Markus Andrews

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

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

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

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

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

The cost of the rechargeable lithium-ion batteries used for phones, laptops, and cars has fallen dramatically over the last three decades, and has been a major driver of the rapid growth of those technologies. But attempting to quantify that cost decline has produced ambiguous and conflicting results that have hampered attempts to project the technology’s future or devise useful policies and research priorities.

Now, MIT researchers have carried out an exhaustive analysis of the studies that have looked at the decline in the prices these batteries, which are the dominant rechargeable technology in today’s world. The new study looks back over three decades, including analyzing the original underlying datasets and documents whenever possible, to arrive at a clear picture of the technology’s trajectory.

The researchers found that the cost of these batteries has dropped by 97 percent since they were first commercially introduced in 1991. This rate of improvement is much faster than many analysts had claimed and is comparable to that of solar photovoltaic panels, which some had considered to be an exceptional case. The new findings are reported today in the journal Energy and Environmental Science, in a paper by MIT postdoc Micah Ziegler and Associate Professor Jessika Trancik.

While it’s clear that there have been dramatic cost declines in some clean-energy technologies such as solar and wind, Trancik says, when they started to look into the decline in prices for lithium-ion batteries, “we saw that there was substantial disagreement as to how quickly the costs of these technologies had come down.” Similar disagreements showed up in tracing other important aspects of battery development, such as the ever-improving energy density (energy stored within a given volume) and specific energy (energy stored within a given mass).

“These trends are so consequential for getting us to where we are right now, and also for thinking about what could happen in the future,” says Trancik, who is an associate professor in MIT’s Institute for Data, Systems and Society. While it was common knowledge that the decline in battery costs was an enabler of the recent growth in sales of electric vehicles, for example, it was unclear just how great that decline had been. Through this detailed analysis, she says, “we were able to confirm that yes, lithium-ion battery technologies have improved in terms of their costs, at rates that are comparable to solar energy technology, and specifically photovoltaic modules, which are often held up as kind of the gold standard in clean energy innovation.”

It may seem odd that there was such great uncertainty and disagreement about how much lithium-ion battery costs had declined, and what factors accounted for it, but in fact much of the information is in the form of closely held corporate data that is difficult for researchers to access. Most lithium-ion batteries are not sold directly to consumers — you can’t run down to your typical corner drugstore to pick up a replacement battery for your iPhone, your PC, or your electric car. Instead, manufacturers buy lithium-ion batteries and build them into electronics and cars. Large companies like Apple or Tesla buy batteries by the millions, or manufacture them themselves, for prices that are negotiated or internally accounted for but never publicly disclosed.

In addition to helping to boost the ongoing electrification of transportation, further declines in lithium-ion battery costs could potentially also increase the batteries’ usage in stationary applications as a way of compensating for the intermittent supply of clean energy sources such as solar and wind. Both applications could play a significant role in helping to curb the world’s emissions of climate-altering greenhouse gases. “I can’t overstate the importance of these trends in clean energy innovation for getting us to where we are right now, where it starts to look like we could see rapid electrification of vehicles and we are seeing the rapid growth of renewable energy technologies,” Trancik says. “Of course, there’s so much more to do to address climate change, but this has really been a game changer.”

The new findings are not just a matter of retracing the history of battery development, but of helping to guide the future, Ziegler points out. Combing all of the published literature on the subject of the cost reductions in lithium-ion cells, he found “very different measures of the historical improvement. And across a variety of different papers, researchers were using these trends to make suggestions about how to further reduce costs of lithium-ion technologies or when they might meet cost targets.” But because the underlying data varied so much, “the recommendations that the researchers were making could be quite different.” Some studies suggested that lithium-ion batteries would not fall in cost quickly enough for certain applications, while others were much more optimistic. Such differences in data can ultimately have a real impact on the setting of research priorities and government incentives.

The researchers dug into the original sources of the published data, in some cases finding that certain primary data had been used in multiple studies that were later cited as separate sources, or that the original data sources had been lost along the way. And while most studies have focused only on the cost, Ziegler says it became clear that such a one-dimensional analysis might underestimate how quickly lithium-ion technologies improved; in addition to cost, weight and volume are also key factors for both vehicles and portable electronics. So, the team added a second track to the study, analyzing the improvements in these parameters as well.

“Lithium-ion batteries were not adopted because they were the least expensive technology at the time,” Ziegler says. “There were less expensive battery technologies available. Lithium-ion technology was adopted because it allows you to put portable electronics into your hand, because it allows you to make power tools that last longer and have more power, and it allows us to build cars” that can provide adequate driving range. “It felt like just looking at dollars per kilowatt-hour was only telling part of the story,” he says.

That broader analysis helps to define what may be possible in the future, he adds: “We’re saying that lithium-ion technologies might improve more quickly for certain applications than would be projected by just looking at one measure of performance. By looking at multiple measures, you get essentially a clearer picture of the improvement rate, and this suggests that they could maybe improve more rapidly for applications where the restrictions on mass and volume are relaxed.”

Trancik adds the new study can play an important role in energy-related policymaking. “Published data trends on the few clean technologies that have seen major cost reductions over time, wind, solar, and now lithium-ion batteries, tend to be referenced over and over again, and not only in academic papers but in policy documents and industry reports,” she says. “Many important climate policy conclusions are based on these few trends. For this reason, it is important to get them right. There’s a real need to treat the data with care, and to raise our game overall in dealing with technology data and tracking these trends.”

“Battery costs determine price parity of electric vehicles with internal combustion engine vehicles,” says Venkat Viswanathan, an associate professor of mechanical engineering at Carnegie Mellon University, who was not associated with this work. “Thus, projecting battery cost declines is probably one of the most critical challenges in ensuring an accurate understanding of adoption of electric vehicles.”

Viswanathan adds that “the finding that cost declines may occur faster than previously thought will enable broader adoption, increasing volumes, and leading to further cost declines. … The datasets curated, analyzed and released with this paper will have a lasting impact on the community.”

The work was supported by the Alfred P. Sloan Foundation. More

WISDM, the Women in Innovation and STEM Database at MIT, celebrated the first anniversary of its digital launch on March 8 — International Women’s Day. To mark the occasion, the tremendous growth of the WISDM community, MIT Innovation Initiative (MITii), which manages the platform/community, announced the WISDM Fellowship Program.
WISDM promotes the visibility of women in the MIT academic community, increases gender diversity in innovation and entrepreneurship, and makes it easier to find talented and diverse speakers for various events. In partnership with MITii, WISDM founder Ritu Raman, an MIT postdoc and AAAS IF/THEN Ambassador, applied for and was awarded a $10,000 AAAS IF/THEN Ambassadors Grant for public engagement with science activities that teach, inspire, and promote the next generation of women in STEM. With this funding, WISDM launched a fellowship program for scientists interested in improving their public speaking capabilities.
“When I had the opportunity to apply for the grant, I immediately thought of WISDM as a great community of women who could benefit from professional development resources supported by this funding,” states Raman. “We all know from experience that the most impactful role models are often highly effective communicators. The WISDM Fellowship Program will help women leverage their scientific expertise by combining it with a formalized speaker training program with The Story Collider, and will also financially reward women for speaking engagements via honorariums. This new program is important to me because it reiterates the core philosophy of WISDM: Women’s expertise and time are valuable assets, and we need to make diverse voices a part of every conversation in STEM.”
Through the WISDM Fellows application and review process, 20 exceptional women were selected to participate in the program. They are:
Taylor Cannon, Department of Electrical Engineering and Computer Science
Cecile Chazot, Department of Materials Science and Engineering
Mara Freilich, MIT-Woods Hole Oceanography/Applied Ocean Science and Engineering
Stephanie Gaglione, Department of Chemical Engineering
Miela Gross, Department of Electrical Engineering and Computer Science
Ayse Guvenilir, Media Lab
Fatima Husain, Department of Earth, Atmospheric and Planetary Sciences
Eugenia Inda, Department of Electrical Engineering and Computer Science
Jessica Ingabire, Institute for Data, Systems, and Society
Lakshmi Amrutha Killada, Department of Mechanical Engineering
Zanele Munyikwa, MIT Sloan School of Management
Ufuoma Ovienmhada, Media Lab
Cadence Payne, Department of Aeronautics and Astronautics
Krista Pullen, Department of Biological Engineering
Julie Rorrer, Department of Chemical Engineering
Erica Salazar, Department of Nuclear Science and Engineering
Stephanie Smelyansky, Department of Chemistry
Kayla Storme, Department of Chemistry
Abigail Taussig, Department of Chemical Engineering
Jacqueline Valeri, Department of Biological Engineering
“In my family, storytelling has always been important,” says WISDM Fellow and Department of Nuclear Science and Engineering graduate student Erica Salazar. “Every holiday season, my family gathers at our annual tamalada (where we make tamales) to tell stories. It has the powerful ability to bring us closer together when we are apart most of the year. Storytelling is another medium to engage an audience to absorb abstract or difficult concepts in a personal way. I am honored, through this fellowship, to learn how to harness storytelling methods to create a personal bond with others — and particularly, to engage young girls and kids in STEM.”
Since its digital launch last March, the community has grown to 135 members, and the platform has received over 16,000 page views from more than 9,000 unique users in 83 countries. Current MIT graduate students, postdocs, technical associates, or research staff members who identify as a woman are eligible to join. More

Public health officials have issued plenty of warnings about people who are reluctant to get vaccinated for Covid-19. But an MIT research team centered at MIT’s Initiative on the Digital Economy (IDE) says this may be counterproductive: When shown basic numbers about how popular Covid-19 vaccines are, the fraction of people reluctant to get the vaccine drops by 5 percent. To reach these conclusions, the researchers drew on a massive international survey about the pandemic, including 1.8 million responses from 67 countries, and developed an experiment covering 300,000 people in 23 countries. The group has described their findings in a working paper and a recent LA Times op-ed.
The MIT team consists of Alex Moehring, a PhD candidate at the MIT Sloan School of Management; Avinash Collis PhD ’20, an assistant professor at the University of Texas at Austin; Kiran Garimella, a postdoc at the MIT Institute for Data, Systems, and Society (IDSS); M. Amin Rahimian, a postdoc at IDSS; Sinan Aral, the David Austin Professor of Management at MIT Sloan, co-director of IDE, and author of the recent book “The Hype Machine”; and Dean Eckles, the Mitsubishi Career Development Professor and an associate professor of marketing at MIT Sloan. MIT News talked to Aral and Eckles about the findings.
Q: You have written that there is a “dangerous irony” in public health officials and other people highlighting those who are reluctant to get a Covid-19 vaccine. Why is that?
Eckles: It makes sense for public health officials and others to be worried about vaccine hesitancy, because we need a very high level of vaccine acceptance. But a lot of the time, officials make it seem as if more people are hesitant than is really the case.
Many people who say they’re unsure if they’ll get the vaccine may be pretty easy to sway, and one way is by telling them, “Actually, a large fraction of people in your country say they’re going to accept the vaccine.” We found that simply by giving people accurate information about the percentage of people in their country who say they will accept a vaccine, it increased vaccine-acceptance intentions across 23 countries. Part of what’s exciting is how consistent this finding is.
Aral: I’d like to add three points. Before this study, there were at least two plausible countervailing hypotheses. One is that if more people heard that others would take the vaccine, the more they [themselves] would be inclined to take the vaccine. The other is that people would free-ride on the vaccine intentions of others: “Well, if they’re going to take it, they can create herd immunity and I can avoid taking a vaccine myself.” Our research shows pretty clearly that the first is true, while the second is not true [on aggregate].
Second, it’s interesting that the treatment most changes the behavior of those people who are most underestimating the amount of vaccine acceptance among others. And third, there’s an overarching theme here: Simply providing people the truth, the accurate information, is also very effective at swaying people to accept the vaccine.
Q: What does this teach us about human behavior, at least in these kinds of situations?
Aral: One really important thing is [the power of] social proof. When you see large portions of people behave in a certain way, it legitimizes that behavior. And there are countless examples of this. When a lot of people say a restaurant is good, you’re swayed. This is another instance of that.
Eckles: There’s an informational process of social learning. People are trying to figure out: What’s the quality of this thing? It might seem weird to some of us following the news more, or watching what’s happening with [vaccine] trials, but a lot of people are not paying attention. They may know there are these vaccines, but even so, other people’s choices can be quite informative to them.
Q: What should be the core of good messaging about vaccination programs, based on your research over the last year?
Aral: As recently as February, a coronavirus task force started its communications by focusing on vaccine hesitancy. That is not, per our findings, as effective as leading with the vast and growing majority who are accepting. That’s not to say we think public health officials shouldn’t talk about vaccine hesitancy, or that people who are hesitant shouldn’t be targeted with outreach to convince them of the safety and efficacy of vaccines — we believe that should all happen. But neglecting to emphasize the vast and growing majorities who are accepting vaccines doesn’t increase vaccine acceptance as much.
Eckles: What we’re saying is one part of a broader messaging strategy. Giving people this information is enough to shift their motivation to get the vaccine in a lot of cases. Though, getting them motivated is not enough if they don’t know what website to go to, or if it’s hard to get an appointment. It’s good to couple motivational messages with actionable information.
Aral: To our knowledge this is the largest global survey of Covid-19 behaviors, norms, and perceptions. We’ve been running it since July. We’ve also done many published studies, whether about social spillovers [during the pandemic], vaccines, vaccine misinformation — all of this is part of a very forceful effort by the Initiative on the Digital Economy to make meaningful contributions to changing the trajectory of this pandemic. More