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    MIT Faculty Founder Initiative announces three winners of entrepreneurship awards

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    Patients with intractable cancers, chronic pain sufferers, and people who depend on battery-powered medical implants may all benefit from the ideas presented at the 2023-24 MIT-Royalty Pharma Prize Competition’s recent awards. This year’s top prizes went to researchers and biotech entrepreneurs Anne Carpenter, Frederike Petzschner, and Betar Gallant ’08, SM ’10, PhD ’13.MIT Faculty Founder Initiative Executive Director Kit Hickey MBA ’13 describes the time and hard work the three awardees and other finalists devoted to the initiative and its mission of cultivating female faculty in biotech to cross the chasm between laboratory research and its clinical application.“They have taken the first brave step of getting off the bench when they already work seven days a week. They have carved out time from their facilities, from their labs, from their lives in order to put themselves out there and leap into entrepreneurship,” Hickey says. “They’ve done it because they each want to see their innovations out in the world improving patients’ lives.”Carpenter, senior director of the Imaging Platform at the Broad Institute of MIT and Harvard, where she is also an institute scientist, won the competition’s $250,000 2023-24 MIT-Royalty Pharma Faculty Founder Prize Competition Grand Prize. Carpenter specializes in using microscopy imaging of cells and computational methods such as machine learning to accelerate the identification of chemical compounds with therapeutic potential to, for instance, shrink tumors. The identified compounds are then tested in biological assays that model the tumor ecosystem to see how the compounds would perform on actual tumors.Carpenter’s startup, SyzOnc, launched in April, a feat Carpenter associates with the assistance provided by the MIT Faculty Founder Initiative. Participants in the program receive mentorship, stipends, and advice from industry experts, as well as help with incorporating, assembling a management team, fundraising, and intellectual property strategy.“The program offered key insights and input at major decision points that gave us the momentum to open our doors,” Carpenter says, adding that participating “offered validation of our scientific ideas and business plan. That kind of credibility is really helpful to raising funding, particularly for those starting their first company.”Carpenter says she and her team will employ “the best biological and computational advancements to develop new therapies to fight tumors such as sarcoma, pancreatic cancer, and glioblastoma, which currently have dismal survival rates.”The MIT Faculty Founder Initiative was begun in 2020 by the School of Engineering and the Martin Trust Center for MIT Entrepreneurship, based on research findings by Sangeeta Bhatia, the Wilson Professor of Health Sciences and Technology, professor of electrical engineering and computer science, and faculty director of the MIT Faculty Founder Initiative; Susan Hockfield, MIT Corporation life member, MIT president emerita, and professor of neuroscience; and Nancy Hopkins, professor emerita of biology. An investigation they conducted showed that only about 9 percent of MIT’s 250 biotech startups were started by women, whereas women made up 22 percent of the faculty, as was presented in a 2021 MIT Faculty Newsletter.That data showed that “technologies from female labs were not getting out in the world, resulting in lost potential,” Hickey says.“The MIT Faculty Founder Initiative plays a pivotal role in MIT’s entrepreneurship ecosystem. It elevates visionary faculty working on solutions in biotech by providing them with critical mentorship and resources, ensuring these solutions can be rapidly scaled to market,” says Anantha Chandrakasan, MIT’s chief innovation and strategy officer, dean of engineering, and Vannevar Bush Professor of Electrical Engineering and Computer Science.The MIT Faculty Founder Initiative Prize Competition was launched in 2021. At this year’s competition, the judges represented academia, health care, biotech, and financial investment. In addition to awarding a grand prize, the competition also distributed two $100,000 prizes, one to a researcher from Brown University, the first university to collaborate with MIT in the entrepreneurship program.This year’s winner of the $100,000 2023-24 MIT-Royalty Pharma Faculty Founder Prize Competition Runner-Up Prize was Frederike Petzschner, assistant professor at the Carney Institute for Brain Science at Brown, for her SOMA startup’s digital pain management system, which helps sufferers to manage and relieve chronic pain.“We leverage cutting-edge technology to provide precision care, focusing specifically on personalized cognitive interventions tailored to each patient’s unique needs,” she says.With her startup on the verge of incorporating, Petzschner says, “without the Faculty Finder Initiative, our startup would still be pursuing commercialization, but undoubtedly at a much earlier and perhaps less structured stage.”“The constant support from the program organizers and our mentors was truly transformative,” she says.Gallant, associate professor of mechanical engineering at MIT and winner of the $100,000 2023-24 MIT-Royalty Pharma Faculty Founder Prize Competition Breakthrough Prize, is leading the startup Halogen. An expert on advanced battery technologies, Gallant and her team have developed high-density battery storage to improve the lifetime and performance of such medical devices as pacemakers.“If you can extend lifetime, you’re talking about longer times between invasive replacement surgeries, which really affects patient quality of life,” Gallant told MIT News in a 2022 interview.Jim Reddoch, executive vice president and chief scientific officer of sponsor Royalty Pharma, emphasized his company’s support for both the competition and the MIT Faculty Finder Initiative program.“Royalty Pharma is thrilled to support the 2023-2024 MIT-Royalty Pharma Prize Competition and accelerate life sciences innovation at leading research institutions such as MIT and Brown,” Reddoch says. “By supporting the amazing female entrepreneurs in this program, we hope to catalyze more ideas from the lab to biotech companies and eventually into the hands of patients.”Bhatia has referred to the MIT Faculty Founder Initiative as a “playbook” on how to direct female faculty’s high-impact technologies that are not being commercialized into the world of health care.“To me, changing the game means that when you have an invention in your lab, you’re connected enough to the ecosystem to know when it should be a company, and to know who to call and how to get your first investors and how to quickly catalyze your team — and you’re off to the races,” Bhatia says. “Every one one of those inventions can be a medicine as quickly as possible. That’s the future I imagine.”Co-founder Hockfield referred to MIT’s role in promoting entrepreneurship in remarks at the award ceremony, alluding to Brown University’s having joined the effort.“MIT has always been a leader in entrepreneurship,” Hockfield says. “Part of leading is sharing with the world. The collaboration with Brown University for this cohort shows that MIT can share our approach with the world, allowing other universities to follow our model of supporting academic entrepreneurship.”Hickey says that when she and Bhatia asked 30 female faculty members three years ago why they were not commercializing their technologies, many said they had no access to the appropriate networks of mentors, investors, role models, and business partners necessary to begin the journey.“We encourage you to become this network that has been missing,” Hickey told the awards event audience, which included an array of leaders in the biotech world. “Get to know our amazing faculty members and continue to support them. Become a part of this movement.” More

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      Improving drug development with a vast map of the immune system

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      The human immune system is a network made up of trillions of cells that are constantly circulating throughout the body. The cellular network orchestrates interactions with every organ and tissue to carry out an impossibly long list of functions that scientists are still working to understand. All that complexity limits our ability to predict which patients will respond to treatments and which ones might suffer debilitating side effects.

      The issue often leads pharmaceutical companies to stop developing drugs that could help certain patients, halting clinical trials even when drugs show promising results for some people.

      Now, Immunai is helping to predict how patients will respond to treatments by building a comprehensive map of the immune system. The company has assembled a vast database it calls AMICA, that combines multiple layers of gene and protein expression data in cells with clinical trial data to match the right drugs to the right patients.

      “Our starting point was creating what I call the Google Maps for the immune system,” Immunai co-founder and CEO Noam Solomon says. “We started with single-cell RNA sequencing, and over time we’ve added more and more ‘omics’: genomics, proteomics, epigenomics, all to measure the immune system’s cellular expression and function, to measure the immune environment holistically. Then we started working with pharmaceutical companies and hospitals to profile the immune systems of patients undergoing treatments to really get to the root mechanisms of action and resistance for therapeutics.”

      Immunai’s big data foundation is a result of its founders’ unique background. Solomon and co-founder Luis Voloch ’13, SM ’15 hold degrees in mathematics and computer science. In fact, Solomon was a postdoc in MIT’s Department of Mathematics at the time of Immunai’s founding.

      Solomon frames Immunai’s mission as stopping the decades-long divergence of computer science and the life sciences. He believes the single biggest factor driving the explosion of computing has been Moore’s Law — our ability to exponentially increase the number of transistors on a chip over the past 60 years. In the pharmaceutical industry, the reverse is happening: By one estimate, the cost of developing a new drug roughly doubles every nine years. The phenomenon has been dubbed Eroom’s Law (“Eroom” for “Moore” spelled backward).

      Solomon sees the trend eroding the case for developing new drugs, with huge consequences for patients.

      “Why should pharmaceutical companies invest in discovery if they won’t get a return on investment?” Solomon asks. “Today, there’s only a 5 to 10 percent chance that any given clinical trial will be successful. What we’ve built through a very robust and granular mapping of the immune system is a chance to improve the preclinical and clinical stages of drug development.”

      A change in plans

      Solomon entered Tel Aviv University when he was 14 and earned his bachelor’s degree in computer science by 19. He earned two PhDs in Israel, one in computer science and the other in mathematics, before coming to MIT in 2017 as a postdoc to continue his mathematical research career.

      That year Solomon met Voloch, who had already earned bachelor’s and master’s degrees in math and computer science from MIT. But the researchers were soon exposed to a problem that would take them out of their comfort zones and change the course of their careers.

      Voloch’s grandfather was receiving a cocktail of treatments for cancer at the time. The cancer went into remission, but he suffered terrible side effects that caused him to stop taking his medication.

      Voloch and Solomon began wondering if their expertise could help patients like Voloch’s grandfather.

      “When we realized we could make an impact, we made the difficult decision to stop our academic pursuits and start a new journey,” Solomon recalls. “That was the starting point for Immunai.”

      Voloch and Solomon soon partnered with Immunai scientific co-founders Ansu Satpathy, a researcher at Stanford University at the time, and Danny Wells, a researcher at the Parker Institute for Cancer Immunotherapy. Satpathy and Wells had shown that single-cell RNA sequencing could be used to gain insights into why patients respond differently to a common cancer treatment.

      The team began analyzing single-cell RNA sequencing data published in scientific papers, trying to link common biomarkers with patient outcomes. Then they integrated data from the United Kingdom’s Biobank public health database, finding they were able to improve their models’ predictions. Soon they were incorporating data from hospitals, academic research institutions, and pharmaceutical companies, analyzing information about the structure, function, and environment of cells — multiomics — to get a clearer picture of immune activity.

      “Single cell sequencing gives you metrics you can measure in thousands of cells, where you can look at 20,000 different genes, and those metrics give you an immune profile,” Solomon explains. “When you measure all of that over time, especially before and after getting therapy, and compare patients who do respond with patients who don’t, you can apply machine learning models to understand why.”

      Those data and models make up AMICA, what Immunai calls the world’s largest cell-level immune knowledge base. AMICA stands for Annotated Multiomic Immune Cell Atlas. It analyzes single cell multiomic data from almost 10,000 patients and bulk-RNA data from 100,000 patients across more than 800 cell types and 500 diseases.

      At the core of Immunai’s approach is a focus on the immune system, which other companies shy away from because of its complexity.

      “We don’t want to be like other groups that are studying mainly tumor microenvironments,” Solomon says. “We look at the immune system because the immune system is the common denominator. It’s the one system that is implicated in every disease, in your body’s response to everything that you encounter, whether it’s a viral infection or bacterial infection or a drug that you are receiving — even how you are aging.”

      Turning data into better treatments

      Immunai has already partnered with some of the largest pharmaceutical companies in the world to help them identify promising treatments and set up their clinical trials for success. Immunai’s insights can help partners make critical decisions about treatment schedules, dosing, drug combinations, patient selection, and more.

      “Everyone is talking about AI, but I think the most exciting aspect of the platform we have built is the fact that it’s vertically integrated, from wet lab to computational modeling with multiple iterations,” Solomon says. “For example, we may do single-cell immune profiling of patient samples, then we upload that data to the cloud and our computational models come up with insights, and with those insights we do in vitro or in vivo validation to see if our models are right and iteratively improve them.”

      Ultimately Immunai wants to enable a future where lab experiments can more reliably turn into impactful new recommendations and treatments for patients.

      “Scientists can cure nearly every type of cancer, but only in mice,” Solomon says. “In preclinical models we know how to cure cancer. In human beings, in most cases, we still don’t. To overcome that, most scientists are looking for better ex vivo or in vivo models. Our approach is to be more agnostic as to the model system, but feed the machine with more and more data from multiple model systems. We’re demonstrating that our algorithms can repeatedly beat the top benchmarks in identifying the top preclinical immune features that match to patient outcomes.” More

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      in Data Management & Statistics

      Using generative AI to improve software testing

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      Generative AI is getting plenty of attention for its ability to create text and images. But those media represent only a fraction of the data that proliferate in our society today. Data are generated every time a patient goes through a medical system, a storm impacts a flight, or a person interacts with a software application.

      Using generative AI to create realistic synthetic data around those scenarios can help organizations more effectively treat patients, reroute planes, or improve software platforms — especially in scenarios where real-world data are limited or sensitive.

      For the last three years, the MIT spinout DataCebo has offered a generative software system called the Synthetic Data Vault to help organizations create synthetic data to do things like test software applications and train machine learning models.

      The Synthetic Data Vault, or SDV, has been downloaded more than 1 million times, with more than 10,000 data scientists using the open-source library for generating synthetic tabular data. The founders — Principal Research Scientist Kalyan Veeramachaneni and alumna Neha Patki ’15, SM ’16 — believe the company’s success is due to SDV’s ability to revolutionize software testing.

      SDV goes viral

      In 2016, Veeramachaneni’s group in the Data to AI Lab unveiled a suite of open-source generative AI tools to help organizations create synthetic data that matched the statistical properties of real data.

      Companies can use synthetic data instead of sensitive information in programs while still preserving the statistical relationships between datapoints. Companies can also use synthetic data to run new software through simulations to see how it performs before releasing it to the public.

      Veeramachaneni’s group came across the problem because it was working with companies that wanted to share their data for research.

      “MIT helps you see all these different use cases,” Patki explains. “You work with finance companies and health care companies, and all those projects are useful to formulate solutions across industries.”

      In 2020, the researchers founded DataCebo to build more SDV features for larger organizations. Since then, the use cases have been as impressive as they’ve been varied.

      With DataCebo’s new flight simulator, for instance, airlines can plan for rare weather events in a way that would be impossible using only historic data. In another application, SDV users synthesized medical records to predict health outcomes for patients with cystic fibrosis. A team from Norway recently used SDV to create synthetic student data to evaluate whether various admissions policies were meritocratic and free from bias.

      In 2021, the data science platform Kaggle hosted a competition for data scientists that used SDV to create synthetic data sets to avoid using proprietary data. Roughly 30,000 data scientists participated, building solutions and predicting outcomes based on the company’s realistic data.

      And as DataCebo has grown, it’s stayed true to its MIT roots: All of the company’s current employees are MIT alumni.

      Supercharging software testing

      Although their open-source tools are being used for a variety of use cases, the company is focused on growing its traction in software testing.

      “You need data to test these software applications,” Veeramachaneni says. “Traditionally, developers manually write scripts to create synthetic data. With generative models, created using SDV, you can learn from a sample of data collected and then sample a large volume of synthetic data (which has the same properties as real data), or create specific scenarios and edge cases, and use the data to test your application.”

      For example, if a bank wanted to test a program designed to reject transfers from accounts with no money in them, it would have to simulate many accounts simultaneously transacting. Doing that with data created manually would take a lot of time. With DataCebo’s generative models, customers can create any edge case they want to test.

      “It’s common for industries to have data that is sensitive in some capacity,” Patki says. “Often when you’re in a domain with sensitive data you’re dealing with regulations, and even if there aren’t legal regulations, it’s in companies’ best interest to be diligent about who gets access to what at which time. So, synthetic data is always better from a privacy perspective.”

      Scaling synthetic data

      Veeramachaneni believes DataCebo is advancing the field of what it calls synthetic enterprise data, or data generated from user behavior on large companies’ software applications.

      “Enterprise data of this kind is complex, and there is no universal availability of it, unlike language data,” Veeramachaneni says. “When folks use our publicly available software and report back if works on a certain pattern, we learn a lot of these unique patterns, and it allows us to improve our algorithms. From one perspective, we are building a corpus of these complex patterns, which for language and images is readily available. “

      DataCebo also recently released features to improve SDV’s usefulness, including tools to assess the “realism” of the generated data, called the SDMetrics library as well as a way to compare models’ performances called SDGym.

      “It’s about ensuring organizations trust this new data,” Veeramachaneni says. “[Our tools offer] programmable synthetic data, which means we allow enterprises to insert their specific insight and intuition to build more transparent models.”

      As companies in every industry rush to adopt AI and other data science tools, DataCebo is ultimately helping them do so in a way that is more transparent and responsible.

      “In the next few years, synthetic data from generative models will transform all data work,” Veeramachaneni says. “We believe 90 percent of enterprise operations can be done with synthetic data.” More

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      Startup accelerates progress toward light-speed computing

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      Our ability to cram ever-smaller transistors onto a chip has enabled today’s age of ubiquitous computing. But that approach is finally running into limits, with some experts declaring an end to Moore’s Law and a related principle, known as Dennard’s Scaling.

      Those developments couldn’t be coming at a worse time. Demand for computing power has skyrocketed in recent years thanks in large part to the rise of artificial intelligence, and it shows no signs of slowing down.

      Now Lightmatter, a company founded by three MIT alumni, is continuing the remarkable progress of computing by rethinking the lifeblood of the chip. Instead of relying solely on electricity, the company also uses light for data processing and transport. The company’s first two products, a chip specializing in artificial intelligence operations and an interconnect that facilitates data transfer between chips, use both photons and electrons to drive more efficient operations.

      “The two problems we are solving are ‘How do chips talk?’ and ‘How do you do these [AI] calculations?’” Lightmatter co-founder and CEO Nicholas Harris PhD ’17 says. “With our first two products, Envise and Passage, we’re addressing both of those questions.”

      In a nod to the size of the problem and the demand for AI, Lightmatter raised just north of $300 million in 2023 at a valuation of $1.2 billion. Now the company is demonstrating its technology with some of the largest technology companies in the world in hopes of reducing the massive energy demand of data centers and AI models.

      “We’re going to enable platforms on top of our interconnect technology that are made up of hundreds of thousands of next-generation compute units,” Harris says. “That simply wouldn’t be possible without the technology that we’re building.”

      From idea to $100K

      Prior to MIT, Harris worked at the semiconductor company Micron Technology, where he studied the fundamental devices behind integrated chips. The experience made him see how the traditional approach for improving computer performance — cramming more transistors onto each chip — was hitting its limits.

      “I saw how the roadmap for computing was slowing, and I wanted to figure out how I could continue it,” Harris says. “What approaches can augment computers? Quantum computing and photonics were two of those pathways.”

      Harris came to MIT to work on photonic quantum computing for his PhD under Dirk Englund, an associate professor in the Department of Electrical Engineering and Computer Science. As part of that work, he built silicon-based integrated photonic chips that could send and process information using light instead of electricity.

      The work led to dozens of patents and more than 80 research papers in prestigious journals like Nature. But another technology also caught Harris’s attention at MIT.

      “I remember walking down the hall and seeing students just piling out of these auditorium-sized classrooms, watching relayed live videos of lectures to see professors teach deep learning,” Harris recalls, referring to the artificial intelligence technique. “Everybody on campus knew that deep learning was going to be a huge deal, so I started learning more about it, and we realized that the systems I was building for photonic quantum computing could actually be leveraged to do deep learning.”

      Harris had planned to become a professor after his PhD, but he realized he could attract more funding and innovate more quickly through a startup, so he teamed up with Darius Bunandar PhD ’18, who was also studying in Englund’s lab, and Thomas Graham MBA ’18. The co-founders successfully launched into the startup world by winning the 2017 MIT $100K Entrepreneurship Competition.

      Seeing the light

      Lightmatter’s Envise chip takes the part of computing that electrons do well, like memory, and combines it with what light does well, like performing the massive matrix multiplications of deep-learning models.

      “With photonics, you can perform multiple calculations at the same time because the data is coming in on different colors of light,” Harris explains. “In one color, you could have a photo of a dog. In another color, you could have a photo of a cat. In another color, maybe a tree, and you could have all three of those operations going through the same optical computing unit, this matrix accelerator, at the same time. That drives up operations per area, and it reuses the hardware that’s there, driving up energy efficiency.”

      Passage takes advantage of light’s latency and bandwidth advantages to link processors in a manner similar to how fiber optic cables use light to send data over long distances. It also enables chips as big as entire wafers to act as a single processor. Sending information between chips is central to running the massive server farms that power cloud computing and run AI systems like ChatGPT.

      Both products are designed to bring energy efficiencies to computing, which Harris says are needed to keep up with rising demand without bringing huge increases in power consumption.

      “By 2040, some predict that around 80 percent of all energy usage on the planet will be devoted to data centers and computing, and AI is going to be a huge fraction of that,” Harris says. “When you look at computing deployments for training these large AI models, they’re headed toward using hundreds of megawatts. Their power usage is on the scale of cities.”

      Lightmatter is currently working with chipmakers and cloud service providers for mass deployment. Harris notes that because the company’s equipment runs on silicon, it can be produced by existing semiconductor fabrication facilities without massive changes in process.

      The ambitious plans are designed to open up a new path forward for computing that would have huge implications for the environment and economy.

      “We’re going to continue looking at all of the pieces of computers to figure out where light can accelerate them, make them more energy efficient, and faster, and we’re going to continue to replace those parts,” Harris says. “Right now, we’re focused on interconnect with Passage and on compute with Envise. But over time, we’re going to build out the next generation of computers, and it’s all going to be centered around light.” More

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      Power when the sun doesn’t shine

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      In 2016, at the huge Houston energy conference CERAWeek, MIT materials scientist Yet-Ming Chiang found himself talking to a Tesla executive about a thorny problem: how to store the output of solar panels and wind turbines for long durations.        

      Chiang, the Kyocera Professor of Materials Science and Engineering, and Mateo Jaramillo, a vice president at Tesla, knew that utilities lacked a cost-effective way to store renewable energy to cover peak levels of demand and to bridge the gaps during windless and cloudy days. They also knew that the scarcity of raw materials used in conventional energy storage devices needed to be addressed if renewables were ever going to displace fossil fuels on the grid at scale.

      Energy storage technologies can facilitate access to renewable energy sources, boost the stability and reliability of power grids, and ultimately accelerate grid decarbonization. The global market for these systems — essentially large batteries — is expected to grow tremendously in the coming years. A study by the nonprofit LDES (Long Duration Energy Storage) Council pegs the long-duration energy storage market at between 80 and 140 terawatt-hours by 2040. “That’s a really big number,” Chiang notes. “Every 10 people on the planet will need access to the equivalent of one EV [electric vehicle] battery to support their energy needs.”

      In 2017, one year after they met in Houston, Chiang and Jaramillo joined forces to co-found Form Energy in Somerville, Massachusetts, with MIT graduates Marco Ferrara SM ’06, PhD ’08 and William Woodford PhD ’13, and energy storage veteran Ted Wiley.

      “There is a burgeoning market for electrical energy storage because we want to achieve decarbonization as fast and as cost-effectively as possible,” says Ferrara, Form’s senior vice president in charge of software and analytics.

      Investors agreed. Over the next six years, Form Energy would raise more than $800 million in venture capital.

      Bridging gaps

      The simplest battery consists of an anode, a cathode, and an electrolyte. During discharge, with the help of the electrolyte, electrons flow from the negative anode to the positive cathode. During charge, external voltage reverses the process. The anode becomes the positive terminal, the cathode becomes the negative terminal, and electrons move back to where they started. Materials used for the anode, cathode, and electrolyte determine the battery’s weight, power, and cost “entitlement,” which is the total cost at the component level.

      During the 1980s and 1990s, the use of lithium revolutionized batteries, making them smaller, lighter, and able to hold a charge for longer. The storage devices Form Energy has devised are rechargeable batteries based on iron, which has several advantages over lithium. A big one is cost.

      Chiang once declared to the MIT Club of Northern California, “I love lithium-ion.” Two of the four MIT spinoffs Chiang founded center on innovative lithium-ion batteries. But at hundreds of dollars a kilowatt-hour (kWh) and with a storage capacity typically measured in hours, lithium-ion was ill-suited for the use he now had in mind.

      The approach Chiang envisioned had to be cost-effective enough to boost the attractiveness of renewables. Making solar and wind energy reliable enough for millions of customers meant storing it long enough to fill the gaps created by extreme weather conditions, grid outages, and when there is a lull in the wind or a few days of clouds.

      To be competitive with legacy power plants, Chiang’s method had to come in at around $20 per kilowatt-hour of stored energy — one-tenth the cost of lithium-ion battery storage.

      But how to transition from expensive batteries that store and discharge over a couple of hours to some as-yet-undefined, cheap, longer-duration technology?

      “One big ball of iron”

      That’s where Ferrara comes in. Ferrara has a PhD in nuclear engineering from MIT and a PhD in electrical engineering and computer science from the University of L’Aquila in his native Italy. In 2017, as a research affiliate at the MIT Department of Materials Science and Engineering, he worked with Chiang to model the grid’s need to manage renewables’ intermittency.

      How intermittent depends on where you are. In the United States, for instance, there’s the windy Great Plains; the sun-drenched, relatively low-wind deserts of Arizona, New Mexico, and Nevada; and the often-cloudy Pacific Northwest.

      Ferrara, in collaboration with Professor Jessika Trancik of MIT’s Institute for Data, Systems, and Society and her MIT team, modeled four representative locations in the United States and concluded that energy storage with capacity costs below roughly $20/kWh and discharge durations of multiple days would allow a wind-solar mix to provide cost-competitive, firm electricity in resource-abundant locations.

      Now that they had a time frame, they turned their attention to materials. At the price point Form Energy was aiming for, lithium was out of the question. Chiang looked at plentiful and cheap sulfur. But a sulfur, sodium, water, and air battery had technical challenges.

      Thomas Edison once used iron as an electrode, and iron-air batteries were first studied in the 1960s. They were too heavy to make good transportation batteries. But this time, Chiang and team were looking at a battery that sat on the ground, so weight didn’t matter. Their priorities were cost and availability.

      “Iron is produced, mined, and processed on every continent,” Chiang says. “The Earth is one big ball of iron. We wouldn’t ever have to worry about even the most ambitious projections of how much storage that the world might use by mid-century.” If Form ever moves into the residential market, “it’ll be the safest battery you’ve ever parked at your house,” Chiang laughs. “Just iron, air, and water.”

      Scientists call it reversible rusting. While discharging, the battery takes in oxygen and converts iron to rust. Applying an electrical current converts the rusty pellets back to iron, and the battery “breathes out” oxygen as it charges. “In chemical terms, you have iron, and it becomes iron hydroxide,” Chiang says. “That means electrons were extracted. You get those electrons to go through the external circuit, and now you have a battery.”

      Form Energy’s battery modules are approximately the size of a washer-and-dryer unit. They are stacked in 40-foot containers, and several containers are electrically connected with power conversion systems to build storage plants that can cover several acres.

      The right place at the right time

      The modules don’t look or act like anything utilities have contracted for before.

      That’s one of Form’s key challenges. “There is not widespread knowledge of needing these new tools for decarbonized grids,” Ferrara says. “That’s not the way utilities have typically planned. They’re looking at all the tools in the toolkit that exist today, which may not contemplate a multi-day energy storage asset.”

      Form Energy’s customers are largely traditional power companies seeking to expand their portfolios of renewable electricity. Some are in the process of decommissioning coal plants and shifting to renewables.

      Ferrara’s research pinpointing the need for very low-cost multi-day storage provides key data for power suppliers seeking to determine the most cost-effective way to integrate more renewable energy.

      Using the same modeling techniques, Ferrara and team show potential customers how the technology fits in with their existing system, how it competes with other technologies, and how, in some cases, it can operate synergistically with other storage technologies.

      “They may need a portfolio of storage technologies to fully balance renewables on different timescales of intermittency,” he says. But other than the technology developed at Form, “there isn’t much out there, certainly not within the cost entitlement of what we’re bringing to market.”  Thanks to Chiang and Jaramillo’s chance encounter in Houston, Form has a several-year lead on other companies working to address this challenge. 

      In June 2023, Form Energy closed its biggest deal to date for a single project: Georgia Power’s order for a 15-megawatt/1,500-megawatt-hour system. That order brings Form’s total amount of energy storage under contracts with utility customers to 40 megawatts/4 gigawatt-hours. To meet the demand, Form is building a new commercial-scale battery manufacturing facility in West Virginia.

      The fact that Form Energy is creating jobs in an area that lost more than 10,000 steel jobs over the past decade is not lost on Chiang. “And these new jobs are in clean tech. It’s super exciting to me personally to be doing something that benefits communities outside of our traditional technology centers.

      “This is the right time for so many reasons,” Chiang says. He says he and his Form Energy co-founders feel “tremendous urgency to get these batteries out into the world.”

      This article appears in the Winter 2024 issue of Energy Futures, the magazine of the MIT Energy Initiative. More

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      Putting AI into the hands of people with problems to solve

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      As Media Lab students in 2010, Karthik Dinakar SM ’12, PhD ’17 and Birago Jones SM ’12 teamed up for a class project to build a tool that would help content moderation teams at companies like Twitter (now X) and YouTube. The project generated a huge amount of excitement, and the researchers were invited to give a demonstration at a cyberbullying summit at the White House — they just had to get the thing working.

      The day before the White House event, Dinakar spent hours trying to put together a working demo that could identify concerning posts on Twitter. Around 11 p.m., he called Jones to say he was giving up.

      Then Jones decided to look at the data. It turned out Dinakar’s model was flagging the right types of posts, but the posters were using teenage slang terms and other indirect language that Dinakar didn’t pick up on. The problem wasn’t the model; it was the disconnect between Dinakar and the teens he was trying to help.

      “We realized then, right before we got to the White House, that the people building these models should not be folks who are just machine-learning engineers,” Dinakar says. “They should be people who best understand their data.”

      The insight led the researchers to develop point-and-click tools that allow nonexperts to build machine-learning models. Those tools became the basis for Pienso, which today is helping people build large language models for detecting misinformation, human trafficking, weapons sales, and more, without writing any code.

      “These kinds of applications are important to us because our roots are in cyberbullying and understanding how to use AI for things that really help humanity,” says Jones.

      As for the early version of the system shown at the White House, the founders ended up collaborating with students at nearby schools in Cambridge, Massachusetts, to let them train the models.

      “The models those kids trained were so much better and nuanced than anything I could’ve ever come up with,” Dinakar says. “Birago and I had this big ‘Aha!’ moment where we realized empowering domain experts — which is different from democratizing AI — was the best path forward.”

      A project with purpose

      Jones and Dinakar met as graduate students in the Software Agents research group of the MIT Media Lab. Their work on what became Pienso started in Course 6.864 (Natural Language Processing) and continued until they earned their master’s degrees in 2012.

      It turned out 2010 wasn’t the last time the founders were invited to the White House to demo their project. The work generated a lot of enthusiasm, but the founders worked on Pienso part time until 2016, when Dinakar finished his PhD at MIT and deep learning began to explode in popularity.

      “We’re still connected to many people around campus,” Dinakar says. “The exposure we had at MIT, the melding of human and computer interfaces, widened our understanding. Our philosophy at Pienso couldn’t be possible without the vibrancy of MIT’s campus.”

      The founders also credit MIT’s Industrial Liaison Program (ILP) and Startup Accelerator (STEX) for connecting them to early partners.

      One early partner was SkyUK. The company’s customer success team used Pienso to build models to understand their customer’s most common problems. Today those models are helping to process half a million customer calls a day, and the founders say they have saved the company over £7 million pounds to date by shortening the length of calls into the company’s call center.

      “The difference between democratizing AI and empowering people with AI comes down to who understands the data best — you or a doctor or a journalist or someone who works with customers every day?” Jones says. “Those are the people who should be creating the models. That’s how you get insights out of your data.”

      In 2020, just as Covid-19 outbreaks began in the U.S., government officials contacted the founders to use their tool to better understand the emerging disease. Pienso helped experts in virology and infectious disease set up machine-learning models to mine thousands of research articles about coronaviruses. Dinakar says they later learned the work helped the government identify and strengthen critical supply chains for drugs, including the popular antiviral remdesivir.

      “Those compounds were surfaced by a team that did not know deep learning but was able to use our platform,” Dinakar says.

      Building a better AI future

      Because Pienso can run on internal servers and cloud infrastructure, the founders say it offers an alternative for businesses being forced to donate their data by using services offered by other AI companies.

      “The Pienso interface is a series of web apps stitched together,” Dinakar explains. “You can think of it like an Adobe Photoshop for large language models, but in the web. You can point and import data without writing a line of code. You can refine the data, prepare it for deep learning, analyze it, give it structure if it’s not labeled or annotated, and you can walk away with fine-tuned, large language model in a matter of 25 minutes.”

      Earlier this year, Pienso announced a partnership with GraphCore, which provides a faster, more efficient computing platform for machine learning. The founders say the partnership will further lower barriers to leveraging AI by dramatically reducing latency.

      “If you’re building an interactive AI platform, users aren’t going to have a cup of coffee every time they click a button,” Dinakar says. “It needs to be fast and responsive.”

      The founders believe their solution is enabling a future where more effective AI models are developed for specific use cases by the people who are most familiar with the problems they are trying to solve.

      “No one model can do everything,” Dinakar says. “Everyone’s application is different, their needs are different, their data is different. It’s highly unlikely that one model will do everything for you. It’s about bringing a garden of models together and allowing them to collaborate with each other and orchestrating them in a way that makes sense — and the people doing that orchestration should be the people who understand the data best.” More

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      Six MIT students selected as spring 2024 MIT-Pillar AI Collective Fellows

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      The MIT-Pillar AI Collective has announced six fellows for the spring 2024 semester. With support from the program, the graduate students, who are in their final year of a master’s or PhD program, will conduct research in the areas of AI, machine learning, and data science with the aim of commercializing their innovations.

      Launched by MIT’s School of Engineering and Pillar VC in 2022, the MIT-Pillar AI Collective supports faculty, postdocs, and students conducting research on AI, machine learning, and data science. Supported by a gift from Pillar VC and administered by the MIT Deshpande Center for Technological Innovation, the mission of the program is to advance research toward commercialization.

      The spring 2024 MIT-Pillar AI Collective Fellows are:

      Yasmeen AlFaraj

      Yasmeen AlFaraj is a PhD candidate in chemistry whose interest is in the application of data science and machine learning to soft materials design to enable next-generation, sustainable plastics, rubber, and composite materials. More specifically, she is applying machine learning to the design of novel molecular additives to enable the low-cost manufacturing of chemically deconstructable thermosets and composites. AlFaraj’s work has led to the discovery of scalable, translatable new materials that could address thermoset plastic waste. As a Pillar Fellow, she will pursue bringing this technology to market, initially focusing on wind turbine blade manufacturing and conformal coatings. Through the Deshpande Center for Technological Innovation, AlFaraj serves as a lead for a team developing a spinout focused on recyclable versions of existing high-performance thermosets by incorporating small quantities of a degradable co-monomer. In addition, she participated in the National Science Foundation Innovation Corps program and recently graduated from the Clean Tech Open, where she focused on enhancing her business plan, analyzing potential markets, ensuring a complete IP portfolio, and connecting with potential funders. AlFaraj earned a BS in chemistry from University of California at Berkeley.

      Ruben Castro Ornelas

      Ruben Castro Ornelas is a PhD student in mechanical engineering who is passionate about the future of multipurpose robots and designing the hardware to use them with AI control solutions. Combining his expertise in programming, embedded systems, machine design, reinforcement learning, and AI, he designed a dexterous robotic hand capable of carrying out useful everyday tasks without sacrificing size, durability, complexity, or simulatability. Ornelas’s innovative design holds significant commercial potential in domestic, industrial, and health-care applications because it could be adapted to hold everything from kitchenware to delicate objects. As a Pillar Fellow, he will focus on identifying potential commercial markets, determining the optimal approach for business-to-business sales, and identifying critical advisors. Ornelas served as co-director of StartLabs, an undergraduate entrepreneurship club at MIT, where he earned an BS in mechanical engineering.

      Keeley Erhardt

      Keeley Erhardt is a PhD candidate in media arts and sciences whose research interests lie in the transformative potential of AI in network analysis, particularly for entity correlation and hidden link detection within and across domains. She has designed machine learning algorithms to identify and track temporal correlations and hidden signals in large-scale networks, uncovering online influence campaigns originating from multiple countries. She has similarly demonstrated the use of graph neural networks to identify coordinated cryptocurrency accounts by analyzing financial time series data and transaction dynamics. As a Pillar Fellow, Erhardt will pursue the potential commercial applications of her work, such as detecting fraud, propaganda, money laundering, and other covert activity in the finance, energy, and national security sectors. She has had internships at Google, Facebook, and Apple and held software engineering roles at multiple tech unicorns. Erhardt earned an MEng in electrical engineering and computer science and a BS in computer science, both from MIT.

      Vineet Jagadeesan Nair

      Vineet Jagadeesan Nair is a PhD candidate in mechanical engineering whose research focuses on modeling power grids and designing electricity markets to integrate renewables, batteries, and electric vehicles. He is broadly interested in developing computational tools to tackle climate change. As a Pillar Fellow, Nair will explore the application of machine learning and data science to power systems. Specifically, he will experiment with approaches to improve the accuracy of forecasting electricity demand and supply with high spatial-temporal resolution. In collaboration with Project Tapestry @ Google X, he is also working on fusing physics-informed machine learning with conventional numerical methods to increase the speed and accuracy of high-fidelity simulations. Nair’s work could help realize future grids with high penetrations of renewables and other clean, distributed energy resources. Outside academics, Nair is active in entrepreneurship, most recently helping to organize the 2023 MIT Global Startup Workshop in Greece. He earned an MS in computational science and engineering from MIT, an MPhil in energy technologies from Cambridge University as a Gates Scholar, and a BS in mechanical engineering and a BA in economics from University of California at Berkeley.

      Mahdi Ramadan

      Mahdi Ramadan is a PhD candidate in brain and cognitive sciences whose research interests lie at the intersection of cognitive science, computational modeling, and neural technologies. His work uses novel unsupervised methods for learning and generating interpretable representations of neural dynamics, capitalizing on recent advances in AI, specifically contrastive and geometric deep learning techniques capable of uncovering the latent dynamics underlying neural processes with high fidelity. As a Pillar Fellow, he will leverage these methods to gain a better understanding of dynamical models of muscle signals for generative motor control. By supplementing current spinal prosthetics with generative AI motor models that can streamline, speed up, and correct limb muscle activations in real time, as well as potentially using multimodal vision-language models to infer the patients’ high-level intentions, Ramadan aspires to build truly scalable, accessible, and capable commercial neuroprosthetics. Ramadan’s entrepreneurial experience includes being the co-founder of UltraNeuro, a neurotechnology startup, and co-founder of Presizely, a computer vision startup. He earned a BS in neurobiology from University of Washington.

      Rui (Raymond) Zhou

      Rui (Raymond) Zhou is a PhD candidate in mechanical engineering whose research focuses on multimodal AI for engineering design. As a Pillar Fellow, he will advance models that could enable designers to translate information in any modality or combination of modalities into comprehensive 2D and 3D designs, including parametric data, component visuals, assembly graphs, and sketches. These models could also optimize existing human designs to accomplish goals such as improving ergonomics or reducing drag coefficient. Ultimately, Zhou aims to translate his work into a software-as-a-service platform that redefines product design across various sectors, from automotive to consumer electronics. His efforts have the potential to not only accelerate the design process but also reduce costs, opening the door to unprecedented levels of customization, idea generation, and rapid prototyping. Beyond his academic pursuits, Zhou founded UrsaTech, a startup that integrates AI into education and engineering design. He earned a BS in electrical engineering and computer sciences from University of California at Berkeley. More

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      Boosting passenger experience and increasing connectivity at the Hong Kong International Airport

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      Recently, a cohort of 36 students from MIT and universities across Hong Kong came together for the MIT Entrepreneurship and Maker Skills Integrator (MEMSI), an intense two-week startup boot camp hosted at the MIT Hong Kong Innovation Node.

      “We’re very excited to be in Hong Kong,” said Professor Charles Sodini, LeBel Professor of Electrical Engineering and faculty director of the Node. “The dream always was to bring MIT and Hong Kong students together.”

      Students collaborated on six teams to meet real-world industry challenges through action learning, defining a problem, designing a solution, and crafting a business plan. The experience culminated in the MEMSI Showcase, where each team presented its process and unique solution to a panel of judges. “The MEMSI program is a great demonstration of important international educational goals for MIT,” says Professor Richard Lester, associate provost for international activities and chair of the Node Steering Committee at MIT. “It creates opportunities for our students to solve problems in a particular and distinctive cultural context, and to learn how innovations can cross international boundaries.” 

      Meeting an urgent challenge in the travel and tourism industry

      The Hong Kong Airport Authority (AAHK) served as the program’s industry partner for the third consecutive year, challenging students to conceive innovative ideas to make passenger travel more personalized from end-to-end while increasing connectivity. As the travel industry resuscitates profitability and welcomes crowds back amidst ongoing delays and labor shortages, the need for a more passenger-centric travel ecosystem is urgent.

      The airport is the third-busiest international passenger airport and the world’s busiest cargo transit. Students experienced an insider’s tour of the Hong Kong International Airport to gain on-the-ground orientation. They observed firsthand the complex logistics, possibilities, and constraints of operating with a team of 78,000 employees who serve 71.5 million passengers with unique needs and itineraries.

      Throughout the program, the cohort was coached and supported by MEMSI alumni, travel industry mentors, and MIT faculty such as Richard de Neufville, professor of engineering systems.

      The mood inside the open-plan MIT Hong Kong Innovation Node was nonstop energetic excitement for the entire program. Each of the six teams was composed of students from MIT and from Hong Kong universities. They learned to work together under time pressure, develop solutions, receive feedback from industry mentors, and iterate around the clock.

      “MEMSI was an enriching and amazing opportunity to learn about entrepreneurship while collaborating with a diverse team to solve a complex problem,” says Maria Li, a junior majoring in computer science, economics, and data science at MIT. “It was incredible to see the ideas we initially came up with as a team turn into a single, thought-out solution by the end.”

      Unsurprisingly given MIT’s focus on piloting the latest technology and the tech-savvy culture of Hong Kong as a global center, many team projects focused on virtual reality, apps, and wearable technology designed to make passengers’ journeys more individualized, efficient, or enjoyable.

      After observing geospatial patterns charting passengers’ movement through an airport, one team realized that many people on long trips aim to meet fitness goals by consciously getting their daily steps power walking the expansive terminals. The team’s prototype, FitAir, is a smart, biometric token integrated virtual coach, which plans walking routes within the airport to promote passenger health and wellness.

      Another team noted a common frustration among frequent travelers who manage multiple mileage rewards program profiles, passwords, and status reports. They proposed AirPoint, a digital wallet that consolidates different rewards programs and presents passengers with all their airport redemption opportunities in one place.

      “Today, there is no loser,” said Vivian Cheung, chief operating officer of AAHK, who served as one of the judges. “Everyone is a winner. I am a winner, too. I have learned a lot from the showcase. Some of the ideas, I believe, can really become a business.”

      Cheung noted that in just 12 days, all teams observed and solved her organization’s pain points and successfully designed solutions to address them.

      More than a competition

      Although many of the models pitched are inventive enough to potentially shape the future of travel, the main focus of MEMSI isn’t to act as yet another startup challenge and incubator.

      “What we’re really focusing on is giving students the ability to learn entrepreneurial thinking,” explains Marina Chan, senior director and head of education at the Node. “It’s the dynamic experience in a highly connected environment that makes being in Hong Kong truly unique. When students can adapt and apply theory to an international context, it builds deeper cultural competency.”

      From an aerial view, the boot camp produced many entrepreneurs in the making and lasting friendships, and respect for other cultural backgrounds and operating environments.

      “I learned the overarching process of how to make a startup pitch, all the way from idea generation, market research, and making business models, to the pitch itself and the presentation,” says Arun Wongprommoon, a senior double majoring in computer science and engineering and linguistics.  “It was all a black box to me before I came into the program.”

      He said he gained tremendous respect for the startup world and the pure hard work and collaboration required to get ahead.

      Spearheaded by the Node, MEMSI is a collaboration among the MIT Innovation Initiative, the Martin Trust Center for Entrepreneurship, the MIT International Science and Technology Initiatives, and Project Manus. Learn more about applying to MEMSI. More

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      Helping companies deploy AI models more responsibly

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      Companies today are incorporating artificial intelligence into every corner of their business. The trend is expected to continue until machine-learning models are incorporated into most of the products and services we interact with every day.

      As those models become a bigger part of our lives, ensuring their integrity becomes more important. That’s the mission of Verta, a startup that spun out of MIT’s Computer Science and Artificial Intelligence Laboratory (CSAIL).

      Verta’s platform helps companies deploy, monitor, and manage machine-learning models safely and at scale. Data scientists and engineers can use Verta’s tools to track different versions of models, audit them for bias, test them before deployment, and monitor their performance in the real world.

      “Everything we do is to enable more products to be built with AI, and to do that safely,” Verta founder and CEO Manasi Vartak SM ’14, PhD ’18 says. “We’re already seeing with ChatGPT how AI can be used to generate data, artefacts — you name it — that look correct but aren’t correct. There needs to be more governance and control in how AI is being used, particularly for enterprises providing AI solutions.”

      Verta is currently working with large companies in health care, finance, and insurance to help them understand and audit their models’ recommendations and predictions. It’s also working with a number of high-growth tech companies looking to speed up deployment of new, AI-enabled solutions while ensuring those solutions are used appropriately.

      Vartak says the company has been able to decrease the time it takes customers to deploy AI models by orders of magnitude while ensuring those models are explainable and fair — an especially important factor for companies in highly regulated industries.

      Health care companies, for example, can use Verta to improve AI-powered patient monitoring and treatment recommendations. Such systems need to be thoroughly vetted for errors and biases before they’re used on patients.

      “Whether it’s bias or fairness or explainability, it goes back to our philosophy on model governance and management,” Vartak says. “We think of it like a preflight checklist: Before an airplane takes off, there’s a set of checks you need to do before you get your airplane off the ground. It’s similar with AI models. You need to make sure you’ve done your bias checks, you need to make sure there’s some level of explainability, you need to make sure your model is reproducible. We help with all of that.”

      From project to product

      Before coming to MIT, Vartak worked as a data scientist for a social media company. In one project, after spending weeks tuning machine-learning models that curated content to show in people’s feeds, she learned an ex-employee had already done the same thing. Unfortunately, there was no record of what they did or how it affected the models.

      For her PhD at MIT, Vartak decided to build tools to help data scientists develop, test, and iterate on machine-learning models. Working in CSAIL’s Database Group, Vartak recruited a team of graduate students and participants in MIT’s Undergraduate Research Opportunities Program (UROP).

      “Verta would not exist without my work at MIT and MIT’s ecosystem,” Vartak says. “MIT brings together people on the cutting edge of tech and helps us build the next generation of tools.”

      The team worked with data scientists in the CSAIL Alliances program to decide what features to build and iterated based on feedback from those early adopters. Vartak says the resulting project, named ModelDB, was the first open-source model management system.

      Vartak also took several business classes at the MIT Sloan School of Management during her PhD and worked with classmates on startups that recommended clothing and tracked health, spending countless hours in the Martin Trust Center for MIT Entrepreneurship and participating in the center’s delta v summer accelerator.

      “What MIT lets you do is take risks and fail in a safe environment,” Vartak says. “MIT afforded me those forays into entrepreneurship and showed me how to go about building products and finding first customers, so by the time Verta came around I had done it on a smaller scale.”

      ModelDB helped data scientists train and track models, but Vartak quickly saw the stakes were higher once models were deployed at scale. At that point, trying to improve (or accidentally breaking) models can have major implications for companies and society. That insight led Vartak to begin building Verta.

      “At Verta, we help manage models, help run models, and make sure they’re working as expected, which we call model monitoring,” Vartak explains. “All of those pieces have their roots back to MIT and my thesis work. Verta really evolved from my PhD project at MIT.”

      Verta’s platform helps companies deploy models more quickly, ensure they continue working as intended over time, and manage the models for compliance and governance. Data scientists can use Verta to track different versions of models and understand how they were built, answering questions like how data were used and which explainability or bias checks were run. They can also vet them by running them through deployment checklists and security scans.

      “Verta’s platform takes the data science model and adds half a dozen layers to it to transform it into something you can use to power, say, an entire recommendation system on your website,” Vartak says. “That includes performance optimizations, scaling, and cycle time, which is how quickly you can take a model and turn it into a valuable product, as well as governance.”

      Supporting the AI wave

      Vartak says large companies often use thousands of different models that influence nearly every part of their operations.

      “An insurance company, for example, will use models for everything from underwriting to claims, back-office processing, marketing, and sales,” Vartak says. “So, the diversity of models is really high, there’s a large volume of them, and the level of scrutiny and compliance companies need around these models are very high. They need to know things like: Did you use the data you were supposed to use? Who were the people who vetted it? Did you run explainability checks? Did you run bias checks?”

      Vartak says companies that don’t adopt AI will be left behind. The companies that ride AI to success, meanwhile, will need well-defined processes in place to manage their ever-growing list of models.

      “In the next 10 years, every device we interact with is going to have intelligence built in, whether it’s a toaster or your email programs, and it’s going to make your life much, much easier,” Vartak says. “What’s going to enable that intelligence are better models and software, like Verta, that help you integrate AI into all of these applications very quickly.” More