Dean of Engineering Paula Hammond ’84 PhD ’93 made a resounding call for the MIT community to “embrace endless hope” and “never stop looking forward,” in a keynote address at the Institute’s annual MLK Celebration on Wednesday, Feb. 11.

“We each have a role to play in contributing to our future, and we each must embrace endless hope and continuously renew our faith in ourselves to accomplish that dream,” Hammond said, to an audience of hundreds at the event.

She added: “Whether it is through caring for those in our community, teaching others, providing inspiration, leadership, or critical support to others in their moment of need, we provide support for one another on our journey … It is that future that will feed the optimism and faith that we need to move forward, to inspire and encourage, and to never stop looking forward.”

The MLK Celebration is an annual tribute to the life and legacy of Martin Luther King Jr., and is always thematically organized around a quotation of King’s. This year, that passage was, “We must accept finite disappointment, but never lose infinite hope.”

Hammond and multiple other speakers at the event organized their remarks around that idea, while weaving in personal reflections about the importance of community, family, and mentorship.

As Hammond noted, “We can lay the path toward a better, greater time with the steps that we take today even in the face of incredible disappointment, shock and disruption.” She added: “Principles founded in fear, ignorance, or injustice ultimately fail because they do not meet the needs of a growing and prosperous nation and world.”

The event, which took place in MIT’s Walker Memorial (Building 50), featured remarks by students, staff, and campus leaders, as well as musical performances by the recently reconstituted MIT Gospel Choir. (Listen to one of those performances by clicking on the player at the end of this article.)

MIT President Sally A. Kornbluth provided introductory remarks, noting that this year’s event was occurring during “a time when feeling fractured, isolated, and pitted against each other feels exhaustingly routine. A time when it’s easy to feel discouraged.” As such, she added, “the solace we take from [coming together at this event] couldn’t be more relevant now.”

Kornbluth also offered laudatory thoughts about Hammond, a highly accomplished research scientist who has held numerous leadership roles at MIT and elsewhere. Hammond, a chemical engineer, was named dean of the MIT School of Engineering in December. Prior to that, she has served as vice provost for faculty, from 2023 to 2025, and head of the Department of Chemical Engineering, from 2015 to 2023. In honor of her accomplishments, Hammond was named an Institute Professor, MIT’s highest faculty honor. A member of MIT’s Koch Institute for Integrative Cancer Research, Hammond has developed polymers and nanoscale materials with multiple applications, including drug delivery, imaging, and even battery advances.

Hammond was awarded the National Medal of Technology and Innovation in 2024. That year she also received MIT’s Killian Award, for faculty achievement. And she has earned the rare distinction of having been elected to all three national academies — the National Academy of Engineering, the National Academy of Medicine, and the National Academy of Sciences.

“I’ve never met anyone who better represents MIT’s highest values and aspirations than Paula Hammond,” Kornbluth said, citing both Hammond’s record of academic excellence and Institute service.

Among other things, Kornbluth observed, “Paula has been a longtime champion of MIT’s culture of openness to people and ideas from everywhere. In fact, it’s hard to think of anyone more open to sharing what she knows — and more interested in hearing your point of view. And the respect she shows to everyone — no matter their job or background — is an example for us all.”

Michael Ewing ’27, a mechanical engineering major, provided welcoming remarks while introducing the speakers as well as the MLK Celebration planning committee.

Ewing noted that the event remains “extremely and vitally important” to the MIT community, and reflected on the meaning of this year’s motif, for individuals and larger communities.

“Dr. King’s hope constitutes the belief that one can make things better, even when current conditions are poor,” Ewing said. “In the face of adversity, we must remain connected to what’s most important, be grateful for both the challenges and the opportunities, and hold on to the long-term belief that no matter what, no matter what, there’s an opportunity for us to learn, grow, and improve.”

The annual MLK Celebration also highlighted further reflections from students and staff on King’s life and legacy and the value of his work.

“Everyone that has fought for a greater good in this world has left the battle without something that they came with,” said Oluwadara Deru, a senior in mechanical engineering and the featured undergraduate speaker. “But what they gained is invaluable.”

Ekua Beneman, a graduate student in chemistry, offered thoughts relating matters of academic achievement, and helping others in a university setting, to the larger themes of the celebration.

“Hope is not pretending disappointment doesn’t exist,” Beneman said. “Hope is choosing to pass forward what was once given to you. At a place like MIT, infinite hope looks like mentorship. It looks like making space. It looks like sharing knowledge instead of guarding or gatekeeping it. If we truly want to honor Dr. King’s legacy, beyond this beautiful celebration today, we do it by choosing community, mentorship, and hope in action.”

Denzil Streete, associate dean and director of the Office of Graduate Education, related the annual theme to everyday life at the Institute, as well as social life everywhere.

“Hope lies in small, often uncelebrated acts,” Streete said. “Showing up. Being present. Responding with patience. Translating complicated processes into next steps. Making one more call. Sending one more email.”

He concluded: “See your daily work as moral work … Every day, through joy and care, we choose infinite hope, for our students, and for one another.”

Reverend Thea Keith-Lucas, chaplain to the Institute and associate dean in the Office of Religious, Spiritual, and Ethical Life, offered both an invocation and a benediction at the event.

The annual celebration includes the Dr. Martin Luther King Jr. Leadership Awards Recipients, given this year to Melissa Smith PhD ’12, Fred Harris, Carissma McGee, Janine Medrano, and Edwin Marrero.

For all the turbulence in the world, Hammond said toward the conclusion of her address, people can continue to make progress in their own communities, and can be intentional about focusing, in part, on the possibilities of progress ahead.

At MIT, Hammond noted, “The commitment of our faculty, students, and staff to continuously learn, to ask deep questions and to apply our knowledge, our perspectives and our insights to the biggest world problems is something that gives me infinite hope and optimism for the future.”

In many academic circles, innovation is imagined as a lab-to-market pipeline that travels through patent filings, venture rounds, and coastal research hubs. But a growing movement inside U.S. universities is pushing students toward a different frontier: solving real engineering problems alongside rural communities whose challenges directly shape national food security. 

A compelling example of this shift can be found in the story of Kiyoko “Kik” Hayano, a second-year mechanical engineering student at MIT, and her work through MIT D-Lab with Keo Fish Farms, a commercial aquaculture operation in the Arkansas Delta.

Hayano’s journey — from a small, windswept town in rural Wyoming to MIT’s campus in Cambridge, Massachusetts, and on to a working Arkansas fish farm — offers a tangible glimpse into how applied engineering, academic partnerships, and on-the-ground innovation can create new models for regenerative agriculture in the United States.

Wyoming childhood and an engineering dream

Hayano grew up in Powell, Wyoming (population ~6,400), a community defined by agriculture, water scarcity, and long distances. Her early interests in gardening with her grandmother and tinkering with irrigation projects through her high school’s agricultural center formed the foundation for a more ambitious goal: studying mechanical engineering at MIT.

That ambition paid off. Shortly after arriving in Cambridge, Hayano connected with MIT D-Lab, a program founded to co-create engineering solutions with communities, rather than for them — especially in regions facing poverty, resource constraints, or climate-related disruptions. For many MIT students, D-Lab is their entry point into field-based development work across Africa, Latin America, and Southeast Asia. Increasingly, however, the program has expanded its domestic mission to include rural areas of the United States experiencing food, water, and energy insecurity.

MIT D-Lab meets the Arkansas Delta

That domestic shift set the stage for a new joint effort. In 2024, Keo Fish Farms — a commercial aquaculture farm near Keo, Arkansas — contacted D-Lab seeking technical collaboration on a growing water quality challenge. The farm had begun to observe elevated iron levels in its groundwater, leading to fish mortality events during peak summer conditions. The problem was both biological and mechanical: Aquaculture species like hybrid striped bass and triploid grass carp require consistent, clean water inputs, and well systems tapping iron-rich geologic layers were compromising fish health, hatchery performance, and long-term viability.

Kendra Leith, MIT D-Lab associate director for research, saw an opportunity. The Delta region represents a collision of three major realities that matter deeply to both public policy and academic research: high-value protein production, aging or inadequate water infrastructure, and generational rural decline.

For Hayano, the chance to work on an important engineering problem with environmental, agricultural, and economic implications was exactly why she chose mechanical engineering in the first place.

Applied engineering in a living laboratory

When Hayano arrived at Keo Fish Farms, the project was structured as a co-creative engineering engagement — D-Lab’s core model. She documented the existing water intake system, analyzed the well depth relative to geological iron strata, and evaluated filtration options including aeration, sedimentation, and emerging biochar-based media.

The collaboration generated three immediate academic values. First, the team reviewed real constraints, a process known as ground truthing. Constraints in this situation included iron levels that shift seasonally, capital budgets that do not assume infinite funding, and labor cycles tied to harvest seasons. The team then scoped out the technology that might be used to mitigate problem areas. Iron-reduction solutions ranged from drilling deeper wells to incorporating biochar and other regenerative filtration mediums capable of binding contaminants while improving soil and plant health elsewhere on the farm. Finally, they reviewed policy relevance: Water quality in aquaculture sits at the intersection of U.S. Department of Agriculture (USDA) conservation, Environmental Protection Agency (EPA) water standards, climate-driven aquifer variability, and domestic protein security — issues central to U.S. food systems.

Leith notes that “the most transformative experiences happen when students and communities learn from one another.” The Keo project, she adds, is an example of how domestic food production systems can act as test beds for innovation that previously would have been deployed exclusively abroad.

Regenerative agriculture as a national opportunity

While Keo Fish Farms played a supporting role in the narrative, the project highlighted a broader challenge and opportunity: Can U.S. aquaculture transition toward regenerative agriculture principles?

Regenerative agriculture — long associated with row crops, grazing systems, and soil carbon — rarely includes aquaculture in the national conversation. Yet aquaculture sits at the nexus of water chemistry, nutrient cycling, renewable energy integration, biochar and filtration research, protein production, and greenhouse gas mitigation.

Hayano’s work helped illuminate that regenerative aquaculture will likely depend on regenerative water systems, where filtration, biochar, solar energy, and nutrient reuse form a closed-loop infrastructure, rather than a linear extract–use–discharge model.

D-Lab’s domestic projects increasingly intersect with this space, creating pathways for MIT students and faculty to collaborate with USDA, the U.S. Department of Energy (DoE), and National Science Foundation (NSF) priorities around rural innovation, renewable energy, and water systems engineering.

The role of industry partners: less spotlight, more signal

Keo Fish Farms’ involvement served as a platform — not a spotlight — for the engineering and policy implications emerging from the project. The farm provided three critical ingredients academic institutions often lack: a real commercial engineering problem with economic consequences, a living laboratory for field research and prototyping, and a pathway for future regenerative adoption at scale.

The farm’s leadership has stated that its long-term goal is to become a first-in-class demonstration site for regenerative aquaculture in the United States, combining advanced iron and sediment filtration, biochar production from local rice hull waste streams, renewable solar energy systems, water recycling and nutrient recovery, reduced chemical inputs, and habitat and biodiversity considerations.

To be sure, the D-Lab collaboration did not solve that entire puzzle, but it created the blueprint for a pathway, showing how academic partnerships can accelerate regenerative transitions in rural U.S. agriculture and aquaculture systems.

Lessons for universities and policymakers

For universities, the Keo–MIT D-Lab partnership offers a replicable model for experiential learning for STEM students, field-based regenerative research, technology validation in live agricultural systems, and cross-disciplinary collaboration. And for federal and state policymakers, it illustrates how rural communities can serve as innovation sites, why water infrastructure modernization matters to food security, how regenerative agriculture can expand beyond soil and grazing, and why public-private-academic partnerships deserve new funding pathways.

All of this aligns with emerging priorities at the USDA, DoE, NSF, and EPA around sustainability, climate resilience, and domestic protein systems.

For Hayano, the experience reinforced that engineering careers can be rooted not only in Silicon Valley labs or aerospace firms, but also in overlooked rural systems that feed the country. 

“I’m really grateful for the experience,” she reflected after the project. “It opened my eyes to how engineering can support sustainable food systems and rural communities.”

The sentiment echoes a broader trend among students seeking careers at the intersection of technology, environment, and public good. Whether Hayano returns to the Arkansas Delta or not, her path captures something deeply relevant to America’s innovation story: talent emerging from rural places, innovating at world-class institutions, and returning engineering capacity back into the country’s agricultural heartland.

It is, in many ways, a modern form of the American dream — one grounded not in abstraction, but in water, food, soil, and the systems that will define our next century.

Industrial yeasts are a powerhouse of protein production, used to manufacture vaccines, biopharmaceuticals, and other useful compounds. In a new study, MIT chemical engineers have harnessed artificial intelligence to optimize the development of new protein manufacturing processes, which could reduce the overall costs of developing and manufacturing these drugs.

Using a large language model (LLM), the MIT team analyzed the genetic code of the industrial yeast Komagataella phaffii — specifically, the codons that it uses. There are multiple possible codons, or three-letter DNA sequences, that can be used to encode a particular amino acid, and the patterns of codon usage are different for every organism.

The new MIT model learned those patterns for K. phaffii and then used them to predict which codons would work best for manufacturing a given protein. This allowed the researchers to boost the efficiency of the yeast’s production of six different proteins, including human growth hormone and a monoclonal antibody used to treat cancer.

“Having predictive tools that consistently work well is really important to help shorten the time from having an idea to getting it into production. Taking away uncertainty ultimately saves time and money,” says J. Christopher Love, the Raymond A. and Helen E. St. Laurent Professor of Chemical Engineering at MIT, a member of the Koch Institute for Integrative Cancer Research, and faculty co-director of the MIT Initiative for New Manufacturing (MIT INM).

Love is the senior author of the new study, which appears this week in the Proceedings of the National Academy of Sciences. Former MIT postdoc Harini Narayanan is the paper’s lead author.

Codon optimization

Yeast such as K. phaffii and Saccharomyces cerevisiae (baker’s yeast) are the workhorses of the biopharmaceutical industry, producing billions of dollars of protein drugs and vaccines every year.

To engineer yeast for industrial protein production, researchers take a gene from another organism, such as the insulin gene, and modify it so that the microbe will produce it in large quantities. This requires coming up with an optimal DNA sequence for the yeast cells, integrating it into the yeast’s genome, devising favorable growth conditions for it, and finally purifying the end product.

For new biologic drugs — large, complex drugs produced by living organisms — this development process might account for 15 to 20 percent of the overall cost of commercializing the drug.

“Today, those steps are all done by very laborious experimental tasks,” Love says. “We have been looking at the question of where could we take some of the concepts that are emerging in machine learning and apply them to make different aspects of the process more reliable and simpler to predict.”

In this study, the researchers wanted to try to optimize the sequence of DNA codons that make up the gene for a protein of interest. There are 20 naturally occurring amino acids, but 64 possible codon sequences, so most of these amino acids can be encoded by more than one codon. Each codon corresponds to a unique transfer RNA (tRNA) molecule, which carries the correct amino acid to the ribosome, where amino acids are strung together into proteins.

Different organisms use each of these codons at different rates, and designers of engineered proteins often optimize the production of their proteins by choosing the codons that occur the most frequently in the host organism. However, this doesn’t necessarily produce the best results. If the same codon is always used to encode arginine, for example, the cell may run low on the tRNA molecules that correspond to that codon.

To take a more nuanced approach, the MIT team deployed a type of large language model known as an encoder-decoder. Instead of analyzing text, the researchers used it to analyze DNA sequences and learn the relationships between codons that are used in specific genes.

Their training data, which came from a publicly available dataset from the National Center for Biotechnology Information, consisted of the amino acid sequences and corresponding DNA sequences for all of the approximately 5,000 proteins naturally produced by K. phaffii.

“The model learns the syntax or the language of how these codons are used,” Love says. “It takes into account how codons are placed next to each other, and also the long-distance relationships between them.”

Once the model was trained, the researchers asked it to optimize the codon sequences of six different proteins, including human growth hormone, human serum albumin, and trastuzumab, a monoclonal antibody used to treat cancer.

They also generated optimized sequences of these proteins using four commercially available codon optimization tools. The researchers inserted each of these sequences into K. phaffii cells and measured how much of the target protein each sequence generated. For five of the six proteins, the sequences from the new MIT model worked the best, and for the sixth, it was the second-best.

“We made sure to cover a variety of different philosophies of doing codon optimization and benchmarked them against our approach,” Narayanan says. “We’ve experimentally compared these approaches and showed that our approach outperforms the others.”

Learning the language of proteins

K. phaffii, formerly known as Pichia pastoris, is used to produce dozens of commercial products, including insulin, hepatitis B vaccines, and a monoclonal antibody used to treat chronic migraines. It is also used in the production of nutrients added to foods, such as hemoglobin.

Researchers in Love’s lab have started using the new model to optimize proteins of interest for K. phaffii, and they have made the code available for other researchers who wish to use it for K. phaffii or other organisms.

The researchers also tested this approach on datasets from different organisms, including humans and cows. Each of the resulting models generated different predictions, suggesting that species-specific models are needed to optimize codons of target proteins.

By looking into the inner workings of the model, the researchers found that it appeared to learn some of the biological principles of how the genome works, including things that the researchers did not teach it. For example, it learned not to include negative repeat elements — DNA sequences that can inhibit the expression of nearby genes. The model also learned to categorize amino acids based on traits such as hydrophobicity and hydrophilicity.

“Not only was it learning this language, but it was also contextualizing it through aspects of biophysical and biochemical features, which gives us additional confidence that it is learning something that’s actually meaningful and not simply an optimization of the task that we gave it,” Love says.

The research was funded by the Daniel I.C. Wang Faculty Research Innovation Fund at MIT, the MIT AltHost Research Consortium, the Mazumdar-Shaw International Oncology Fellowship, and the Koch Institute.

America has been making steel from iron ore the same way for hundreds of years. Unfortunately, it hasn’t been making enough of it. Today the U.S. is the world’s largest steel importer, relying on other countries to produce a material that serves as the backbone of our society.

That’s not to say the U.S. is alone: Globally, most steel today is made in enormous, multi-billion-dollar plants using a coal-based process that hasn’t changed much in 300 years.

Now Hertha Metals, founded by CEO Laureen Meroueh SM ’18, PhD ’20, is scaling up a new steel production system powered by natural gas and electricity. The process, which can also run on hydrogen, uses a continuous electric arc furnace within which iron ore of any grade and format is reduced and carburized into molten steel in a single step. It also eliminates the need for coking and sintering plants, along with other dangerous and expensive components of traditional systems. As a result, the company says its process uses 30 percent less energy and costs less to operate than conventional steel mills in America.

“The real headline is the fact that we can make steel from iron ore more cost-competitive by 25 percent in the United States, while also reducing emissions.” Meroueh says. “The United States hasn’t been competitive in steelmaking in decades. Now we’re enabling that.”

Since late 2024, Hertha has been operating a 1-tonne-per-day pilot plant at its first production facility outside Houston, Texas. The company calls it the world’s largest demonstration of a single-step steelmaking process. This year, the company will begin construction of a plant that will be able to produce 10,000 tons of steel each year. That plant, which Hertha expects to reach full production capacity at the end 2027, will also produce high-purity iron for the magnet industry, helping America onshore another critical material.

“By importing so much of our pig iron and steel, we are completely reliant on global trade mechanisms and geopolitics remaining the way they are today for us to continue making the materials that are critical for our infrastructure, our defense systems, and our energy systems,” Meroueh says. “Steel is the most foundational material to our society. It is simply irreplaceable.”

Streamlining steelmaking

Meroueh earned her master’s degree in the lab of Gang Chen, MIT’s Carl Richard Soderberg Professor of Power Engineering. She studied thermal energy storage and the fundamental physics of heat transfer, eventually getting her first taste of entrepreneurship when she explored commercializing some of that research. Meroueh received a grant from the MIT Sandbox Innovation Fund and considers Executive Director Jinane Abounadi a close mentor today.

The experience taught Meroueh a lot about startups, but she ultimately decided to stay at MIT to pursue her PhD in metallurgy and hydrogen production in the lab of Douglas Hart, MIT professor of mechanical engineering. After earning her PhD in 2020, she was recruited to lead a hydrogen production startup for a year and a half.

“After that experience, I was looking at all of the hard-to-abate, high-emissions sectors of the economy to find the one receiving the least attention,” Meroueh says. “I stumbled onto steel and fell in love.”

Meroueh became an Innovators Fellow at the climate and energy startup investment firm Breakthrough Energy and officially founded Hertha Metals in 2022.

The company is named after Hertha Ayrton, a 19th-century physicist and inventor who advanced our understanding of electric arcs, which the company uses in its furnaces.

Globally, most steel today is made by combining iron ore with coke (from coal) and limestone in a blast furnace to make molten iron. That “pig iron” is then sent to another furnace to burn off excess carbon and impurities. Alloying elements are then added, and the steel is sent for casting and finishing, requiring additional machinery.

The U.S. makes most of its steel from recycled scrap metal, but it still must import iron made from a blast furnace to reach useful grades of steel.

“The United States has a massive need to make steel from iron ore, not just scrap, so we can stop relying on importing so much,” Meroueh explains. “We only have about 11 operational blast furnaces in the U.S., so we end up importing about 90 percent of the pig iron needed to feed into domestic scrap steel furnaces.”

To solve the problem, Meroueh leveraged a fuel America has in abundance: natural gas. Hertha’s system uses natural gas (the process also works with hydrogen) to reduce iron ore while using electricity to melt it in a single step. She says the closest competing technology requires scarce and expensive pelletized, high-grade iron ore and multiple furnaces to produce liquid steel. Meroueh’s process uses iron ore of any format or grade, producing refined liquid steel in a single furnace, cutting both cost and emissions.

“Many reactions that were previously run sequentially though a conventional steelmaking process are now occurring simultaneously, within a single furnace,” Meroueh explains. “We’re melting, we’re reducing, and we’re carburizing the steel to the exact amount we need. What exits our furnace is a refined molten steel. We can process any grade and format of iron ore because everything is occurring in the molten phase. It doesn’t matter whether the ore came in as a pellet or clumps and fines out of the ground.”

Meroueh says the company’s biggest innovation is performing the gaseous reduction when the iron oxide is a molten liquid using proprietary gas technologies.     

“All of the conventional steelmaking technologies perform reduction while the iron ore is in a solid state, and they use gas — whether that’s combusted coke or natural gas — to perform that reduction,” Meroueh says. “We saw the inefficiency in doing that and how it restricted the grade and form of usable iron ore, because at the end of the day you have to melt the ore anyway.”

Hertha’s system is modular and uses standard off-gas handling equipment, steam turbines, and heat exchangers. It also recycles natural gas to regenerate electricity from the hot off-gas leaving the furnace.

“Our steel mill has its own little power plant attached that leads to 35 percent recovery in energy and minimizes grid power demand in an age in which we are competing with data centers,” Meroueh says.

Onshoring critical materials

Today’s steel mills are the result of enormous investments and are designed to run for at least 50 years. Hertha Metals doesn’t envision replacing those entirely — at least not anytime soon.

“You’re not just going to shut off a steel mill in the middle of its life,” Meroueh says. “Sure, you can build new steel mills, but we really want to be able to displace the blast furnace and the basic oxygen furnace while still utilizing all the mill’s downstream equipment.”

The company’s Houston plant began producing one ton of steel per day just two years after Hertha’s founding and less than one year after Meroueh opened up Hertha’s headquarters. She calls it an important first step.

“This is the largest-scale demonstration of a single-step steelmaking company,” Meroueh says. “It’s a true breakthrough in terms of scalability, pace of progress, and capital efficiency.”

The company’s next plant, which will be capable of producing 10,000 tons of steel each year, will also be producing high-purity iron for permanent magnets, which are used in electric motors, robotics, consumer electronics, aerospace and military hardware.

“It’s insane that we don’t make rare earth magnets domestically,” Meroueh says. “It’s insane that any country doesn’t make their own rare earth magnets. Most rare earth magnets are permanent magnets, so neodymium magnets. What’s interesting is that by weight, 70 percent of that magnet is not a rare earth, it’s high-purity iron. America doesn’t currently make any high-purity iron, but Hertha has already made it in our pilot plant.”

Hertha plans to quickly scale up its production of high-purity iron so that, by 2030, it will be able to meet about a quarter of total projected demand for magnets in the U.S.

After that, the company plans to run a full-scale commercial steel plant in partnership with a steel manufacturer in America. Meroueh says that plant, which will be able to produce around half a million tons of steel each year, should be operational by 2030.

“We are eager to partner with today’s steel producers so that we can collectively leverage the existing infrastructure alongside Hertha’s innovation,” Meroueh says. “That includes the $1.5 billion of capital downstream of a melt shop that Hertha’s process can integrate into. The melt shop is the ore-to-liquid steel portion of the steel mill. That’s just the start.  It’s a smaller scale than a conventional plant in which we still economically out compete traditional production processes. Then we’re going to scale to 2 million tons per year once we build up our balance sheet.”

The Abdul Latif Jameel Poverty Action Lab (J-PAL) at MIT has awarded funding to eight new research studies to understand how artificial intelligence innovations can be used in the fight against poverty through its new Project AI Evidence.

The age of AI has brought wide-ranging optimism and skepticism about its effects on society. To realize AI’s full potential, Project AI Evidence (PAIE) will identify which AI solutions work and for whom, and scale only the most effective, inclusive, and responsible solutions — while scaling down those that may potentially cause harm.

PAIE will generate evidence on what works by connecting governments, tech companies, and nonprofits with world-class economists at MIT and across J-PAL’s global network to evaluate and improve AI solutions to entrenched social challenges.

The new initiative is prioritizing questions policymakers are already asking: Do AI-assisted teaching tools help all children learn? How can early-warning flood systems help people affected by natural disasters? Can machine learning algorithms help reduce deforestation in the Amazon? Can AI-powered chatbots help improve people’s health? In the coming years, PAIE will run a series of funding competitions to invite proposals for evaluations of AI tools that address questions like these, and many more.

PAIE is financially supported by a grant from Google.org, philanthropic support from Community Jameel, a grant from Canada’s International Development Research Centre and UK International Development, and a collaboration agreement with Amazon Web Services. Through a grant from Eric and Wendy Schmidt, awarded by recommendation of Schmidt Sciences, the initiative will also study generative AI in the workplace, particularly in low- and middle-income countries.

Alex Diaz, head of AI for social good at Google.org, says, “we’re thrilled to collaborate with MIT and J-PAL, already leaders in this space, on Project AI Evidence. AI has great potential to benefit all people, but we urgently need to study what works, what doesn’t, and why, if we are to realize this potential.”

“Artificial intelligence holds extraordinary potential, but only if the tools, knowledge, and power to shape it are accessible to all — that includes contextually grounded research and evidence on what works and what does not,” adds Maggie Gorman-Velez, vice president of strategy, regions, and policies at IDRC. “That is why IDRC is proud to be supporting this new evaluation work as part of our ongoing commitment to the responsible scaling of proven safe, inclusive, and locally relevant AI innovations.”

J-PAL is uniquely positioned to help understand AI’s effects on society: Since its inception in 2003, J-PAL’s network of researchers has led over 2,500 rigorous evaluations of social policies and programs around the world. Through PAIE, J-PAL will bring together leading experts in AI technology, research, and social policy, in alignment with MIT president Sally Kornbluth’s focus on generative AI as a strategic priority.

PAIE is chaired by Professor Joshua Blumenstock of the University of California at Berkeley; J-PAL Global Executive Director Iqbal Dhaliwal; and Professor David Yanagizawa-Drott of the University of Zurich.

New evaluations of urgent policy questions

The studies funded in PAIE’s first round of competition explore urgent questions in key sectors like education, health, climate, and economic opportunity.

How can AI be most effective in classrooms, helping both students and teachers?

Existing research shows that personalized learning is important for students, but challenging to implement with limited resources. In Kenya, education social enterprise EIDU has developed an AI tool that helps teachers identify learning gaps and adapt their daily lesson plans. In India, the nongovernmental organization (NGO) Pratham is developing an AI tool to increase the impact and scale of the evidence-informed Teaching at the Right Level approach. J-PAL researchers Daron Acemoglu, Iqbal Dhaliwal, and Francisco Gallego will work with both organizations to study the effects and potential of these different use cases on teachers’ productivity and students’ learning.

Can AI tools reduce gender bias in schools?

Researchers are collaborating with Italy’s Ministry of Education to evaluate whether AI tools can help close gender gaps in students’ performance by addressing teachers’ unconscious biases. J-PAL affiliates Michela Carlana and Will Dobbie, along with Francesca Miserocchi and Eleonora Patacchini, will study the impacts of two AI tools, one that helps teachers predict performance and a second that gives real-time feedback on the diversity of their decisions.

Can AI help career counselors uncover more job opportunities?

In Kenya, researchers are evaluating if an AI tool can identify overlooked skills and unlock employment opportunities, particularly for youth, women, and those without formal education. In collaboration with NGOs Swahilipot and Tabiya, Jasmin Baier and J-PAL researcher Christian Meyer will evaluate how the tool changes people’s job search strategies and employment. This study will shed light on AI as a complement, rather than a substitute, for human expertise in career guidance.

Looking forward

As use of AI in the social sector evolves, these evaluations are a first step in discovering effective, responsible solutions that will go the furthest in alleviating poverty and inequality.

J-PAL’s Dhaliwal notes, “J-PAL has a long history of evaluating innovative technology and its ability to improve people’s lives. While AI has incredible potential, we need to maximize its benefits and minimize possible harms. We’re grateful to our donors, sponsors, and collaborators for their catalytic support in launching PAIE, which will help us do exactly that by continuing to expand evidence on the impacts of AI innovations.”

J-PAL is also seeking new collaborators who share its vision of discovering and scaling up real-world AI solutions. It aims to support more governments and social sector organizations that want to adopt AI responsibly, and will continue to expand funding for new evaluations and provide policy guidance based on the latest research.

To learn more about Project AI Evidence, subscribe to J-PAL's newsletter or contact paie@povertyactionlab.org.

Maria Yang ’91, the William E. Leonhard (1940) Professor in the Department of Mechanical Engineering, has been appointed vice provost for faculty at MIT, a role in which she will oversee programs and strategies to recruit and retain faculty members and support them throughout their careers.

Provost Anantha Chandrakasan announced Yang’s appointment, which is effective Feb. 16, in an email to MIT faculty and staff today.

“In the nearly two decades since Maria joined the MIT faculty, she has exemplified dedicated service to the Institute and deep interdisciplinary collaboration,” Chandrakasan wrote. He added that, in a series of leadership positions within the School of Engineering, Yang “consistently demonstrated her skill as a leader, her empathy as a colleague, and her values-driven decision-making.”

As vice provost for faculty, Yang will play a pivotal role in creating an environment where MIT’s faculty members are able to do their best work, “pursuing bold ideas with excellence and creativity,” according to Chandrakasan’s letter. She will partner with school and department leaders on faculty recruitment and retention, mentorship, and strategic planning, and she will oversee programs to support faculty members’ professional development at every stage of their careers.

“Part of what makes MIT unique is the way it provides faculty the room and the encouragement to do work that they think is important, impactful, and sometimes unexpected,” says Yang. “I think it’s vital to foster a culture and a sense of community that really enables our faculty to perform at their best — as researchers, of course, but also as educators and mentors, and as citizens of MIT.”

In addition to her role supporting MIT faculty, Yang will also handle oversight and planning responsibilities for campus academic and research spaces, in partnership with the Office of the Executive Vice President and Treasurer. She will also serve as the principal investigator for the National Science Foundation’s New England Innovation Corps Hub, oversee MIT Solve, and represent the provost on various boards and committees, such as MIT International and the Axim Collaborative.

Yang, who attended MIT as an undergraduate in mechanical engineering as part of the Class of 1991 before earning her master’s and PhD degrees from the design division of the mechanical engineering department at Stanford University, returned to MIT in 2007 as an assistant professor. She has held a number of leadership positions at MIT, including associate dean, deputy dean, and interim dean of the School of Engineering. 

In 2021, Yang co-chaired an Institute-wide committee on the future of design, which recommended the creation of a center to support design opportunities at MIT. Through a generous gift from the Morningside Foundation, the recommendation came to life as the interdisciplinary Morningside Academy for Design (MAD), where Yang has served as associate director since inception. Yang has been instrumental in the development of several new programs at MAD, including design-focused graduate fellowships open to students across MIT and a new design-themed first-year learning community.

Since 2017, Yang has also served as academic faculty director for MIT D-Lab, which uses participatory design to collaborate with communities around the world on the development of solutions to poverty challenges. And since 2024, Yang has served as a co-chair of the SHASS+ Connectivity Fund, which funds research projects in which scholars in the School of Humanities, Arts, and Social Sciences collaborate with faculty colleagues from other schools at MIT.

Given Yang’s extensive track record of working across disciplinary lines, Chandrakasan said in his letter that he had “no doubt that in her new role she will be an effective and trusted champion for colleagues across the Institute.”

An internationally recognized leader in design theory and methodology, Yang is currently focused on researching the early-stage processes used to create successful designs for everything from consumer products to complex, large-scale engineering systems, and the role that these early-stage processes play in determining design outcomes.

Yang, a fellow of the American Society of Mechanical Engineers (ASME), received the 2024 ASME Design Theory and Methodology Award, recognizing “sustained and meritorious contributions” in the field. She has also been recognized with a National Science Foundation CAREER award and the American Society of Engineering Education Fred Merryfield Design Award. In 2017 Yang was named a MacVicar Faculty Fellow, one of MIT’s highest teaching honors.

Yang succeeds Institute Professor Paula Hammond, who served in the role from 2023 before being named dean of the School of Engineering, a role she assumed in January.

For more than a decade, MIT Associate Professor Rafael Gómez-Bombarelli has used artificial intelligence to create new materials. As the technology has expanded, so have his ambitions.

Now, the newly tenured professor in materials science and engineering believes AI is poised to transform science in ways never before possible. His work at MIT and beyond is devoted to accelerating that future.

“We’re at a second inflection point,” Gómez-Bombarelli says. “The first one was around 2015 with the first wave of representation learning, generative AI, and high-throughput data in some areas of science. Those are some of the techniques I first brought into my lab at MIT. Now I think we’re at a second inflection point, mixing language and merging multiple modalities into general scientific intelligence. We’re going to have all the model classes and scaling laws needed to reason about language, reason over material structures, and reason over synthesis recipes.”

Gómez Bombarelli’s research combines physics-based simulations with approaches like machine learning and generative AI to discover new materials with promising real-world applications. His work has led to new materials for batteries, catalysts, plastics, and organic light-emitting diodes (OLEDs). He has also co-founded multiple companies and served on scientific advisory boards for startups applying AI to drug discovery, robotics, and more. His latest company, Lila Sciences, is working to build a scientific superintelligence platform for the life sciences, chemical, and materials science industries.

All of that work is designed to ensure the future of scientific research is more seamless and productive than research today.

“AI for science is one of the most exciting and aspirational uses of AI,” Gómez-Bombarelli says. “Other applications for AI have more downsides and ambiguity. AI for science is about bringing a better future forward in time.”

From experiments to simulations

Gómez-Bombarelli grew up in Spain and gravitated toward the physical sciences from an early age. In 2001, he won a Chemistry Olympics competition, setting him on an academic track in chemistry, which he studied as an undergraduate at his hometown college, the University of Salamanca. Gómez-Bombarelli stuck around for his PhD, where he investigated the function of DNA-damaging chemicals.

“My PhD started out experimental, and then I got bitten by the bug of simulation and computer science about halfway through,” he says. “I started simulating the same chemical reactions I was measuring in the lab. I like the way programming organizes your brain; it felt like a natural way to organize one’s thinking. Programming is also a lot less limited by what you can do with your hands or with scientific instruments.”

Next, Gómez-Bombarelli went to Scotland for a postdoctoral position, where he studied quantum effects in biology. Through that work, he connected with Alán Aspuru-Guzik, a chemistry professor at Harvard University, whom he joined for his next postdoc in 2014.

“I was one of the first people to use generative AI for chemistry in 2016, and I was on the first team to use neural networks to understand molecules in 2015,” Gómez-Bombarelli says. “It was the early, early days of deep learning for science.”

Gómez-Bombarelli also began working to eliminate manual parts of molecular simulations to run more high-throughput experiments. He and his collaborators ended up running hundreds of thousands of calculations across materials, discovering hundreds of promising materials for testing.

After two years in the lab, Gómez-Bombarelli and Aspuru-Guzik started a general-purpose materials computation company, which eventually pivoted to focus on producing organic light-emitting diodes. Gómez-Bombarelli joined the company full-time and calls it the hardest thing he’s ever done in his career.

“It was amazing to make something tangible,” he says. “Also, after seeing Aspuru-Guzik run a lab, I didn’t want to become a professor. My dad was a professor in linguistics, and I thought it was a mellow job. Then I saw Aspuru-Guzik with a 40-person group, and he was on the road 120 days a year. It was insane. I didn’t think I had that type of energy and creativity in me.”

In 2018, Aspuru-Guzik suggested Gómez-Bombarelli apply for a new position in MIT’s Department of Materials Science and Engineering. But, with his trepidation about a faculty job, Gómez-Bombarelli let the deadline pass. Aspuru-Guzik confronted him in his office, slammed his hands on the table, and told him, “You need to apply for this.” It was enough to get Gómez-Bombarelli to put together a formal application.

Fortunately at his startup, Gómez-Bombarelli had spent a lot of time thinking about how to create value from computational materials discovery. During the interview process, he says, he was attracted to the energy and collaborative spirit at MIT. He also began to appreciate the research possibilities.

“Everything I had been doing as a postdoc and at the company was going to be a subset of what I could do at MIT,” he says. “I was making products, and I still get to do that. Suddenly, my universe of work was a subset of this new universe of things I could explore and do.”

It’s been nine years since Gómez Bombarelli joined MIT. Today his lab focuses on how the composition, structure, and reactivity of atoms impact material performance. He has also used high-throughput simulations to create new materials and helped develop tools for merging deep learning with physics-based modeling.

“Physics-based simulations make data and AI algorithms get better the more data you give them,” Gómez Bombarelli’s says. “There are all sorts of virtuous cycles between AI and simulations.”

The research group he has built is solely computational — they don’t run physical experiments.

“It’s a blessing because we can have a huge amount of breadth and do lots of things at once,” he says. “We love working with experimentalists and try to be good partners with them. We also love to create computational tools that help experimentalists triage the ideas coming from AI .”

Gómez-Bombarelli is also still focused on the real-world applications of the materials he invents. His lab works closely with companies and organizations like MIT’s Industrial Liaison Program to understand the material needs of the private sector and the practical hurdles of commercial development.

Accelerating science

As excitement around artificial intelligence has exploded, Gómez-Bombarelli has seen the field mature. Companies like Meta, Microsoft, and Google’s DeepMind now regularly conduct physics-based simulations reminiscent of what he was working on back in 2016. In November, the U.S. Department of Energy launched the Genesis Mission to accelerate scientific discovery, national security, and energy dominance using AI.

“AI for simulations has gone from something that maybe could work to a consensus scientific view,” Gómez-Bombarelli says. “We’re at an inflection point. Humans think in natural language, we write papers in natural language, and it turns out these large language models that have mastered natural language have opened up the ability to accelerate science. We’ve seen that scaling works for simulations. We’ve seen that scaling works for language. Now we’re going to see how scaling works for science.”

When he first came to MIT, Gómez-Bombarelli says he was blown away by how non-competitive things were between researchers. He tries to bring that same positive-sum thinking to his research group, which is made up of about 25 graduate students and postdocs.

“We’ve naturally grown into a really diverse group, with a diverse set of mentalities,” Gomez-Bombarelli says. “Everyone has their own career aspirations and strengths and weaknesses. Figuring out how to help people be the best versions of themselves is fun. Now I’ve become the one insisting that people apply to faculty positions after the deadline. I guess I’ve passed that baton.”

James J. Collins, the Termeer Professor of Medical Engineering and Science at MIT and faculty co-lead of the Abdul Latif Jameel Clinic for Machine Learning in Health, is embarking on a multidisciplinary research project that applies synthetic biology and generative artificial intelligence to the growing global threat of antimicrobial resistance (AMR).

The research project is sponsored by Jameel Research, part of the Abdul Latif Jameel International network. The initial three-year, $3 million research project in MIT’s Department of Biological Engineering and Institute of Medical Engineering and Science focuses on developing and validating programmable antibacterials against key pathogens.

AMR — driven by the overuse and misuse of antibiotics — has accelerated the rise of drug-resistant infections, while the development of new antibacterial tools has slowed. The impact is felt worldwide, especially in low- and middle-income countries, where limited diagnostic infrastructure causes delays or ineffective treatment.

The project centers on developing a new generation of targeted antibacterials using AI to design small proteins to disable specific bacterial functions. These designer molecules would be produced and delivered by engineered microbes, providing a more precise and adaptable approach than traditional antibiotics.

“This project reflects my belief that tackling AMR requires both bold scientific ideas and a pathway to real-world impact,” Collins says. “Jameel Research is keen to address this crisis by supporting innovative, translatable research at MIT.”

Mohammed Abdul Latif Jameel ’78, chair of Abdul Latif Jameel, says, “antimicrobial resistance is one of the most urgent challenges we face today, and addressing it will require ambitious science and sustained collaboration. We are pleased to support this new research, building on our long-standing relationship with MIT and our commitment to advancing research across the world, to strengthen global health and contribute to a more resilient future.”

The signals that drive many of the brain and body’s most essential functions — consciousness, sleep, breathing, heart rate, and motion — course through bundles of “white matter” fibers in the brainstem, but imaging systems so far have been unable to finely resolve these crucial neural cables. That has left researchers and doctors with little capability to assess how they are affected by trauma or neurodegeneration. 

In a new study, a team of MIT, Harvard University, and Massachusetts General Hospital researchers unveil AI-powered software capable of automatically segmenting eight distinct bundles in any diffusion MRI sequence.

In the open-access study, published Feb. 6 in the Proceedings of the National Academy Sciences, the research team led by MIT graduate student Mark Olchanyi reports that their BrainStem Bundle Tool (BSBT), which they’ve made publicly available, revealed distinct patterns of structural changes in patients with Parkinson’s disease, multiple sclerosis, and traumatic brain injury, and shed light on Alzheimer’s disease as well. Moreover, the study shows, BSBT retrospectively enabled tracking of bundle healing in a coma patient that reflected the patient’s seven-month road to recovery.

“The brainstem is a region of the brain that is essentially not explored because it is tough to image,” says Olchanyi, a doctoral candidate in MIT’s Medical Engineering and Medical Physics Program. “People don't really understand its makeup from an imaging perspective. We need to understand what the organization of the white matter is in humans and how this organization breaks down in certain disorders.”

Adds Professor Emery N. Brown, Olchanyi’s thesis supervisor and co-senior author of the study, “the brainstem is one of the body’s most important control centers. Mark’s algorithms are a significant contribution to imaging research and to our ability to the understand regulation of fundamental physiology. By enhancing our capacity to image the brainstem, he offers us new access to vital physiological functions such as control of the respiratory and cardiovascular systems, temperature regulation, how we stay awake during the day and how sleep at night.”

Brown is the Edward Hood Taplin Professor of Computational Neuroscience and Medical Engineering in The Picower Institute for Learning and Memory, the Institute for Medical Engineering and Science, and the Department of Brain and Cognitive Sciences at MIT. He is also an anesthesiologist at MGH and a professor at Harvard Medical School.

Building the algorithm

Diffusion MRI helps trace the long branches, or “axons,” that neurons extend to communicate with each other. Axons are typically clad in a sheath of fat called myelin, and water diffuses along the axons within the myelin, which is also called the brain’s “white matter.” Diffusion MRI can highlight this very directed displacement of water. But segmenting the distinct bundles of axons in the brainstem has proved challenging, because they are small and masked by flows of brain fluids and the motions produced by breathing and heart beats.

As part of his thesis work to better understand the neural mechanisms that underpin consciousness, Olchanyi wanted to develop an AI algorithm to overcome these obstacles. BSBT works by tracing fiber bundles that plunge into the brainstem from neighboring areas higher in the brain, such as the thalamus and the cerebellum, to produce a “probabilistic fiber map.” An artificial intelligence module called a “convolutional neural network” then combines the map with several channels of imaging information from within the brainstem to distinguish eight individual bundles.

To train the neural network to segment the bundles, Olchanyi “showed” it 30 live diffusion MRI scans from volunteers in the Human Connectome Project (HCP). The scans were manually annotated to teach the neural network how to identify the bundles. Then he validated BSBT by testing its output against “ground truth” dissections of post-mortem human brains where the bundles were well delineated via microscopic inspection or very slow but ultra-high-resolution imaging. After training, BSBT became proficient in automatically identifying the eight distinct fiber bundles in new scans.

In an experiment to test its consistency and reliability, Olchanyi tasked BSBT with finding the bundles in 40 volunteers who underwent separate scans two months apart. In each case, the tool was able to find the same bundles in the same patients in each of their two scans. Olchanyi also tested BSBT with multiple datasets (not just the HCP), and even inspected how each component of the neural network contributed to BSBT’s analysis by hobbling them one by one.

“We put the neural network through the wringer,” Olchanyi says. “We wanted to make sure that it’s actually doing these plausible segmentations and it is leveraging each of its individual components in a way that improves the accuracy.”

Potential novel biomarkers

Once the algorithm was properly trained and validated, the research team moved on to testing whether the ability to segment distinct fiber bundles in diffusion MRI scans could enable tracking of how each bundle’s volume and structure varied with disease or injury, creating a novel kind of biomarker. Although the brainstem has been difficult to examine in detail, many studies show that neurodegenerative diseases affect the brainstem, often early on in their progression.

Olchanyi, Brown and their co-authors applied BSBT to scores of datasets of diffusion MRI scans from patients with Alzheimer’s, Parkinson’s, MS, and traumatic brain injury (TBI). Patients were compared to controls and sometimes to themselves over time. In the scans, the tool measured bundle volume and “fractional anisotropy,” (FA) which tracks how much water is flowing along the myelinated axons versus how much is diffusing in other directions, a proxy for white matter structural integrity.

In each condition, the tool found consistent patterns of changes in the bundles. While only one bundle showed significant decline in Alzheimer’s, in Parkinson’s the tool revealed a reduction in FA in three of the eight bundles. It also revealed volume loss in another bundle in patients between a baseline scan and a two-year follow-up. Patients with MS showed their greatest FA reductions in four bundles and volume loss in three. Meanwhile, TBI patients didn’t show significant volume loss in any bundles, but FA reductions were apparent in the majority of bundles.

Testing in the study showed that BSBT proved more accurate than other classifier methods in discriminating between patients with health conditions versus controls.

BSBT, therefore, can be “a key adjunct that aids current diagnostic imaging methods by providing a fine-grained assessment of brainstem white matter structure and, in some cases, longitudinal information,” the authors wrote.

Finally, in the case of a 29-year-old man who suffered a severe TBI, Olchanyi applied BSBT to a scans taken during the man’s seven-month coma. The tool showed that the man’s brainstem bundles had been displaced, but not cut, and showed that over his coma, the lesions on the nerve bundles decreased by a factor of three in volume. As they healed, the bundles moved back into place as well.

The authors wrote that BSBT “has substantial prognostic potential by identifying preserved brainstem bundles that can facilitate coma recovery.”

The study’s other senior authors are Juan Eugenio Iglesias and Brian Edlow. Other co-authors are David Schreier, Jian Li, Chiara Maffei, Annabel Sorby-Adams, Hannah Kinney, Brian Healy, Holly Freeman, Jared Shless, Christophe Destrieux, and Hendry Tregidgo.

Funding for the study came from the National Institutes of Health, U.S. Department of Defense, James S. McDonnell Foundation, Rappaport Foundation, American SidS Institute, American Brain Foundation, American Academy of Neurology, Center for Integration of Medicine and Innovative Technology, Blueprint for Neuroscience Research, and Massachusetts Life Sciences Center.

3D bioprinting, in which living tissues are printed with cells mixed into soft hydrogels, or “bio-inks,” is widely used in the field of bioengineering for modeling or replacing the tissues in our bodies. The print quality and reproducibility of tissues, however, can face challenges. One of the most significant challenges is created simply by gravity — cells naturally sink to the bottom of the bioink-extruding printer syringe because the cells are heavier than the hydrogel around them.

“This cell settling, which becomes worse during the long print sessions required to print large tissues, leads to clogged nozzles, uneven cell distribution, and inconsistencies between printed tissues,” explains Ritu Raman, the Eugene Bell Career Development Professor of Tissue Engineering and assistant professor of mechanical engineering at MIT. “Existing solutions, such as manually stirring bioinks before loading them into the printer, or using passive mixers, cannot maintain uniformity once printing begins.”

In a study published Feb. 2 in the journal Device, Raman’s team introduces a new approach that aims to solve this core limitation by actively preventing cell sedimentation within bioinks during printing, allowing for more reliable and biologically consistent 3D printed tissues.

“Precise control over the bioink’s physical and biological properties is essential for recreating the structure and function of native tissues,” says Ferdows Afghah, a postdoc in mechanical engineering at MIT and lead author of the study.

“If we can print tissues that more closely mimic those in our bodies, we can use them as models to understand more about human diseases, or to test the safety and efficacy of new therapeutic drugs,” adds Raman. Such models could help researchers move away from techniques like animal testing, which supports recent interest from the U.S. Food and Drug Administration in developing faster, less expensive, and more informative new approaches to establish the safety and efficacy of new treatment paths.

“Eventually, we are working towards regenerative medicine applications such as replacing diseased or injured tissues in our bodies with 3D printed tissues that can help restore healthy function,” says Raman.

MagMix, a magnetically actuated mixer, is composed of two parts: a small magnetic propeller that fits inside the syringes used by bioprinters to deposit bioinks, layer by layer, into 3D tissues, and a permanent magnet attached to a motor that moves up and down near the syringe, controlling the movement of the propeller inside. Together, this compact system can be mounted onto any standard 3D bioprinter, keeping bioinks uniformly mixed during printing without changing the bioink formulation or interfering with the printer’s normal operation. To test the approach, the team used computer simulations to design the optimal mixing propeller geometry and speed and then validated its performance experimentally.

“Across multiple bioink types, MagMix prevented cell settling for more than 45 minutes of continuous printing, reducing clogging and preserving high cell viability,” says Raman. “Importantly, we showed that mixing speeds could be adjusted to balance effective homogenization for different bioinks while inducing minimal stress on the cells. As a proof-of-concept, we demonstrated that MagMix could be used to 3D print cells that could mature into muscle tissues over the course of several days.”

By maintaining uniform cell distribution throughout long or complex print jobs, MagMix enables the fabrication of high-quality tissues with more consistent biological function. Because the device is compact, low-cost, customizable, and easily integrated into existing 3D printers, it offers a broadly accessible solution for laboratories and industries working toward reproducible engineered tissues for applications in human health including disease modeling, drug screening, and regenerative medicine.

This work was supported, in part, by the Safety, Health, and Environmental Discovery Lab (SHED) at MIT, which provides infrastructure and interdisciplinary expertise to help translate biofabrication innovations from lab-scale demonstrations to scalable, reproducible applications.

“At the SHED, we focus on accelerating the translation of innovative methods into practical tools that researchers can reliably adopt,” says Tolga Durak, the SHED’s founding director. “MagMix is a strong example of how the right combination of technical infrastructure and interdisciplinary support can move biofabrication technologies toward scalable, real-world impact.”

The SHED’s involvement reflects a broader vision of strengthening technology pathways that enhance reproducibility and accessibility across engineering and the life sciences by providing equitable access to advanced equipment and fostering cross-disciplinary collaboration.

“As the field advances toward larger-scale and more standardized systems, integrated labs like SHED are essential for building sustainable capacity,” Durak adds. “Our goal is not only to enable discovery, but to ensure that new technologies can be reliably adopted and sustained over time.”

The team is also interested in non-medical applications of engineered tissues, such as using printed muscles to power safer and more efficient “biohybrid” robots.

The researchers believe this work can improve the reliability and scalability of 3D bioprinting, making the potential impacts on the field of 3D bioprinting and on human health significant. Their paper, “Advancing Bioink Homogeneity in Extrusion 3D Bioprinting with Active In Situ Magnetic Mixing,” is available now from the journal Device. 

Olympic figure skating looks effortless. Athletes sail across the ice, then soar into the air, spinning like a top, before landing on a single blade just 4-5 millimeters wide. To help figure skaters land quadruple axels, Salchows, Lutzes, and maybe even the elusive quintuple without looking the least bit stressed, Jerry Lu MFin ’24 developed an optical tracking system called OOFSkate that uses artificial intelligence to analyze video of a figure skater’s jump and make recommendations on how to improve. Lu, a former researcher at the MIT Sports Lab, has been aiding elite skaters on Team USA with their technical performance and will be working with NBC Sports during the 2026 Winter Olympics to help commentators and TV viewers make better sense of the complex scoring system in figure skating, snowboarding, and skiing. He’ll be applying AI technologies to explain nuanced judging decisions and demonstrate just how technically challenging these sports can be.

Meanwhile, Professor Anette “Peko” Hosoi, co-founder and faculty director of the MIT Sports Lab, is embarking on new research aimed at understanding how AI systems evaluate aesthetic performance in figure skating. Hosoi and Lu recently chatted with MIT News about applying AI to sports, whether AI systems could ever be used to judge Olympic figure skating, and when we might see a skater land a quint.

Q: Why apply AI to figure skating?

Lu: Skaters can always keep pushing, higher, faster, stronger. OOFSkate is all about helping skaters figure out a way to rotate a little bit faster in their jumps or jump a little bit higher. The system helps skaters catch things that perhaps could pass an eye test, but that might allow them to target some high-value areas of opportunity. The artistic side of skating is much harder to evaluate than the technical elements because it’s subjective.

To use mobile training app, you just need to take a video of an athlete’s jump, and it will spit out the physical metrics that drive how many rotations you can do. It tracks those metrics and builds in all of the other current elite and former elite athletes. You can see your data and then see, “This is how an Olympic champion did this element, perhaps I should try that.” You get the comparison and the automated classifier, which shows you if you did this trick at World Championships and it were judged by an international panel, this is approximately the grade of execution score they would give you.

Hosoi: There are a lot of AI tools that are coming online, especially things like pose estimators, where you can approximate skeletal configurations from video. The challenge with these pose estimators is that if you only have one camera angle, they do very well in the plane of the camera, but they do very poorly with depth. For example, if you’re trying to critique somebody’s form in fencing, and they’re moving toward the camera, you get very bad data. But with figure skating, Jerry has found one of the few areas where depth challenges don’t really matter. In figure skating, you need to understand: How high did this person jump, how many times did they go around, and how well did they land? None of those rely on depth. He’s found an application that pose estimators do really well, and that doesn’t pay a penalty for the things they do badly.

Q: Could you ever see a world in which AI is used to evaluate the artistic side of figure skating?

Hosoi: When it comes to AI and aesthetic evaluation, we have new work underway thanks to a MIT Human Insight Collaborative (MITHIC) grant. This work is in collaboration with Professor Arthur Bahr and IDSS graduate student Eric Liu. When you ask an AI platform for an aesthetic evaluation such as “What do you think of this painting?” it will respond with something that sounds like it came from a human. What we want to understand is, to get to that assessment, are the AIs going through the same sort of reasoning pathways or using the same intuitive concepts that humans go through to arrive at, “I like that painting,” or “I don’t like that painting”? Or are they just parrots? Are they just mimicking what they heard a person say? Or is there some concept map of aesthetic appeal? Figure skating is a perfect place to look for this map because skating is aesthetically judged. And there are numbers. You can’t go around a museum and find scores, “This painting is a 35.” But in skating, you’ve got the data.

That brings up another even more interesting question, which is the difference between novices and experts. It’s known that expert humans and novice humans will react differently to seeing the same thing. Somebody who is an expert judge may have a different opinion of a skating performance than a member of the general population. We’re trying to understand differences between reactions from experts, novices, and AI. Do these reactions have some common ground in where they are coming from, or is the AI coming from a different place than both the expert and the novice?

Lu: Figure skating is interesting because everybody working in the field of AI is trying to figure out AGI or artificial general intelligence and trying to build this extremely sound AI that replicates human beings. Working on applying AI to sports like figure skating helps us understand how humans think and approach judging. This has down-the-line impacts for AI research and companies that are developing AI models. By gaining a deeper understanding of how current state-of-the-art AI models work with these sports, and how you need to do training and fine-tuning of these models to make them work for specific sports, it helps you understand how AI needs to advance.

Q: What will you be watching for in the Milan Cortina Olympics figure skating competitions, now that you’ve been studying and working in this area? Do you think someone will land a quint?

Lu: For the winter games, I am working with NBC for the figure skating, ski, and snowboarding competitions to help them tell a data-driven story for the American people. The goal is to make these sports more relatable. Skating looks slow on television, but it’s not. Everything is supposed to look effortless. If it looks hard, you are probably going to get penalized. Skaters need to learn how to spin very fast, jump extremely high, float in the air, and land beautifully on one foot. The data we are gathering can help showcase how hard skating actually is, even though it is supposed to look easy.

I’m glad we are working in the Olympics sports realm because the world watches once every four years, and it is traditionally coaching-intensive and talent-driven sports, unlike a sport like baseball, where if you don’t have an elite-level optical tracking system you are not maximizing the value that you currently have. I’m glad we get to work with these Olympic sports and athletes and make an impact here.

Hosoi: I have always watched Olympic figure skating competitions, ever since I could turn on the TV. They’re always incredible. One of the things that I’m going to be practicing is identifying the jumps, which is very hard to do if you’re an amateur “judge.”

I have also done some back-of-the-envelope calculations to see if a quint is possible. I am now totally convinced it’s possible. We will see one in our lifetime, if not relatively soon. Not in this Olympics, but soon. When I saw we were so close on the quint, I thought, what about six? Can we do six rotations? Probably not. That’s where we start to come up against the limits of human physical capability. But five, I think, is in reach.