate, innovation and deployment, science and entrepreneurship, and competitiveness and collaboration, she said. Campbell Flatter feels this expansive lens is especially important in our increasingly polarized world.

At its core, Greentown Labs is a place to cluster innovators together. “We have to be very intentional about how we support and accelerate and help those entrepreneurs,” said Campbell Flatter. There is a science behind this “innovation infrastructure” that involves not only bringing creative minds together, but also removing friction so startups can move faster. The majority of this friction exists in the gaps between innovation and deployment, often referred to as the “valleys of death.” The first valley of death happens between idea and prototype; the second valley of death happens between prototype and the first commercial pilot. Greentown often asks where their ecosystems can be most helpful, which has led them to focus on helping entrepreneurs bridge that second valley, according to Campbell Flatter.

“Entrepreneurs at the stage where they can’t quite afford space on their own, and maybe it takes six to 12 months to figure out the permitting anyway, come to Greentown,” said Campbell Flatter. “We’re actively thinking about the customers, the capital, the infrastructure needs that you have in order for you to move your way through this second valley.”

Part of Greentown’s decision to focus on the second valley came from MIT’s unique ability to bring innovators across the first valley of death — an ability that Campbell Flatter deemed “truly world class.” Referencing startups born from universities like MIT and Harvard, Campbell Flatter said “they're far more likely to be successful and scale because of the ecosystem they’re surrounded in. You’re getting feedback constantly from your peers, you’re getting support and mentorship — that all matters for the ecosystem.”

MIT also helps build this ecosystem by attracting innovators to the area. “Thirty percent of our entrepreneurs at Greentown are coming from out of state and moving to Massachusetts,” she said. “One, because Greentown’s a great home for them, but two, because of MIT and the talent that they can source from the ecosystem, which they are well aware of, and the knowledge, IP [intellectual property], and credibility.”

Not only is the symbiotic relationship between MIT and Greentown a powerful entrepreneurial ecosystem, but MIT has also been instrumental in Campbell Flatter’s own journey toward her current body of work. After completing her master’s degree in materials science at Oxford University, she graduated from the MIT Technology and Policy Program. Campbell Flatter credited her time as a graduate student at MIT for giving her an appreciation for how hard it is to commercialize technology, and for the importance of ecosystems, and for giving her an early sense of how energy and climate would define this century. “I think it is really important to recognize the intentionality behind MIT’s commitment to energy and climate,” said Campbell Flatter.

While at MIT, she ran the third iteration of the MIT Clean Energy Prize, advocating for the inclusion of a non-renewables chapter of the prize because she saw “how important it was to continue to decarbonize and bring efficiencies to the traditional energy sectors while we work on all these amazing new energy initiatives.” Greentown has put this into practice through their wide network of industry partners. 

“I guess this early lesson I took from MIT was this idea that we must embrace the power of ‘and,’” said Campbell Flatter. “It slows innovation down when we don’t embrace and work together.”

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

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MIT scientists build the world’s largest collection of Olympiad-level math problems, and open it to everyone

Every year, the countries competing in the International Mathematical Olympiad (IMO) arrive with a booklet of their best, most original problems. Those booklets get shared among delegations, then quietly disappear. No one had ever collected them systematically, cleaned them, and made them available, not for AI researchers testing the limits of mathematical reasoning, and not for the students around the world training for these competitions largely on their own.

Researchers at MIT’s Computer Science and Artificial Intelligence Laboratory (CSAIL), King Abdullah University of Science and Technology (KAUST), and the company HUMAIN have now done exactly that.

MathNet is the largest high-quality dataset of proof-based math problems ever created. Comprising more than 30,000 expert-authored problems and solutions spanning 47 countries, 17 languages, and 143 competitions, it is five times larger than the next-biggest dataset of its kind. The work will be presented at the International Conference on Learning Representations (ICLR) in Brazil later this month.

What makes MathNet different is not only its size, but its breadth. Previous Olympiad-level datasets draw almost exclusively from competitions in the United States and China. MathNet spans dozens of countries across six continents, covers 17 languages, includes both text- and image-based problems and solutions, and spans four decades of competition mathematics. The goal is to capture the full range of mathematical perspectives and problem-solving traditions that exist across the global math community, not just the most visible ones.

"Every country brings a booklet of its most novel and most creative problems," says Shaden Alshammari, an MIT PhD student and lead author on the paper. "They share the booklets with each other, but no one had made the effort to collect them, clean them, and upload them online."

Building MathNet required tracking down 1,595 PDF volumes totaling more than 25,000 pages, spanning digital documents and decades-old scans in more than a dozen languages. A significant portion of that archive came from an unlikely source: Navid Safaei, a longtime IMO community figure and co-author who had been collecting and scanning those booklets by hand since 2006. His personal archive formed much of the backbone of the dataset.

The sourcing matters as much as the scale. Where most existing math datasets pull problems from community forums like Art of Problem Solving (AoPS), MathNet draws exclusively from official national competition booklets. The solutions in those booklets are expert-written and peer-reviewed, and they often run to multiple pages, with authors walking through several approaches to the same problem. That depth gives AI models a far richer signal for learning mathematical reasoning than the shorter, informal solutions typical of community-sourced datasets. It also means the dataset is genuinely useful for students: Anyone preparing for the IMO or a national competition now has access to a centralized, searchable collection of high-quality problems and worked solutions from traditions around the world.

"I remember so many students for whom it was an individual effort. No one in their country was training them for this kind of competition," says Alshammari, who competed in the IMO as a student herself. "We hope this gives them a centralized place with high-quality problems and solutions to learn from."

The team has deep roots in the IMO community. Sultan Albarakati, a co-author, currently serves on the IMO board, and the researchers are working to share the dataset with the IMO foundation directly. To validate the dataset, they assembled a grading group of more than 30 human evaluators from countries including Armenia, Russia, Ukraine, Vietnam, and Poland, who coordinated together to verify thousands of solutions.

"The MathNet database has the potential to be an excellent resource for both students and leaders seeking new problems to work on or looking for the solution to a difficult question," says Tanish Patil, deputy leader of Switzerland's IMO. "Whilst other archives of Olympiad problems do exist (notably, the Contest Collections forums on AoPS), these resources lack standardized formatting system, verified solutions, and important problem metadata that topics and theory require. It will also be interesting to see how this dataset is used to improve the performance of reasoning models, and if we will soon be able to reliably answer an important issue when creating novel Olympiad questions: determining if a problem is truly original."

MathNet also functions as a rigorous benchmark for AI performance, and the results reveal a more complicated picture than recent headlines about AI math prowess might suggest. Frontier models have made extraordinary progress: Some have reportedly achieved gold-medal performance at the IMO, and on standard benchmarks they now solve problems that would stump most humans. But MathNet shows that progress is uneven. Even GPT-5, the top-performing model tested, averaged around 69.3 percent on MathNet's main benchmark of 6,400 problems, failing nearly one-in-three Olympiad-level problems. And when problems include figures, performance drops significantly across the board, exposing visual reasoning as a consistent weak point for even the most capable models.

Several open-source models scored 0 percent on Mongolian-language problems, highlighting another dimension where current AI systems fall short despite their overall strength.

"GPT models are equally good in English and other languages," Alshammari says. "But many of the open-source models fail completely at less-common languages, such as Mongolian."

The diversity of MathNet is also designed to address a deeper limitation in how AI models learn mathematics. When training data skews toward English and Chinese problems, models absorb a narrow slice of mathematical culture. A Romanian combinatorics problem or a Brazilian number theory problem may approach the same underlying concept from a completely different angle. Exposure to that range, the researchers argue, makes both humans and AI systems better mathematical thinkers.

Beyond problem-solving, MathNet introduces a retrieval benchmark that asks whether models can recognize when two problems share the same underlying mathematical structure, a capability that matters both for AI development and for the math community itself. Near-duplicate problems have appeared in real IMO exams over the years because finding mathematical equivalences across different notations, languages, and formats is genuinely hard, even for expert human committees. Testing eight state-of-the-art embedding models, the researchers found that even the strongest identified the correct match only about 5 percent of the time on the first try, with models frequently ranking structurally unrelated problems as more similar than equivalent ones.

The dataset also includes a retrieval-augmented generation benchmark, testing whether giving a model a structurally related problem before asking it to solve a new one improves performance. It does, but only when the retrieved problem is genuinely relevant. DeepSeek-V3.2-Speciale gained up to 12 percentage points with well-matched retrieval, while irrelevant retrieval degraded performance in roughly 22 percent of cases.

Alshammari wrote the paper with Safaei, HUMAIN AI engineer Abrar Zainal, KAUST Academy Director Sultan Albarakati, and MIT CSAIL colleagues: master's student Kevin Wen SB ’25; Microsoft Principal Engineering Manager Mark Hamilton SM ’22, PhD ‘25; and professors William Freeman and Antonio Torralba. Their work was funded, in part, by the Schwarzman College of Computing Fellowship and the National Science Foundation.

MathNet is publicly available at mathnet.csail.mit.edu.

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Faces of MIT: Gabi Hott Soares

Gabi Hott Soares, associate director of student organizations and programming for the Student Organizations, Leadership, and Engagement Office (SOLE) in the Division of Student Life (DSL), empowers and equips students to lead and serve not only during their time at MIT, but also as they venture into their professional lives. With enthusiasm and a global mindset, she is dedicated to helping students thrive and reach their goals. 

Hott Soares was working in Brazil in corporate communication and social responsibility for heavy‑industry companies, including metals, mining, steel, and oil and gas, when she moved to the United States in 2017 to attend the Hult International Business School in Cambridge. After graduating, she hoped to fulfill her dream of working in the United States, and initially planned to continue in the same industry. Once she arrived in Boston, however, she saw the potential of working in higher education and identified it as a field she wanted to pursue. The challenge, Hott Soares noted, was that as an international professional, she did not have anyone stateside who could recommend her. 

Taking matters into her own hands, Hott Soares began attending meetups of Brazilian students and researchers in the Boston area to make connections. At one, she met an MIT student who invited her to volunteer as a marketing chair for his startup. Hott Soares worked with the startup for three months when she met another member of the team — the girlfriend of an MIT student — who mentioned that she was leaving a part‑time position within the MIT Spouses and Partners Connect (MS&PC) program. She asked Hott Soares if she would be interested in the role, and Hott Soares jumped at the opportunity to work at the Institute. 

In her first position at MIT, Hott Soares worked directly with Aaron Donaghey, manager of event scheduling and special projects in the Campus Activities Complex (CAC), in a temporary office assistant position supporting CAC and SOLE. Located on the fifth floor of the Stratton Student Center, she greeted students and provided resources related to both offices. Intent on learning as much as she could about how both offices operated, she dedicated time to familiarizing herself with their functions, which was no small task. CAC, for example, manages several event spaces, including Kresge Auditorium and the MIT Chapel, and oversees thousands of events each year. Meanwhile, SOLE advises hundreds of student organizations recognized by the Association of Student Activities.  

Six months later, when Hott Soares told Donaghey about her background and hope for a career at MIT, he encouraged her to apply to be the event support assistant within CAC. She was selected for the role, marking her first permanent role at MIT. On her path to continued growth at the Institute, and confident that new opportunities would come, she took advantage of the Institute’s career planning and development resources offered to employees. She worked one-on-one with Michele King Harrington, career development program administrator in human resources, and attended her workshops. King Harrington encouraged her to stay open to emerging opportunities, and in turn, Hott Soares immersed herself in learning everything she could about the Institute.  

In 2021, she was promoted to senior administrative assistant for what is now known as Student Engagement and Campus Activities within DSL. A year later, she became assistant director of student organizations and programming in SOLE. In 2023, she was again promoted to associate director of student organizations and programming and received a DSL Infinite Mile Award in the category “Here for the Students.” 

In her current role, Hott Soares leads the student events and programming boards area, which includes the Class Councils, Ring Committee, Senior Ball and Week Committees, and the Student Events Board. She interfaces daily with the student groups, helping them build community and plan activities and programs both on and off campus. While the skills she teaches students are applicable for their task at hand, they are also life skills that students will carry with them long after their time at MIT.  

Serving people and nurturing the MIT community are what Hott Soares enjoys most. She reminds students that amid a rigorous course load and demanding commitments, it’s important to have fun — especially when they are celebrating an event they worked hard to plan. “Their time at MIT is one of the most beautiful times of their lives,” she says. “I want them to remember that.” 

Soundbytes 

Q: What part of your work makes you feel most proud?

Hott Soares: I am proud of being able to work with the most brilliant minds in the world and still be myself. When I am interacting with students, we want to help each other, and we can create a relationship that is based on empathy, respect, trust, and humility. I am grateful that I get to work with so many wonderful people. 

Q: What advice would you give to a new staff member at MIT?

Hott Soares: Introduce yourself to people and take time to build relationships. Let others know what you do, what you want to do, and how you want to collaborate. Be humble, stay curious, and open to learning. MIT can feel fast-paced, but it is also a community full of people who genuinely care. You will thrive by being your true self!

Q: How would you describe the community at MIT?

Hott Soares: The people at MIT are amazing. Because I don’t have my family here, MIT is like home. The community is made up of people from different backgrounds and cultures, and I’ve always felt respected and like I belong. It is welcoming, safe, and compassionate. A shared sense of purpose, collaboration, creativity, and drive make MIT an inspiring place to work. 

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Three from MIT named 2026 Goldwater Scholars

Three MIT rising seniors have been selected to receive a 2026 Barry Goldwater Scholarship, including Deeksha Kumaresh in the School of Engineering and Anna Liu and Charlotte Myersin the School of Science. An estimated 5,000 college sophomores and juniors from across the United States were nominated for the scholarships, of whom only 454 were selected.

The Goldwater Scholarships have been conferred since 1989 by the Barry Goldwater Scholarship and Excellence in Education Foundation. These scholarships have supported undergraduates who go on to become leading scientists, engineers, and mathematicians in their respective fields.

Deeksha Kumaresh, a third-year biological engineering major, is an undergraduate researcher at the Hammond Lab. The Hammond Research Group at the MIT Koch Institute for Integrative Cancer Research focuses on the self-assembly of polymeric nanomaterials, with a major emphasis on the use of electrostatics and other complementary interactions to generate multifunctional materials with highly controlled architecture.

“Hands down, the mentors I’ve encountered have been the most significant part of my MIT journey,” Kumaresh says. “I’m also extremely grateful to the Hammond Lab, which has provided a supportive environment where I can make mistakes, learn, and grow as a researcher. I treasure the spontaneous conversations with lab members (about science or life) and their willingness to treat me seriously as an independent researcher, even as an undergraduate.”

Kumaresh is mentored by Paula Hammond, dean of the School of Engineering, Institute Professor, and professor of chemical engineering. Kumaresh's career goals are to pursue an MD/PhD. In the long term, she seeks to lead a bioengineering research lab to predict the efficacy and side effects of cancer therapies by developing systems-level computational and biological preclinical models.

“Receiving this scholarship has been incredibly meaningful, because it offered me the chance to reflect critically on my post-graduate goals and receive recognition for my journey for them,” Kumaresh says. “Earning this scholarship has welcomed me into a tight-knit community where I’ve already found so much guidance. Everyone is genuinely curious about everyone else’s interests and are eager to lend a hand however they can.”

Anna Liu, a third-year chemistry major, is an undergraduate researcher in the Radosevich Group. The overarching objective of the group’s research is to develop new catalysts, strategies, and reagents for synthetic chemistry. By designing and synthesizing new molecular compounds with unknown structure and function, the group hopes to learn more about the general principles enabling new chemical transformations.

Liu is mentored by professor of chemistry Alexander Radosevich. She plans to pursue a PhD in organic or inorganic chemistry and eventually lead research developing sustainable synthetic transformations informed by fundamental mechanistic and reactivity studies, and teach at the university level.

“Going through the Goldwater application process gave me a deeper understanding of my research project and helped me reflect on my intrinsic motivations to pursue research. I’m excited to use what I’ve learned to keep growing as a researcher,” Liu says. “I am so grateful for the countless mentors, teachers, labmates, classmates, friends, and family in my life who have believed in me, fostered my passion for chemistry, and taught me so much. Receiving this scholarship is truly a testament to their outstanding support!"

Charlotte Myers, a third-year physics and astronomy major, conducts research at the Kavli Institute for Astrophysics and Space Research, where she applies machine learning to model galactic structure, and at the Center for Theoretical Physics, where she studies theoretical models of dark matter. Her research interests center on the physics of dark matter, which she approaches from multiple perspectives — from its distribution on galactic scales to particle-level models.

Myers is mentored by Lina Necib, an assistant professor in the Department of Physics. She plans to pursue a PhD in theoretical physics and conduct research in cosmology and astroparticle physics, with a focus on the fundamental physics of dark matter, and teach at the university level.

“I am very grateful to my research advisors, Professor Necib, Dr. Starkman, and Professor Slatyer, for their guidance and support in helping me develop as a researcher,” Myers says. “I find it deeply rewarding to engage with open questions in physics, and I am excited to continue pursuing this work in graduate school and beyond. Receiving this scholarship has given me both the resources and the confidence to continue on that path, even when progress is not always linear.”

The scholarship program honoring Senator Barry Goldwater was designed to identify, encourage, and financially support outstanding undergraduates interested in pursuing research careers in the sciences, engineering, and mathematics. The Goldwater Scholarship is the preeminent undergraduate award of its type in these fields.

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MIT takes top team honors in 86th Putnam Math Competition

In an outstanding performance at the 86th William Lowell Putnam Mathematical Competition, MIT’s team once again took the top spot for the sixth consecutive year. MIT secured four of the five Putnam Fellows, who are the five highest-ranking students, and the Elizabeth Lowell Putnam Prize, which is given to a woman whose “performance in the competition is particularly meritorious.”

The members of the winning team, consisting of junior Cheng Jiang, senior Luke Robitaille, and first-year Chunji Wang, were all awarded as Putnam Fellows alongside senior Zixiang Zhou, each receiving a $2,500 award for their performance. Notably, Robitaille is a four-time Putnam Fellow, having received the award for each year of his studies. For a second consecutive year, sophomore Jessica Wan was awarded the Elizabeth Lowell Putnam Prize and received $1,000.

Wan was also among the top 25 scorers, amongst 16 others from MIT: Warren Bei, Reagan Choi, Pico Gilman, Henry Jiang, Zhicheng Jiang, Papon Lapate, Gyudong Lee, Derek Liu, Maximus Lu, Krishna Pothapragada, Pitchayut Saengrungkongka, Qiao Sun, Allen Wang, Kevin Wang, and Yichen Xiao.

A legacy of success

“I was delighted to see how well the MIT students did on the Putnam exam this year, which reflects their hard work, talent, and enthusiasm,” says Professor Henry Cohn, who led class 18.A34 (Mathematical Problem Solving) this year, also informally known as the Putnam seminar.

MIT’s continued success in the Putnam competition stems from a variety of sources. Some of this is built on things like the seminar, where students get together to sharpen their skills by diving deep into tough problems and discussing solutions.

Cohn, a former participant in the Putnam, comments on the joy of teaching the seminar and seeing students’ progress. “When you spend a semester watching students present solutions to difficult problems, you start to understand how they think,” says Cohn. “It’s exciting to see them apply their abilities to new, difficult problems."

Professor Bjorn Poonen, who also led the seminar in previous years (and is a four-time Putnam Fellow), describes it as an opportunity to hone a spectrum of skills in competition preparation. “Knowing how to explain things well is really important for doing well on the Putnam and for everything else, and for this it really helps to have experience communicating with others, which is what the problem-solving seminar is all about.”

A shared passion for problem-solving

The students who take the Putnam thrive on all aspects of the competition, from the social to the exam itself.

“It’s not a school day, and we still get to do math,” Jiang describes his excitement for the competition. Indeed, getting to “do math” extends beyond formally sitting for the exam, to breaks and opportunities for discussion that are interspersed throughout the day. The students take each opportunity to come together as seriously as they do the competition, and it is this collective passion for problem-solving that builds a strong sense of community and brings students back year after year.

“The competition brings together hundreds of students from across campus representing many majors, years of graduation, and degrees of math contest experience, but what brings everyone together is a shared love of solving problems,” Cohn says. “You can see this in the clusters of students who stay to discuss the problems long after the exam has ended. Mathematics can sometimes feel like a solitary pursuit, but at this level, collaboration is key.”

Community complements the shared passion the math enthusiasts share for problems and puzzles. “You get a kind of satisfaction similar to when you get unstuck while doing a crossword puzzle and everything falls into place,” Poonen describes his own experience solving Putnam problems.

Consistency in certainty

The competition is also an opportunity to see familiar faces. Robitaille recalls his experiences in high school math olympiads, and highlights the friendly atmosphere at the Putnam. “Throughout college, I have stayed close with people I met at competitions,” Robitaille says. “There’s the whole background of times spent together, not just on contest day.”

An event for both community and challenge, the consistency and certainty of competition day is what brought Robitaille and Zhou back year after year. “Each time, you have a set amount of time to sit in the room and work on the problems,” Robitaille says. “If you were the type of person for whom that would be a fun thing, like me, it’s nice to have an opportunity to do it again occasionally.”

“It’s more fun than the real world, where everything is complicated,” Zhou adds with a smile.

The full list of 2025 winners can be found on the Putnam website.

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