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    Understanding viral justice

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    In the wake of the Covid-19 pandemic, the word “viral” has a new resonance, and it’s not necessarily positive. Ruha Benjamin, a scholar who investigates the social dimensions of science, medicine, and technology, advocates a shift in perspective. She thinks justice can also be contagious. That’s the premise of Benjamin’s award-winning book “Viral Justice: How We Grow the World We Want,” as she shared with MIT Libraries staff on a June 14 visit. 

    “If this pandemic has taught us anything, it’s that something almost undetectable can be deadly, and that we can transmit it without even knowing,” said Benjamin, professor of African American studies at Princeton University. “Doesn’t this imply that small things, seemingly minor actions, decisions, or habits, could have exponential effects in the other direction, tipping the scales towards justice?” 

    To seek a more just world, Benjamin exhorted library staff to notice the ways exclusion is built into our daily lives, showing examples of park benches with armrests at regular intervals. On the surface they appear welcoming, but they also make lying down — or sleeping — impossible. This idea is taken to the extreme with “Pay and Sit,” an art installation by Fabian Brunsing in the form of a bench that deploys sharp spikes on the seat if the user doesn’t pay a meter. It serves as a powerful metaphor for discriminatory design. 

    “Dr. Benjamin’s keynote was seriously mind-blowing,” said Cherry Ibrahim, human resources generalist in the MIT Libraries. “One part that really grabbed my attention was when she talked about benches purposely designed to prevent unhoused people from sleeping on them. There are these hidden spikes in our community that we might not even realize because they don’t directly impact us.” 

    Benjamin urged the audience to look for those “spikes,” which new technologies can make even more insidious — gender and racial bias in facial recognition, the use of racial data in software used to predict student success, algorithmic bias in health care — often in the guise of progress. She coined the term “the New Jim Code” to describe the combination of coded bias and the imagined objectivity we ascribe to technology. 

    “At the MIT Libraries, we’re deeply concerned with combating inequities through our work, whether it’s democratizing access to data or investigating ways disparate communities can participate in scholarship with minimal bias or barriers,” says Director of Libraries Chris Bourg. “It’s our mission to remove the ‘spikes’ in the systems through which we create, use, and share knowledge.”

    Calling out the harms encoded into our digital world is critical, argues Benjamin, but we must also create alternatives. This is where the collective power of individuals can be transformative. Benjamin shared examples of those who are “re-imagining the default settings of technology and society,” citing initiatives like Data for Black Lives movement and the Detroit Community Technology Project. “I’m interested in the way that everyday people are changing the digital ecosystem and demanding different kinds of rights and responsibilities and protections,” she said.

    In 2020, Benjamin founded the Ida B. Wells Just Data Lab with a goal of bringing together students, educators, activists, and artists to develop a critical and creative approach to data conception, production, and circulation. Its projects have examined different aspects of data and racial inequality: assessing the impact of Covid-19 on student learning; providing resources that confront the experience of Black mourning, grief, and mental health; or developing a playbook for Black maternal mental health. Through the lab’s student-led projects Benjamin sees the next generation re-imagining technology in ways that respond to the needs of marginalized people.

    “If inequity is woven into the very fabric of our society — we see it from policing to education to health care to work — then each twist, coil, and code is a chance for us to weave new patterns, practices, and politics,” she said. “The vastness of the problems that we’re up against will be their undoing.” More

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      Festival of Learning 2023 underscores importance of well-designed learning environments

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      During its first in-person gathering since 2020, MIT’s Festival of Learning 2023 explored how the learning sciences can inform the Institute on how to best support students. Co-sponsored by MIT Open Learning and the Office of the Vice Chancellor (OVC), this annual event celebrates teaching and learning innovations with MIT instructors, students, and staff.

      Bror Saxberg SM ’85, PhD ’89, founder of LearningForge LLC and former chief learning officer at Kaplan, Inc., was invited as keynote speaker, with opening remarks by MIT Chancellor Melissa Nobles and Vice President for Open Learning Eric Grimson, and discussion moderated by Senior Associate Dean of Open Learning Christopher Capozzola. This year’s festival focused on how creating well-designed learning environments using learning engineering can increase learning success.

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      2023 Festival of Learning: Highlights

      Well-designed learning environments are key

      In his keynote speech “Learning Engineering: What We Know, What We Can Do,” Saxberg defined “learning engineering” as the practical application of learning sciences to real-world problems at scale. He said, “High levels can be reached by all learners, given access to well-designed instruction and motivation for enough practice opportunities.”

      Informed by decades of empirical evidence from the field of learning science, Saxberg’s own research, and insights from Kaplan, Inc., Saxberg finds that a hands-on strategy he calls “prepare, practice, perform” delivers better learning outcomes than a traditional “read, write, discuss” approach. Saxberg recommends educators devote at least 60 percent of learning time to hands-on approaches, such as producing, creating, and engaging. Only 20-30 percent of learning time should be spent in the more passive “knowledge acquisition” modes of listening and reading.

      “Here at MIT, a place that relies on data to make informed decisions, learning engineering can provide a framework for us to center in on the learner to identify the challenges associated with learning, and to apply the learning sciences in data-driven ways to improve instructional approaches,” said Nobles. During their opening remarks, Nobles and Grimson both emphasized how learning engineering at MIT is informed by the Institute’s commitment to educating the whole student, which encompasses student well-being and belonging in addition to academic rigor. “What lessons can we take away to change the way we think about education moving forward? This is a chance to iterate,” said Grimson.

      Well-designed learning environments are informed by understanding motivation, considering the connection between long-term and working memory, identifying the range of learners’ prior experience, grounding practice in authentic contexts (i.e., work environments), and using data-driven instructional approaches to iterate and improve.

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      2023 Festival of Learning: Keynote by Bror Saxberg

      Understand learner motivation

      Saxberg asserted that before developing course structures and teaching approaches known to encourage learning, educators must first examine learner motivation. Motivation doesn’t require enjoyment of the subject or task to spur engagement. Similar to how a well-designed physical training program can change your muscle cells, if a learner starts, persists, and exerts mental effort in a well-designed learning environment, they can change their neurons — they learn. Saxberg described four main barriers to learner motivation, and solutions for each:

      The learner doesn’t see the value of the lesson. Ways to address this include helping the learners find value; leveraging the learner’s expertise in another area to better understand the topic at hand; and making the activity itself enjoyable. “Finding value” could be as simple as explaining the practical applications of this knowledge in their future work in the field, or how this lesson prepares learners for their advanced level courses. 
      Self-efficacy for learners who don’t think they’re capable. Educators can point to parallel experiences with similar goals that students may have already achieved in another context. Alternatively, educators can share stories of professionals who have successfully transitioned from one area of expertise to another. 
      “Something” in the learner’s way, such as not having the time, space, or correct materials. This is an opportunity to demonstrate how a learner can use problem-solving skills to find a solution to their perceived problem. As with the barrier of self-efficacy, educators can assure learners that they are in control of the situation by sharing similar stories of those who’ve encountered the same problem and the solution they devised.
      The learner’s emotional state. This is no small barrier to motivation. If a learner is angry, depressed, scared, or grieving, it will be challenging for them to switch their mindset into learning mode. A wide array of emotions require a wide array of possible solutions, from structured conversation techniques to recommending professional help.
      Consider the cognitive load

      Saxberg has found that learning occurs when we use working memory to problem-solve, but our working memory can only process three to five verbal or conscious thoughts at a time. Long-term memory stores knowledge that can be accessed non-verbally and non-consciously, which is why experts appear to remember information effortlessly. Until a learner develops that expertise, extraneous information in a lesson will occupy space in their working memory, running the risk of distracting the learner from the desired learning outcome.

      To accommodate learners’ finite cognitive load, Saxberg suggested the solution of reevaluating which material is essential, then simplifying the exercise or removing unnecessary material accordingly. “That notion of, ‘what do we really need students to be able to do?’ helps you focus,” said Saxberg.

      Another solution is to leverage the knowledge, skills, and interests learners already bring to the course — these long-term memories can scaffold the new material. “What do you have in your head already, what do you love, what’s easy to draw from long-term memory? That would be the starting point for challenging new skills. It’s not the ending point because you want to use your new skills to then find out new things,” Saxberg said. Finally, consider how your course engages with the syllabi. Do you explain the reasoning behind the course structure? Do you show how the exercises or material will be applied to future courses or the field? Do you share best practices for engaging working memory and learning? By acknowledging and empathizing with the practical challenges that learners face, you can remove a barrier from their cognitive load.

      Ground practice in authentic contexts

      Saxberg stated that few experts read textbooks to learn new information — they discover what they need to know while working in the field, using those relevant facts in context. As such, students will have an easier time remembering facts if they’re practicing in relevant or similar environments to their future work.

      If students can practice classifying problems in real work contexts rather than theoretical practice problems, they can build a framework to classify what’s important. That helps students recognize the type of problem they’re trying to solve before trying to solve the problem itself. With enough hands-on practice and examples of how experts use processes and identify which principles are relevant, learners can holistically learn entire procedures. And that learning continues once learners graduate to the workforce: professionals often meet to exchange knowledge at conferences, charrettes, and other gatherings.

      Enhancing teaching at MIT

      The Festival of Learning furthers the Office of the Chancellor’s mission to advance academic innovation that will foster the growth of MIT students. The festival also aligns with the MIT Open Learning’s Residential Education team’s goal of making MIT education more effective and efficient. Throughout the year, their team offers continuous support to MIT faculty and instructors using digital technologies to augment and transform how they teach.

      “We are doubling down on our commitment to continuous growth in how we teach,” said Nobles. More

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      Subtle biases in AI can influence emergency decisions

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      It’s no secret that people harbor biases — some unconscious, perhaps, and others painfully overt. The average person might suppose that computers — machines typically made of plastic, steel, glass, silicon, and various metals — are free of prejudice. While that assumption may hold for computer hardware, the same is not always true for computer software, which is programmed by fallible humans and can be fed data that is, itself, compromised in certain respects.

      Artificial intelligence (AI) systems — those based on machine learning, in particular — are seeing increased use in medicine for diagnosing specific diseases, for example, or evaluating X-rays. These systems are also being relied on to support decision-making in other areas of health care. Recent research has shown, however, that machine learning models can encode biases against minority subgroups, and the recommendations they make may consequently reflect those same biases.

      A new study by researchers from MIT’s Computer Science and Artificial Intelligence Laboratory (CSAIL) and the MIT Jameel Clinic, which was published last month in Communications Medicine, assesses the impact that discriminatory AI models can have, especially for systems that are intended to provide advice in urgent situations. “We found that the manner in which the advice is framed can have significant repercussions,” explains the paper’s lead author, Hammaad Adam, a PhD student at MIT’s Institute for Data Systems and Society. “Fortunately, the harm caused by biased models can be limited (though not necessarily eliminated) when the advice is presented in a different way.” The other co-authors of the paper are Aparna Balagopalan and Emily Alsentzer, both PhD students, and the professors Fotini Christia and Marzyeh Ghassemi.

      AI models used in medicine can suffer from inaccuracies and inconsistencies, in part because the data used to train the models are often not representative of real-world settings. Different kinds of X-ray machines, for instance, can record things differently and hence yield different results. Models trained predominately on white people, moreover, may not be as accurate when applied to other groups. The Communications Medicine paper is not focused on issues of that sort but instead addresses problems that stem from biases and on ways to mitigate the adverse consequences.

      A group of 954 people (438 clinicians and 516 nonexperts) took part in an experiment to see how AI biases can affect decision-making. The participants were presented with call summaries from a fictitious crisis hotline, each involving a male individual undergoing a mental health emergency. The summaries contained information as to whether the individual was Caucasian or African American and would also mention his religion if he happened to be Muslim. A typical call summary might describe a circumstance in which an African American man was found at home in a delirious state, indicating that “he has not consumed any drugs or alcohol, as he is a practicing Muslim.” Study participants were instructed to call the police if they thought the patient was likely to turn violent; otherwise, they were encouraged to seek medical help.

      The participants were randomly divided into a control or “baseline” group plus four other groups designed to test responses under slightly different conditions. “We want to understand how biased models can influence decisions, but we first need to understand how human biases can affect the decision-making process,” Adam notes. What they found in their analysis of the baseline group was rather surprising: “In the setting we considered, human participants did not exhibit any biases. That doesn’t mean that humans are not biased, but the way we conveyed information about a person’s race and religion, evidently, was not strong enough to elicit their biases.”

      The other four groups in the experiment were given advice that either came from a biased or unbiased model, and that advice was presented in either a “prescriptive” or a “descriptive” form. A biased model would be more likely to recommend police help in a situation involving an African American or Muslim person than would an unbiased model. Participants in the study, however, did not know which kind of model their advice came from, or even that models delivering the advice could be biased at all. Prescriptive advice spells out what a participant should do in unambiguous terms, telling them they should call the police in one instance or seek medical help in another. Descriptive advice is less direct: A flag is displayed to show that the AI system perceives a risk of violence associated with a particular call; no flag is shown if the threat of violence is deemed small.  

      A key takeaway of the experiment is that participants “were highly influenced by prescriptive recommendations from a biased AI system,” the authors wrote. But they also found that “using descriptive rather than prescriptive recommendations allowed participants to retain their original, unbiased decision-making.” In other words, the bias incorporated within an AI model can be diminished by appropriately framing the advice that’s rendered. Why the different outcomes, depending on how advice is posed? When someone is told to do something, like call the police, that leaves little room for doubt, Adam explains. However, when the situation is merely described — classified with or without the presence of a flag — “that leaves room for a participant’s own interpretation; it allows them to be more flexible and consider the situation for themselves.”

      Second, the researchers found that the language models that are typically used to offer advice are easy to bias. Language models represent a class of machine learning systems that are trained on text, such as the entire contents of Wikipedia and other web material. When these models are “fine-tuned” by relying on a much smaller subset of data for training purposes — just 2,000 sentences, as opposed to 8 million web pages — the resultant models can be readily biased.  

      Third, the MIT team discovered that decision-makers who are themselves unbiased can still be misled by the recommendations provided by biased models. Medical training (or the lack thereof) did not change responses in a discernible way. “Clinicians were influenced by biased models as much as non-experts were,” the authors stated.

      “These findings could be applicable to other settings,” Adam says, and are not necessarily restricted to health care situations. When it comes to deciding which people should receive a job interview, a biased model could be more likely to turn down Black applicants. The results could be different, however, if instead of explicitly (and prescriptively) telling an employer to “reject this applicant,” a descriptive flag is attached to the file to indicate the applicant’s “possible lack of experience.”

      The implications of this work are broader than just figuring out how to deal with individuals in the midst of mental health crises, Adam maintains.  “Our ultimate goal is to make sure that machine learning models are used in a fair, safe, and robust way.” More

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      Meet the 2022-23 Accenture Fellows

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      Launched in October 2020, the MIT and Accenture Convergence Initiative for Industry and Technology underscores the ways in which industry and technology can collaborate to spur innovation. The five-year initiative aims to achieve its mission through research, education, and fellowships. To that end, Accenture has once again awarded five annual fellowships to MIT graduate students working on research in industry and technology convergence who are underrepresented, including by race, ethnicity, and gender.This year’s Accenture Fellows work across research areas including telemonitoring, human-computer interactions, operations research,  AI-mediated socialization, and chemical transformations. Their research covers a wide array of projects, including designing low-power processing hardware for telehealth applications; applying machine learning to streamline and improve business operations; improving mental health care through artificial intelligence; and using machine learning to understand the environmental and health consequences of complex chemical reactions.As part of the application process, student nominations were invited from each unit within the School of Engineering, as well as from the Institute’s four other schools and the MIT Schwarzman College of Computing. Five exceptional students were selected as fellows for the initiative’s third year.Drew Buzzell is a doctoral candidate in electrical engineering and computer science whose research concerns telemonitoring, a fast-growing sphere of telehealth in which information is collected through internet-of-things (IoT) connected devices and transmitted to the cloud. Currently, the high volume of information involved in telemonitoring — and the time and energy costs of processing it — make data analysis difficult. Buzzell’s work is focused on edge computing, a new computing architecture that seeks to address these challenges by managing data closer to the source, in a distributed network of IoT devices. Buzzell earned his BS in physics and engineering science and his MS in engineering science from the Pennsylvania State University.

      Mengying (Cathy) Fang is a master’s student in the MIT School of Architecture and Planning. Her research focuses on augmented reality and virtual reality platforms. Fang is developing novel sensors and machine components that combine computation, materials science, and engineering. Moving forward, she will explore topics including soft robotics techniques that could be integrated with clothes and wearable devices and haptic feedback in order to develop interactions with digital objects. Fang earned a BS in mechanical engineering and human-computer interaction from Carnegie Mellon University.

      Xiaoyue Gong is a doctoral candidate in operations research at the MIT Sloan School of Management. Her research aims to harness the power of machine learning and data science to reduce inefficiencies in the operation of businesses, organizations, and society. With the support of an Accenture Fellowship, Gong seeks to find solutions to operational problems by designing reinforcement learning methods and other machine learning techniques to embedded operational problems. Gong earned a BS in honors mathematics and interactive media arts from New York University.

      Ruby Liu is a doctoral candidate in medical engineering and medical physics. Their research addresses the growing pandemic of loneliness among older adults, which leads to poor health outcomes and presents particularly high risks for historically marginalized people, including members of the LGBTQ+ community and people of color. Liu is designing a network of interconnected AI agents that foster connections between user and agent, offering mental health care while strengthening and facilitating human-human connections. Liu received a BS in biomedical engineering from Johns Hopkins University.

      Joules Provenzano is a doctoral candidate in chemical engineering. Their work integrates machine learning and liquid chromatography-high resolution mass spectrometry (LC-HRMS) to improve our understanding of complex chemical reactions in the environment. As an Accenture Fellow, Provenzano will build upon recent advances in machine learning and LC-HRMS, including novel algorithms for processing real, experimental HR-MS data and new approaches in extracting structure-transformation rules and kinetics. Their research could speed the pace of discovery in the chemical sciences and benefits industries including oil and gas, pharmaceuticals, and agriculture. Provenzano earned a BS in chemical engineering and international and global studies from the Rochester Institute of Technology. More

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      How artificial intelligence can help combat systemic racism

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      In 2020, Detroit police arrested a Black man for shoplifting almost $4,000 worth of watches from an upscale boutique. He was handcuffed in front of his family and spent a night in lockup. After some questioning, however, it became clear that they had the wrong man. So why did they arrest him in the first place?

      The reason: a facial recognition algorithm had matched the photo on his driver’s license to grainy security camera footage.

      Facial recognition algorithms — which have repeatedly been demonstrated to be less accurate for people with darker skin — are just one example of how racial bias gets replicated within and perpetuated by emerging technologies.

      “There’s an urgency as AI is used to make really high-stakes decisions,” says MLK Visiting Professor S. Craig Watkins, whose academic home for his time at MIT is the Institute for Data, Systems, and Society (IDSS). “The stakes are higher because new systems can replicate historical biases at scale.”

      Watkins, a professor at the University of Texas at Austin and the founding director of the Institute for Media Innovation​, researches the impacts of media and data-based systems on human behavior, with a specific concentration on issues related to systemic racism. “One of the fundamental questions of the work is: how do we build AI models that deal with systemic inequality more effectively?”

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      Artificial Intelligence and the Future of Racial Justice | S. Craig Watkins | TEDxMIT

      Ethical AI

      Inequality is perpetuated by technology in many ways across many sectors. One broad domain is health care, where Watkins says inequity shows up in both quality of and access to care. The demand for mental health care, for example, far outstrips the capacity for services in the United States. That demand has been exacerbated by the pandemic, and access to care is harder for communities of color.

      For Watkins, taking the bias out of the algorithm is just one component of building more ethical AI. He works also to develop tools and platforms that can address inequality outside of tech head-on. In the case of mental health access, this entails developing a tool to help mental health providers deliver care more efficiently.

      “We are building a real-time data collection platform that looks at activities and behaviors and tries to identify patterns and contexts in which certain mental states emerge,” says Watkins. “The goal is to provide data-informed insights to care providers in order to deliver higher-impact services.”

      Watkins is no stranger to the privacy concerns such an app would raise. He takes a user-centered approach to the development that is grounded in data ethics. “Data rights are a significant component,” he argues. “You have to give the user complete control over how their data is shared and used and what data a care provider sees. No one else has access.”

      Combating systemic racism

      Here at MIT, Watkins has joined the newly launched Initiative on Combatting Systemic Racism (ICSR), an IDSS research collaboration that brings together faculty and researchers from the MIT Stephen A. Schwarzman College of Computing and beyond. The aim of the ICSR is to develop and harness computational tools that can help effect structural and normative change toward racial equity.

      The ICSR collaboration has separate project teams researching systemic racism in different sectors of society, including health care. Each of these “verticals” addresses different but interconnected issues, from sustainability to employment to gaming. Watkins is a part of two ICSR groups, policing and housing, that aim to better understand the processes that lead to discriminatory practices in both sectors. “Discrimination in housing contributes significantly to the racial wealth gap in the U.S.,” says Watkins.

      The policing team examines patterns in how different populations get policed. “There is obviously a significant and charged history to policing and race in America,” says Watkins. “This is an attempt to understand, to identify patterns, and note regional differences.”

      Watkins and the policing team are building models using data that details police interventions, responses, and race, among other variables. The ICSR is a good fit for this kind of research, says Watkins, who notes the interdisciplinary focus of both IDSS and the SCC. 

      “Systemic change requires a collaborative model and different expertise,” says Watkins. “We are trying to maximize influence and potential on the computational side, but we won’t get there with computation alone.”

      Opportunities for change

      Models can also predict outcomes, but Watkins is careful to point out that no algorithm alone will solve racial challenges.

      “Models in my view can inform policy and strategy that we as humans have to create. Computational models can inform and generate knowledge, but that doesn’t equate with change.” It takes additional work — and additional expertise in policy and advocacy — to use knowledge and insights to strive toward progress.

      One important lever of change, he argues, will be building a more AI-literate society through access to information and opportunities to understand AI and its impact in a more dynamic way. He hopes to see greater data rights and greater understanding of how societal systems impact our lives.

      “I was inspired by the response of younger people to the murders of George Floyd and Breonna Taylor,” he says. “Their tragic deaths shine a bright light on the real-world implications of structural racism and has forced the broader society to pay more attention to this issue, which creates more opportunities for change.” More

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      New integrative computational neuroscience center established at MIT’s McGovern Institute

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      With the tools of modern neuroscience, researchers can peer into the brain with unprecedented accuracy. Recording devices listen in on the electrical conversations between neurons, picking up the voices of hundreds of cells at a time. Genetic tools allow us to focus on specific types of neurons based on their molecular signatures. Microscopes zoom in to illuminate the brain’s circuitry, capturing thousands of images of elaborately branched dendrites. Functional MRIs detect changes in blood flow to map activity within a person’s brain, generating a complete picture by compiling hundreds of scans.

      This deluge of data provides insights into brain function and dynamics at different levels — molecules, cells, circuits, and behavior — but the insights remain compartmentalized in separate research silos for each level. An innovative new center at MIT’s McGovern Institute for Brain Research aims to leverage them into powerful revelations of the brain’s inner workings.

      The K. Lisa Yang Integrative Computational Neuroscience (ICoN) Center will create advanced mathematical models and computational tools to synthesize the deluge of data across scales and advance our understanding of the brain and mental health.

      The center, funded by a $24 million donation from philanthropist Lisa Yang and led by McGovern Institute Associate Investigator Ila Fiete, will take a collaborative approach to computational neuroscience, integrating cutting-edge modeling techniques and data from MIT labs to explain brain function at every level, from the molecular to the behavioral.

      “Our goal is that sophisticated, truly integrated computational models of the brain will make it possible to identify how ‘control knobs’ such as genes, proteins, chemicals, and environment drive thoughts and behavior, and to make inroads toward urgent unmet needs in understanding and treating brain disorders,” says Fiete, who is also a brain and cognitive sciences professor at MIT.

      “Driven by technologies that generate massive amounts of data, we are entering a new era of translational neuroscience research,” says Yang, whose philanthropic investment in MIT research now exceeds $130 million. “I am confident that the multidisciplinary expertise convened by the ICoN center will revolutionize how we synthesize this data and ultimately understand the brain in health and disease.”

      Connecting the data

      It is impossible to separate the molecules in the brain from their effects on behavior — although those aspects of neuroscience have traditionally been studied independently, by researchers with vastly different expertise. The ICoN Center will eliminate the divides, bringing together neuroscientists and software engineers to deal with all types of data about the brain.

      “The center’s highly collaborative structure, which is essential for unifying multiple levels of understanding, will enable us to recruit talented young scientists eager to revolutionize the field of computational neuroscience,” says Robert Desimone, director of the McGovern Institute. “It is our hope that the ICoN Center’s unique research environment will truly demonstrate a new academic research structure that catalyzes bold, creative research.”

      To foster interdisciplinary collaboration, every postdoc and engineer at the center will work with multiple faculty mentors. In order to attract young scientists and engineers to the field of computational neuroscience, the center will also provide four graduate fellowships to MIT students each year in perpetuity. Interacting closely with three scientific cores, engineers and fellows will develop computational models and technologies for analyzing molecular data, neural circuits, and behavior, such as tools to identify patterns in neural recordings or automate the analysis of human behavior to aid psychiatric diagnoses. These technologies and models will be instrumental in synthesizing data into knowledge and understanding.

      Center priorities

      In its first five years, the ICoN Center will prioritize four areas of investigation: episodic memory and exploration, including functions like navigation and spatial memory; complex or stereotypical behavior, such as the perseverative behaviors associated with autism and obsessive-compulsive disorder; cognition and attention; and sleep. Models of complex behavior will be created in collaboration with clinicians and researchers at Children’s Hospital of Philadelphia.

      The goal, Fiete says, is to model the neuronal interactions that underlie these functions so that researchers can predict what will happen when something changes — when certain neurons become more active or when a genetic mutation is introduced, for example. When paired with experimental data from MIT labs, the center’s models will help explain not just how these circuits work, but also how they are altered by genes, the environment, aging, and disease. These focus areas encompass circuits and behaviors often affected by psychiatric disorders and neurodegeneration, and models will give researchers new opportunities to explore their origins and potential treatment strategies.

      “Lisa Yang is focused on helping the scientific community realize its goals in translational research,” says Nergis Mavalvala, dean of the School of Science and the Curtis and Kathleen Marble Professor of Astrophysics. “With her generous support, we can accelerate the pace of research by connecting the data to the delivery of tangible results.” More

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      Finding common ground in Malden

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      When disparate groups convene around a common goal, exciting things can happen.

      That is the inspiring story unfolding in Malden, Massachusetts, a city of about 60,000 — nearly half people of color — where a new type of community coalition continues to gain momentum on its plan to build a climate-resilient waterfront park along its river. The Malden River Works (MRW) project, recipient of the inaugural Leventhal City Prize, is seeking to connect to a contiguous greenway network where neighboring cities already have visitors coming to their parks and enjoying recreational boating. More important, the MRW is changing the model for how cities address civic growth, community engagement, equitable climate resilience, and environmental justice.                                                                                        

      The MRW’s steering committee consists of eight resident leaders of color, a resident environmental advocate, and three city representatives. One of the committee’s primary responsibilities is providing direction to the MRW’s project team, which includes urban designers, watershed and climate resilience planners, and a community outreach specialist. MIT’s Kathleen Vandiver, director of the Community Outreach Education and Engagement Core at MIT’s Center for Environmental Health Sciences (CEHS), and Marie Law Adams MArch ’06, a lecturer in the School of Architecture and Planning’s Department of Urban Studies and Planning (DUSP), serve on the project team.

      “This governance structure is somewhat unusual,” says Adams. “More typical is having city government as the primary decision-maker. It is important that one of the first things our team did was build a steering committee that is the decision maker on this project.”

      Evan Spetrini ’18 is the senior planner and policy manager for the Malden Redevelopment Authority and sits on both the steering committee and project team. He says placing the decision-making power with the steering committee and building it to be representative of marginalized communities was intentional. 

      “Changing that paradigm of power and decision-making in planning processes was the way we approached social resilience,” says Spetrini. “We have always intended this project to be a model for future planning projects in Malden.”

      This model ushers in a new history chapter for a city founded in 1640.

      Located about six miles north of Boston, Malden was home to mills and factories that used the Malden River for power, and a site for industrial waste over the last two centuries. Decades after the city’s industrial decline, there is little to no public access to the river. Many residents were not even aware there was a river in their city. Before the project was under way, Vandiver initiated a collaborative effort to evaluate the quality of the river’s water. Working with the Mystic River Watershed Association, Gradient Corporation, and CEHS, water samples were tested and a risk analysis conducted.

      “Having the study done made it clear the public could safely enjoy boating on the water,” says Vandiver. “It was a breakthrough that allowed people to see the river as an amenity.”

      A team effort

      Marcia Manong had never seen the river, but the Malden resident was persuaded to join the steering committee with the promise the project would be inclusive and of value to the community. Manong has been involved with civic engagement most of her life in the United States and for 20 years in South Africa.

      “It wasn’t going to be a marginalized, token-ized engagement,” says Manong. “It was clear to me that they were looking for people that would actually be sitting at the table.”

      Manong agreed to recruit additional people of color to join the team. From the beginning, she says, language was a huge barrier, given that nearly half of Malden’s residents do not speak English at home. Finding the translation efforts at their public events to be inadequate, the steering committee directed more funds to be made available for translation in several languages when public meetings began being held over Zoom this past year.

      “It’s unusual for most cities to spend this money, but our population is so diverse that we require it,” says Manong. “We have to do it. If the steering committee wasn’t raising this issue with the rest of the team, perhaps this would be overlooked.”

      Another alteration the steering committee has made is how the project engages with the community. While public attendance at meetings had been successful before the pandemic, Manong says they are “constantly working” to reach new people. One method has been to request invitations to attend the virtual meetings of other organizations to keep them apprised of the project.

      “We’ve said that people feel most comfortable when they’re in their own surroundings, so why not go where the people are instead of trying to get them to where we are,” says Manong.

      Buoyed by the $100,000 grant from MIT’s Norman B. Leventhal Center for Advanced Urbanism (LCAU) in 2019, the project team worked with Malden’s Department of Public Works, which is located along the river, to redesign its site and buildings and to study how to create a flood-resistant public open space as well as an elevated greenway path, connecting with other neighboring cities’ paths. The park’s plans also call for 75 new trees to reduce urban heat island effect, open lawn for gathering, and a dock for boating on the river.

      “The storm water infrastructure in these cities is old and isn’t going to be able to keep up with increased precipitation,” says Adams. “We’re looking for ways to store as much water as possible on the DPW site so we can hold it and release it more gradually into the river to avoid flooding.”

      The project along the 2.3-mile-long river continues to receive attention. Recently, the city of Malden was awarded a 2021 Accelerating Climate Resilience Grant of more than $50,000 from the state’s Metropolitan Area Planning Council and the Barr Foundation to support the project. Last fall, the project was awarded a $150,015 Municipal Vulnerability Preparedness Action Grant. Both awards are being directed to fund engineering work to refine the project’s design.

      “We — and in general, the planning profession — are striving to create more community empowerment in decision-making as to what happens to their community,” says Spetrini. “Putting the power in the community ensures that it’s actually responding to the needs of the community.”

      Contagious enthusiasm

      Manong says she’s happy she got involved with the project and believes the new governance structure is making a difference.

      “This project is definitely engaging with communities of color in a manner that is transformative and that is looking to build a long-lasting power dynamic built on trust,” she says. “It’s a new energized civic engagement and we’re making that happen. It’s very exciting.”

      Spetrini finds the challenge of creating an open space that’s publicly accessible and alongside an active work site professionally compelling.

      “There is a way to preserve the industrial employment base while also giving the public greater access to this natural resource,” he says. “It has real implications for other communities to follow this type of model.”

      Despite the pandemic this past year, enthusiasm for the project is palpable. For Spetrini, a Malden resident, it’s building “the first significant piece of what has been envisioned as the Malden River Greenway.” Adams sees the total project as a way to build social resilience as well as garnering community interest in climate resilience. For Vandiver, it’s the implications for improved community access.

      “From a health standpoint, everybody has learned from Covid-19 that the health aspects of walking in nature are really restorative,” says Vandiver. “Creating greater green space gives more attention to health issues. These are seemingly small side benefits, but they’re huge for mental health benefits.”

      Leventhal City Prize’s next cycle

      The Leventhal City Prize was established by the LCAU to catalyze innovative, interdisciplinary urban design, and planning approaches worldwide to improve both the environment and the quality of life for residents. Support for the LCAU was provided by the Muriel and Norman B. Leventhal Family Foundation and the Sherry and Alan Leventhal Family Foundation.

      “We’re thrilled with inaugural recipients of the award and the extensive work they’ve undertaken that is being held up as an exemplary model for others to learn from,” says Sarah Williams, LCAU director and a professor in DUSP. “Their work reflects the prize’s intent. We look forward to catalyzing these types of collaborative partnership in the next prize cycle.”

      Submissions for the next cycle of the Leventhal City Prize will open in early 2022.    More