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      3 Questions: Catherine D’Ignazio on data science and a quest for justice

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      As long as we apply data science to society, we should remember that our data may have flaws, biases, and absences. That is one motif of MIT Associate Professor Catherine D’Ignazio’s new book, “Counting Feminicide,” published this spring by the MIT Press. In it, D’Ignazio explores the world of Latin American activists who began using media accounts and other sources to tabulate how many women had been killed in their countries as the result of gender-based violence — and found that their own numbers differed greatly from official statistics.Some of these activists have become prominent public figures, and others less so, but all of them have produced work providing lessons about collecting data, sharing it, and applying data to projects supporting human liberty and dignity. Now, their stories are reaching a new audience thanks to D’Ignazio, an associate professor of urban science and planning in MIT’s Department of Urban Studies and Planning, and director of MIT’s Data and Feminism Lab. She is also hosting an ongoing, transnational book club about the work. MIT News spoke with D’Ignazio about the new book and how activists are expanding the traditional practice of data science.Q: What is your book about?A: Three things. It’s a book that documents the rise of data activism as a really interesting form of citizen data science. Increasingly, because of the availability of data and tools, gathering and doing your own data analysis is a growing form of social activism. We characterize it in the book as a citizenship practice. People are using data to make knowledge claims and put political demands out there for their institutions to respond to.Another takeaway is that from observing data activists, there are ways they approach data science that are very different from how it’s usually taught. Among other things, when undertaking work about inequality and violence, there’s a connection with the rows of data. It’s about memorializing people who have been lost. Mainstream data scientists can learn a lot from this.The third thing is about feminicide itself and missing information. The main reason people start collecting data about feminicide is because their institutions aren’t doing it. This includes our institutions here in the United States. We’re talking about violence against women that the state is neglecting to count, classify, or take action on. So, activists step into these gaps and do this to the best of their ability, and they have been quite effective. The media will go to the activists, who end up becoming authorities on feminicide.Q: Can you elaborate on the differences between the practices of these data activists and more standard data science?A: One difference is what I’ll call the intimacy and proximity to the rows of the data set. In conventional data science, when you’re analyzing data, typically you’re not also the data collector. However these activists and groups are involved across the entire pipeline. As a result, there’s a connection and humanization to each line of the data set. For example, there is a school nurse in Texas who runs the site Women Count USA, and she will spend many hours trying to find photographs of victims of feminicide, which represents unusual care paid to each row of a dataset.Another point is the sophistication that the data activists have around what their data represent and what the biases are in the data. In mainstream AI and data science, we’re still having conversations where people seem surprised that there is bias in datasets. But I was impressed with the critical sophistication with which the activists approached their data. They gather information from the media and are familiar with the biases media have, and are aware their data is not comprehensive but is still useful. We can hold those two things together. It’s often more comprehensive data than what the institutions themselves have or will release to the public.Q: You did not just chronicle the work of activists, but engaged with them as well, and report about that in the book. What did you work on with them?A: One big component in the book is the participatory technology development that we engaged in with the activists, and one chapter is a case study of our work with activists to co-design machine learning and AI technology that supports their work. Our team was brainstorming about a system for the activists that would automatically find cases, verify them, and put them right in the database. Interestingly, the activists pushed back on that. They did not want full automation. They felt being, in effect, witnesses is an important part of the work. The emotional burden is an important part of the work and very central to it, too. That’s not something I might always expect to hear from data scientists.Keeping the human in the loop also means the human makes the final decision over whether a specific item constitutes feminicide or not. Handling it like that aligns with the fact that there are multiple definitions of feminicide, which is a complicated thing from a computational perspective. The proliferation of definitions about what counts as feminicide is a reflection of the fact that this is an ongoing global, transnational conversation. Feminicide has been codified in many laws, especially in Latin American countries, but none of those single laws is definitive. And no single activist definition is definitive. People are creating this together, through dialogue and struggle, so any computational system has to be designed with that understanding of the democratic process in mind. More

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      Arvind, longtime MIT professor and prolific computer scientist, dies at 77

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      Arvind Mithal, the Charles W. and Jennifer C. Johnson Professor in Computer Science and Engineering at MIT, head of the faculty of computer science in the Department of Electrical Engineering and Computer Science (EECS), and a pillar of the MIT community, died on June 17. Arvind, who went by the mononym, was 77 years old.A prolific researcher who led the Computation Structures Group in the Computer Science and Artificial Intelligence Laboratory (CSAIL), Arvind served on the MIT faculty for nearly five decades.“He was beloved by countless people across the MIT community and around the world who were inspired by his intellectual brilliance and zest for life,” President Sally Kornbluth wrote in a letter to the MIT community today.As a scientist, Arvind was well known for important contributions to dataflow computing, which seeks to optimize the flow of data to take advantage of parallelism, achieving faster and more efficient computation.In the last 25 years, his research interests broadened to include developing techniques and tools for formal modeling, high-level synthesis, and formal verification of complex digital devices like microprocessors and hardware accelerators, as well as memory models and cache coherence protocols for parallel computing architectures and programming languages.Those who knew Arvind describe him as a rare individual whose interests and expertise ranged from high-level, theoretical formal systems all the way down through languages and compilers to the gates and structures of silicon hardware.The applications of Arvind’s work are far-reaching, from reducing the amount of energy and space required by data centers to streamlining the design of more efficient multicore computer chips.“Arvind was both a tremendous scholar in the fields of computer architecture and programming languages and a dedicated teacher, who brought systems-level thinking to our students. He was also an exceptional academic leader, often leading changes in curriculum and contributing to the Engineering Council in meaningful and impactful ways. I will greatly miss his sage advice and wisdom,” says Anantha Chandrakasan, chief innovation and strategy officer, dean of engineering, and the Vannevar Bush Professor of Electrical Engineering and Computer Science.“Arvind’s positive energy, together with his hearty laugh, brightened so many people’s lives. He was an enduring source of wise counsel for colleagues and for generations of students. With his deep commitment to academic excellence, he not only transformed research in computer architecture and parallel computing but also brought that commitment to his role as head of the computer science faculty in the EECS department. He left a lasting impact on all of us who had the privilege of working with him,” says Dan Huttenlocher, dean of the MIT Schwarzman College of Computing and the Henry Ellis Warren Professor of Electrical Engineering and Computer Science.Arvind developed an interest in parallel computing while he was a student at the Indian Institute of Technology in Kanpur, from which he received his bachelor’s degree in 1969. He earned a master’s degree and PhD in computer science in 1972 and 1973, respectively, from the University of Minnesota, where he studied operating systems and mathematical models of program behavior. He taught at the University of California at Irvine from 1974 to 1978 before joining the faculty at MIT.At MIT, Arvind’s group studied parallel computing and declarative programming languages, and he led the development of two parallel computing languages, Id and pH. He continued his work on these programming languages through the 1990s, publishing the book “Implicit Parallel Programming in pH” with co-author R.S. Nikhil in 2001, the culmination of more than 20 years of research.In addition to his research, Arvind was an important academic leader in EECS. He served as head of computer science faculty in the department and played a critical role in helping with the reorganization of EECS after the establishment of the MIT Schwarzman College of Computing.“Arvind was a force of nature, larger than life in every sense. His relentless positivity, unwavering optimism, boundless generosity, and exceptional strength as a researcher was truly inspiring and left a profound mark on all who had the privilege of knowing him. I feel enormous gratitude for the light he brought into our lives and his fundamental impact on our community,” says Daniela Rus, the Andrew and Erna Viterbi Professor of Electrical Engineering and Computer Science and the director of CSAIL.His work on dataflow and parallel computing led to the Monsoon project in the late 1980s and early 1990s. Arvind’s group, in collaboration with Motorola, built 16 dataflow computing machines and developed their associated software. One Monsoon dataflow machine is now in the Computer History Museum in Mountain View, California.Arvind’s focus shifted in the 1990s when, as he explained in a 2012 interview for the Institute of Electrical and Electronics Engineers (IEEE), funding for research into parallel computing began to dry up.“Microprocessors were getting so much faster that people thought they didn’t need it,” he recalled.Instead, he began applying techniques his team had learned and developed for parallel programming to the principled design of digital hardware.In addition to mentoring students and junior colleagues at MIT, Arvind also advised universities and governments in many countries on research in parallel programming and semiconductor design.Based on his work on digital hardware design, Arvind founded Sandburst in 2000, a fabless manufacturing company for semiconductor chips. He served as the company’s president for two years before returning to the MIT faculty, while continuing as an advisor. Sandburst was later acquired by Broadcom.Arvind and his students also developed Bluespec, a programming language designed to automate the design of chips. Building off this work, he co-founded the startup Bluespec, Inc., in 2003, to develop practical tools that help engineers streamline device design.Over the past decade, he was dedicated to advancing undergraduate education at MIT by bringing modern design tools to courses 6.004 (Computation Structures) and 6.191 (Introduction to Deep Learning), and incorporating Minispec, a programming language that is closely related to Bluespec.Arvind was honored for these and other contributions to data flow and multithread computing, and the development of tools for the high-level synthesis of hardware, with membership in the National Academy of Engineering in 2008 and the American Academy of Arts and Sciences in 2012. He was also named a distinguished alumnus of IIT Kanpur, his undergraduate alma mater.“Arvind was more than a pillar of the EECS community and a titan of computer science; he was a beloved colleague and a treasured friend. Those of us with the remarkable good fortune to work and collaborate with Arvind are devastated by his sudden loss. His kindness and joviality were unwavering; his mentorship was thoughtful and well-considered; his guidance was priceless. We will miss Arvind deeply,” says Asu Ozdaglar, deputy dean of the MIT Schwarzman College of Computing and head of EECS.Among numerous other awards, including membership in the Indian National Academy of Sciences and fellowship in the Association for Computing Machinery and IEEE, he received the Harry H. Goode Memorial Award from IEEE in 2012, which honors significant contributions to theory or practice in the information processing field.A humble scientist, Arvind was the first to point out that these achievements were only possible because of his outstanding and brilliant collaborators. Chief among those collaborators were the undergraduate and graduate students he felt fortunate to work with at MIT. He maintained excellent relationships with them both professionally and personally, and valued these relationships more than the work they did together, according to family members.In summing up the key to his scientific success, Arvind put it this way in the 2012 IEEE interview: “Really, one has to do what one believes in. I think the level at which most of us work, it is not sustainable if you don’t enjoy it on a day-to-day basis. You can’t work on it just because of the results. You have to work on it because you say, ‘I have to know the answer to this,’” he said.He is survived by his wife, Gita Singh Mithal, their two sons Divakar ’01 and Prabhakar ’04, their wives Leena and Nisha, and two grandchildren, Maya and Vikram.  More

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      MIT-Takeda Program wraps up with 16 publications, a patent, and nearly two dozen projects completed

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      When the Takeda Pharmaceutical Co. and the MIT School of Engineering launched their collaboration focused on artificial intelligence in health care and drug development in February 2020, society was on the cusp of a globe-altering pandemic and AI was far from the buzzword it is today.As the program concludes, the world looks very different. AI has become a transformative technology across industries including health care and pharmaceuticals, while the pandemic has altered the way many businesses approach health care and changed how they develop and sell medicines.For both MIT and Takeda, the program has been a game-changer.When it launched, the collaborators hoped the program would help solve tangible, real-world problems. By its end, the program has yielded a catalog of new research papers, discoveries, and lessons learned, including a patent for a system that could improve the manufacturing of small-molecule medicines.Ultimately, the program allowed both entities to create a foundation for a world where AI and machine learning play a pivotal role in medicine, leveraging Takeda’s expertise in biopharmaceuticals and the MIT researchers’ deep understanding of AI and machine learning.“The MIT-Takeda Program has been tremendously impactful and is a shining example of what can be accomplished when experts in industry and academia work together to develop solutions,” says Anantha Chandrakasan, MIT’s chief innovation and strategy officer, dean of the School of Engineering, and the Vannevar Bush Professor of Electrical Engineering and Computer Science. “In addition to resulting in research that has advanced how we use AI and machine learning in health care, the program has opened up new opportunities for MIT faculty and students through fellowships, funding, and networking.”What made the program unique was that it was centered around several concrete challenges spanning drug development that Takeda needed help addressing. MIT faculty had the opportunity to select the projects based on their area of expertise and general interest, allowing them to explore new areas within health care and drug development.“It was focused on Takeda’s toughest business problems,” says Anne Heatherington, Takeda’s research and development chief data and technology officer and head of its Data Sciences Institute.“They were problems that colleagues were really struggling with on the ground,” adds Simon Davies, the executive director of the MIT-Takeda Program and Takeda’s global head of statistical and quantitative sciences. Takeda saw an opportunity to collaborate with MIT’s world-class researchers, who were working only a few blocks away. Takeda, a global pharmaceutical company with global headquarters in Japan, has its global business units and R&D center just down the street from the Institute.As part of the program, MIT faculty were able to select what issues they were interested in working on from a group of potential Takeda projects. Then, collaborative teams including MIT researchers and Takeda employees approached research questions in two rounds. Over the course of the program, collaborators worked on 22 projects focused on topics including drug discovery and research, clinical drug development, and pharmaceutical manufacturing. Over 80 MIT students and faculty joined more than 125 Takeda researchers and staff on teams addressing these research questions.The projects centered around not only hard problems, but also the potential for solutions to scale within Takeda or within the biopharmaceutical industry more broadly.Some of the program’s findings have already resulted in wider studies. One group’s results, for instance, showed that using artificial intelligence to analyze speech may allow for earlier detection of frontotemporal dementia, while making that diagnosis more quickly and inexpensively. Similar algorithmic analyses of speech in patients diagnosed with ALS may also help clinicians understand the progression of that disease. Takeda is continuing to test both AI applications.Other discoveries and AI models that resulted from the program’s research have already had an impact. Using a physical model and AI learning algorithms can help detect particle size, mix, and consistency for powdered, small-molecule medicines, for instance, speeding up production timelines. Based on their research under the program, collaborators have filed for a patent for that technology.For injectable medicines like vaccines, AI-enabled inspections can also reduce process time and false rejection rates. Replacing human visual inspections with AI processes has already shown measurable impact for the pharmaceutical company.Heatherington adds, “our lessons learned are really setting the stage for what we’re doing next, really embedding AI and gen-AI [generative AI] into everything that we do moving forward.”Over the course of the program, more than 150 Takeda researchers and staff also participated in educational programming organized by the Abdul Latif Jameel Clinic for Machine Learning in Health. In addition to providing research opportunities, the program funded 10 students through SuperUROP, the Advanced Undergraduate Research Opportunities Program, as well as two cohorts from the DHIVE health-care innovation program, part of the MIT Sandbox Innovation Fund Program.Though the formal program has ended, certain aspects of the collaboration will continue, such as the MIT-Takeda Fellows, which supports graduate students as they pursue groundbreaking research related to health and AI. During its run, the program supported 44 MIT-Takeda Fellows and will continue to support MIT students through an endowment fund. Organic collaboration between MIT and Takeda researchers will also carry forward. And the programs’ collaborators are working to create a model for similar academic and industry partnerships to widen the impact of this first-of-its-kind collaboration.  More

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      Scientists preserve DNA in an amber-like polymer

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      In the movie “Jurassic Park,” scientists extracted DNA that had been preserved in amber for millions of years, and used it to create a population of long-extinct dinosaurs.Inspired partly by that film, MIT researchers have developed a glassy, amber-like polymer that can be used for long-term storage of DNA, whether entire human genomes or digital files such as photos.Most current methods for storing DNA require freezing temperatures, so they consume a great deal of energy and are not feasible in many parts of the world. In contrast, the new amber-like polymer can store DNA at room temperature while protecting the molecules from damage caused by heat or water.The researchers showed that they could use this polymer to store DNA sequences encoding the theme music from Jurassic Park, as well as an entire human genome. They also demonstrated that the DNA can be easily removed from the polymer without damaging it.“Freezing DNA is the number one way to preserve it, but it’s very expensive, and it’s not scalable,” says James Banal, a former MIT postdoc. “I think our new preservation method is going to be a technology that may drive the future of storing digital information on DNA.”Banal and Jeremiah Johnson, the A. Thomas Geurtin Professor of Chemistry at MIT, are the senior authors of the study, published yesterday in the Journal of the American Chemical Society. Former MIT postdoc Elizabeth Prince and MIT postdoc Ho Fung Cheng are the lead authors of the paper.Capturing DNADNA, a very stable molecule, is well-suited for storing massive amounts of information, including digital data. Digital storage systems encode text, photos, and other kind of information as a series of 0s and 1s. This same information can be encoded in DNA using the four nucleotides that make up the genetic code: A, T, G, and C. For example, G and C could be used to represent 0 while A and T represent 1.DNA offers a way to store this digital information at very high density: In theory, a coffee mug full of DNA could store all of the world’s data. DNA is also very stable and relatively easy to synthesize and sequence.In 2021, Banal and his postdoc advisor, Mark Bathe, an MIT professor of biological engineering, developed a way to store DNA in particles of silica, which could be labeled with tags that revealed the particles’ contents. That work led to a spinout called Cache DNA.One downside to that storage system is that it takes several days to embed DNA into the silica particles. Furthermore, removing the DNA from the particles requires hydrofluoric acid, which can be hazardous to workers handling the DNA.To come up with alternative storage materials, Banal began working with Johnson and members of his lab. Their idea was to use a type of polymer known as a degradable thermoset, which consists of polymers that form a solid when heated. The material also includes cleavable links that can be easily broken, allowing the polymer to be degraded in a controlled way.“With these deconstructable thermosets, depending on what cleavable bonds we put into them, we can choose how we want to degrade them,” Johnson says.For this project, the researchers decided to make their thermoset polymer from styrene and a cross-linker, which together form an amber-like thermoset called cross-linked polystyrene. This thermoset is also very hydrophobic, so it can prevent moisture from getting in and damaging the DNA. To make the thermoset degradable, the styrene monomers and cross-linkers are copolymerized with monomers called thionolactones. These links can be broken by treating them with a molecule called cysteamine.Because styrene is so hydrophobic, the researchers had to come up with a way to entice DNA — a hydrophilic, negatively charged molecule — into the styrene.To do that, they identified a combination of three monomers that they could turn into polymers that dissolve DNA by helping it interact with styrene. Each of the monomers has different features that cooperate to get the DNA out of water and into the styrene. There, the DNA forms spherical complexes, with charged DNA in the center and hydrophobic groups forming an outer layer that interacts with styrene. When heated, this solution becomes a solid glass-like block, embedded with DNA complexes.The researchers dubbed their method T-REX (Thermoset-REinforced Xeropreservation). The process of embedding DNA into the polymer network takes a few hours, but that could become shorter with further optimization, the researchers say.To release the DNA, the researchers first add cysteamine, which cleaves the bonds holding the polystyrene thermoset together, breaking it into smaller pieces. Then, a detergent called SDS can be added to remove the DNA from polystyrene without damaging it.Storing informationUsing these polymers, the researchers showed that they could encapsulate DNA of varying length, from tens of nucleotides up to an entire human genome (more than 50,000 base pairs). They were able to store DNA encoding the Emancipation Proclamation and the MIT logo, in addition to the theme music from “Jurassic Park.”After storing the DNA and then removing it, the researchers sequenced it and found that no errors had been introduced, which is a critical feature of any digital data storage system.The researchers also showed that the thermoset polymer can protect DNA from temperatures up to 75 degrees Celsius (167 degrees Fahrenheit). They are now working on ways to streamline the process of making the polymers and forming them into capsules for long-term storage.Cache DNA, a company started by Banal and Bathe, with Johnson as a member of the scientific advisory board, is now working on further developing DNA storage technology. The earliest application they envision is storing genomes for personalized medicine, and they also anticipate that these stored genomes could undergo further analysis as better technology is developed in the future.“The idea is, why don’t we preserve the master record of life forever?” Banal says. “Ten years or 20 years from now, when technology has advanced way more than we could ever imagine today, we could learn more and more things. We’re still in the very infancy of understanding the genome and how it relates to disease.”The research was funded by the National Science Foundation. More

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      Researchers use large language models to help robots navigate

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      Someday, you may want your home robot to carry a load of dirty clothes downstairs and deposit them in the washing machine in the far-left corner of the basement. The robot will need to combine your instructions with its visual observations to determine the steps it should take to complete this task.For an AI agent, this is easier said than done. Current approaches often utilize multiple hand-crafted machine-learning models to tackle different parts of the task, which require a great deal of human effort and expertise to build. These methods, which use visual representations to directly make navigation decisions, demand massive amounts of visual data for training, which are often hard to come by.To overcome these challenges, researchers from MIT and the MIT-IBM Watson AI Lab devised a navigation method that converts visual representations into pieces of language, which are then fed into one large language model that achieves all parts of the multistep navigation task.Rather than encoding visual features from images of a robot’s surroundings as visual representations, which is computationally intensive, their method creates text captions that describe the robot’s point-of-view. A large language model uses the captions to predict the actions a robot should take to fulfill a user’s language-based instructions.Because their method utilizes purely language-based representations, they can use a large language model to efficiently generate a huge amount of synthetic training data.While this approach does not outperform techniques that use visual features, it performs well in situations that lack enough visual data for training. The researchers found that combining their language-based inputs with visual signals leads to better navigation performance.“By purely using language as the perceptual representation, ours is a more straightforward approach. Since all the inputs can be encoded as language, we can generate a human-understandable trajectory,” says Bowen Pan, an electrical engineering and computer science (EECS) graduate student and lead author of a paper on this approach.Pan’s co-authors include his advisor, Aude Oliva, director of strategic industry engagement at the MIT Schwarzman College of Computing, MIT director of the MIT-IBM Watson AI Lab, and a senior research scientist in the Computer Science and Artificial Intelligence Laboratory (CSAIL); Philip Isola, an associate professor of EECS and a member of CSAIL; senior author Yoon Kim, an assistant professor of EECS and a member of CSAIL; and others at the MIT-IBM Watson AI Lab and Dartmouth College. The research will be presented at the Conference of the North American Chapter of the Association for Computational Linguistics.Solving a vision problem with languageSince large language models are the most powerful machine-learning models available, the researchers sought to incorporate them into the complex task known as vision-and-language navigation, Pan says.But such models take text-based inputs and can’t process visual data from a robot’s camera. So, the team needed to find a way to use language instead.Their technique utilizes a simple captioning model to obtain text descriptions of a robot’s visual observations. These captions are combined with language-based instructions and fed into a large language model, which decides what navigation step the robot should take next.The large language model outputs a caption of the scene the robot should see after completing that step. This is used to update the trajectory history so the robot can keep track of where it has been.The model repeats these processes to generate a trajectory that guides the robot to its goal, one step at a time.To streamline the process, the researchers designed templates so observation information is presented to the model in a standard form — as a series of choices the robot can make based on its surroundings.For instance, a caption might say “to your 30-degree left is a door with a potted plant beside it, to your back is a small office with a desk and a computer,” etc. The model chooses whether the robot should move toward the door or the office.“One of the biggest challenges was figuring out how to encode this kind of information into language in a proper way to make the agent understand what the task is and how they should respond,” Pan says.Advantages of languageWhen they tested this approach, while it could not outperform vision-based techniques, they found that it offered several advantages.First, because text requires fewer computational resources to synthesize than complex image data, their method can be used to rapidly generate synthetic training data. In one test, they generated 10,000 synthetic trajectories based on 10 real-world, visual trajectories.The technique can also bridge the gap that can prevent an agent trained with a simulated environment from performing well in the real world. This gap often occurs because computer-generated images can appear quite different from real-world scenes due to elements like lighting or color. But language that describes a synthetic versus a real image would be much harder to tell apart, Pan says. Also, the representations their model uses are easier for a human to understand because they are written in natural language.“If the agent fails to reach its goal, we can more easily determine where it failed and why it failed. Maybe the history information is not clear enough or the observation ignores some important details,” Pan says.In addition, their method could be applied more easily to varied tasks and environments because it uses only one type of input. As long as data can be encoded as language, they can use the same model without making any modifications.But one disadvantage is that their method naturally loses some information that would be captured by vision-based models, such as depth information.However, the researchers were surprised to see that combining language-based representations with vision-based methods improves an agent’s ability to navigate.“Maybe this means that language can capture some higher-level information than cannot be captured with pure vision features,” he says.This is one area the researchers want to continue exploring. They also want to develop a navigation-oriented captioner that could boost the method’s performance. In addition, they want to probe the ability of large language models to exhibit spatial awareness and see how this could aid language-based navigation.This research is funded, in part, by the MIT-IBM Watson AI Lab. More

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      Making climate models relevant for local decision-makers

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      Climate models are a key technology in predicting the impacts of climate change. By running simulations of the Earth’s climate, scientists and policymakers can estimate conditions like sea level rise, flooding, and rising temperatures, and make decisions about how to appropriately respond. But current climate models struggle to provide this information quickly or affordably enough to be useful on smaller scales, such as the size of a city. Now, authors of a new open-access paper published in the Journal of Advances in Modeling Earth Systems have found a method to leverage machine learning to utilize the benefits of current climate models, while reducing the computational costs needed to run them. “It turns the traditional wisdom on its head,” says Sai Ravela, a principal research scientist in MIT’s Department of Earth, Atmospheric and Planetary Sciences (EAPS) who wrote the paper with EAPS postdoc Anamitra Saha. Traditional wisdomIn climate modeling, downscaling is the process of using a global climate model with coarse resolution to generate finer details over smaller regions. Imagine a digital picture: A global model is a large picture of the world with a low number of pixels. To downscale, you zoom in on just the section of the photo you want to look at — for example, Boston. But because the original picture was low resolution, the new version is blurry; it doesn’t give enough detail to be particularly useful. “If you go from coarse resolution to fine resolution, you have to add information somehow,” explains Saha. Downscaling attempts to add that information back in by filling in the missing pixels. “That addition of information can happen two ways: Either it can come from theory, or it can come from data.” Conventional downscaling often involves using models built on physics (such as the process of air rising, cooling, and condensing, or the landscape of the area), and supplementing it with statistical data taken from historical observations. But this method is computationally taxing: It takes a lot of time and computing power to run, while also being expensive. A little bit of both In their new paper, Saha and Ravela have figured out a way to add the data another way. They’ve employed a technique in machine learning called adversarial learning. It uses two machines: One generates data to go into our photo. But the other machine judges the sample by comparing it to actual data. If it thinks the image is fake, then the first machine has to try again until it convinces the second machine. The end-goal of the process is to create super-resolution data. Using machine learning techniques like adversarial learning is not a new idea in climate modeling; where it currently struggles is its inability to handle large amounts of basic physics, like conservation laws. The researchers discovered that simplifying the physics going in and supplementing it with statistics from the historical data was enough to generate the results they needed. “If you augment machine learning with some information from the statistics and simplified physics both, then suddenly, it’s magical,” says Ravela. He and Saha started with estimating extreme rainfall amounts by removing more complex physics equations and focusing on water vapor and land topography. They then generated general rainfall patterns for mountainous Denver and flat Chicago alike, applying historical accounts to correct the output. “It’s giving us extremes, like the physics does, at a much lower cost. And it’s giving us similar speeds to statistics, but at much higher resolution.” Another unexpected benefit of the results was how little training data was needed. “The fact that that only a little bit of physics and little bit of statistics was enough to improve the performance of the ML [machine learning] model … was actually not obvious from the beginning,” says Saha. It only takes a few hours to train, and can produce results in minutes, an improvement over the months other models take to run. Quantifying risk quicklyBeing able to run the models quickly and often is a key requirement for stakeholders such as insurance companies and local policymakers. Ravela gives the example of Bangladesh: By seeing how extreme weather events will impact the country, decisions about what crops should be grown or where populations should migrate to can be made considering a very broad range of conditions and uncertainties as soon as possible.“We can’t wait months or years to be able to quantify this risk,” he says. “You need to look out way into the future and at a large number of uncertainties to be able to say what might be a good decision.”While the current model only looks at extreme precipitation, training it to examine other critical events, such as tropical storms, winds, and temperature, is the next step of the project. With a more robust model, Ravela is hoping to apply it to other places like Boston and Puerto Rico as part of a Climate Grand Challenges project.“We’re very excited both by the methodology that we put together, as well as the potential applications that it could lead to,” he says.  More

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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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      in Data Management & Statistics

      A data-driven approach to making better choices

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      Imagine a world in which some important decision — a judge’s sentencing recommendation, a child’s treatment protocol, which person or business should receive a loan — was made more reliable because a well-designed algorithm helped a key decision-maker arrive at a better choice. A new MIT economics course is investigating these interesting possibilities.Class 14.163 (Algorithms and Behavioral Science) is a new cross-disciplinary course focused on behavioral economics, which studies the cognitive capacities and limitations of human beings. The course was co-taught this past spring by assistant professor of economics Ashesh Rambachan and visiting lecturer Sendhil Mullainathan.Rambachan studies the economic applications of machine learning, focusing on algorithmic tools that drive decision-making in the criminal justice system and consumer lending markets. He also develops methods for determining causation using cross-sectional and dynamic data.Mullainathan will soon join the MIT departments of Electrical Engineering and Computer Science and Economics as a professor. His research uses machine learning to understand complex problems in human behavior, social policy, and medicine. Mullainathan co-founded the Abdul Latif Jameel Poverty Action Lab (J-PAL) in 2003.The new course’s goals are both scientific (to understand people) and policy-driven (to improve society by improving decisions). Rambachan believes that machine-learning algorithms provide new tools for both the scientific and applied goals of behavioral economics.“The course investigates the deployment of computer science, artificial intelligence (AI), economics, and machine learning in service of improved outcomes and reduced instances of bias in decision-making,” Rambachan says.There are opportunities, Rambachan believes, for constantly evolving digital tools like AI, machine learning, and large language models (LLMs) to help reshape everything from discriminatory practices in criminal sentencing to health-care outcomes among underserved populations.Students learn how to use machine learning tools with three main objectives: to understand what they do and how they do it, to formalize behavioral economics insights so they compose well within machine learning tools, and to understand areas and topics where the integration of behavioral economics and algorithmic tools might be most fruitful.Students also produce ideas, develop associated research, and see the bigger picture. They’re led to understand where an insight fits and see where the broader research agenda is leading. Participants can think critically about what supervised LLMs can (and cannot) do, to understand how to integrate those capacities with the models and insights of behavioral economics, and to recognize the most fruitful areas for the application of what investigations uncover.The dangers of subjectivity and biasAccording to Rambachan, behavioral economics acknowledges that biases and mistakes exist throughout our choices, even absent algorithms. “The data used by our algorithms exist outside computer science and machine learning, and instead are often produced by people,” he continues. “Understanding behavioral economics is therefore essential to understanding the effects of algorithms and how to better build them.”Rambachan sought to make the course accessible regardless of attendees’ academic backgrounds. The class included advanced degree students from a variety of disciplines.By offering students a cross-disciplinary, data-driven approach to investigating and discovering ways in which algorithms might improve problem-solving and decision-making, Rambachan hopes to build a foundation on which to redesign existing systems of jurisprudence, health care, consumer lending, and industry, to name a few areas.“Understanding how data are generated can help us understand bias,” Rambachan says. “We can ask questions about producing a better outcome than what currently exists.”Useful tools for re-imagining social operationsEconomics doctoral student Jimmy Lin was skeptical about the claims Rambachan and Mullainathan made when the class began, but changed his mind as the course continued.“Ashesh and Sendhil started with two provocative claims: The future of behavioral science research will not exist without AI, and the future of AI research will not exist without behavioral science,” Lin says. “Over the course of the semester, they deepened my understanding of both fields and walked us through numerous examples of how economics informed AI research and vice versa.”Lin, who’d previously done research in computational biology, praised the instructors’ emphasis on the importance of a “producer mindset,” thinking about the next decade of research rather than the previous decade. “That’s especially important in an area as interdisciplinary and fast-moving as the intersection of AI and economics — there isn’t an old established literature, so you’re forced to ask new questions, invent new methods, and create new bridges,” he says.The speed of change to which Lin alludes is a draw for him, too. “We’re seeing black-box AI methods facilitate breakthroughs in math, biology, physics, and other scientific disciplines,” Lin  says. “AI can change the way we approach intellectual discovery as researchers.”An interdisciplinary future for economics and social systemsStudying traditional economic tools and enhancing their value with AI may yield game-changing shifts in how institutions and organizations teach and empower leaders to make choices.“We’re learning to track shifts, to adjust frameworks and better understand how to deploy tools in service of a common language,” Rambachan says. “We must continually interrogate the intersection of human judgment, algorithms, AI, machine learning, and LLMs.”Lin enthusiastically recommended the course regardless of students’ backgrounds. “Anyone broadly interested in algorithms in society, applications of AI across academic disciplines, or AI as a paradigm for scientific discovery should take this class,” he says. “Every lecture felt like a goldmine of perspectives on research, novel application areas, and inspiration on how to produce new, exciting ideas.”The course, Rambachan says, argues that better-built algorithms can improve decision-making across disciplines. “By building connections between economics, computer science, and machine learning, perhaps we can automate the best of human choices to improve outcomes while minimizing or eliminating the worst,” he says.Lin remains excited about the course’s as-yet unexplored possibilities. “It’s a class that makes you excited about the future of research and your own role in it,” he says. 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