Ila Kumar: Innovating with communities

Before Ila Kumar thinks about how to build technology, she asks a different question: What is the context that technology will operate in, and who needs to be involved in the design? 

For Kumar, meaningful innovation doesn’t result from engineers or designers working in isolation. Instead, she believes the best innovations emerge when the people who stand to benefit from a technology help create it from the very beginning. 

That philosophy has guided her research in the Lifelong Kindergarten group, where she works alongside young people who have experienced trauma during childhood, particularly those involved in the child welfare system, to reimagine how technology can support healing, connection, and independence. 

“I really think that community-based design is the only way that we can make technology that accounts for communities’ needs, but also their barriers, their cultures, their concerns,” Kumar says. “It’s the only way that we can make really sustainable and positively impactful technology.” 

Today, Kumar is preparing to enter the sixth and final year of her PhD. But when she first arrived at MIT in 2021, she envisioned staying only long enough to complete a master’s degree. However, Kumar quickly fell in love with her work and decided to stay at MIT and pursue her doctorate.

Before graduate school, Kumar grew up in Philadelphia, attended the University of Pennsylvania, and worked on several projects at the intersection of technology and mental health or psychology research. 

Through those experiences, Kumar began to question whether the technology she was helping to develop was having the sustained impact she hoped for. “I had done a number of projects that were ‘tech for good,’” she says. “And I wasn’t seeing that what I was doing had a long-term impact.” 

Rather than walking away from technology altogether, Kumar began to rethink how it was created. “If we design technology in community-based ways and really think about holistic well-being,” she says, “maybe we can actually create things that help people.” 

That conviction eventually became the foundation of her doctoral research, and over the course of her PhD, Kumar has increasingly moved from simply listening to communities to building with them. 

Public conversations about technology often present a choice: Embrace it or reject it. Kumar believes that it’s not that simple. 

Kumar sees the way digital platforms have the potential to both harm young people’s mental health and development, and help young people process emotions, strengthen relationships, and practice healthy vulnerability — if those tools are designed thoughtfully and embedded in the systems where young people already receive care and support. 

Much of her work explores exactly what that could look like. 

One project Kumar worked on, in partnership with Stepping Forward LA and with the input of the young people who would use the app, replaces text-heavy communication with a visual collage system to help young people impacted by trauma and the child welfare system to express emotions that may be difficult to put into words, and to build a sense of connectedness with one another. 

In an ongoing project, Kumar is collaborating with the Justice Resource Institute to design a mobile app that supports youth in playing an active role in their treatment-planning process and helps them work toward the goals they set outside of the therapy office. The group is working with clinicians and youth to design and evaluate the system.

“We are not sitting at MIT designing tools and just throwing them at people,” Kumar says.  “We’re designing it together. We need to actually have the folks that are relevant to providing the care in the room.” 

That idea became even clearer to Kumar through a 10-month technology leadership circle she co-facilitated with Foster America. The program brought together people with lived experience of foster care and technology experts to envision how digital technologies could fill gaps in care for young people in the child welfare system. 

This project surfaced the importance of not just designing tools that center youths’ needs but also considering the ways in which social services need to be brought into the innovation process. 

Those ideas have also led Kumar to explorations that involve one of technology’s newest frontiers: artificial intelligence. She began asking questions after she realized that young people who had experienced trauma had already been turning to AI to make critical life decisions, even as many caregivers were not aware of it.

As a result, Kumar has increasingly focused on supporting care providers in talking with young people about AI. She has led training workshops with organizations that serve young people impacted by trauma or involved in the child welfare system.

Kumar’s passion for advocating for young people extends far beyond the lab. She also volunteers as a court-appointed special advocate, working one-on-one with a young person in the child welfare system while pursuing her PhD. 

The role has deepened both her understanding of the challenges young people face and her belief that lasting change depends on relationships.

Some of her most meaningful moments have come while working directly with young people.  Last summer, she, alongside another graduate student in her lab, mentored two interns with foster care experience during a six-week program that blended technology, creativity, and personal growth. 

“It felt like a real privilege,” Kumar says. “Even the six weeks was not enough.” 

Those relationships have also inspired Kumar to address how community-based research is conducted at MIT. 

Recognizing that many students interested in community-engaged work often feel isolated, she collaborated with the Priscilla King Gray Public Service Center to co-teach a course on community-driven innovation. She later established a biweekly community of practice connecting researchers across MIT and Harvard University who are navigating the benefits and challenges of conducting research alongside communities rather than simply studying them. 

Outside of research, Kumar enjoys birdwatching, cooking with friends, and creating graphic illustrations — creative pursuits that, much like her research, reward patience, observation, and careful attention. 

As technology becomes increasingly woven into young people’s lives, Kumar hopes innovation will move beyond the lab and into the communities it is meant to serve. 

“The future of actually impactful technologies,” Kumar says, “is when researchers are making decisions with communities instead of for them.”

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Playing against climate risk

Sai Ravela, principal research scientist in MIT’s Department of Earth, Atmospheric and Planetary Sciences (EAPS), works with a team of researchers, local partners, and community collaborators to develop game-based computer models to help local communities find solutions to their unique geographical and environmental challenges.

Ravela came to MIT as a postdoc in 2002. Prior to that, he had been working on robotics and computer vision, but he was excited by the idea of studying the climate system and wanted to work in the field of sustainability. “Suddenly, overnight, I became a climate person,” Ravela says.

His project, funded by a 2025 Abdul Latif Jameel Water and Food Systems Lab (J-WAFS) India Grant, explores how agricultural decision-making occurs under climate stress. Using localized climate projections and a participatory approach, the project aims to help communities discover ways to improve their collective agricultural resilience.

EAPS postdoc Anamitra Saha is a key contributor on the grant, working with Ravela and local collaborators to combine downscaled climate modeling, participatory decision-making, and community-based adaptation planning. Other team members include Myisha Ahmad (Carthago Consultancy), Jayanta Basu (University of Calcutta), Anusree Ghosh (Bangladesh Open University), Showmitra Sarkar (Khulna University of Engineering and Technology), and Bivuti Sikder (Dhaka University). 

In a process known as downscaling, researchers take large-scale climate projections and turn them into highly detailed local projections. From these hazard maps, Ravela and Saha can estimate the risk of extreme weather phenomena such as flooding, drought, heat waves, and salinity-related stress. 

“We kind of simulate what the outcome could be in that region,” Ravela explains. “Would it improve agricultural productivity? Would it reduce agricultural productivity? Would it change certain land use patterns? Would the land be less livable, more livable?” 

The team combines surveys, scientific models, and local knowledge to build an impact graph that allows them to explore what might happen to a region during simulated weather events.

Although Ravela knew hazard maps could be useful, he was troubled by how rarely they reached the people whose lives were most affected by the risks they described. “We had clients like insurance companies,” he says. “But I never saw it reach people in a way that made a difference in their lives. And that really bothered me.”

To address this gap, he began thinking about how to help communities engage with hazard maps directly and take part in the decision-making process. In conversations that informed the game’s development, Ravela heard people whose livelihoods are vulnerable to climate events voice immediate concerns about what would happen if a future season failed: “If I don’t plant next season — if I can’t — what would I do?” Ravela wanted to help people think instead about possible choices, different paths, and their respective risks.

When he asked himself what circumstances allow someone to think about risk, the answer began to take shape. “Well, roll a die. Toss a coin,” he thought. “And where do you do these things? In a game.”

How it works

The process the collaborating team developed takes place in three stages. The first is a “snakes and ladders” game, played with physical game pieces and tokens. The second is a mixed game that still uses the gameboard, but a computer generates events and manages portfolios, allowing the system to calculate risk percentages. Once players become comfortable with the mixed game, the final stage, developed by Ravela, abandons the board game and moves fully into a more detailed computer simulation that can be played on a cellphone app.

“We tried this in different stages in three places,” says Ravela. Two villages, Bally Island and Joygopalpur, are in India's Sundarbans region. The third is a village in Bangladesh just across the border. In each location, the work depends on collaboration with local residents, community organizers, and regional partners who help shape the game around local land, water, livelihood, and governance conditions. During development, informal community-engagement sessions helped the team refine and adapt the game. Those interactions also led to intriguing observations that are now helping the team formulate hypotheses for future formal research.

The three villages lie in a coastal region that faces numerous extreme weather events threatening water availability and agricultural productivity. As riverbeds rise from sediment accumulation over time and the land sinks from groundwater extraction, saltwater can more easily intrude into groundwater aquifers, while freshwater drainage, recharge, and flushing become increasingly difficult, intensifying waterlogging and drought. 

“There’s a vicious cycle that’s happening with salinization of the soil,” Ravela explains. 

One visible result is that Boro rice leaves now often begin browning far too early in the season, as salinity and water stress damage crops before they can mature. This cycle occurs in many coastal communities, suggesting to Ravela that the outcomes of the J-WAFS project could have applications around the world.

That broader potential comes from what the game is able to reveal. Instead of treating potential interventions — such as embankments, canals, recharge, crops, fisheries, and energy — as separate choices, the simulation lets players see how each intervention affects the coupled system of land, water, salinity, and livelihoods. When players test different options, simply raising embankments often proves less effective than expected, because it does not break the underlying cycle that causes the land to flood. 

More-integrated strategies — combining mangrove restoration, canal excavation, groundwater recharge, diversified agriculture and fisheries, better water management, and merging solar panels into farming with agrivoltaics or aquavoltaics — can generate better long-term returns while also making the landscape more resilient.

The game also creates space to consider dramatic alternatives to embankment-based protection, including seasonal migration, livelihood shifts, and other difficult choices. These possibilities can be explored safely inside the game, even when they would be almost unimaginable to raise in real life. In this way, difficult questions that might otherwise be avoided can be explored, rather than ignored. And if the game reveals that a difficult choice could lead to better long-term outcomes, that result is not a prescription, but a basis for informed conversation between the community, government, and other decision-makers.

Competition or cooperation?

To make the game effective at developing strategies, Ravela’s team had to understand how many people should play at one time. Too few players may not generate enough diversity of ideas, while too many can slow the process significantly. During game development, groups of roughly ten to twelve people seemed especially workable: large enough to support active interaction, but small enough for practical discussion and learning. 

“Once it crosses a dozen people,” Ravela explains, “it becomes very, very viable as a way to solve problems.”

The games have sparked interest and generated new strategies. People are often excited by the prospect of playing, and repeated play reveals different kinds of expertise. Some participants become especially engaged strategy-explorers; others contribute through discussion, critique, memory, and local knowledge. Together, the process helps identify players who are especially adept at thinking across different dimensions of the problem.

Ravela emphasizes the social aspect of the games as central to their efficacy. “Even though the game is on a phone,” he says, “players are within each other’s reach.” An emcee or facilitator encourages players to engage with one another by asking them to explain their gameplay, discuss their reasoning, and learn from one another’s choices.

While competition is not an explicit feature of the game, there can be zero-sum outcomes. One household’s decision about land, water, drainage, or energy may improve its own outcome while making conditions worse for others. Initially, players may aim for individual success. As they explore longer simulated time horizons, they often shift toward cooperative strategies. 

After each game, the research team and local facilitators lead an educational session where people can learn from each other’s strategies. At first, players often attempt to copy the previous winner’s gameplay — usually, making as much money as possible and saving it in case of disaster. But some disasters are too large for one person to handle alone. 

“That strategy is only optimal up to a certain horizon,” Ravela explains, “because when everyone replicates that strategy, the community doesn’t necessarily thrive.”

As players recognize this, they begin to evolve collective modes of behavior, such as creating a common insurance pool where everyone contributes money to a disaster relief fund. Through multiple iterations of the game, players often appeared to converge on cooperative solutions. 

“The community in this way, playing a game against nature, simulated nature, comes upon solutions that work for them,” says Ravela. “We would love to formally explore this in the future,” Ravela adds.

Why the game works

Ravela’s team sees three advantages to game-based decision-making. First, the game brings new perspectives to the table that formal decision-making often misses. Many communities have strong hierarchies that can discourage women or less powerful community members from participating openly. The game allows people to offer insight without necessarily violating cultural norms. One recurring impression was that women — often responsible for managing family affairs — diversified their portfolios earlier, while men more often concentrated on a single livelihood strategy. The observation was striking enough that the team hopes to test and quantify it formally in future studies.

Second, in the game, all players begin on a level playing field, regardless of status, gender, or wealth. “It democratizes the process,” explains Ravela. In the simulation, a wealthy, influential community figure has no intrinsic advantage over a seamstress. The game reduces natural biases by giving everyone’s ideas a chance to be tested under the same conditions.

Third, because the game is a simulation, people can explore choices that might be too risky, too expensive, or too socially difficult to consider in real life. People may not want to discuss a large aquifer management system, a new land-use arrangement, or a difficult livelihood transition if the real-world implications feel too overwhelming. But inside the game, they can test possibilities without immediate consequence. “So, what, you lose? You start again,” says Ravela.

This is where the game becomes more than a communication tool. It turns uncertainty into a shared decision space. Players can test interventions, observe trade-offs, compare outcomes, and discover strategies before real disasters force those choices upon them. The game shifts the conversation from avoiding risk to reasoning about it, and from fatalistic thinking to collective agency.

Ravela and his collaborators also see the games as a way to address roadblocks in policy implementation by allowing community members to own the solutions they discover. Traditionally, donors may give money to a nongovernmental organization (NGO) that has proposed a project, and the NGO then distributes resources in the community. But it is not always obvious what has actually been implemented, or whether the community has had meaningful ownership of the decision. “In seeking solutions to problems, often the difficulty is developing the policy that provides metrics for the effectiveness of those solutions,” Ravela says. “Games enable people to quickly see the policy space, rather than approaching problems only reactively.”

When people test policies in the game, see how they work, and revise them through repeated play and refinement, they can begin to propose those policies themselves. The result is not simply a technical recommendation from outside experts, but a community-informed basis for action.

What's next?

The broader project, developed with collaborators and community partners in India and Bangladesh, has attracted interest in Bangladesh and Thailand, where similar game-based coastal agricultural resilience projects are being explored. Some customization is necessary to adjust the game to local conditions, but the simulations are highly adaptable. Between 75 and 80 percent of the game can remain the same across locations, while the rest can be tuned to local geography, livelihoods, hazards, and governance structures. Although each place brings its own challenges, “the way land and water and people interact is very similar,” says Ravela.

Building on insights from these game-development and informal community-engagement sessions, Ravela hopes the project can eventually expand to other locations, including members of the Association of Southeast Asian Nations and some places in Latin America. But he emphasizes the importance of establishing longitudinal outcomes before scaling. “The critical question is, does it answer real problems?” he says.

Future formal research will test these emerging hypotheses prospectively and longitudinally. The resulting evidence will help determine whether, where, and how to scale the approach.

If computationally assisted decision-making proves useful over time, the impact could spread far beyond the initial development locations. But the work is not only about finding an optimal solution. It is also about helping people work with one another. As Ravela puts it, “the process really is about helping the people work with each other as much as it is about finding an optimal solution, because part of finding the optimal solution is finding people to work with each other.”

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How an MIT research project became a global programming language

It all started with some exasperated emails. Back in 2009, a group of researchers began venting their frustration with the programming languages designed to help scientists and other researchers perform complex mathematical operations and statistical simulations without learning how to code. These programming languages were rigid and slow. If scientists built something that really worked, they’d need to rewrite the entire program in another language just to run it more quickly.

The emails turned into a research project at MIT with the mission of building an easy-to-use, high-performance programming language called Julia, which is designed for scientific research, data analysis, and modeling complex systems such as jet engines, drugs, financial markets, and robots, to name a few examples.

That research project turned into a lab at MIT, and the lab turned into the company JuliaHub. Along the way, Julia gained a loyal following among scientists, engineers, mathematicians, and others. Today, the free and open-source language counts more than 1 million users, including people working in thousands of companies and universities around the world.

It is only a slight exaggeration to say Julia has been used to model everything under the sun, from the behavior of tiny atoms to semiconductors, neural networks, race cars, and airplanes. It has also been used to study much beyond the sun, with astronomers using Julia for imaging black holes.

Julia’s secret sauce is in the way it compiles code depending on the type of data being used. Such “just-in-time compilation” makes Julia faster and more flexible than other numerical programming languages.

“Scientists and engineers are not programmers. Building scientific applications with multidisciplinary teams of scientists, engineers, and programmers is challenging,” JuliaHub co-founder and CEO Viral Shah says. “We asked: What if you could equip the scientists and engineers with a programming language that allowed them to express their ideas at a high level and also get great software performance?”

Making programming easy for non-programmers has been a north star for JuliaHub’s founders, who include Julia co-creators Shah, MIT professor of mathematics Alan Edelman, Jeff Bezanson SM ’12, PhD ’15, and former MIT research scientist Stefan Karpinski.

In April, JuliaHub’s team took another big step in that direction with the launch of Dyad 3.0, the latest version of its AI platform to help engineering teams accelerate the development of complex physical systems like rockets, heat pumps, and satellites. Engineers are already using Dyad to direct autonomous AI agents as they work through physics simulations, safety analyses, quality controls, and more.

“With Dyad 3.0, you can upload data and design documents and the system will design an entire aircraft for you,” Shah says. “Working with customers like Boeing, we are building agentic hardware design capabilities for engineers. Simplistically, you want to say, ‘Okay computer, build me a plane’; upload the design documents; and have the system account for all the physics, compile all the code, verify everything, and build the entire design agentically.”

Humble beginnings

After discussing the need for better programming languages for scientists and other researchers, Julia’s co-creators started the Julia Lab around 2009. The Julia Lab remains active in MIT’s Computer Science and Artificial Intelligence Laboratory.

The core idea was to create a high-performance platform that would excel at engineering, scientific, and mathematics applications. Shah says before Julia, scientists and engineers would either have to hire someone to build software for them or accept the slow performance of the few programming languages designed for them.

“We wanted to create something as easy to use as Python or MATLAB but as fast as the C programming language,” Shah says. “We built Julia for ourselves.”

Edelman says at first, the researchers didn’t think anyone would want their creation.

“We figured it would take 10 years before anyone was interested, but we said, ‘Patience is a virtue, so let’s do it,’” Edelman recalls.

The MIT researchers announced Julia with a blog post in 2012. They quickly realized many other researchers shared their frustration.

“When we first started, we were targeting interactive research workflows, but increasingly people are using it for everything,” Bezanson says. “Now we’re moving the whole stack of the language onto smaller, embedded devices as we evolve with our users.”

Since those early days, Edelman has taught a class on Julia with students from nearly every department at MIT. Today, he often learns students are already using Julia when they enroll in the class for applications as wide ranging as robotics, astronomy, physics simulations, and finance.

“Researchers come up to me and say, ‘I tell my supervisor I’m using Julia because it’s fast, but don’t tell them I’m using Julia because it’s really fun,’” Edelman says. “The key thing is Julia’s abstractions. A lot of times a coding language forces you to solve the one problem you’re thinking about. Julia’s language makes it so you’re solving not only the problem you’re thinking about, but other people’s problems around the world too. It encourages you to solve problems more generally.”

As Julia gained popularity, researchers around the world started asking the Julia team for support. By 2015, the demand became strong enough that they decided to start JuliaHub and help users through the company full-time. They received support from the MIT Deshpande Center for Technological Innovation and others at MIT to get the company off the ground.

JuliaHub’s work has evolved from simply helping users to advancing the language more generally. That’s powered an impressive list of creations from Julia’s loyal users. Julia has been used to simulate computer circuits, detect health disparities, model global climates and oceans, analyze brain activity, and more. 

After someone built a pharmaceutical modeling platform in Julia, it was used to accelerate development of Moderna’s Covid-19 vaccine. In another case, researchers used Julia to create a program for avoiding aircraft collisions. They found it ran about 50 times faster than an earlier version built on Python. Engineers at Meta used Julia to develop a better audio codec for WhatsApp’s 4 billion users.

“Over the years we’ve seen industrial, government, and academic users doing all kinds of interesting things with the Julia language,” Edelman says. “It’s honestly surprised us in many ways, the wide-ranging things people are using it for.”

Autonomous design

JuliaHub launched Dyad 1.0 in June of 2025 as a research agent to accelerate programming and Dyad 2.0 in December. The founders believe Dyad 3.0 represents a new level of ability and autonomy for designing complex systems.

“One important thing about Dyad is that it is a physics compiler and hence enforces physical laws,” Shah explains. “General AI systems often solve physical problems in ways that violate physical laws. When using the Dyad agent, it will detect such violations and guide the agent in the direction of the physically correct solution. We expect it will decrease design times in product engineering by orders of magnitude, leading to months of work being accomplished in hours.”

One way Edelman sees the impact of Julia is through his class. One student recently used Dyad to model how robots move around in space. Another used it to build a rocket engine.

“At the end he said, ‘I couldn’t believe how easy that was — I just got a rocket engine!’” Edelman recalls.

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How an MIT graduate student helped a team of young scientists test their experiment at CERN

This past spring, MIT physics graduate student Manu Srivastava opened an email from a group of high school students in India he had never met.

They were hoping to enter Beamline for Schools, an international competition that gives secondary school students the chance to design and carry out experiments using particle accelerator beams. And they were looking for a mentor.

Srivastava, who studies quantum gravity as a PhD student in the MIT Center for Theoretical Physics – a Leinweber Institute, with Professor Hong Liu, gets other requests to mentor students, often through companies charging families for access to scientists or students at prestigious universities. He usually declines, but this message came directly from the students.

“I've also cold-emailed a lot in my early career, and it usually never works,” he says. “But this email seemed very genuine. They wanted to do something nice and they just needed some guidance.”

Many months and many more emails and calls later, the students secured a place with Srivastava to attend CERN, in Geneva, where they spent two weeks turning their proposed idea into a real experiment. 

Finding an experiment worth doing

Calling themselves Team attoPION, the students are one of five teams selected in the 13th annual Beamline for Schools competition from a record 712 teams representing 89 countries and more than 4,500 students. The six high schoolers met through a combination of science competitions and mutual friends, and attend four schools in four cities across India.

When they first met with Srivastava, the students already had several experimental ideas. His role, he says, was to help determine which directions were practical and scientifically interesting.

They settled on measuring pion charge exchange. Pions are short-lived subatomic particles that can carry positive, negative, or neutral charge. In the process the students want to study, a positively charged pion interacts with a neutron in a target material, producing a neutral pion and a positively charged proton. The team wants to characterize how often that reaction occurs.

Srivastava suspected such a measurement could have relevance to the Deep Underground Neutrino Experiment, or DUNE, a major international experiment designed to study neutrinos.

Dave Newbold, a co-spokesperson for DUNE, says understanding how pions interact with matter helps researchers quantify uncertainties in DUNE’s measurements. In particular, pion interactions can affect estimates of a neutrino’s flavor and energy, which researchers need to measure accurately to determine whether they have observed something new.

And although Beamline for Schools has an educational mission, Newbold says the students aren't simply reproducing a classroom demonstration. “The proposal is real experimental particle physics!” he notes.

If successful, Newbold believes the work could improve scientists' understanding of this particular interaction and potentially lead to a publishable result. Similar “test beam” experiments remain important tools in particle physics: DUNE's detector designs were themselves demonstrated using the (albeit much larger) ProtoDUNE experiments at CERN.

“This [proposal] stands out because of the work the students have put into motivating their measurement, and demonstrating that the experiment is feasible,” Newbold says. “It's certainly at a level far above anything I was thinking about at high school.”

Learning to navigate uncertainty

At CERN, the students worked hands-on with detectors and data-acquisition systems, collected and analyze data, and attended talks by CERN scientists.

In advance of the trip, the team worked with Berare Göktürk, one of the support scientists for Beamline for Schools. In their preparation sessions for the experiment, they realized that the charge-exchange process they hope to observe is extremely rare, forcing them to think through how they might reliably detect it.

With just a few months months to prepare and only 12 days of test-beam time, Göktürk cautioned that producing a result useful to a much larger experiment would be an ambitious outcome.

“We prepare in the best way possible, but we also stay humble and we are aware of the limitations we have,” she says. Her priority is for the students to “understand the journey of a scientist” as they encounter technical problems and work together to solve them.

For Srivastava, mentoring an experiment has also taken him well outside his own specialty. A theoretical physicist, he credits MIT's culture with encouraging him to follow questions beyond the boundaries of his research, including by attending seminars, colloquia, and research meetings across physics.

The experience has been personally meaningful for Srivastava, who grew up in India and sees the mentorship as a way to encourage young people there to pursue fundamental science. 

“I didn't even know what CERN was in high school,” he says. “But these students, they are just that good. They deserve all the credit.”

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How MIT Sandbox has turned student ideas into $8.7 billion in global impact

Although Jacob Becraft had two swings and two misses when he first tried to become an entrepreneur as a graduate student, the MIT Sandbox Innovation Fund Program allowed him to keep at it. This especially benefited cancer patients, as Becraft went on to co-found Strand Therapeutics: a $550-million firm whose programmable mRNA drug has shrunk tumors in patients who had exhausted all other treatment options.

Stories like Becraft’s took center stage at the recent 10-year anniversary celebration of the MIT Sandbox Innovation Fund Program, where student founders, alumni, mentors, and university leaders gathered to reflect on a decade of empowering student entrepreneurs. Speaking at the event, Becraft referred to Strand as "our third swing at the plate," explaining that the Sandbox model gave him "the freedom and ability to fail fast" — letting previous venture ideas "blow up in our faces" before moving on.

For Strand, Becraft says, MIT Sandbox helped him and his co-founder, Tasuku Kitada, to "get out, do some travel, some market research, meet with experts in the field, meet with mentors who could help us build the company — and eventually find investors who were going to back this big vision to transform medicine."

MIT Sandbox was launched in 2016 by Ian Waitz, then-dean of the School of Engineering and now MIT's vice president for research, to lower the barrier for students to try entrepreneurship. The concept of a new program focused on student-led entrepreneurship was developed in consultation with internal MIT leaders and supporters of MIT, including Alan Spoon, a life member emeritus of the MIT Corporation. From its inception, MIT Sandbox has been open to all MIT students, from undergraduates to PhD students. Teams are awarded between $500 and $5,000 to begin their process, and they are matched with two mentors and connected with other expert advisors. 

As they make progress, students can go before the program’s funding board to ask for up to $25,000. Supported entirely by alumni, corporate sponsors, entrepreneurs, and investors, the program has grown to include about 350 teams each semester, some of which are new and some continuing their participation according to their own timelines.

Anantha P. Chandrakasan, MIT provost, explained in the program's decade-in-review report: "Since its inception 10 years ago, MIT Sandbox has been a defining part of MIT's innovation ecosystem, ensuring that every student with the curiosity to explore entrepreneurship has the resources, mentorship, and community to take their first steps."

MIT Sandbox is a "home," where students can "explore, seriously test assumptions, talk to customers, build prototypes, fail, pivot, learn, and grow," says Jinane Abounadi, founding executive director of Sandbox. "And they can do that with a lot of support — and I don't just mean financial support. I mean a lot of support from a lot of people."

For Samuel Udotong, co-founder and CTO of Fireflies.ai, early funding was the difference between an idea and a company. "I think largely because we had gotten a little bit of Sandbox funding, we were actually able to take the risk to move out to San Francisco and try to build the company," he says. "But it would have been really a money barrier if we hadn't gotten the initial $5,000 from Sandbox."

Startup investor and advisor Sophie V. Vandebroek says, "MIT has extraordinary students from around the globe as well as faculty who are top experts in their fields. What’s often lacking," she says, "is confidence. That is where Sandbox plays a vital role. Sandbox enables every individual student to believe that they can be an entrepreneur."

At the anniversary celebration, Fred Parietti, co-founder and CEO of Multiply Labs, recounted how his early product prototypes were developed on his kitchen table and had to be moved regularly according to the dictates of his grad school housemates. Those prototypes wouldn't have been built at all, he said, without MIT Sandbox.

The first funding he received was minimal, "but it wasn't zero, and zero represented my resources as a student. That belief in us and the possibility to build a prototype were game-changers," Parietti said.

Multiply Labs, with 60-plus employees, has raised $36 million and develops robotics technology to manufacture biological drugs safely and economically. The firm supplies pharmaceutical customers including AstraZeneca and Kyverna Therapeutics, whose chief medical and development officer, Naji Gehchan, is an MIT Sandbox mentor.

That same willingness to back an unconventional approach helped AeroShield get off the ground. "One of the things that enables me to stand here today is that Sandbox created a safe environment where it was encouraged to look at this problem backwards, rather than from the nanostructure up," says Elise Strobach, CEO and founder of AeroShield.

The anniversary celebration speakers also included Ross Finman, CEO and founder of Augmodo; Laureen Meroueh, CEO and founder of Hertha Metals; and Daris Bunadar, chief scientist at Lightmatter. All were working on their PhDs when they started exploring commercial applications of their research. All recognize the critical role that MIT Sandbox, in addition to other programs — such as the MIT I-Corps Program, the Martin Trust Center for MIT Entrepreneurship, MIT Venture Mentoring Service (VMS), and the Bernard M. Gordon-MIT Engineering Leadership Program — played in their development as entrepreneurs. These programs offered the space to explore the possibility of not only founding a deep tech company, but also taking on an executive role as their ventures raised venture capital and grew into substantial companies. Today they all have big ambitions for the growth and impact of their companies — ambitions that are made possible only thanks to innovative technologies and an entrepreneurial drive. 

Over its decade of existence, MIT Sandbox has supported over 4,000 teams, representing 8,000 participants associated with a wide range of industries and nonprofit endeavors. It has disbursed more than $11 million in non-dilutive funding, meaning the program takes no stake in the resulting ventures. MIT Sandbox has been involved in the creation of 475 companies in more than 30 countries, and companies that were started in the program have raised $8.7 billion in venture funding.

MIT Sandbox collaborates with other programs across MIT — including the Martin Trust Center, VMS, Kuo Sharp Center, MITdesignX, the PKG Center for Social Impact, the MIT Climate Project, I-Corps, and others — and its teams have excelled in innovation accelerators and competitions. Nine out of 10 winners of MIT's $100K Entrepreneurship Competition have been MIT Sandbox participants.

Apart from the program's impressive results, MIT Sandbox aims to first and foremost serve as a great educational tool, developing the innovators themselves.

"From an educator's perspective, this is just another incredible way to teach," said Abounadi at the anniversary celebration. "MIT Sandbox is a place where students can start seeing themselves as people who can create a meaningful impact in the world," she said, "and that is really what innovation and entrepreneurship are all about."

Paula T. Hammond, School of Engineering dean and Institute Professor, echoed the same sentiments: "What I find most compelling, year after year, is not only what students build, but how they change. They gain confidence, learn to refine before they scale, and begin to see themselves as people who can create meaningful impact, strengthening not only their own trajectories, but the broader MIT community."

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Gage Coon: An Earth scientist exploring the power of microbes

Growing up in Waverly, Tennessee, Gage Coon spent much of his childhood outside. His family had everything from chickens to horses and even an emu named Big Bird. Coon and his cousins would explore the woods surrounding their home, and his father, a mechanic, taught him how to build and repair things around the house. His mother, a secretary at the local high school’s vocational school who loves gardening and birdwatching, encouraged him to experience as much of the world around him as he could.

That hands-on upbringing, which taught Coon to appreciate the natural world and the processes that sustain it, continues to influence how he approaches science today.

Now entering his third year as a PhD student in MIT’s Department of Earth, Atmospheric and Planetary Sciences, Coon studies some of the smallest organisms on Earth: microbes. His research focuses on how microorganisms cycle carbon and sulfur through the environment and how to leverage those processes to help address climate change. Though he studies organisms too small to see with the naked eye, the experimental nature of his work — whether in the lab or on a research vessel in the open ocean — is especially satisfying.

“I think I enjoy that physicality of seeing what I’m working with, seeing its change, and being able to touch it,” Coon says.

Coon did not initially set out to study microbiology. His interest in science began with chemistry. A high school chemistry teacher and a summer program introduced him to the subject. But later, at the University of Tennessee at Knoxville, he joined a lab focused on microbial biogeochemistry and was delighted to find a field that brought together the different areas that interested him: chemistry, the environment, and the larger climate processes shaping our Earth.

The transition from rural Tennessee to Cambridge, Massachusetts, and MIT has been a significant one. As a first-generation student, he did not learn about PhD programs until several years into college.

Once he discovered academic research, however, Coon was drawn to the possibility of spending his career learning.

“I discovered this world of academia, and so I was really excited when I learned about it,” he says. “I was like, ‘Oh my god, constant learning. That is exactly what I want to do forever.’”

Coon began studying the microbes that drive carbon and sulfur cycling in marine sediments as an undergraduate, eventually joining research cruises to investigate these processes firsthand.

His first research cruise, in 2022 after his second year of college, took him to the Atlantic continental slope to study methane seeps and how microbes prevent this methane from escaping to our atmosphere. For Coon, experiencing the ocean up close changed the way he understood the microscopic organisms he was studying.

“It is very powerful seeing yourself in the middle of the ocean, with a whole other world of complex life beneath you,” he says.

At MIT, working with his advisor Tanja Bosak, a professor of geobiology, Coon has continued studying microbial carbon and sulfur cycling, but with a greater emphasis on the applications. One of his major projects explores how microbes could be used to reduce methane emissions from wastewater treatment.

When wastewater is treated, microbes break down organic material in large tanks called anaerobic digesters. One of the final products of this process is the powerful greenhouse gas methane. However, Coon and his colleagues found a way to change what the microbes produce by adding gypsum, a waste product that is created from fertilizer manufacturing

The system uses the added gypsum to turn the methane into carbonate, which can be used to make cement, agriculture, and pharmaceuticals. The process also produces elemental sulfur, necessary for global fertilizer production, which is currently sources from oil and gas refinement. The approach effectively turns two waste products, sewage and waste gypsum, into useful materials while reducing greenhouse gas emissions.

For Coon, the possibility of creating a system that is both environmentally beneficial and economically useful is central to the project. Now that the laboratory experiments have ended, the researchers are looking toward conducting pilot-scale testing. Coon and his advisors have been communicating with companies interested in adapting the system to larger facilities, and hope the technology can eventually move beyond the laboratory.

“If enough small places start doing their pilot-scale studies, then hopefully you could convince some place like Boston or another big city to do this and really make a contribution to our global goal to decrease emissions on the gigaton scale,” he says.

The wastewater project is only one part of Coon’s PhD research. He also studies geological processes that could produce molecular hydrogen, a potential carbon-free energy source. His work examines how iron-rich rocks break down and generate hydrogen underground. He is continuing his thesis work by focusing on microbial competition for acetate, and what this means for global methane emissions from coastal wetlands. This work could improve future climate predictions and support engineered mitigation efforts to decrease emissions from these wetlands. 

Across these projects, Coon is interested in the connection between the microscopic and the massive. But Coon’s PhD has also given him an opportunity to think about science beyond his own research.

One of the parts of graduate school he has enjoyed most is mentoring younger researchers. He has worked with a handful of students through MIT’s Undergraduate Research Opportunities Program and from Tufts University, teaching them laboratory techniques and experimental geobiology.

Outside the lab, Coon maintains some of the same connection to the natural world that characterized his childhood in Tennessee. He spends time hiking to explore local geology, playing bluegrass guitar, and speed-solving Rubik’s Cubes. 

Looking ahead, Coon sees himself continuing in academia, working in government, or helping to bring environmental technologies into practice.

What matters most, he says, is continuing to produce knowledge that can help people understand and potentially improve the world around them.

“I do think, no matter what,” he says, “I’ll be somewhere thinking about how microscopic life connects to the global ecosystem and carbon emissions.”

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Looking beyond natural sequences

A protein’s function is determined by its structure, and structure — the way a protein folds — is determined by its sequence of amino acids, the building blocks of proteins. 

Many methods for designing novel proteins, including examples that could bind to a disease-causing molecule in our cells, involve a two-step process: The structure comes first, and then a machine-learning framework generates a repertoire of sequences that could potentially adopt that structure. 

In nature, many different amino acid sequences can fold into the same structure. At the same time, one amino acid sequence can potentially adopt different structures depending on the protein’s flexibility or a functional trigger. Therefore, when researchers use artificial intelligence to design new proteins, the challenge is to guide AI to “see” that there are many potentially useful answers — that many sequences can adopt the same fold

“For years, the field has measured success by asking whether a model can reproduce the protein sequence that evolution happened to select — our work shows that this isn’t the best metric for protein design,” says Amy E. Keating, Department of Biology head, Jay A. Stein (1968) Professor of Biology, professor of biological engineering, and senior author of a paper recently published in PNAS. 

PottsMPNN, a new machine-learning framework developed in the Department of Biology, incorporates the physical principles that govern protein structure and stability, improving sequence generation and the ability to predict how mutations will affect a protein’s stability. In other words, the model has a better understanding of the sequence-energy landscape, meaning the relationship between the identity of each amino acid and the stability of the protein.

Adding this framework to a protein design pipeline will allow researchers to design structurally feasible proteins with sequences that don’t resemble those of any native protein. 

“If we’re thinking about a completely novel, designed structure, there would be no native sequence to compare it to,” says graduate student and lead author Foster Birnbaum. “What we actually care about is how likely the generated sequences are to fold into the desired structures, how well the model understands the sequence-energy landscape, and how well it can predict the effect of mutations on the stability of the protein.” 

Beyond the noise 

In the same way that AI has recently powered some dramatic social changes, so too has machine learning impacted the pace and breadth of fundamental biological research. Only recently has it become possible to reliably use a computational model to generate a protein structure or sequence. Perhaps the most widely used model today, however, was released in 2022. 

“For a field that’s moving as fast as machine learning in biology, that model has not been surpassed — we’ve been trying to understand why that is, and what it is about that model that makes it so useful,” Birnbaum says. 

Birnbaum was first interested in strategic applications of something researchers call “noise,” or adding variations to a protein structure during training. Noise decreases the tendency of the model to overly mimic native sequences, increasing the diversity of structures for which it’s able to generate sequences.

PottsMPNN also uses a pairwise distribution to capture interactions between amino acids. The ability to account for the physical interactions between all 20 possible sequence options at a pair of positions in the protein is a key reason that PottsMPNN more accurately models the sequence-energy landscape than other methods. 

Finally, Birnbaum says, they introduced sets of evolutionarily related sequences into training the PottsMPNN framework to teach the model how different sequences can adopt the same folded structure.

Birnbaum acknowledges that in trying to shift away from adhering to native sequences, incorporating evolutionary information is, in some ways, still a reliance on them. But PottsMPNN succeeded in demonstrating that as the model depends less and less on native sequences, structural compatibility and energy prediction, including for novel proteins, improve. 

Protein design in the age of AI

“Once we can design any protein we want, that enables us to do a potentially scary amount of biological engineering,” Birnbaum says. “It’s a difficult task, but I’m really optimistic about this century’s progress in biology.”

Birnbaum hopes that the model could be further improved and fine-tuned for a specific task, which has in the past led to better predictions, for example, on the outcome or consequence of a particular mutation. 

Ultimately, according to Keating, “Our methods move the field toward designing useful new-to-nature proteins for diverse applications while providing a stronger foundation for future advances.” 

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