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    Physicists find a novel way to switch antiferromagnetism on and off

    When you save an image to your smartphone, those data are written onto tiny transistors that are electrically switched on or off in a pattern of “bits” to represent and encode that image. Most transistors today are made from silicon, an element that scientists have managed to switch at ever-smaller scales, enabling billions of bits, and therefore large libraries of images and other files, to be packed onto a single memory chip.

    But growing demand for data, and the means to store them, is driving scientists to search beyond silicon for materials that can push memory devices to higher densities, speeds, and security.

    Now MIT physicists have shown preliminary evidence that data might be stored as faster, denser, and more secure bits made from antiferromagnets.

    Antiferromagnetic, or AFM materials are the lesser-known cousins to ferromagnets, or conventional magnetic materials. Where the electrons in ferromagnets spin in synchrony — a property that allows a compass needle to point north, collectively following the Earth’s magnetic field — electrons in an antiferromagnet prefer the opposite spin to their neighbor, in an “antialignment” that effectively quenches magnetization even at the smallest scales.

    The absence of net magnetization in an antiferromagnet makes it impervious to any external magnetic field. If they were made into memory devices, antiferromagnetic bits could protect any encoded data from being magnetically erased. They could also be made into smaller transistors and packed in greater numbers per chip than traditional silicon.

    Now the MIT team has found that by doping extra electrons into an antiferromagnetic material, they can turn its collective antialigned arrangement on and off, in a controllable way. They found this magnetic transition is reversible, and sufficiently sharp, similar to switching a transistor’s state from 0 to 1. The results, published today in Physical Review Letters, demonstrate a potential new pathway to use antiferromagnets as a digital switch.

    “An AFM memory could enable scaling up the data storage capacity of current devices — same volume, but more data,” says the study’s lead author Riccardo Comin, assistant professor of physics at MIT.

    Comin’s MIT co-authors include lead author and graduate student Jiarui Li, along with Zhihai Zhu, Grace Zhang, and Da Zhou; as well as Roberg Green of the University of Saskatchewan; Zhen Zhang, Yifei Sun, and Shriram Ramanathan of Purdue University; Ronny Sutarto and Feizhou He of Canadian Light Source; and Jerzy Sadowski at Brookhaven National Laboratory.

    Magnetic memory

    To improve data storage, some researchers are looking to MRAM, or magnetoresistive RAM, a type of memory system that stores data as bits made from conventional magnetic materials. In principle, an MRAM device would be patterned with billions of magnetic bits. To encode data, the direction of a local magnetic domain within the device is flipped, similar to switching a transistor from 0 to 1.

    MRAM systems could potentially read and write data faster than silicon-based devices and could run with less power. But they could also be vulnerable to external magnetic fields.

    “The system as a whole follows a magnetic field like a sunflower follows the sun, which is why, if you take a magnetic data storage device and put it in a moderate magnetic field, information is completely erased,” Comin says.

    Antiferromagnets, in contrast, are unaffected by external fields and could therefore be a more secure alternative to MRAM designs. An essential step toward encodable AFM bits is the ability to switch antiferromagnetism on and off. Researchers have found various ways to accomplish this, mostly by using electric current to switch a material from its orderly antialignment, to a random disorder of spins.

    “With these approaches, switching is very fast,” says Li. “But the downside is, everytime you need a current to read or write, that requires a lot of energy per operation. When things get very small, the energy and heat generated by running currents are significant.”

    Doped disorder

    Comin and his colleagues wondered whether they could achieve antiferromagnetic switching in a more efficient manner. In their new study, they work with neodymium nickelate, an antiferromagnetic oxide grown in the Ramanathan lab. This material exhibits nanodomains that consist of nickel atoms with an opposite spin to that of its neighbor, and held together by oxygen and neodymium atoms. The researchers had previously mapped the material’s fractal properties.

    Since then, the researchers have looked to see if they could manipulate the material’s antiferromagnetism via doping — a process that intentionally introduces impurities in a material to alter its electronic properties. In their case, the researchers doped neodymium nickel oxide by stripping the material of its oxygen atoms.

    When an oxygen atom is removed, it leaves behind two electrons, which are redistributed among the other nickel and oxygen atoms. The researchers wondered whether stripping away many oxygen atoms would result in a domino effect of disorder that would switch off the material’s orderly antialignment.

    To test their theory, they grew 100-nanometer-thin films of neodymium nickel oxide and placed them in an oxygen-starved chamber, then heated the samples to temperatures of 400 degrees Celsius to encourage oxygen to escape from the films and into the chamber’s atmosphere.

    As they removed progressively more oxygen, they studied the films using advanced magnetic X-ray crystallography techniques to determine whether the material’s magnetic structure was intact, implying that its atomic spins remained in their orderly antialignment, and therefore retained antiferomagnetism. If their data showed a lack of an ordered magnetic structure, it would be evidence that the material’s antiferromagnetism had switched off, due to sufficient doping.

    Through their experiments, the researchers were able to switch off the material’s antiferromagnetism at a certain critical doping threshold. They could also restore antiferromagnetism by adding oxygen back into the material.

    Now that the team has shown doping effectively switches AFM on and off, scientists might use more practical ways to dope similar materials. For instance, silicon-based transistors are switched using voltage-activated “gates,” where a small voltage is applied to a bit to alter its electrical conductivity. Comin says that antiferromagnetic bits could also be switched using suitable voltage gates, which would require less energy than other antiferromagnetic switching techniques.

    “This could present an opportunity to develop a magnetic memory storage device that works similarly to silicon-based chips, with the added benefit that you can store information in AFM domains that are very robust and can be packed at high densities,” Comin says. “That’s key to addressing the challenges of a data-driven world.”

    This research was supported, in part, by the Air Force Office of Scientific Research Young Investigator Program and the Natural Sciences and Engineering Research Council of Canada. This research used resources of the Center for Functional Nanomaterials and National Synchrotron Light Source II, both U.S. Department of Energy Office of Science User Facilities located at Brookhaven National Laboratory. More

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    Robotic solution for disinfecting food production plants wins agribusiness prize

    The winners of this year’s Rabobank-MIT Food and Agribusiness Innovation Prize got a good indication their pitch was striking a chord when a judge offered to have his company partner with the team for an early demonstration. The offer signified demand for their solution — to say nothing of their chances of winning the pitch competition.

    The annual competition’s MIT-based grand-prize winner, Human Dynamics, is seeking to improve sanitation in food production plants with a robotic drone — a “drobot” — that flies through facilities spraying soap and disinfectant.

    The company says the product addresses major labor shortages for food production facilities, which often must carry out daily sanitation processes.

    “They have to sanitize every night, and it’s extremely labor intensive and expensive,” says co-founder Tom Okamoto, a master’s student in MIT’s System Design and Management (SDM) program.

    In the winning pitch, Okamoto said the average large food manufacturer spends $13 million on sanitation annually. When you combine the time sanitation processes takes away from production and delays due to human error, Human Dynamics estimates it’s tackling an $80 billion problem.

    The company’s prototype uses a quadcopter drone that carries a tank, nozzle, and spray hose. Underneath the hood, the drone uses visual detection technology to validate that each area is clean, LIDAR to map out its path, and algorithms for route optimization.

    The product is designed to automate repetitive tasks while complementing other cleaning efforts currently done by humans. Workers will still be required for certain aspects of cleaning and tasks like preparing and inspecting facilities during sanitation.

    The company has already developed several proofs of concept and is planning to run a pilot project with a local food producer and distributor this summer.

    The Human Dynamics team also includes MIT researcher Takahiro Nozaki, MIT master’s student Julia Chen, and Harvard Business School students Mike Mancinelli and Kaz Yoshimaru.

    The company estimates that the addressable market for sanitation in food production facilities in the country is $3 billion.

    The second-place prize went to Resourceful, which aims to help connect buyers and sellers of food waste byproducts through an online platform. The company says there’s a growing market for upcycled products made by companies selling things like edible chips made from juice pulp, building materials made from potato skins, and eyeglasses made from orange peels. But establishing a byproduct supply chain can be difficult.

    “Being paid for byproducts should be low-hanging fruit for food manufacturers, but the system is broken,” says co-founder and CEO Kyra Atekwana, an MBA candidate at the University of Chicago’s Booth School of Business. “There are tens of millions of pounds of food waste produced in the U.S. every year, and there’s a variety of tech solutions … enabling this food waste and surplus to be captured by consumers. But there’s virtually nothing in the middle to unlock access to the 10.6 million tons of byproduct waste produced every year.”

    Buyers and sellers can offer and browse food waste byproducts on the company’s subscription-based platform. The businesses can also connect and establish contracts through the platform. Resourceful charges a small fee for each transaction.

    The company is currently launching pilots in the Chicago region before making a public launch later this year. It has also partnered with the Upcycled Food Association, a nonprofit focused on reducing food waste.

    The winners were chosen from a group of seven finalist teams. Other finalists included:

    Chicken Haus, a vertically integrated, fast-casual restaurant concept dedicated to serving locally sourced, bone-in fried chicken;
    Joise Food Technologies, which is 3-D printing the next-generation of meat alternatives and other foods using 3-D biofabrication technology and sustainable food ink formulation;
    Marble, which is developing a small-footprint robot to remove fat from the surface of meat cuts to achieve optimal yield;
    Nice Rice, which is developing a rice alternative made from pea starch, which can be upcycled; and
    Roofscapes, which deploys accessible wooden platforms to “vegetalize” roofs in dense urban areas to combat food insecurity and climate change.

    This was the sixth year of the event, which was hosted by the MIT Food and Agriculture Club. The event was sponsored by Rabobank and MIT’s Abdul Latif Jameel World Water and Food Systems Lab (J-WAFS). More

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    Five from MIT elected to American Academy of Arts and Sciences for 2021

    Five MIT faculty members are among more than 250 leaders from academia, business, public affairs, the humanities, and the arts elected to the American Academy of Arts and Sciences, the academy announced Thursday.

    One of the nation’s most prestigious honorary societies, the academy is also a leading center for independent policy research. Members contribute to academy publications, as well as studies of science and technology policy, energy and global security, social policy and American institutions, the humanities and culture, and education.

    Those elected from MIT this year are:

    Linda Griffith, the School of Engineering Professor of Teaching Innovation, Biological Engineering, and Mechanical engineering;
    Muriel Médard, the Cecil H. Green Professor in the Department of Electrical Engineering;
    Leona Samson, professor of biological engineering and biology;
    Scott Sheffield, the Leighton Family Professor in the Department of Mathematics; and
    Li-Huei Tsai, the Picower Professor in the Department of Brain and Cognitive Sciences.

    “We are honoring the excellence of these individuals, celebrating what they have achieved so far, and imagining what they will continue to accomplish,” says David Oxtoby, president of the academy. “The past year has been replete with evidence of how things can get worse; this is an opportunity to illuminate the importance of art, ideas, knowledge, and leadership that can make a better world.”

    Since its founding in 1780, the academy has elected leading thinkers from each generation, including George Washington and Benjamin Franklin in the 18th century, Maria Mitchell and Daniel Webster in the 19th century, and Toni Morrison and Albert Einstein in the 20th century. The current membership includes more than 250 Nobel and Pulitzer Prize winners. More

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    A guide to when and how to build technology for social good

    People frequently try to participate in political processes, from organizing to hold government to account for providing quality health care and education to participating in elections. But sometimes these systems are set up in a way that makes it difficult for people and government to engage effectively with each other. How can technology help?

    In a new how-to guide, Luke Jordan, an MIT Governance Lab (MIT GOV/LAB) practitioner-in-residence, advises on how — and more importantly, when — to put together a team to build such a piece of “civic technology.” 

    Jordan is the founder and executive director of Grassroot, a civic technology platform for community organizing in South Africa. “With Grassroot, I learned a lot about building technology on a very limited budget in difficult contexts for complex problems,” says Jordan. “The guide codifies some of what I learned.” 

    While the guide is aimed at people interested in designing technology that has a social impact, some parts might also be useful more broadly to anyone designing technology in a small team. 

    The “don’t build it” principle 

    The guide’s first lesson is its title: “Don’t Build It.” Because an app can be designed cheaply and easily, many get built when the designer hasn’t found a good solution to the problem they’re trying to solve or doesn’t even understand the problem in the first place. 

    Koketso Moeti, founding executive director of amandla.mobi, says she is regularly approached by people with an idea for a piece of civic technology. “Often after a discussion, it is either realized that there is something that already exists that can do what is desired, or that the problem was misdiagnosed and is sometimes not even a technical problem,” she says. The “don’t build it” principle serves as a reminder that you have to work hard to convince yourself that your project is worth starting. 

    The guide offers several litmus tests for whether or not an idea is a good one, one of which is that the technology should help people do something that they’re already trying to do, but are finding it difficult. “Unless you’re the Wright brothers,” says Jordan, “you have to know if people are actually going to want to use this.” 

    This means developing a deep understanding of the context you’re trying to solve a problem in. Jordan’s original conception of Grassroot was an alert for when services weren’t working. But after walking around and talking to people in communities that might use the product, his team found that people were already alerting each other. “But when we asked, ‘how do people come together when you need to do something about it,’” says Jordan, “we were told over and over, ‘that’s actually really difficult.’” And so Grassroot became a platform activists could use to organize gatherings. 

    Building a team: hire young engineers

    One section of the guide advises on how to put together a team to build a project, such as what qualities one should want in a chief technology officer (CTO) who will help run things; where to look for engineers; and how a tech team should work with one’s field staff. 

    The guide suggests hiring entry-level engineers as a way to get some talented people on board while operating on a limited budget. “When I’ve hired, I’ve tended to find most of the value among very unconventional and raw junior hires,” says Jordan. “I think if you put in the work in the hiring process, you get fantastic people at junior levels.”

    “Civic tech is one exciting area where promising young engineers, like MIT students, can apply computer science skills for the public good,” says Professor Lily L. Tsai, MIT GOV/LAB’s director and founder. “The guide provides advice on how you can find, hire, and mentor new talent.”

    Jordan says the challenge is that while people in computer science find these “tech for good” projects appealing, they often don’t pay nearly as well as other opportunities. Like in other startup contexts, though, young engineers have the opportunity to learn a lot in an engaging environment. “I tell people, ‘come and do this for a year-and-a-half, two years,’” he says. “‘You’ll get paid perhaps significantly below industry rate, but you’ll get to do a really interesting thing, and you’ll work in a small team directly with the CTO. You’ll get a lot more experience a lot more quickly.’” 

    How to work: learn early, quickly, and often

    Jordan says that both a firm and its engineers must have “a real thirst to learn.” This includes being able to identify when things aren’t working and using that knowledge to make something better. The guide emphasizes the importance of ignoring “vanity metrics,” like the total number of users. They might look flashy and impress donors, but they don’t actually describe whether or not people are using the app, or if it’s helping people engage with their governments. Total user numbers “will always go up except in a complete catastrophe,” Jordan writes in the guide. 

    The biggest challenge is convincing partners and donors to also be willing to accept mistakes and ignore vanity metrics. Tsai thinks that getting governments to buy into civic tech projects can help create an innovation culture that values failure and rapid learning, and thus leads to more productive work. “Many times, civic tech projects start and end with citizens as users, and leave out the government side,” she says. “Designing with government as an end user is critical to the success of any civic tech project.” More

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    Fair ball! Sports analytics reckons with equity

    Fairness is part of the promise of sports analytics. By judging an athlete’s performance through good data — as opposed to reputation, image, or outworn clichés — analytics creates the possibility that people can be judged more consistently on merit than often occurs elsewhere in life.

    But that promise of fairness only goes so far in a sports world shaped by the same social forces as everything else: Men’s sports have traditionally commanded more resources than women’s sports, including access to data, and the analytics industry has not employed many women or people of color.

    The 15th annual MIT Sloan Sports Analytics Conference (SSAC), held online April 8 and 9, placed these issues in the middle of its 2021 agenda. The industry-leading event, a high-profile yearly gathering hosted by the MIT Sloan School of Management, featured numerous panels and speakers focused on the crossroads of sports and society.

    That emphasis follows a year of social change and protest, but it’s also borne out by viewership numbers. For instance, across all sports, viewership on television has been almost uniformly down during the Covid-19 pandemic — but, for the women’s NCAA basketball Final Four earlier this month, the semifinal ratings were up 22 percent compared to 2019, and the title game’s ratings were up 11 percent.

    And at a time when sports executives and sponsors have fretted over athlete activism possibly conflicting with fan sensibilities, some conference participants offered that women’s sports are better-positioned to thrive through turbulence. WNBA star Sue Bird, for one, observed that women have long had to engage in battles for equal treatment and fair pay, meaning that being a high-flying female professional athlete has often necessitated having an activist’s outlook.

    “I think our fanbase already knew what we were about,” said Bird, referring to the long-time embrace of social issues by many of the sport’s stars. She added: “It pays, metaphorically and literally, to be authentic.”

    Whatever gains have been made, equity issues remain ever-present in sports, as evidenced by a controversy a few weeks ago over a strikingly substandard weight room provided for the women’s teams in the NCAA basketball tournament — itself a topic of conversation at SSAC.

    “I don’t think women coming from the college basketball world were surprised by that,” said Sonia Raman, the former long-time women’s basketball coach at MIT. “At the NCAA level, the student-athlete experience, there needs to be parity in that experience.”

    But equity in sports does look a bit different compared to even a couple years ago. Last fall, Raman accepted an assistant coach job with the NBA’s Memphis Grizzlies, in good part because of her reputation for intense preparation and openness to analytics, something she would share with her players at MIT.

    “Analytics never gives you a cut and dried answer,” said Raman. “It might make you lean one way or another.” At MIT, she added, the coaching staff’s attitude toward metrics was, “Let’s have a conversation about it. We’d get to that point with our players where there was such a high level of trust, we could include them in the decisions, too.”

    Players today increasingly say they are receptive to analytics — and not just marginal athletes looking for an edge to make a roster, but major stars.

    “Hockey is so dynamic, I think there are endless opportunities [to find] things to measure,” said Hilary Knight, superstar of U.S. women’s hockey — and part of an all-female panel on hockey analytics at SSAC, something the sport’s old hands might have found mind-bending a few years ago.

    J.J. Watt, the star defensive end of the NFL’s Arizona Cardinals, suggested that players will buy into analytics-based decisions — like aggressively going for it on fourth downs in football — as long as coaches are consistently committed to such tactics.

    “If you’re going to believe the analytics and be an analytics team, you have to be an analytics team 100 percent of the time,” said Watt, making his first appearance at SSAC. If a team reverses course midseason and starts punting or kicking field goals more on the fourth down, he noted, “Then the players start to say, okay, what are we doing here?”

    There are plenty of questions sports analysts are still trying to understand better, of course.

    “It’s pretty hard to quantify defense with publicly available data,” said Alexandra Mandrycky, director of hockey strategy and research for the Seattle Kraken, the NHL’s new expansion team.

    On the other hand, noted Andrew Friedman, president of baseball operation for the World Series champion Los Angeles Dodgers, baseball managers are making decisions by the numbers much more often than they used to: “Fifteen years ago you saw a lot more bad bets happening a lot more frequently,” he noted.

    While demonstrating the evolving trends in analytics, the Sloan conference also offers historical perspective. The SSAC baseball panel this year included pioneering analyst Bill James, whose annual “Baseball Abstract” book, published from 1977 to 1988, brought “sabermetrics,” as he then called systematic baseball analysis, to a mainstream national audience for the first time.

    Regarding the analytics boom, James said, a bit modestly, “I’ve always been given more credit” than is merited. He added: “I absolutely never envisioned to any extent whatsoever that sabermetrics might come to have the influence that it has had. That was a great shock to me, and still is every day.”

    For a younger generation, though, there is no shock involved in using analytics — and if current trends continue, that should apply to teams of any gender, and at any level of sports.

    “Embrace data,” said Knight. “It’s here, and it’s the future.” More

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    Job connectivity improves resiliency in US cities, study finds

    What makes urban labor markets more resilient? This is the question at the heart of a new study published in Nature Communications by members of MIT’s Connection Science Group. The researchers in this study, including MIT research scientist and Universidad Carlos III (Spain) Professor Esteban Moro; University of Pittsburgh professor and former MIT postdoc Frank Morgan, MIT Professor Alex “Sandy” Pentland, and Max Planck professor and former MIT professor Iyad Rahwan, drew on prior network modeling research to map the job landscapes in cities across the United States, and showed that job “connectedness” is a key determinant of the resilience of local economies. 

    Economists, policymakers, city planners, and companies have a strong interest in determining what factors contribute to healthy job markets, including what factors can help promote faster recovery after a shock, such as a major recession or the current Covid-19 pandemic. Traditional modeling approaches in this realm have treated workers as narrowly linked to specific jobs. In the real world, however, jobs and sectors are linked. Displaced workers can often transition to another job or sector requiring similar skills. In this way, job markets are much like ecosystems, where organisms are linked in a complex web of relationships.

    In ecology and other domains where complex networks are present, resilience has been closely linked to the “connectedness” of the networks. In nature, for example, ecosystems with many mutualistic connections have proven more resistant to shocks, such as changes in acidity or temperature, than those with fewer connections. By drawing on ecosystem-inspired network models and extending the Nobel Prize-winning Pissarides-Mortensen job matching framework, the authors of the new study modeled the relationships between jobs in cities across the United States. Just as connectedness in nature fosters resilience, they predicted that cities with jobs connected by overlapping skills and geography would fare better in the face of economic shock than those without such networks.

    To validate this, the researchers examined data from the Bureau of Labor Statistics for all metropolitan areas in the country from the onset to the end of the Great Recession (December 2007-June 2009). They were able to create job landscape maps for each area, including not just the numbers of specific jobs, but also their geographical distribution and the extent to which the skills they required overlapped with other jobs in the area. The size of a given city, as well as its employment diversity, played a role in resilience, with bigger, more diverse cities faring better than smaller and less-diverse ones. However, controlling for size and diversity, factoring in job connectivity significantly improved predictions of peak unemployment rates during the recession. Cities where job connectivity was highest leading up to the crash were significantly more resilient and recovered faster than those with less-connected markets.

    Even in the absence of temporary crises like the Great Recession or the Covid-19 pandemic, automation promises to upend the employment landscapes of many areas in coming years. How can cities prepare for this disruption? The researchers in this study extended their model to predict how job markets would behave when facing job loss due to automation. They found that while cities of similar sizes would be affected similarly in the beginning phases of automation shocks, those with well-connected job networks would provide better opportunities for displaced workers to find other jobs. This provides a buffer against widespread unemployment, and in some cases even leads to more jobs being created in the aftermath of the initial automation shock. A city like Burlington, Vermont, where job connectivity is high, would fare much better than Bloomington, Indiana, a similar-sized city where job connectivity is low.

    The findings of the study suggest that policymakers should consider job connectivity when planning for the future of work in their regions, especially where automation is expected to replace large numbers of jobs. Not only does increased connectivity result in lower unemployment — it also contributes to a rise in overall wages. Furthermore, in individual occupations, workers in jobs that are more “embedded” (connected to other jobs) in a region earn higher wages than similar workers in areas where those jobs are not as connected.

    These results offer fresh insight to help steer discussions about the Future of Work and may help guide and complement current decisions about where to invest in job creation and training programs.

    MIT Connection Science is a research group hosted by the Sociotechnical Systems Research Center, a part of the Institute for Data, Systems, and Society. More

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    Seeking the cellular mechanisms of disease, with help from machine learning

    Caroline Uhler’s research blends machine learning and statistics with biology to better understand gene regulation, health, and disease. Despite this lofty mission, Uhler remains dedicated to her original career passion: teaching. “The students at MIT are amazing,” says Uhler. “That’s what makes it so fun to work here.”

    Uhler recently received tenure in the Department of Electrical Engineering and Computer Science. She is also an associate member of the Broad Institute of MIT and Harvard, and a researcher at the MIT Institute for Data, Systems, and Society, and the Laboratory for Information and Decision Systems.

    Growing up along Lake Zurich in Switzerland, Uhler knew early on she wanted to teach. After high school, she spent a year gaining classroom experience — and didn’t discriminate by subject. “I taught Latin, German, math, and biology,” she says. But by year’s end, she found herself enjoying teaching math and biology best. So she enrolled at ETH Zurich to study those subjects and earn a master’s of education that would allow her to become a full-time high school teacher.

    But Uhler’s plans changed, thanks to a class she took from a visiting professor from the University of California at Berkeley named Bernd Sturmfels. “He taught a course called algebraic statistics for computational biology,” says Uhler. The course title alone may sound like a mouthful, but to Uhler, the class was an elegant link between her passions for math and biology. “It basically connected everything that I liked in one course,” she recalls.

    Algebraic statistics provided Uhler with a unique set of tools for representing the mathematics of complex biological systems. She was so intrigued she decided to postpone her dreams of teaching and pursue a PhD in statistics.

    Uhler enrolled at UC Berkeley, completing her dissertation with Sturmfels as her advisor. “I loved it,” Uhler says of her time at Berkeley, where she dove deeper into the nexus of math and biology using algebra and statistics. “Berkeley was very open in the sense that you can take all kinds of courses,” she says, “and really pursue your diverse research interests early on. It was a great experience.”

    Much of her work was theoretical, attempting to answer questions about network models in statistics. But toward the end of her PhD, her questions took on a more applied approach. “I got really interested in causality and gene regulation — how can we learn something about what is going on in the cell?” Uhler says gene regulation provides ample opportunities to apply causal analysis, because changes in one gene can have cascading effects on the expression of genes downstream.

    She carried these causality questions forward to MIT, where she accepted a role as assistant professor in 2015. Her first impressions of the Institute? “The place was very collaborative and a hub for machine learning and genomics,” says Uhler. “I was excited to find a place with so many people working in my field. Here, everyone wants to discuss research. It’s just really, really fun.”

    The Broad Institute, which uses genomics to better understand the genetic basis of disease and seek solutions, has also been a good fit for Uhler’s academic interests and her cooperative approach to research. The Broad announced last month that Uhler will co-direct its new Eric and Wendy Schmidt Center, which will promote interdisciplinary research between the data and life sciences.

    Uhler now works to synthesize two distinct types of genomic information: sequencing and the 3D packing of DNA. The nucleus of each cell in a person’s body contains an identical sequence of DNA, but the physical arrangement of that DNA — how it kinks and winds — varies among cell types. “In understanding gene regulation, it’s becoming clear that the packing of the DNA matters very much,” says Uhler. “If some genes in the DNA are not used, you can just close them off and pack them very densely. But if you have other genes that you need often in a particular cell, you’ll have them open and maybe even close together so they can be co-regulated.”

    Learning the interplay of the genetic code and the 3D packing of the DNA could help reveal how a particular disease impacts the body on a cellular level, and it could help point to targeted treatments. To achieve this synthesis, Uhler develops machine-learning methods, in particular based on autoencoders, which can be used to integrate sequencing data and packing data to generate a representation of a cell. “You can represent the data in a space where the two modalities are integrated,” says Uhler. “It’s a question I’m very excited about because of its importance in biology as well as my background in mathematics. It’s an interesting packing problem.”

    Recently, Uhler has focused on one disease in particular. Her research group co-authored a paper that uses autoencoders and causal networks to identify drugs that could be repurposed to fight Covid-19. The approach could help pinpoint drug candidates to be tested in clinical trials, and it is adaptable to other diseases where detailed gene expression data are available.

    Research accomplishments aside, Uhler hasn’t relinquished her earliest career aspirations to be a teacher and mentor. In fact, it’s become one of her most cherished roles at MIT. “The students are incredible,” says Uhler, highlighting their intellectual curiosity. “You can just go up to the whiteboard and start a conversation about research. Everyone is so driven to learn and cares so deeply.” More

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    School of Engineering welcomes new faculty

    The School of Engineering is welcoming 15 new faculty members to its departments, institutes, labs, and centers. With research and teaching activities ranging from the development of robotics and AI technologies to the modeling and optimization of renewable energy systems, they are poised to make significant contributions in new directions across the school and to a wide range of research efforts around the Institute.

    “I am happy to welcome our wonderful new faculty,” says Anantha Chandrakasan, dean of the School of Engineering. “Their talents and expertise as educators, researchers, collaborators, and mentors will enhance the engineering community and strengthen our global impact.”

    Navid Azizan will join the MIT faculty with dual appointments in the Department of Mechanical Engineering and the Institute for Data, Systems, and Society (IDSS) as an assistant professor in September. He is currently a postdoc in the Autonomous Systems Laboratory at Stanford University. He received his PhD in computing and mathematical sciences from Caltech in 2020, his MS from the University of Southern California in 2015, and his BS from Sharif University of Technology in 2013. Additionally, he was a research intern at Google DeepMind in 2019. Azizan’s research interests broadly lie in machine learning, control theory, mathematical optimization, and network science. He has made fundamental contributions to various aspects of intelligent systems, including the design and analysis of optimization algorithms for nonconvex and networked problems with applications to the smart grid, distributed computation, epidemics, and autonomy. Azizan’s work has been recognized by several awards, including the 2020 Information Theory and Applications (ITA) Graduation-Day Gold Award. He was named an Amazon Fellow in Artificial Intelligence in 2017 and a PIMCO Fellow in Data Science in 2018. His research on electricity markets received the ACM GREENMETRICS Best Student Paper Award in 2016. He was also the first-place winner and a gold medalist at the 2008 National Physics Olympiad in Iran. He co-organizes the popular “Control meets Learning” virtual seminar series.

    Rodrigo Freitas joined the Department of Materials Science and Engineering (DMSE) in January as the AMAX Assistant Professor. He received his BS and MS degrees in physics from the University of Campinas in Brazil, and MS and PhD degrees in materials science and engineering from the University of California at Berkeley, followed by postdoctoral work at Stanford University. During his PhD, he was also a Livermore Graduate Scholar in the Materials Science Division of the Lawrence Livermore National Laboratory. He uses a combination of theoretical, computational, and data-driven approaches to study the mechanisms of microstructure evolution in materials. This research area is critical to understand and control materials kinetics at the microstructure level, and it has broad potential impact and application, which will lead to collaborations across DMSE and in the MIT Stephen A. Schwarzman College of Computing.

    Marzyeh Ghassemi will join the Institute for Medical Engineering and Science and the Department of Electrical Engineering and Computer Science as an assistant professor in July. She received her PhD in computer science from MIT; her MS in biomedical engineering from Oxford University; and two BS degrees, in electrical engineering and computer science, from New Mexico State University. Her research focuses on creating and applying machine learning to human health improvement. Ghassemi’s work has been published in top conferences and journals including NeurIPS, FaCCT, The Lancet Digital Health, JAMA, the AMA Journal of Ethics, and Nature Medicine, and featured in media outlets such as MIT News, NVIDIA, and the Huffington Post. A British Marshall Scholar and American Goldwater Scholar who has completed graduate fellowships at organizations including Xerox and the NIH, Ghassemi has been named one of MIT Technology Review’s 35 Innovators Under 35. Ghassemi organized MIT’s first Hacking Discrimination event and was awarded MIT’s 2018 Seth J. Teller Award for Excellence, Inclusion and Diversity. She also is on the Senior Advisory Council of Women in Machine Learning (WiML) and founded the ACM Conference on Health, Inference and Learning (ACM CHIL).

    Dylan Hadfield-Menell will join the Department of Electrical Engineering and Computer Science as an assistant professor in July. Hadfield-Menell received his PhD in computer science from the University of California at Berkeley, and his MS and BS (both in computer science and electrical engineering) from MIT. His research focuses on the value alignment problem in artificial intelligence, and aims to help create algorithms that pursue the intended goals of their users. He is also interested in work that bridges the gap between AI theory and practical robotics, and the problem of integrated task and motion planning. Hadfield-Menell is an NSF Graduate Research Fellowship Recipient and a Berkeley Fellow, with multiple conference papers published in the AAAI/ACM Conference on AI, Ethics, and Society and the ACM/IEEE International Conference on Human-Robot Interaction, among others. He was the technical lead on The Future Starts Here Exhibit for the Victoria and Albert Museum, and has interned at Facebook and Microsoft.

    Jack Hare joined the Department of Nuclear Science and Engineering as an assistant professor in January. He received his BA (2010) and his MS (2011) in natural sciences from the University of Cambridge, his MA in plasma physics from Princeton University in 2013, and his PhD in plasma physics from Imperial College London in 2017. After his PhD, he held postdoc appointments at Imperial College London, where he has researched magnetized turbulence in high-energy-density plasmas, and at the Max-Planck Institute for Plasma Physics, where he worked on the design of diagnostics for the ITER fusion reactor project. At MIT, his research will focus on fundamental plasma processes in magnetized high energy density plasmas, such as magnetic reconnection and magneto-hydrodynamic turbulence. These plasmas are created using intense pulses of electrical current generated by the new PUFFIN pulsed-power facility, hosted on campus at the Plasma Science and Fusion Center.Samuel Hopkins will join the Department of Electrical Engineering and Computer Science as an assistant professor in January 2022. Hopkins received his PhD in computer science from Cornell University, and his BS in computer science and mathematics from the University of Washington. His research focuses on algorithms, optimization, and theoretical machine learning, especially through the lens of convex programming relaxations. He is a Miller Fellow, an NSF Graduate Research Fellow, a Microsoft Research Fellow, and has won the Cornell Computer Science Dissertation Award. Hopkins’ publications include papers in FOCS, STOC, and the Annals of Statistics. Before coming to MIT, Hopkins was a Miller Fellow in the theory group at the University of California at Berkeley.

    Michael F. Howland will join the department of Civil and Environmental Engineering as an assistant professor in September. Currently he is a postdoc at Caltech in the Department of Aerospace Engineering. He received his BS from Johns Hopkins University and his MS from Stanford University, both in mechanical engineering. He received his PhD from Stanford University in the Department of Mechanical Engineering. His research encompasses the flow physics of Earth’s atmosphere and the modeling, optimization, and control of renewable energy generation systems. Howland’s work is focused at the intersection of fluid mechanics, weather and climate modeling, uncertainty quantification, and optimization and control with an emphasis on renewable energy systems. He uses synergistic approaches including simulations, laboratory and field experiments, and modeling to understand the operation of renewable energy systems, with the goal of improving the efficiency, predictability, and reliability of low-carbon energy generation. He was the recipient of the Robert George Gerstmyer Award, the Creel Family Teaching Award, and the James F. Bell Award from Johns Hopkins University. He received the Tau Beta Pi scholarship, NSF Graduate Research Fellowship, and a Stanford Graduate Fellowship.

    Yoon Kim will join the Department of Electrical Engineering and Computer Science as an assistant professor in July. Currently a research scientist at the MIT-IBM Watson AI Lab, Kim received a PhD in computer science from Harvard University, an MS in data science from New York University, an MA in statistics from Columbia, and dual BA degrees in mathematics and economics from Cornell University. Kim’s research focuses on machine learning and natural language processing. He is the recipient of a Google Fellowship.

    Adrián Lozano-Duran joined the Department of Aeronautics and Astronautics at MIT as an Assistant Professor in January. He received his PhD in aerospace engineering from the Technical University of Madrid in 2015 on the use of graph theory to unravel the dynamics of chaotic patterns in fluids. From 2016 to 2020, he was a postdoc at Stanford University working on high-fidelity simulations of external aerodynamic applications. His research is focused on solving outstanding problems in physics and modeling of turbulent flows using transformative tools and creativity. His work includes turbulence theory and modeling by artificial intelligence, information theory, and quantum computing, with applications ranging from unmanned aerial vehicles and commercial airliners to hypersonic vehicles. He is the recipient of the Milton van Dyke Award from the American Physical Society (2017), the Center for Turbulence Research Fellowship from Stanford University (2016), and the Da Vinci Award for the top five European dissertations on Fluid Mechanics (2015).

    Kelly A. Metcalf Pate joined the Department of Biological Engineering as an assistant professor and director of the Division of Comparative Medicine in March. As director of DCM, Pate will oversee the group of veterinarians and staff who serve as experts in animal models for the MIT community. Pate’s research focuses on the role of platelets in the pathogenesis of viral infection, with an emphasis on HIV and cytomegalovirus, and on the refinement and development of animal models. 

    Anand Natarajan joined the Department of Electrical Engineering and Computer Science as an assistant professor in September 2020. Natarajan received his PhD in physics from MIT, and an MS in computer science and BS in physics from Stanford University. Natarajan’s interests center upon theoretical quantum information, particularly nonlocality (e.g., Bell inequalities and nonlocal games), quantum complexity theory (especially the power of quantum interactive proof systems), and semidefinite programming hierarchies. He is the co-winner of the Best Paper Award at FOCS ’19 for paper NEEXP ⊆ MIP*, with John Wright, and is a gold medalist in the International Physics Olympiad. His conference papers have been published in the Proceedings of ITCS, Proceedings of FOCS, and Proceedings of CCC, among others. Before joining MIT, Natarajan was a postdoc at the Institute for Quantum Information and Matter at Caltech.

    Jelena Notaros joined the Department of Electrical Engineering and Computer Science in June 2020 as an assistant professor, a principal investigator in the Research Laboratory of Electronics, and a core faculty member of the Microsystems Technology Laboratories. Notaros received her PhD and MS degrees from MIT in 2020 and 2017, respectively, and BS degree from the University of Colorado Boulder in 2015. Her research interests are in integrated silicon photonics devices, systems, and applications, with an emphasis on integrated optical phased arrays for lidar and augmented reality. Notaros was a Top-Three DARPA Riser, a 2018 DARPA D60 Plenary Speaker, a 2021 Forbes 30 Under 30 Listee, a 2020 MIT RLE Early Career Development Award recipient, an MIT Presidential Fellow, a National Science Foundation Graduate Research Fellow, a 2019 OSA CLEO Chair’s Pick Award recipient, a 2014 IEEE R5 Student Paper Competition First Place Award recipient, a 2019 MIT MARC Best Paper Award recipient, a 2018 MIT EECS Rising Star, and a 2015 CU Boulder Chancellor’s Recognition Award recipient, among other honors.

    Carlos Portela joined the Department of Mechanical Engineering as an assistant professor in August 2020. He received his PhD in mechanical engineering from Caltech in 2019. He was a postdoc at Caltech under the guidance of professors Julia Greer, Dennis Kochmann, and Chiara Daraio. Portela’s research lies at the intersection of materials science, mechanics, and nano-to-macro fabrication with the objective of designing and testing novel materials — with features spanning from nanometers to centimeters — that yield unprecedented mechanical, optical, and acoustic properties. His recent accomplishments have provided routes for fabrication of these so-called “nano-architected materials” in scalable processes as well as testing nanomaterials in real-world conditions such as supersonic impact, in collaboration with researchers at MIT’s Institute for Soldier Nanotechnologies. His present application areas involve the creation of novel lightweight armor materials, ultrasonic devices for medical purposes, and new generations of ultra-strong structural materials. Portela is the recipient of several awards including the Gold Paper Award at the Society of Engineering Science Meeting in 2019, the Centennial Award for the Best Thesis in Mechanical and Civil Engineering at Caltech, and the Caltech Rolf H. Sabersky Graduate Fellowship.

    Ashia Wilson joined the Department of Electrical Engineering and Computer Science as an assistant professor in January. Wilson received her PhD in statistics from the University of California at Berkeley, and her BA in applied mathematics from Harvard University. Her research centers upon optimization, algorithmic decision-making, dynamical systems, and fairness within large-scale machine learning. A National Science Foundation Graduate Research Fellow, Wilson has received the NeurIPS ’17 Spotlight Paper Award for “The Marginal Value of Adaptive Methods in Machine Learning,” and has performed research with Microsoft and Google AI. Her papers have been published in the Proceedings of the National Academy of Science, Advances in Neural Information Processing Systems, and the International Conference of Machine Learning, among others. Additionally, she has served as a reviewer for NeurIPS and the Journal of Machine Learning.

    Sixian You joined the Department of Electrical Engineering and Computer Science as an assistant professor in March. You received her PhD and MS in bioengineering from the University of Illinois at Urbana-Champaign, and her BS in optical and electronic information from Huazhong University of Science and Technology. Her research interests are biophotonics, microscopy, and computational imaging. She has won the Microscopy Innovation Award and the Nikon Photomicrography Competition Image of Distinction award, and her work has been featured on the PNAS cover and as a Nature Communications Editors’ Highlight, among other honors. The You Lab focuses on developing optical imaging tools to enable noninvasive, deeper, faster, and richer visualization of dynamic biological processes and disease pathology. She was recently a postdoc at the University of California at Berkeley, and has been an engineer intern for Apple. More