Listening to immigrant and indigenous Pacific Islander voices

After Kevin Lujan Lee came out to his parents, he found another family in Improving Dreams, Equality, Access, and Success (IDEAS), an undocumented student advocacy and support group at the University of California at Los Angeles. After joining the organization to support his undocumented partner at the time, he fell in love with the group and community around it, and became involved in organizing alongside undocumented youth. When Lee found himself struggling to make ends meet upon graduation, it was his then-partner’s parents who took him in and cared for him despite their limited means and the constant threat of deportation.

“I would be nothing if it weren’t for IDEAS, if it weren’t for undocumented people sheltering me and giving me food,” Lee says. “This was the spirit that the group embodied. People who were more than willing to just fork out what they didn’t have.”

The third-year PhD candidate credits the family and mission of IDEAS for every subsequent step he has taken, from his master’s degree at the University of Chicago to his current research in MIT’s Department of Urban Studies and Planning.

“I really don’t think of myself as a researcher,” Lee says. “I’m first and foremost an organizer. That’s where I gained my purpose. That’s where I learned what love is in its most unconditional and revolutionary form.”

Lee’s priority is to give back that sense of unconditional love to the communities who have made him who he is, a scholar with broad interests in equitable community and economic development. His research has ranged from the political engagement of Pacific Islanders in Hawai‘i and Guåhan, to the role of worker centers — many of which serve undocumented immigrants in the informal economy –– in California’s workforce development system.

“When you work with people and in geographies that are invisible, you need power,” Lee says. “That’s why I’m here.”

“You have to make a decision”

The people Lee met through IDEAS have become crucial points of access that make his research possible on a day-to-day basis. Organizing has always built itself on interpersonal trust, and for Lee, shared connections in the immigrant labor world allow him to engage with the organizations he studies. For instance, it was through a collaboration with Sasha Feldstein at the California Immigrant Policy Center that he pursued his first research project as an MIT student, which looked at the way gaps in organizational networks prevent marginalized populations from accessing the resources that are supposedly for them.

When it comes to workforce development, social scientists have identified the problem of “creaming,” wherein nonprofits focus their resources on the populations that are easiest to serve in the face of tightened budgets, leaving behind the most marginalized in the process.

Inspired by the centrality of interpersonal relationships in the organizing world, Lee identified an additional, network-based mechanism through which the most marginalized are excluded from workforce development nonprofits. He calls this “structural creaming.” One of the ways in which federally funded job training providers exclude marginalized populations, he says, is by failing to establish or maintain relationships with smaller nonprofits specifically oriented toward meeting those populations’ needs.

Without those relationships, such providers simply don’t reach these populations, and if they do, they might not provide them with the appropriate support or refer them to the right employers. As a result, people slip through the cracks. Small-scale service providers don’t receive the funding they could use to provide greater assistance to the marginalized populations they already reach.

“These small-scale organizations are not really in conversation with the mainstream workforce development systems,” explains Lee, who works on this issue along with Ana Luz Gonzalez-Vasquez and Magaly López at the UCLA Labor Center. “They often have a fraught relationship with American Job Centers [federally funded job-training providers under the U.S. Department of Labor]. And, these organizations are not often studied by economists, who wield tremendous influence in workforce development policymaking.”

Conversations about workforce development for the most marginalized need to move away from prioritizing the “average client,” scalability, and cost efficiency, Lee says.

“You have to make a decision,” he says of the nonprofits and agencies in this field. “Serving immigrants is not always cost-effective. It requires conducting targeted outreach, providing English as Second Language classes, offering ongoing support to address barriers to program participation, and high-quality employment — it’s a difficult process. But if you care about these populations, that’s what you'll do.”

A life of many edges

An organizing-first approach was what brought Lee to MIT’s Department of Urban Studies and Planning (DUSP) to begin with. Advised by Associate Professor Justin Steil, Lee feels the department’s emphasis on interdisciplinary, applied research has allowed him to pursue his interests within the context of the academy.

“What he provides is unconditional support, a ready smile, and a lot of space to do what I want,” Lee says of his advisor. “There are a lot of wonderful junior faculty who are phenomenal, and I’m very grateful to them.”

Lee’s ability to range broadly over his interests in immigrant rights and equitable development has led him toward several collaborative projects on an issue that reaches into his own ancestry and past: indigenous Pacific Islander sovereignty.

An initial project with the Center for Pacific Island Studies at the University of Hawai‘i at Mānoa prompted him to learn more about indigenous sovereignty and colonialism. Now, he is collaborating with Ngoc Phan, a political scientist at Hawai‘i Pacific University, to analyze her Native Hawaiian Survey. And, alongside Patrick Thomsen of the University of Auckland and Lana Lopesi of the Auckland University of Technology, he is theorizing Pacific Islander mobilities. The Pacific has many “edges,” Lee says, alluding to the work of Pacific Studies scholar and activist Teresia Teaiwa, who emphasizes the deep heterogeneity of land, history, culture, language, religion and spirituality across the Pacific.

Within his own life, Lee has been grappling with the relationship between indigeneity and his own position as an immigrant and settler on Native American lands. Lee is an indigenous Pacific Islander himself; his mother is CHamoru, from Guåhan, and his father is Chinese, from Malaysia. Growing up in Malaysia, he remembers how his mother maintained her relationship to her family and to the island, in the face of a hierarchical and colorist society where she was required to overcome inordinate obstacles as a young mother. Through his research, he aims to reconnect to his Pacific Islander heritage and takes inspiration from his mother’s resilience.

“She is my connection,” he says. “She continuously demonstrates what it for me what it means to be CHamoru and what it means to be an Islander. It’s to have strength, and to stay connected to your homeland. You do it because it’s in your blood. You just have to.”

Since at least 2010, DUSP has only ever enrolled one PhD student who identifies as “Native Hawaiian or Other Pacific Islander” — Lee himself. Thus, he feels a responsibility to make sure he is not the last Pacific Islander to come through his department. Inspired by the recent release of the Black DUSP Thesis, he also works alongside his colleagues to advance equity within his department. In the future, he hopes to help establish a pipeline of Pacific Islanders into urban planning.

“Sovereignty movements are very much alive in the Pacific, and people are trying to build their nations,” Lee says. “But there is no pipeline for Pacific Islanders into urban planning. How are you going to engage the World Bank and the Asian Development Bank about measures of development, how are you going to talk about community control, how are you going to talk about the military’s role in land use, if you don’t have these skills?”

With both his academic and organizing work, Lee acknowledges he has a lot on his plate. “I am deeply imperfect and often thinly stretched,” he says. “But when things matter so deeply in your bones, the energy just comes. It has to.”

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Making health care more personal

The health care system today largely focuses on helping people after they have problems. When they do receive treatment, it’s based on what has worked best on average across a huge, diverse group of patients.

Now the company Health at Scale is making health care more proactive and personalized — and, true to its name, it’s doing so for millions of people.

Health at Scale uses a new approach for making care recommendations based on new classes of machine-learning models that work even when only small amounts of data on individual patients, providers, and treatments are available.

The company is already working with health plans, insurers, and employers to match patients with doctors. It’s also helping to identify people at rising risk of visiting the emergency department or being hospitalized in the future, and to predict the progression of chronic diseases. Recently, Health at Scale showed its models can identify people at risk of severe respiratory infections like influenza or pneumonia, or, potentially, Covid-19.

“From the beginning, we decided all of our predictions would be related to achieving better outcomes for patients,” says John Guttag, chief technology officer of Health at Scale and the Dugald C. Jackson Professor of Computer Science and Electrical Engineering at MIT. “We’re trying to predict what treatment or physician or intervention would lead to better outcomes for people.”

A new approach to improving health

Health at Scale co-founder and CEO Zeeshan Syed met Guttag while studying electrical engineering and computer science at MIT. Guttag served as Syed’s advisor for his bachelor’s and master’s degrees. When Syed decided to pursue his PhD, he only applied to one school, and his advisor was easy to choose.

Syed did his PhD through the Harvard-MIT Program in Health Sciences and Technology (HST). During that time, he looked at how patients who’d had heart attacks could be better managed. The work was personal for Syed: His father had recently suffered a serious heart attack.

Through the work, Syed met Mohammed Saeed SM ’97, PhD ’07, who was also in the HST program. Syed, Guttag, and Saeed founded Health at Scale in 2015 along with  David Guttag ’05, focusing on using core advances in machine learning to solve some of health care’s hardest problems.

“It started with the burning itch to address real challenges in health care about personalization and prediction,” Syed says.

From the beginning, the founders knew their solutions needed to work with widely available data like health care claims, which include information on diagnoses, tests, prescriptions, and more. They also sought to build tools for cleaning up and processing raw data sets, so that their models would be part of what Guttag refers to as a “full machine-learning stack for health care.”

Finally, to deliver effective, personalized solutions, the founders knew their models needed to work with small numbers of encounters for individual physicians, clinics, and patients, which posed severe challenges for conventional AI and machine learning.

“The large companies getting into [the health care AI] space had it wrong in that they viewed it as a big data problem,” Guttag says. “They thought, ‘We’re the experts. No one’s better at crunching large amounts of data than us.’ We thought if you want to make the right decision for individuals, the problem was a small data problem: Each patient is different, and we didn’t want to recommend to patients what was best on average. We wanted what was best for each individual.”

The company’s first models helped recommend skilled nursing facilities for post-acute care patients. Many such patients experience further health problems and return to the hospital. Health at Scale’s models showed that some facilities were better at helping specific kinds of people with specific health problems. For example, a 64-year-old man with a history of cardiovascular disease may fare better at one facility compared to another.

Today the company’s recommendations help guide patients to the primary care physicians, surgeons, and specialists that are best suited for them. Guttag even used the service when he got his hip replaced last year.

Health at Scale also helps organizations identify people at rising risk of specific adverse health events, like heart attacks, in the future.

“We’ve gone beyond the notion of identifying people who have frequently visited emergency departments or hospitals in the past, to get to the much more actionable problem of finding those people at an inflection point, where they are likely to experience worse outcomes and higher costs,” Syed says.

The company’s other solutions help determine the best treatment options for patients and help reduce health care fraud, waste, and abuse. Each use case is designed to improve patient health outcomes by giving health care organizations decision-support for action.

“Broadly speaking, we are interested in building models that can be used to help avoid problems, rather than simply predict them,” says Guttag. “For example, identifying those individuals at highest risk for serious complications of a respiratory infection [enables care providers] to target them for interventions that reduce their chance of developing such an infection.”

Impact at scale

Earlier this year, as the scope of the Covid-19 pandemic was becoming clear, Health at Scale began considering ways its models could help.

“The lack of data in the beginning of the pandemic motivated us to look at the experiences we have gained from combatting other respiratory infections like influenza and pneumonia,” says Saeed, who serves as Health at Scale’s chief medical officer.

The idea led to a peer-reviewed paper where researchers affiliated with the company, the University of Michigan, and MIT showed Health at Scale’s models could accurately predict hospitalizations and visits to the emergency department related to respiratory infections.

“We did the work on the paper using the tech we’d already built,” Guttag says. “We had interception products deployed for predicting patients at-risk of emergent hospitalizations for a variety of causes, and we saw that we could extend that approach. We had customers that we gave the solution to for free.”

The paper proved out another use case for a technology that is already being used by some of the largest health plans in the U.S. That’s an impressive customer base for a five-year-old company of only 20 people — about half of which have MIT affiliations.

“The culture MIT creates to solve problems that are worth solving, to go after impact, I think that’s been reflected in the way the company got together and has operated,” Syed says. “I’m deeply proud that we’ve maintained that MIT spirit.”

And, Syed believes, there’s much more to come.

“We set out with the goal of driving impact,” Syed says. “We currently run some of the largest production deployments of machine learning at scale, affecting millions, if not tens of millions, of patients, and we  are only just getting started.”

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Robot takes contact-free measurements of patients’ vital signs

The research described in this article has been published on a preprint server but has not yet been peer-reviewed by scientific or medical experts.

During the current coronavirus pandemic, one of the riskiest parts of a health care worker’s job is assessing people who have symptoms of Covid-19. Researchers from MIT and Brigham and Women’s Hospital hope to reduce that risk by using robots to remotely measure patients’ vital signs.

The robots, which are controlled by a handheld device, can also carry a tablet that allows doctors to ask patients about their symptoms without being in the same room.

“In robotics, one of our goals is to use automation and robotic technology to remove people from dangerous jobs,” says Henwei Huang, an MIT postdoc. “We thought it should be possible for us to use a robot to remove the health care worker from the risk of directly exposing themselves to the patient.”

Using four cameras mounted on a dog-like robot developed by Boston Dynamics, the researchers have shown that they can measure skin temperature, breathing rate, pulse rate, and blood oxygen saturation in healthy patients, from a distance of 2 meters. They are now making plans to test it in patients with Covid-19 symptoms.

“We are thrilled to have forged this industry-academia partnership in which scientists with engineering and robotics expertise worked with clinical teams at the hospital to bring sophisticated technologies to the bedside,” says Giovanni Traverso, an MIT assistant professor of mechanical engineering, a gastroenterologist at Brigham and Women’s Hospital, and the senior author of the study.

The researchers have posted a paper on their system on the preprint server techRxiv, and have submitted it to a peer-reviewed journal. Huang is one of the lead authors of the study, along with Peter Chai, an assistant professor of emergency medicine at Brigham and Women’s Hospital, and Claas Ehmke, a visiting scholar from ETH Zurich.

Measuring vital signs

When Covid-19 cases began surging in Boston in March, many hospitals, including Brigham and Women’s, set up triage tents outside their emergency departments to evaluate people with Covid-19 symptoms. One major component of this initial evaluation is measuring vital signs, including body temperature.

The MIT and BWH researchers came up with the idea to use robotics to enable contactless monitoring of vital signs, to allow health care workers to minimize their exposure to potentially infectious patients. They decided to use existing computer vision technologies that can measure temperature, breathing rate, pulse, and blood oxygen saturation, and worked to make them mobile.

To achieve that, they used a robot known as Spot, which can walk on four legs, similarly to a dog. Health care workers can maneuver the robot to wherever patients are sitting, using a handheld controller. The researchers mounted four different cameras onto the robot — an infrared camera plus three monochrome cameras that filter different wavelengths of light.

The researchers developed algorithms that allow them to use the infrared camera to measure both elevated skin temperature and breathing rate. For body temperature, the camera measures skin temperature on the face, and the algorithm correlates that temperature with core body temperature. The algorithm also takes into account the ambient temperature and the distance between the camera and the patient, so that measurements can be taken from different distances, under different weather conditions, and still be accurate.

Measurements from the infrared camera can also be used to calculate the patient’s breathing rate. As the patient breathes in and out, wearing a mask, their breath changes the temperature of the mask. Measuring this temperature change allows the researchers to calculate how rapidly the patient is breathing.

The three monochrome cameras each filter a different wavelength of light — 670, 810, and 880 nanometers. These wavelengths allow the researchers to measure the slight color changes that result when hemoglobin in blood cells binds to oxygen and flows through blood vessels. The researchers’ algorithm uses these measurements to calculate both pulse rate and blood oxygen saturation.

“We didn’t really develop new technology to do the measurements,” Huang says. “What we did is integrate them together very specifically for the Covid application, to analyze different vital signs at the same time.”

Continuous monitoring

In this study, the researchers performed the measurements on healthy volunteers, and they are now making plans to test their robotic approach in people who are showing symptoms of Covid-19, in a hospital emergency department.

While in the near term, the researchers plan to focus on triage applications, in the longer term, they envision that the robots could be deployed in patients’ hospital rooms. This would allow the robots to continuously monitor patients and also allow doctors to check on them, via tablet, without having to enter the room. Both applications would require approval from the U.S. Food and Drug Administration.

The research was funded by the MIT Department of Mechanical Engineering and the Karl van Tassel (1925) Career Development Professorship.

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National Science Foundation announces MIT-led Institute for Artificial Intelligence and Fundamental Interactions

The U.S. National Science Foundation (NSF) announced today an investment of more than $100 million to establish five artificial intelligence (AI) institutes, each receiving roughly $20 million over five years. One of these, the NSF AI Institute for Artificial Intelligence and Fundamental Interactions (IAIFI), will be led by MIT’s Laboratory for Nuclear Science (LNS) and become the intellectual home of more than 25 physics and AI senior researchers at MIT and Harvard, Northeastern, and Tufts universities. 

By merging research in physics and AI, the IAIFI seeks to tackle some of the most challenging problems in physics, including precision calculations of the structure of matter, gravitational-wave detection of merging black holes, and the extraction of new physical laws from noisy data.

“The goal of the IAIFI is to develop the next generation of AI technologies, based on the transformative idea that artificial intelligence can directly incorporate physics intelligence,” says Jesse Thaler, an associate professor of physics at MIT, LNS researcher, and IAIFI director.  “By fusing the ‘deep learning’ revolution with the time-tested strategies of ‘deep thinking’ in physics, we aim to gain a deeper understanding of our universe and of the principles underlying intelligence.”

IAIFI researchers say their approach will enable making groundbreaking physics discoveries, and advance AI more generally, through the development of novel AI approaches that incorporate first principles from fundamental physics.  

“Invoking the simple principle of translational symmetry — which in nature gives rise to conservation of momentum — led to dramatic improvements in image recognition,” says Mike Williams, an associate professor of physics at MIT, LNS researcher, and IAIFI deputy director. “We believe incorporating more complex physics principles will revolutionize how AI is used to study fundamental interactions, while simultaneously advancing the foundations of AI.”

In addition, a core element of the IAIFI mission is to transfer their technologies to the broader AI community.

“Recognizing the critical role of AI, NSF is investing in collaborative research and education hubs, such as the NSF IAIFI anchored at MIT, which will bring together academia, industry, and government to unearth profound discoveries and develop new capabilities,” says NSF Director Sethuraman Panchanathan. “Just as prior NSF investments enabled the breakthroughs that have given rise to today’s AI revolution, the awards being announced today will drive discovery and innovation that will sustain American leadership and competitiveness in AI for decades to come.”

Research in AI and fundamental interactions

Fundamental interactions are described by two pillars of modern physics: at short distances by the Standard Model of particle physics, and at long distances by the Lambda Cold Dark Matter model of Big Bang cosmology. Both models are based on physical first principles such as causality and space-time symmetries.  An abundance of experimental evidence supports these theories, but also exposes where they are incomplete, most pressingly that the Standard Model does not explain the nature of dark matter, which plays an essential role in cosmology.

AI has the potential to help answer these questions and others in physics.

For many physics problems, the governing equations that encode the fundamental physical laws are known. However, undertaking key calculations within these frameworks, as is essential to test our understanding of the universe and guide physics discovery, can be computationally demanding or even intractable. IAIFI researchers are developing AI for such first-principles theory studies, which naturally require AI approaches that rigorously encode physics knowledge. 

“My group is developing new provably exact algorithms for theoretical nuclear physics,” says Phiala Shanahan, an assistant professor of physics and LNS researcher at MIT. “Our first-principles approach turns out to have applications in other areas of science and even in robotics, leading to exciting collaborations with industry partners.”

Incorporating physics principles into AI could also have a major impact on many experimental applications, such as designing AI methods that are more easily verifiable. IAIFI researchers are working to enhance the scientific potential of various facilities, including the Large Hadron Collider (LHC) and the Laser Interferometer Gravity Wave Observatory (LIGO). 

“Gravitational-wave detectors are among the most sensitive instruments on Earth, but the computational systems used to operate them are mostly based on technology from the previous century,” says Principal Research Scientist Lisa Barsotti of the MIT Kavli Institute for Astrophysics and Space Research. “We have only begun to scratch the surface of what can be done with AI; just enough to see that the IAIFI will be a game-changer.”

The unique features of these physics applications also offer compelling research opportunities in AI more broadly. For example, physics-informed architectures and hardware development could lead to advances in the speed of AI algorithms, and work in statistical physics is providing a theoretical foundation for understanding AI dynamics. 

“Physics has inspired many time-tested ideas in machine learning: maximizing entropy, Boltzmann machines, and variational inference, to name a few,” says Pulkit Agrawal, an assistant professor of electrical engineering and computer science at MIT, and researcher in the Computer Science and Artificial Intelligence Laboratory (CSAIL). “We believe that close interaction between physics and AI researchers will be the catalyst that leads to the next generation of machine learning algorithms.” 

Cultivating early-career talent

AI technologies are advancing rapidly, making it both important and challenging to train junior researchers at the intersection of physics and AI. The IAIFI aims to recruit and train a talented and diverse group of early-career researchers, including at the postdoc level through its IAIFI Fellows Program.  

“By offering our fellows their choice of research problems, and the chance to focus on cutting-edge challenges in physics and AI, we will prepare many talented young scientists to become future leaders in both academia and industry,” says MIT professor of physics Marin Soljacic of the Research Laboratory of Electronics (RLE). 

IAIFI researchers hope these fellows will spark interdisciplinary and multi-investigator collaborations, generate new ideas and approaches, translate physics challenges beyond their native domains, and help develop a common language across disciplines. Applications for the inaugural IAIFI fellows are due in mid-October. 

Another related effort spearheaded by Thaler, Williams, and Alexander Rakhlin, an associate professor of brain and cognitive science at MIT and researcher in the Institute for Data, Systems, and Society (IDSS), is the development of a new interdisciplinary PhD program in physics, statistics, and data science, a collaborative effort between the Department of Physics and the Statistics and Data Science Center.

“Statistics and data science are among the foundational pillars of AI. Physics joining the interdisciplinary doctoral program will bring forth new ideas and areas of exploration, while fostering a new generation of leaders at the intersection of physics, statistics, and AI," says Rakhlin.  

Education, outreach, and partnerships 

The IAIFI aims to cultivate “human intelligence” by promoting education and outreach. For example, IAIFI members will contribute to establishing a MicroMasters degree program at MIT for students from non-traditional backgrounds.    

“We will increase the number of students in both physics and AI from underrepresented groups by providing fellowships for the MicroMasters program,” says Isaac Chuang, professor of physics and electrical engineering, senior associate dean for digital learning, and RLE researcher at MIT. “We also plan on working with undergraduate MIT Summer Research Program students, to introduce them to the tools of physics and AI research that they might not have access to at their home institutions.”

The IAIFI plans to expand its impact via numerous outreach efforts, including a K-12 program in which students are given data from the LHC and LIGO and tasked with rediscovering the Higgs boson and gravitational waves. 

“After confirming these recent Nobel Prizes, we can ask the students to find tiny artificial signals embedded in the data using AI and fundamental physics principles,” says assistant professor of physics Phil Harris, an LNS researcher at MIT. “With projects like this, we hope to disseminate knowledge about — and enthusiasm for — physics, AI, and their intersection.”

In addition, the IAIFI will collaborate with industry and government to advance the frontiers of both AI and physics, as well as societal sectors that stand to benefit from AI innovation. IAIFI members already have many active collaborations with industry partners, including DeepMind, Microsoft Research, and Amazon. 

“We will tackle two of the greatest mysteries of science: how our universe works and how intelligence works,” says MIT professor of physics Max Tegmark, an MIT Kavli Institute researcher. “Our key strategy is to link them, using physics to improve AI and AI to improve physics. We're delighted that the NSF is investing the vital seed funding needed to launch this exciting effort.”

Building new connections at MIT and beyond

Leveraging MIT’s culture of collaboration, the IAIFI aims to generate new connections and to strengthen existing ones across MIT and beyond.

Of the 27 current IAIFI senior investigators, 16 are at MIT and members of the LNS, RLE, MIT Kavli Institute, CSAIL, and IDSS. In addition, IAIFI investigators are members of related NSF-supported efforts at MIT, such as the Center for Brains, Minds, and Machines within the McGovern Institute for Brain Research and the MIT-Harvard Center for Ultracold Atoms.  

“We expect a lot of creative synergies as we bring physics and computer science together to study AI," says Bill Freeman, the Thomas and Gerd Perkins Professor of Electrical Engineering and Computer Science and researcher in CSAIL. "I'm excited to work with my physics colleagues on topics that bridge these fields."

More broadly, the IAIFI aims to make Cambridge, Massachusetts, and the surrounding Boston area a hub for collaborative efforts to advance both physics and AI. 

“As we teach in 8.01 and 8.02, part of what makes physics so powerful is that it provides a universal language that can be applied to a wide range of scientific problems,” says Thaler. “Through the IAIFI, we will create a common language that transcends the intellectual borders between physics and AI to facilitate groundbreaking discoveries.”

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Face-specific brain area responds to faces even in people born blind

More than 20 years ago, neuroscientist Nancy Kanwisher and others discovered that a small section of the brain located near the base of the skull responds much more strongly to faces than to other objects we see. This area, known as the fusiform face area, is believed to be specialized for identifying faces.

Now, in a surprising new finding, Kanwisher and her colleagues have shown that this same region also becomes active in people who have been blind since birth, when they touch a three-dimensional model of a face with their hands. The finding suggests that this area does not require visual experience to develop a preference for faces.

“That doesn’t mean that visual input doesn’t play a role in sighted subjects — it probably does,” she says. “What we showed here is that visual input is not necessary to develop this particular patch, in the same location, with the same selectivity for faces. That was pretty astonishing.”

Kanwisher, the Walter A. Rosenblith Professor of Cognitive Neuroscience and a member of MIT’s McGovern Institute for Brain Research, is the senior author of the study. N. Apurva Ratan Murty, an MIT postdoc, is the lead author of the study, which appears this week in the Proceedings of the National Academy of Sciences. Other authors of the paper include Santani Teng, a former MIT postdoc; Aude Oliva, a senior research scientist, co-director of the MIT Quest for Intelligence, and MIT director of the MIT-IBM Watson AI Lab; and David Beeler and Anna Mynick, both former lab technicians.

Selective for faces

Studying people who were born blind allowed the researchers to tackle longstanding questions regarding how specialization arises in the brain. In this case, they were specifically investigating face perception, but the same unanswered questions apply to many other aspects of human cognition, Kanwisher says.

“This is part of a broader question that scientists and philosophers have been asking themselves for hundreds of years, about where the structure of the mind and brain comes from,” she says. “To what extent are we products of experience, and to what extent do we have built-in structure? This is a version of that question asking about the particular role of visual experience in constructing the face area.”

The new work builds on a 2017 study from researchers in Belgium. In that study, congenitally blind subjects were scanned with functional magnetic resonance imaging (fMRI) as they listened to a variety of sounds, some related to faces (such as laughing or chewing), and others not. That study found higher responses in the vicinity of the FFA to face-related sounds than to sounds such as a ball bouncing or hands clapping.

In the new study, the MIT team wanted to use tactile experience to measure more directly how the brains of blind people respond to faces. They created a ring of 3D-printed objects that included faces, hands, chairs, and mazes, and rotated them so that the subject could handle each one while in the fMRI scanner.

They began with normally sighted subjects and found that when they handled the 3D objects, a small area that corresponded to the location of the FFA was preferentially active when the subjects touched the faces, compared to when they touched other objects. This activity, which was weaker than the signal produced when sighted subjects looked at faces, was not surprising to see, Kanwisher says.

“We know that people engage in visual imagery, and we know from prior studies that visual imagery can activate the FFA. So the fact that you see the response with touch in a sighted person is not shocking because they’re visually imagining what they’re feeling,” she says.

The researchers then performed the same experiments, using tactile input only, with 15 subjects who reported being blind since birth. To their surprise, they found that the brain showed face-specific activity in the same area as the sighted subjects, at levels similar to when sighted people handled the 3D-printed faces.

“When we saw it in the first few subjects, it was really shocking, because no one had seen individual face-specific activations in the fusiform gyrus in blind subjects previously,” Murty says.

Patterns of connection

The researchers also explored several hypotheses that have been put forward to explain why face-selectivity always seems to develop in the same region of the brain. One prominent hypothesis suggests that the FFA develops face-selectivity because it receives visual input from the fovea (the center of the retina), and we tend to focus on faces at the center of our visual field. However, since this region developed in blind people with no foveal input, the new findings do not support this idea.

Another hypothesis is that the FFA has a natural preference for curved shapes. To test that idea, the researchers performed another set of experiments in which they asked the blind subjects to handle a variety of 3D-printed shapes, including cubes, spheres, and eggs. They found that the FFA did not show any preference for the curved objects over the cube-shaped objects.

The researchers did find evidence for a third hypothesis, which is that face selectivity arises in the FFA because of its connections to other parts of the brain. They were able to measure the FFA’s “connectivity fingerprint” — a measure of the correlation between activity in the FFA and activity in other parts of the brain — in both blind and sighted subjects.

They then used the data from each group to train a computer model to predict the exact location of the brain’s selective response to faces based on the FFA connectivity fingerprint. They found that when the model was trained on data from sighted patients, it could accurately predict the results in blind subjects, and vice versa. They also found evidence that connections to the frontal and parietal lobes of the brain, which are involved in high-level processing of sensory information, may be the most important in determining the role of the FFA.

“It’s suggestive of this very interesting story that the brain wires itself up in development not just by taking perceptual information and doing statistics on the input and allocating patches of brain, according to some kind of broadly agnostic statistical procedure,” Kanwisher says. “Rather, there are endogenous constraints in the brain present at birth, in this case, in the form of connections to higher-level brain regions, and these connections are perhaps playing a causal role in its development.”

The research was funded by the National Institutes of Health Shared Instrumentation Grant to the Athinoula Martinos Center at MIT, a National Eye Institute Training Grant, the Smith-Kettlewell Eye Research Institute’s Rehabilitation Engineering Research Center, an Office of Naval Research Vannevar Bush Faculty Fellowship, an NIH Pioneer Award, and a National Science Foundation Science and Technology Center Grant.

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Uncertainty, belief, and economic outcomes

In late 1994 Mexico suffered a severe currency crisis, with attacks on the peso by international traders that led to inflation, bailouts, and macroeconomic woes. Some experts had thought Mexico was ripe for a currency crisis a couple of years before it happened. So if the peso was already vulnerable to attack, why didn’t that occur earlier?

Stephen Morris has some ideas about that. Influential ideas. The MIT economist is the co-author of “Unique Equilibrium in a Model of Self-Fulfilling Currency Attacks,” a widely cited 1998 paper co-authored with economist Hyun Song Shin, who is now at the Bank of International Settlements. The paper changed the way many people in economics and finance think about market dynamics.

Before Morris and Shin published their paper, a common line of thought was that there were multiple points of equilibrium at which currencies (among other tradeable things) would rest. Investors might short-sell a currency, leading to its collapse — which would establish one equilibrium. Alternately, the currency might avoid attacks and remain robust — representing another equilibrium. Either way, large numbers of people would be acting similarly based on the same information.

But Morris and Shin posited a new, more realistic view about how events like this happen. They stipulated that there is often uncertainty about some of the fundamentals concerning a country’s currency, and also uncertainty among investors about what other investors will do.

“If you say there are multiple equilibria, and everybody attacks or doesn’t attack, that’s on the assumption that [there] is common knowledge among the agents,” Morris says. “And that’s surely not going to be true in reality. In reality, there is going to be uncertainty about what other people think about the situation and what they think other people think.”

For that very reason, Morris says, “An attack would tend to occur when it made sense for you to attack, when you were very uncertain about what other people were doing.”

His paper with Shin codified this point, modeling how the behavior of investors hinges greatly on their beliefs about what other investors will do — and laying out how, in this situation, a single equilibrium for a given currency will result. The model made waves: The finance world used the paper by applying the model to their own decisions, while scholars used it to rethink, in general terms, existing assumptions about the way markets work. The model helped persuade people that markets did not operate with utmost efficiency and that “higher-order” beliefs among investors — what you think I will do, or what I think you think — matter hugely.

“I think people liked it because under some circumstances it delivered a unique prediction,” Morris says. “So it got widely used — people could use this and plug it in to different economic problems. I was happy for people to do that, but what got lost a little bit was the idea about people’s higher-order beliefs and the rich modeling of the information structure lying behind this.”

That kind of work is the through-line in Morris’ career: He takes thorny problems about information and beliefs, and finds sophisticated yet useful ways of modeling them, in areas applicable to finance, central banking, firm decisions, and even nonfinancial markets such as school-choice plans.

“I’ve always been very interested in information,” Morris says. “And in trying to take a richer perspective on information and how that affects economic outcomes.”

With a broad and deep portfolio of research that he is still building upon, Morris was hired with tenure at MIT, joining the Institute’s Department of Economics in 2019. He was recently named the inaugural Peter A. Diamond Professor in Economics.

Morris did not always think economics was something he would pursue. As an undergraduate at Cambridge University, he studied math and, for the first time, economics.

“I think I have an origin story which a reasonable number of economists have,” Morris says. “You’re interested in math and analytical reasoning, and then you discover you’re interested in the world and social science as well, and then you discover economics is a subject addressing big, real-world problems where these analytical tools are being used in a significant way.

Still, Morris did not instantly jump into graduate work in economics. First he attended Yale University as part of an exchange program, then spent two years in Uganda, working as what he calls a “practicing development economist,” before entering Yale’s PhD program in economics.

“At the end of the day I missed academia, came back, and did a very different type of economics,” Morris says. “I do theoretical microeconomics.”

Morris obtained his PhD from Yale, then joined the faculty at the University of Pennsylvania straight out of graduate school. He subsequently taught at Yale and at Princeton University, before joining MIT.

In his years as a practicing theoretical microeconomist, Morris’ work has ranged across a number of problems and bridged the gap between pure theory and more applied theoretical endeavors. A 2002 paper he wrote with Shin, “The Social Value of Private Information,” looked at the ways different market participants may coordinate, crowd out useful public information, and limit the spread of useful knowledge in markets — a work that has also been widely cited, and which generated considerable follow-up research among economists.

On a different note, a 2005 paper Morris wrote with economist Dirk Bergemann, “Robust Mechanism Design,” was influential in the field of mechanism design — the development of nonfinancial markets that apply to things like school choice or medical matches. In it, Morris and his colleagues queried whether such markets can reach optimal outcomes for everyone in them. One key point of the paper was to question how well we can know, and model, the beliefs of — say — parents choosing schools for children. The paper did not lead to a single outcome in the way the currency-attack model did, but it also generated a large follow-up literature in the field about assumptions inherent in mechanism-design work.

“To me it’s all unified,” Morris says of the different branches of his work. “What people may remember from the currency attack paper was that this was a useful trick to get the unique equilibrium. Whereas the robust mechanism design paper was saying there are lots of different things that can happen. So in that sense they may seem to be going in different directions, but in my mind, it was all about taking a richer perspective on information structures and what their consequences are.”

At MIT, he is returning to the question of when the economy switches between equilibria, started in his 1998 “Unique Equilibrium” paper, sometimes in tandem with MIT economist Muhamet Yildiz. Morris is also interested in the crossover between his work and that of computer scientists, and views MIT as a place with significant potential for collaborative, interdisciplinary research. He also finds the Department of Economics to be a highly productive place for him to work. 

“It’s collegial, but in particular that means there are more intellectual interactions as well,” Morris observes.

He notes that he came to the Institute partly for the teaching opportunities, as well. In his first semester at MIT, Morris taught MIT second-year PhD students in a course about writing effective papers; he anticipates extensive advising of graduate students, as well as good in-class experiences.

“The main thing that drew me to be here was the PhD program,” Morris says. “I’d heard great things about it over the years.”

Between research and teaching, Morris will no doubt find his own unique equilibrium at MIT, too.

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Syringe technology could enable injection of concentrated biologic drugs

MIT researchers have developed a simple, low-cost technology to administer powerful drug formulations that are too viscous to be injected using conventional medical syringes.

The technology, which is described in a paper published today in the journal Advanced Healthcare Materials, makes it possible to inject high-concentration drugs and other therapies subcutaneously. It was developed as a solution for highly effective, and extremely concentrated, biopharmaceuticals, or biologics, which typically are diluted and injected intravenously.

“Where drug delivery and biologics are going, injectability is becoming a big bottleneck, preventing formulations that could treat diseases more easily,” says Kripa Varanasi, MIT professor of mechanical engineering. “Drug makers need to focus on what they do best, and formulate drugs, not be stuck by this problem of injectability.”

Leaders at the Bill and Melinda Gates Foundation brought the injectability problem to Varanasi after reading about his previous work on dispensing liquids, which has attracted the attention of industries ranging from aviation to makers of toothpaste. A main concern of the foundation, Varanasi says, was with providing high-concentration vaccines and biologic therapies to people in developing countries who could not travel from remote areas to a medical setting.

In the current pandemic, Varanasi adds, being able to stay home and subcutaneously self-administer medication to treat diseases such as cancer or auto-immune disorders is also important in developed countries such as the United States.

“Self-administration of drugs or vaccines can help democratize access to health care,” he says.

Varanasi and Vishnu Jayaprakash, a graduate student in MIT’s mechanical engineering department who is the first author on the paper, designed a system that would make subcutaneous injection of high-concentration drug formulations possible by reducing the required injection force, which exceeded what is possible with manual subcutaneous injection with a conventional syringe.

In their system, the viscous fluid to be injected is surrounded with a lubricating fluid, easing the fluid’s flow through the needle. With the lubricant, just one-seventh of the injection force was needed for the highest viscosity tested, effectively allowing subcutaneous injection of any of the more than 100 drugs otherwise considered too viscous to be administered in that way.

“We can enable injectability of these biologics,” Jayaprakash says. “Regardless of how viscous your drug is, you can inject it, and this is what made this approach very attractive to us.”

Biologic drugs include protein-based formulations and are harvested from living cells. They are used to treat a wide range of diseases and disorders, and can bind with specific tissues or immune cells as desired, provoking fewer unwanted reactions and bringing about particular immune responses that don’t occur with other drugs.

“You can tailor very specific proteins or molecules that bind to very specific receptors in the body,” says Jayaprakash. “They enable a degree of personalization, specificity, and immune response that just isn’t available with small-molecule drugs. That’s why, globally, people are pushing toward biologic drugs.”

Because of their high viscosities, administering the drugs subcutaneously has involved methods that have turned out to be impractical and expensive. Generally, the drugs are diluted and given intravenously, which requires a visit to a hospital or doctor’s office. Jet injectors, which shoot the drugs through the skin without a needle, are expensive and prone to contamination from backsplash. Injecting encapsulated drugs often results in their clogging the needle and additional complexity in drug manufacturing and release profiles. EpiPen-style syringes are also too expensive to be used widely.

To develop their technology, the MIT researchers began by defining theoretical parameters and testing them before designing their device. The device consists of a syringe with two barrels, one inside of the other, with the inner tube delivering the viscous drug fluid and the surrounding tube delivering a thin coating of lubricant to the drug as it enters the needle.

Because the lubricated fluid passes more easily through the needle, the viscous payload undergoes minimal shear stress. For this reason, Jayaprakash says, the system could also be useful for 3D bioprinting of tissues made of natural components and administering cell therapies, both cases where tissues and cells can be destroyed by shear damage.

Therapeutic gels — used in bone and join therapies, as well as for timed-release drug delivery, among other uses — could also be more easily administered using the syringe developed by the researchers.

“The technique works as a platform for all of these other applications,” Jayaprakash says.

Pramod Bonde, Yale School of Medicine associate professor of surgery, says the technique could greatly affect the field of medicine.

“This innovative technology has the potential to have a fundamental and wide-ranging impact on how drugs are delivered in the body,” Bonde says.

Whether the technology will make a difference as researchers hunt for Covid-19 vaccine possibilities and treatments is unclear. The researchers say, however that it widens the options as different drug formulations are considered.

“Once you have the story about the technology out there, the industry might say they could consider things that had previously been impossible,” Varanasi says.

With his previous work having spurred the creation of four companies, Varanasi says he and his team are hopeful this technology will also be commercialized.

“There should be no reason why this approach, given its simplicity, can’t help solve what we’ve heard from industry is an emerging problem,” he says. “The foundational work is done. Now it’s just applying it to different formulations.”

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Real-time data for a better response to disease outbreaks

Kinsa was founded by MIT alumnus Inder Singh MBA ’06, SM ’07 in 2012, with the mission of collecting information about when and where infectious diseases are spreading in real-time. Today the company is fulfilling that mission along several fronts.

It starts with families. More than 1.5 million of Kinsa’s “smart” thermometers have been sold or given away across the country, including hundreds of thousands to families from low-income school districts. The thermometers link to an app that helps users decide if they should seek medical attention based on age, fever, and symptoms.

At the community level, the data generated by the thermometers are anonymized and aggregated, and can be shared with parents and school officials, helping them understand what illnesses are going around and prevent the spread of disease in classrooms.

By working with over 2,000 schools to date in addition to many businesses, Kinsa has also developed predictive models that can forecast flu seasons each year. In the spring of this year, the company showed it could predict flu spread 12-20 weeks in advance at the city level.

The milestone prepared Kinsa for its most profound scale-up yet. When Covid-19 came to the U.S., the company was able to estimate its spread in real-time by tracking fever levels above what would normally be expected. Now Kinsa is working with health officials in five states and three cities to help contain and control the virus.

“By the time the CDC [U.S. Centers for Disease Control] gets the data, it has been processed, deidentified, and people have entered the health system to see a doctor,” say Singh, who is Kinsa’s CEO as well as its founder. “There’s a huge delay from when someone contracts an illness and when they see a doctor. The current health care system only sees the latter; we see the former.”

Today Kinsa finds itself playing a central role in America’s Covid-19 response. In addition to its local partnerships, the company has become a central information hub for the public, media, and researchers with its Healthweather tool, which maps unusual rates of fevers — among the most common symptom of Covid-19 — to help visualize the prevalence of illness in communities.

Singh says Kinsa’s data complement other methods of containing the virus like testing, contact tracing, and the use of face masks.

Better data for better responses

Singh’s first exposure to MIT came while he was attending the Harvard University Kennedy School of Government as a graduate student.

“I remember I interacted with some MIT undergrads, we brainstormed some social-impact ideas,” Singh recalls. “A week later I got an email from them saying they’d prototyped what we were talking about. I was like, ‘You prototyped what we talked about in a week!?’ I was blown away, and it was an insight into how MIT is such a do-er campus. It was so entrepreneurial. I was like, ‘I want to do that.’”

Soon Singh enrolled in the Harvard-MIT Program in Health Sciences and Technology, an interdisciplinary program where Singh earned his master’s and MBA degrees while working with leading research hospitals in the area. The program also set him on a course to improve the way we respond to infectious disease.

Following his graduation, he joined the Clinton Health Access Initiative (CHAI), where he brokered deals between pharmaceutical companies and low-resource countries to lower the cost of medicines for HIV, malaria, and tuberculosis. Singh described CHAI as a dream job, but it opened his eyes to several shortcomings in the global health system.

“The world tries to curb the spread of infectious illness with almost zero real-time information about when and where disease is spreading,” Singh says. “The question I posed to start Kinsa was ‘how do you stop the next outbreak before it becomes an epidemic if you don’t know where and when it’s starting and how fast it’s spreading’?”

Kinsa was started in 2012 with the insight that better data were needed to control infectious diseases. In order to get that data, the company needed a new way of providing value to sick people and families.

“The behavior in the home when someone gets sick is to grab the thermometer,” Singh says. “We piggy-backed off of that to create a communication channel to the sick, to help them get better faster.”

Kinsa started by selling its thermometers and creating a sponsorship program for corporate donors to fund thermometer donations to Title 1 schools, which serve high numbers of economically disadvantaged students. Singh says 40 percent of families that receive a Kinsa thermometer through that program did not previously have any thermometer in their house.

The company says its program has been shown to help schools improve attendance, and has yielded years of real-time data on fever rates to help compare to official estimates and develop its models.

“We had been forecasting flu incidence accurately several weeks out for years, and right around early 2020, we had a massive breakthrough,” Singh recalls. “We showed we could predict flu 12 to 20 weeks out — then March hit. We said, let’s try to remove the fever levels associated with cold and flu from our observed real time signal. What’s left over is unusual fevers, and we saw hotspots across the country. We observed six years of data and there’d been hot spots, but nothing like we were seeing in early March.”

The company quickly made their real-time data available to the public, and on March 14, Singh got on a call with the former New York State health commissioner, the former head of the U.S. Food and Drug Administration, and the man responsible for Taiwan’s successful Covid-19 response.

“I said, ‘There’s hotspots everywhere,” Singh recalls. “They’re in New York, around the Northeast, Texas, Michigan. They said, ‘This is interesting, but it doesn’t look credible because we’re not seeing case reports of Covid-19.’ Low and behold, days and weeks later, we saw the Covid cases start building up.”

A tool against Covid-19

Singh says Kinsa’s data provide an unprecedented look into the way a disease is spreading through a community.

“We can predict the entire incidence curve [of flu season] on a city-by-city basis,” Singh says. “The next best model is [about] three weeks out, at a multistate level. It’s not because we’re smarter than others; it’s because we have better data. We found a way to communicate with someone consistently when they’ve just fallen ill.”

Kinsa has been working with health departments and research groups around the country to help them interpret the company’s data and react to early warnings of Covid-19’s spread. It’s also helping companies around the country as they begin bringing employees back to offices.

Now Kinsa is working on expanding its international presence to help curb infectious diseases on multiple fronts around the world, just like it’s doing in the U.S. The company’s progress promises to help authorities monitor diseases long after Covid-19.

“I started Kinsa to create a global, real-time outbreak monitoring and detection system, and now we have predictive power beyond that,” Singh says. “When you know where and when symptoms are starting and how fast their spreading, you can empower local individuals, families, communities, and governments.”

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Economist Antoine Levy is all over the map

Some of the stereotypical differences between the United States and France do check out, according to Antoine Levy: The weather and the food are much worse in New England, he says, and the people are much more welcoming. But for Levy, who is about to start the fifth year of his MIT PhD program in economics, the U.S. is starting to feel like his native France in some ways.

“For a long time, I thought France was obsessed by politics and the United States was not,” he recalls. However, his impression has changed over the last five years. In France, from urban neighborhoods to small villages, he says everyone has an opinion on every government minister. Lately, he has felt a transformation around him, and has observed his peers in the U.S. becoming more interested in local politics as well.

While this may be a reflection of recent changes in the American political climate, a local perspective on policy is also a key signature of Levy’s research at MIT. Whether in France or the U.S., the economist has long been fascinated by how politics and economics converge in different ways from one region or locality to another.

All over the place

Levy’s research looks at how different sociodemographic markers within a country, such as population density, can shape economic activity and policy across these areas.

His current projects focus on harnessing the power of regional data to inform economic policy, from housing development to unemployment to political influence. For example, he has studied the Economic and Monetary Union of the E.U. after the Great Recession, in relation to the Phillips curve, which, somewhat controversially, suggests there is an inverse relationship between unemployment and wage growth. While aggregated national data do not demonstrate a clear Phillips curve, Levy has found that regional European data do follow the pattern –– indicating that policy informed by regional data might be more important than ever.

“We’ve talked a lot about political polarization, but there’s also been a massive spatial polarization over the last 25 years,” he explains. “That conjunction of economic geography and political geography has massive implications for the relative influence of places, and for the policy and politics of trade, social insurance, and redistribution.”

His latest work has been inspired by recent historical events –– Brexit, the election of Donald Trump, the “yellow vest” protests in his native France –– which have exposed the way one-size-fits-all economic policies have left behind people in vastly different geographical situations. Too often, Levy says, people rely on a mythicized idea of a region without drilling down into the patterns of population and economic behavior there. For example, in one working paper, he argues that a significant part of Emmanuel Macron’s success in the 2017 French presidential election can be attributed to a specific campaign promise to abolish a housing tax that affected 80 percent of households in the country.

A key theme in his work is how regional economics have an important influence on individuals’ political decisions — though this is often overlooked by economists.

“There’s this thing in economics where people are called agents,” Levy says. “People do stuff. People write laws, people vote, people get jobs and consume. And at some point, you have to still ask what you would do in their place.”

Taking it all in

Part of Levy’s interest in regional variations comes from personal experience. Growing up, he moved around often for his father’s work as an executive in the food industry, which took the family from the midsized city of Lyon, in the southeast, to the much smaller Périgueux, in the southwest; eventually they moved to Paris for his mother’s medical care and school. Experiencing the daily economic differences between those places, even commonplace details like the cost of coffee, have impressed upon him the way one’s economic circumstances affect one’s choices.

“The fate of places and how it’s tied to economics: I think that’s something that you get to experience very concretely when you move around,” Levy says. “Especially in a country as diverse as France.”

Levy’s penchant for variety followed him to college, where he couldn’t bring himself to choose between a more academically oriented education at École Normale Supérieure and business school at HEC Paris. In an unusual move, he ended up enrolling in both. He says he wanted to keep an eye on everything in economics –– from fundamental research to more applied areas. His embrace of interdisciplinary approaches ultimately brought him to MIT, where he appreciates how his program has allowed him to fold together his early interests in macroeconomics and international finance, and his current work on microeconomic and spatial topics.

“The professors tend to always push you to explore your interests and be very open about your interests,” Levy says of the MIT economics department, where he is advised by professors Arnaud Costinot and Ivan Werning. “They were never excessively restrictive about what I should work on or what I should study, they were always very open to hear new ideas.”

That doesn’t mean the path has always been easy, especially with the sheer time investment of a doctoral degree. “I used to be the one who wanted to experience satisfaction in the very short run,” Levy says. “Sometimes you have to slow down and go back to the beginning instead of going through a project very quickly.” To keep himself going he also takes on smaller projects, like writing short proposals, book reviews, and popular press articles.

He also takes the time to read the news or a favorite Philip Roth novel, and has fond memories of playing squash, picnicking on the Charles River, and bouncing research ideas with friends from his cohort and the French community at MIT. He has an affinity for his fellow ex-pats: “They made a choice of leaving France, and I think that’s always a sign of being ready to find out the limits of your openness.”

As he continues with his research, Levy plans to stay focused on issues that matter to the people around him, and remaining open to topics outside his expertise and immediate research field. Knowing that his work could have an impact on people’s lives keeps him passionate about economics, wherever it might take him in the future.

“It’s not something that you do for the sake of beauty,” he says of economics. “When you say you’re an economist, and you’re at the dinner table, people have tons of questions. If people have a question that they think is relevant for economics, then maybe it should be. You have to have an answer.”

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Two projects receive funding for technologies that avoid carbon emissions

The Carbon Capture, Utilization, and Storage Center, one of the MIT Energy Initiative (MITEI)’s Low-Carbon Energy Centers, has awarded $900,000 in funding to two new research projects to advance technologies that avoid carbon dioxide (CO₂) emissions into the atmosphere and help address climate change. The winning project is receiving $750,000, and an additional project receives $150,000.

The winning project, led by principal investigator Asegun Henry, the Robert N. Noyce Career Development Professor in the Department of Mechanical Engineering, and co-principal investigator Paul Barton, the Lammot du Pont Professor of Chemical Engineering, aims to produce hydrogen without CO₂ emissions while creating a second revenue stream of solid carbon. The additional project, led by principal investigator Matěj Peč, the Victor P. Starr Career Development Chair in the Department of Earth, Atmospheric and Planetary Sciences, seeks to expand understanding of new processes for storing CO₂ in basaltic rocks by converting it from an aqueous solution into carbonate minerals.

Carbon capture, utilization, and storage (CCUS) technologies have the potential to play an important role in limiting or reducing the amount of CO₂ in the atmosphere, as part of a suite of approaches to mitigating to climate change that includes renewable energy and energy efficiency technologies, as well as policy measures. While some CCUS technologies are being deployed at the million-ton-of-CO₂ per year scale, there are substantial needs to improve costs and performance of those technologies and to advance more nascent technologies. MITEI’s CCUS center is working to meet these challenges with a cohort of industry members that are supporting promising MIT research, such as these newly funded projects.

A new process for producing hydrogen without CO₂ emissions

Henry and Barton’s project, “Lower cost, CO₂-free, H₂ production from CH₄ using liquid tin,” investigates the use of methane pyrolysis instead of steam methane reforming (SMR) for hydrogen production.

Currently, hydrogen production accounts for approximately 1 percent of global CO₂ emissions, and the predominant production method is SMR. The SMR process relies on the formation of CO₂, so replacing it with another economically competitive approach to making hydrogen would avoid emissions. 

“Hydrogen is essential to modern life, as it is primarily used to make ammonia for fertilizer, which plays an indispensable role in feeding the world’s 7.5 billion people,” says Henry. “But we need to be able to feed a growing population and take advantage of hydrogen’s potential as a carbon-free fuel source by eliminating CO₂ emissions from hydrogen production. Our process results in a solid carbon byproduct, rather than CO₂ gas. The sale of the solid carbon lowers the minimum price at which hydrogen can be sold to break even with the current, CO₂ emissions-intensive process.”

Henry and Barton’s work is a new take on an existing process, pyrolysis of methane. Like SMR, methane pyrolysis uses methane as the source of hydrogen, but follows a different pathway. SMR uses the oxygen in water to liberate the hydrogen by preferentially bonding oxygen to the carbon in methane, producing CO₂ gas in the process. In methane pyrolysis, the methane is heated to such a high temperature that the molecule itself becomes unstable and decomposes into hydrogen gas and solid carbon — a much more valuable byproduct than CO₂ gas. Although the idea of methane pyrolysis has existed for many years, it has been difficult to commercialize because of the formation of the solid byproduct, which can deposit on the walls of the reactor, eventually plugging it up. This issue makes the process impractical. Henry and Barton’s project uses a new approach in which the reaction is facilitated with inert molten tin, which prevents the plugging from occurring. The proposed approach is enabled by recent advances in Henry’s lab that enable the flow and containment of liquid metal at extreme temperatures without leakage or material degradation. 

Studying CO₂ storage in basaltic reservoirs

With his project, “High-fidelity monitoring for carbon sequestration: integrated geophysical and geochemical investigation of field and laboratory data,” Peč plans to conduct a comprehensive study to gain a holistic understanding of the coupled chemo-mechanical processes that accompany CO₂ storage in basaltic reservoirs, with hopes of increasing adoption of this technology.

The Intergovernmental Panel on Climate Change estimates that 100 to 1,000 gigatonnes of CO₂ must be removed from the atmosphere by the end of the century. Such large volumes can only be stored below the Earth’s surface, and that storage must be accomplished safely and securely, without allowing any leakage back into the atmosphere.

One promising storage strategy is CO₂ mineralization — specifically by dissolving gaseous CO₂ in water, which then reacts with reservoir rocks to form carbonate minerals. Of the technologies proposed for carbon sequestration, this approach is unique in that the sequestration is permanent: the CO₂ becomes part of an inert solid, so it cannot escape back into the environment. Basaltic rocks, the most common volcanic rock on Earth, present good sites for CO₂ injection due to their widespread occurrence and high concentrations of divalent cations such as calcium and magnesium that can form carbonate minerals. In one study, more than 95 percent of the CO₂ injected into a pilot site in Iceland was precipitated as carbonate minerals in less than two years.

However, ensuring the subsurface integrity of geological formations during fluid injection and accurately evaluating the reaction rates in such reservoirs require targeted studies such as Peč’s.

“The funding by MITEI’s Low-Carbon Energy Center for Carbon Capture, Utilization, and Storage allows me to start a new research direction, bringing together a group of experts from a range of disciplines to tackle climate change, perhaps the greatest scientific challenge our generation is facing,” says Peč.

The two projects were selected from a call for proposals that resulted in 15 entries by MIT researchers. “The application process revealed a great deal of interest from MIT researchers in advancing carbon capture, utilization, and storage processes and technologies,” says Bradford Hager, the Cecil and Ida Green Professor of Earth Sciences, who co-directs the CCUS center with T. Alan Hatton, the Ralph Landau Professor of Chemical Engineering. “The two projects funded through the center will result in fundamental, higher-risk research exploring novel approaches that have the potential to have high impact in the longer term. Given the short-term focus of the industry, projects like this might not have otherwise been funded, so having support for this kind of early-stage fundamental research is crucial.”

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Are we still listening to space?

When LIGO, the Laser Interferometer Gravitational-Wave Observatory, and its European counterpart, Virgo, detect a gravitational ripple from space, a public alert is sent out. That alert lets researchers know with a decently high confidence that this ripple was probably caused by an exceptional cosmic event, such as the collision of neutron stars or the merging of black holes, somewhere in the universe.

Then starts the scramble. A pair of researchers is assigned to the incoming event, analyzing the data to get a preliminary location in the sky whence the ripple emanated. Telescopes are pointed in that direction, more data is amassed, and the pair of researchers conducts further followup studies to try to determine what kind of event caused the wave.

“I often think of it as if we’re in a dark forest and listening to the ground,” says Eva Huang, a third-year Department of Physics graduate student in Assistant Professor Salvatore Vitale’s lab in the MIT Kavli Institute for Astrophysics and Space Research (MKI). “From the footsteps, we’re trying to guess what kind of animal is passing by.”

The LIGO-Virgo Collaboration keeps a rotation system to determine which researchers get to investigate the latest detection. Sylvia Biscoveanu, a second-year graduate student also in Vitale’s lab, was next on the list when LIGO suspended its third observational run due to Covid-19. If a cosmic event happens in the universe and there’s no one there to detect it, did it even happen?

Data analysis in isolation

When MIT similarly scaled back on-campus research in mid-March due to the coronavirus pandemic, the LIGO team at MKI adapted quickly to the new work-from-home normal. “Our work is physically less dependent on being at MIT,” says Vitale, who is also a member of the LIGO Scientific Collaboration. “Still, there are consequences.”

For Biscoveanu, working from home has entailed being at her computer for at least eight hours a day. “In terms of actually being able to do my research, I haven’t suffered,” she says. What has suffered is her ability to exchange ideas with other members of the LIGO group at MIT. “I had just moved to a bigger office with a bunch of graduate students, and we were really looking forward to being able to talk to each other and ask questions regularly,” says Biscoveanu. “I definitely don’t get as much of that at home.”

Mentorship also looks different when everyone is at home. Vitale has always had an open-door policy with his graduate students. “I do weekly meetings with my students, but on top of that I had close-to-daily interactions with them,” he says. Unless his door was closed, Vitale says, his students could come in and talk anytime. That immediate connection, he has found, is hard to replicate in the digital world.

“The thing I tell my students is that we don’t work in a hut where everyone is making their own project and then it’s done,” says Vitale. “Research is more than the sum of its parts.” One advantage of working in a group is the ability to turn to a colleague to discuss a paper you just read, a problem you’re facing, or a crazy idea you had the night before. That’s harder to do when everyone is stuck in their own hut.

“Now you have to go in the chat room or arrange a telecon if you want to ask a question,” says Ken Ng, a third-year graduate student in the Vitale group. Ng uses gravitational waves to study particle physics, with his work focusing on axions, a proposed elementary particle that is orders of magnitude smaller than the tiniest particle observed. Telecons and Slack, he has found, can be particularly inefficient when you’re trying to quickly sketch out an idea. “I’m actually thinking of buying a white board,” he says.

Space never stops

When the third observation run was suspended a month before it was supposed to end, it had collected 56 gravitational wave candidates. In comparison, the first two runs combined amassed a total of 11 candidates. So even though fresh data isn’t arriving in the lab, the work hasn’t ceased, and LIGO scientists are scrutinizing the data from home. “If the pandemic had happened a few months before, we could have missed half the data,” says Ng, looking on the positive side.  

Compared to the other members of the lab, Ng is no pandemic rookie. When the Covid-19 pandemic struck, he thought, “Again?” Ng, who is from Hong Kong, faced the SARS outbreak in 2002 and considers himself the pandemic veteran of the group. That experience has kept him from panicking these days. “I know the importance of social distancing and mask-wearing,” he explains.

Still, for some in the group, social distancing has led to less productivity and feelings of guilt. “I sometimes feel that, because my work is less impacted, I cannot allow myself to feel frustrated,” says Huang. Her work — analyzing LIGO data to decipher the cosmic events responsible for detected waves — can be done at home, unlike researchers who need to be physically in-lab. Throughout the pandemic, Huang has worked hard to combat the feeling that she needs to earn permission to be self-compassionate. “I can be, and need to be, kind to myself during this time.

All are looking forward to the day when they can come back to campus. Partly, Ng confesses, for the free food. But mostly to continue studying gravitational waves in the same space. “I miss being able to chat randomly when people are in an office,” he says.

Vitale acknowledges that there have been some benefits of working from home. “This has obliged everyone to think a bit harder about how to express what we want to say,” he says. Still, like his students, he also can’t wait to leave his hut and get back to campus. “I think for all of us, it will also just be nice to be back at the office and re-establish a clear separation between our living and our working spaces, that right now are collapsed in the same entity.”

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The factory of the future, batteries not included

Many analysts have predicted an explosion in the number of industrial “internet of things” (IoT) devices that will come online over the next decade. Sensors play a big role in those forecasts.

Unfortunately, sensors come with their own drawbacks, many of which are due to the limited energy supply and finite lifetime of their batteries.

Now the startup Everactive has developed industrial sensors that run around the clock, require minimal maintenance, and can last over 20 years. The company created the sensors not by redesigning its batteries, but by eliminating them altogether.

The key is Everactive’s ultra-low-power integrated circuits, which harvest energy from sources like indoor light and vibrations to generate data. The sensors continuously send that data to Everactive’s cloud-based dashboard, which gives users real time insights, analysis, and alerts to help them leverage the full power of industrial IoT devices.

“It’s all enabled by the ultra-low-power chips that support continuous monitoring,” says Everactive Co-Chief Technology Officer David Wentzloff SM ’02, PhD ’07. “Because our source of power is unlimited, we’re not making tradeoffs like keeping radios off or doing something else [limiting] to save battery life.”

Everactive builds finished products on top of its chips that customers can quickly deploy in large numbers. Its first product monitors steam traps, which release condensate out of steam systems. Such systems are used in a variety of industries, and Everactive’s customers include companies in sectors like oil and gas, paper, and food production. Everactive has also developed a sensor to monitor rotating machinery, like motors and pumps, that runs on the second generation of its battery-free chips.

By avoiding the costs and restrictions associated with other sensors, the company believes it’s well-positioned to play a role in the IoT-powered transition to the factory of the future.

“This is technology that’s totally maintenance free, with no batteries, powered by harvested energy, and always connected to the cloud. There’s so many things you can do with that, it’s hard to wrap your head around,” Wentzloff says.

Breaking free from batteries

Wentzloff and his Everactive co-founder and co-CTO Benton Calhoun SM ’02, PhD ’06 have been working on low-power circuit design for more than a decade, beginning with their time at MIT. They both did their PhD work in the lab of Anantha Chandrakasan, who is currently the Vannevar Bush Professor of Electrical Engineering and Computer Science and the dean of MIT’s School of Engineering. Calhoun’s research focused on low-power digital circuits and memory while Wentzloff’s focused on low power radios.

After earning their PhDs, both men became assistant professors at the schools they attended as undergraduates — Wentzloff at the University of Michigan and Calhoun at the University of Virginia — where they still teach today. Even after settling in different parts of the country, they continued collaborating, applying for joint grants and building circuit-based systems that combined their areas of research.

The collaboration was not an isolated incident: The founders have maintained relationships with many of their contacts from MIT.

“To this day I stay in touch with my colleagues and professors,” Wentzloff says. “It’s a great group to be associated with, especially when you talk about the integrated circuit space. It’s a great community, and I really value and appreciate that experience and those connections that have come out of it. That’s far and away the longest impression MIT has left on my career, those people I continue to stay in touch with. We’re all helping each other out.”

Wentzloff and Calhoun’s academic labs eventually created a battery-free physiological monitor that could track a user’s movement, temperature, heart rate, and other signals and send that data to a phone, all while running on energy harvested from body heat.

“That’s when we decided we should look at commercializing this technology,” Wentzloff says.

In 2014, they partnered with semiconductor industry veteran Brendan Richardson to launch the company, originally called PsiKick.

In the beginning, when Wentzloff describes the company as “three guys and a dog in a garage,” the founders sought to reimagine circuit designs that included features of full computing systems like sensor interfaces, processing power, memory, and radio signals. They also needed to incorporate energy harvesting mechanisms and power management capabilities.

“We wiped the slate clean and had a fresh start,” Wentzloff recalls.

The founders initially attempted to sell their chips to companies to build solutions on top of, but they quickly realized the industry wasn’t familiar enough with battery-free chips.

“There’s an education level to it, because there’s a generation of engineers used to thinking of systems design with battery-operated chips,” Wentzloff says.

The learning curve led the founders to start building their own solutions for customers. Today Everactive offers its sensors as part of a wider service that incorporates wireless networks and data analytics.

The company’s sensors can be powered by small vibrations, lights inside a factory as dim as 100 lux, and heat differentials below 10 degrees Fahrenheit. The devices can sense temperature, acceleration, vibration, pressure, and more.

The company says its sensors cost significantly less to operate than traditional sensors and avoid the maintenance headache that comes with deploying thousands of battery-powered devices.

For instance, Everactive considered the cost of deploying 10,000 traditional sensors. Assuming a three-year battery life, the customer would need to replace an average of 3,333 batteries each year, which comes out to more than nine a day.

The next technological revolution

By saving on maintenance and replacement costs, Everactive customers are able to deploy more sensors. That, combined with the near-continuous operation of those sensors, brings a new level of visibility to operations.

“[Removing restrictions on sensor installations] starts to give you a sixth sense, if you will, about how your overall operations are running,” Calhoun says. “That’s exciting. Customers would like to wave a magic wand and know exactly what’s going on wherever they’re interested. The ability to deploy tens of thousands of sensors gets you close to that magic wand.”

With thousands of Everactive’s steam trap sensors already deployed, Wentzloff believes its sensors for motors and other rotating machinery will make an even bigger impact on the IoT market.

Beyond Everactive’s second generation of products, the founders say their sensors are a few years away from being translucent, flexible, and the size of a postage stamp. At that point customers will simply need to stick the sensors onto machines to start generating data. Such ease of installation and use would have implications far beyond the factory floor.

“You hear about smart transportation, smart agriculture, etc.,” Calhoun says. “IoT has this promise to make all of our environments smart, meaning there’s an awareness of what’s going on and use of that information to have these environments behave in ways that anticipate our needs and are as efficient as possible. We believe battery-less sensing is required and inevitable to bring about that vision, and we’re excited to be a part of that next computing revolution.”

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3 Questions: Historian Emma Teng on face masks as 公德心

As The Washington Post has reported, “at the heart of the dismal U.S. coronavirus response” is a “fraught relationship with masks.” With this “meaning of masks” series, which explores the myriad historic, creative, and cultural meanings of masks, we aim to offer our fellow Americans more ways to appreciate and practice protective masking — a primary means for containing the Covid-19 pandemic.

Emma J. Teng is the T.T. and Wei Fong Chao Professor of Asian Civilizations at MIT and director of MIT Global Languages. A member of the History Section faculty, she teaches courses in Chinese and East Asian culture, migration, Asian American history, and women’s and gender studies. A Margaret MacVicar Faculty Fellow, Teng is the author of “Taiwan's Imagined Geography: Chinese Colonial Travel Writing and Pictures, 1683-1895” (Harvard, 2004) and “Eurasian: Mixed Identities in the United States, China, and Hong Kong, 1842-1943” (University of California, 2013). SHASS Communications spoke with her in July.

Q: Humans use masks for a variety of purposes, ranging from protection to play to artistic performance. Can you provide some examples of masks and masking drawn from your discipline?

A: In East Asia, face masks are worn for a wide range of purposes. Combating urban pollution is a top reason for many, but it’s also common to wear a face mask to prevent allergies, for extra sun protection, or even because you want to run down to the corner shop anonymously and it’s too early to face your neighbors.

Masks can sometimes provide a little extra privacy in densely populated East Asian cities — on the subway or train, for example. In cold and flu season, many consider it prudent to wear a face mask and carry disposable hand wipes to fight contagion. And if you have a cold yourself, you are expected as a matter of basic etiquette to wear a face mask out in public, in the office, and at school in order to protect others from possible infection.

The earliest uses of face coverings in East Asia were likely for sun protection, especially for those working long hours in the fields, or for feminine modesty in eras when it was considered inappropriate for women (particularly elite women) to show their faces in the presence of men outside their family. The use of masks as a public health measure in East Asia seems to have arisen with the 1918 influenza pandemic, becoming commonplace first in Japan. After the SARS outbreak of 2002, face masks became even more common, as did other public health measures such as incorporating antimicrobial materials into such surfaces as escalator handrails.

Q: Many countries have adopted mask-wearing as a politically neutral health measure, but that hasn’t been universally true. Can you comment on the ways that culture impacts the wearing of masks?

A: In an era of globalization, it’s tempting to imagine that culture doesn’t matter, or is invisible. The pandemic, however, has made cultural differences highly visible, in some disturbing ways. Just months into the outbreak of Covid-19, I received an email from a reporter from The Los Angeles Times who was hoping to interview me on the seemingly strange (to many in the U.S.) Asian custom of wearing face masks. Closer to home, I heard people question why Asians in Somerville [Massachusetts] were wearing face masks “as if they aren't the problem.”

With U.S. leaders referring to SARS-CoV-2 as the “Wuhan virus,” “China virus,” or even worse, the “kung flu,” Chinese immigrants and Asian Americans more broadly suddenly found themselves objects of suspicion, xenophobia, and hate. Face masks made them all the more visible. Not surprisingly, the face mask has become one symbol of the “I am not a virus” (#JeNeSuisPasUnVirus) movement that first emerged among Asians in France.

In general, I think too much has been made of the supposed difference between American “individualism” and Asian “collectivism.” However, when it comes to wearing face masks, certain aspects of culture have almost certainly been coming into play. At an MIT Starr Forum faculty panel on “When Culture Meets Covid-19,” Professor Yasheng Huang of the MIT Sloan School suggested that communitarian norms in East Asian countries support the ethos that “doing something for the community good is good for me also.”

This value is known as 公德心: in Mandarin, gongdexin; in Japanese, kootokushin; in Korean, kongdkshim; and in English, public-spiritedness.

Confucianism, a philosophy that has significantly influenced East Asian cultures, encourages respect for elders and care for young children. It would therefore be largely unthinkable to discuss sacrificing older people to the pandemic using a cost-benefit analysis. If wearing a face mask can help protect someone’s grandparents, that is your duty. It is also considered a social responsibility to do one’s part in controlling the pandemic to ensure that schools remain open for the younger generation.

Research that has emerged from East Asia over the past several months supports the efficacy of community mask wearing, even for the asymptomatic or presymptomatic, as a public health measure. Findings of a Hong Kong study published in The Journal of Infection (April 2020) showed that: “Community-wide mask wearing may contribute to the control of Covid-19 by reducing the amount of emission of infected saliva and respiratory droplets from individuals with subclinical or mild Covid-19.”

The authors of “Covid-19 and Public Interest in Face Mask Use,” which appeared in the American Journal of Respiratory and Critical Care Medicine in June, noted that: “In many Asian countries like China and Japan, the use of face masks in this pandemic is ubiquitous and is considered as a hygiene etiquette, whereas in many Western countries, its use in the public is less common.” Comparing rates of infection in East Asia with Western countries such as the United States, their study suggests that early public interest in using face masks “may be an independently important factor in controlling the Covid-19 epidemic on a population scale.”

As an Asian studies scholar, I see this as a valuable opportunity to learn from the approaches and successes of Asian countries — for example, South Korea, Taiwan, and Singapore — in controlling the pandemic. I hope we can observe how these countries look to science to guide their public health policy and responses to the pandemic, in addition to cultural factors that support community mask-wearing. This would be far more productive than blaming the emergence of this novel coronavirus on “weird” Chinese food habits — as we saw with the so-called bat soup controversy and the media attention to wet markets — or stigmatizing mask wearing as a “strange” Asian custom.

Q: Given this history, can you speculate on ways in which people today might explore the creative possibilities of masks that are needed for protection from the virus?

A: I see face masks as an outlet for creativity and self-expression. In early April, when the U.S. Centers for Disease Control and Prevention finally caught up and reversed its position on face masks, surgical masks were impossible to find. The first thing I did after sewing masks for my family was to reach out to my team to say, “Who needs a mask?!”

Owning a sewing machine and a large collection of fabrics, I thought sewing masks would be a useful and creative distraction. It was fun to choose fabrics reflecting my colleagues’ color and pattern preferences and to experiment with different mask designs. Reaching out to various members of the MIT community to see who might need a mask at that time of shortage helped me feel connected while working remotely. Crafting is also a good way to slow down and practice mindfulness.

Among the favorite masks I made are the one for Associate Provost Krystyn Van Vliet, which features floor plans, and the one I call “I Know Why the Caged Bird Sings” (after the book by Maya Angelou) for Associate Professor of Literature Sandy Alexandre. I wanted to thank both of them for their invaluable contributions to our MIT communty during this difficult time.

Another fun, creative outlet was to team up with my friend Associate Dean of Engineering Anette “Peko” Hosoi to develop an online exploration of the science and craft of face masks. This project was a good way to bring together cross-disciplinary knowledge of fabrics, designs, and usage in a very practical way and share with others.

Wearing a face mask yourself is a good way to say “I care about your health” when out in public; using your creativity to make a personalized mask for someone else is another way to say you care.

Prepared by MIT SHASS Communications
Editorial Team: Emily Hiestand and Kathryn O’Neill

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