Markus Andrews

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    Supporting sustainability, digital health, and the future of work

    The MIT and Accenture Convergence Initiative for Industry and Technology has selected three new research projects that will receive support from the initiative. The research projects aim to accelerate progress in meeting complex societal needs through new business convergence insights in technology and innovation.

    Established in MIT’s School of Engineering and now in its third year, the MIT and Accenture Convergence Initiative is furthering its mission to bring together technological experts from across business and academia to share insights and learn from one another. Recently, Thomas W. Malone, the Patrick J. McGovern (1959) Professor of Management, joined the initiative as its first-ever faculty lead. The research projects relate to three of the initiative’s key focus areas: sustainability, digital health, and the future of work.

    “The solutions these research teams are developing have the potential to have tremendous impact,” says Anantha Chandrakasan, dean of the School of Engineering and the Vannevar Bush Professor of Electrical Engineering and Computer Science. “They embody the initiative’s focus on advancing data-driven research that addresses technology and industry convergence.”

    “The convergence of science and technology driven by advancements in generative AI, digital twins, quantum computing, and other technologies makes this an especially exciting time for Accenture and MIT to be undertaking this joint research,” says Kenneth Munie, senior managing director at Accenture Strategy, Life Sciences. “Our three new research projects focusing on sustainability, digital health, and the future of work have the potential to help guide and shape future innovations that will benefit the way we work and live.”

    The MIT and Accenture Convergence Initiative charter project researchers are described below.

    Accelerating the journey to net zero with industrial clusters

    Jessika Trancik is a professor at the Institute for Data, Systems, and Society (IDSS). Trancik’s research examines the dynamic costs, performance, and environmental impacts of energy systems to inform climate policy and accelerate beneficial and equitable technology innovation. Trancik’s project aims to identify how industrial clusters can enable companies to derive greater value from decarbonization, potentially making companies more willing to invest in the clean energy transition.

    To meet the ambitious climate goals that have been set by countries around the world, rising greenhouse gas emissions trends must be rapidly reversed. Industrial clusters — geographically co-located or otherwise-aligned groups of companies representing one or more industries — account for a significant portion of greenhouse gas emissions globally. With major energy consumers “clustered” in proximity, industrial clusters provide a potential platform to scale low-carbon solutions by enabling the aggregation of demand and the coordinated investment in physical energy supply infrastructure.

    In addition to Trancik, the research team working on this project will include Aliza Khurram, a postdoc in IDSS; Micah Ziegler, an IDSS research scientist; Melissa Stark, global energy transition services lead at Accenture; Laura Sanderfer, strategy consulting manager at Accenture; and Maria De Miguel, strategy senior analyst at Accenture.

    Eliminating childhood obesity

    Anette “Peko” Hosoi is the Neil and Jane Pappalardo Professor of Mechanical Engineering. A common theme in her work is the fundamental study of shape, kinematic, and rheological optimization of biological systems with applications to the emergent field of soft robotics. Her project will use both data from existing studies and synthetic data to create a return-on-investment (ROI) calculator for childhood obesity interventions so that companies can identify earlier returns on their investment beyond reduced health-care costs.

    Childhood obesity is too prevalent to be solved by a single company, industry, drug, application, or program. In addition to the physical and emotional impact on children, society bears a cost through excess health care spending, lost workforce productivity, poor school performance, and increased family trauma. Meaningful solutions require multiple organizations, representing different parts of society, working together with a common understanding of the problem, the economic benefits, and the return on investment. ROI is particularly difficult to defend for any single organization because investment and return can be separated by many years and involve asymmetric investments, returns, and allocation of risk. Hosoi’s project will consider the incentives for a particular entity to invest in programs in order to reduce childhood obesity.

    Hosoi will be joined by graduate students Pragya Neupane and Rachael Kha, both of IDSS, as well a team from Accenture that includes Kenneth Munie, senior managing director at Accenture Strategy, Life Sciences; Kaveh Safavi, senior managing director in Accenture Health Industry; and Elizabeth Naik, global health and public service research lead.

    Generating innovative organizational configurations and algorithms for dealing with the problem of post-pandemic employment

    Thomas Malone is the Patrick J. McGovern (1959) Professor of Management at the MIT Sloan School of Management and the founding director of the MIT Center for Collective Intelligence. His research focuses on how new organizations can be designed to take advantage of the possibilities provided by information technology. Malone will be joined in this project by John Horton, the Richard S. Leghorn (1939) Career Development Professor at the MIT Sloan School of Management, whose research focuses on the intersection of labor economics, market design, and information systems. Malone and Horton’s project will look to reshape the future of work with the help of lessons learned in the wake of the pandemic.

    The Covid-19 pandemic has been a major disrupter of work and employment, and it is not at all obvious how governments, businesses, and other organizations should manage the transition to a desirable state of employment as the pandemic recedes. Using natural language processing algorithms such as GPT-4, this project will look to identify new ways that companies can use AI to better match applicants to necessary jobs, create new types of jobs, assess skill training needed, and identify interventions to help include women and other groups whose employment was disproportionately affected by the pandemic.

    In addition to Malone and Horton, the research team will include Rob Laubacher, associate director and research scientist at the MIT Center for Collective Intelligence, and Kathleen Kennedy, executive director at the MIT Center for Collective Intelligence and senior director at MIT Horizon. The team will also include Nitu Nivedita, managing director of artificial intelligence at Accenture, and Thomas Hancock, data science senior manager at Accenture. More

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    M’Care and MIT students join forces to improve child health in Nigeria

    Through a collaboration between M’Care, a 2021 Health Security and Pandemics Solver team, and students from MIT, the landscape of child health care in Nigeria could undergo a transformative change, wherein the power of data is harnessed to improve child health outcomes in economically disadvantaged communities. 

    M’Care is a mobile application of Promane and Promade Limited, developed by Opeoluwa Ashimi, which gives community health workers in Nigeria real-time diagnostic and treatment support. The application also creates a dashboard that is available to government health officials to help identify disease trends and deploy timely interventions. As part of its work, M’Care is working to mitigate malnutrition by providing micronutrient powder, vitamin A, and zinc to children below the age of 5. To help deepen its impact, Ashimi decided to work with students in the MIT Computer Science and Artificial Intelligence Laboratory (CSAIL) course 6.S897 (Machine Learning for Healthcare) — instructed by professors Peter Szolovits and Manolis Kellis — to leverage data in order to improve nutrient delivery to children across Nigeria. The collaboration also enabled students to see real-world applications for data analysis in the health care space.

    A meeting of minds: M’Care, MIT, and national health authorities

    “Our primary goal for collaborating with the ML for Health team was to spot the missing link in the continuum of care. With over 1 million cumulative consultations that qualify for a continuum of care evaluation, it was important to spot why patients could be lost to followup, prevent this, and ensure completion of care to successfully address the health needs of our patients,” says Ashimi, founder and CEO of M’Care.

    In May 2023, Ashimi attended a meeting that brought together key national stakeholders, including the representatives of the National Ministry of Health in Nigeria. This gathering served as a platform to discuss the profound impact of M’Care’s and ML for Health team’s collaboration — bolstered by data analysis provided on dosage regimens and a child’s age to enhance continuum of care with its attendant impact on children’s health, particularly in relation to brain development with regards to the use of essential micronutrients. The data analyzed by the students using ML methods that were shared during the meeting provided strong supporting evidence to individualize dosage regimens for children based on their age in months for the ANRIN project — a national nutrition project supported by the World Bank — as well as policy decisions to extend months of coverage for children, redefining health care practices in Nigeria.

    MIT students drive change by harnessing the power of data

    At the heart of this collaboration lies the contribution of MIT students. Armed with their dedication and skill in data analysis and machine learning, they played a pivotal role in helping M’Care analyze their data and prepare for their meeting with the Ministry of Health. Their most significant findings included ways to identify patients at risk of not completing their full course of micronutrient powder and/or vitamin A, and identifying gaps in M’Care’s data, such as postdated delivery dates and community demographics. These findings are already helping M’Care better plan its resources and adjust the scope of its program to ensure more children complete the intervention.

    Darcy Kim, an undergraduate at Wellesley College studying math and computer science, who is cross-registered for the MIT machine learning course, expresses enthusiasm about the practical applications found within the project: “To me, data and math is storytelling, and the story is why I love studying it. … I learned that data exploration involves asking questions about how the data is collected, and that surprising patterns that arise often have a qualitative explanation. Impactful research requires radical collaboration with the people the research intends to help. Otherwise, these qualitative explanations get lost in the numbers.”

    Joyce Luo, a first-year operations research PhD student at the Operations Research Center at MIT, shares similar thoughts about the project: “I learned the importance of understanding the context behind data to figure out what kind of analysis might be most impactful. This involves being in frequent contact with the company or organization who provides the data to learn as much as you can about how the data was collected and the people the analysis could help. Stepping back and looking at the bigger picture, rather than just focusing on accuracy or metrics, is extremely important.”

    Insights to implementation: A new era for micronutrient dosing

    As a direct result of M’Care’s collaboration with MIT, policymakers revamped the dosing scheme for essential micronutrient administration for children in Nigeria to prevent malnutrition. M’Care and MIT’s data analysis unearthed critical insights into the limited frequency of medical visits caused by late-age enrollment. 

    “One big takeaway for me was that the data analysis portion of the project — doing a deep dive into the data; understanding, analyzing, visualizing, and summarizing the data — can be just as important as building the machine learning models. M’Care shared our data analysis with the National Ministry of Health, and the insights from it drove them to change their dosing scheme and schedule for delivering micronutrient powder to young children. This really showed us the value of understanding and knowing your data before modeling,” shares Angela Lin, a second-year PhD student at the Operations Research Center.

    Armed with this knowledge, policymakers are eager to develop an optimized dosing scheme that caters to the unique needs of children in disadvantaged communities, ensuring maximum impact on their brain development and overall well-being.

    Siddharth Srivastava, M’Care’s corporate technology liaison, shares his gratitude for the MIT student’s input. “Collaborating with enthusiastic and driven students was both empowering and inspiring. Each of them brought unique perspectives and technical skills to the table. Their passion for applying machine learning to health care was evident in their unwavering dedication and proactive approach to problem-solving.”

    Forging a path to impact

    The collaboration between M’Care and MIT exemplifies the remarkable achievements that arise when academia, innovative problem-solvers, and policy authorities unite. By merging academic rigor with real-world expertise, this partnership has the potential to revolutionize child health care not only in Nigeria but also in similar contexts worldwide.

    “I believe applying innovative methods of machine learning, data gathering, instrumentation, and planning to real problems in the developing world can be highly effective for those countries and highly motivating for our students. I was happy to have such a project in our class portfolio this year and look forward to future opportunities,” says Peter Szolovits, professor of computer science and engineering at MIT.

    By harnessing the power of data, innovation, and collective expertise, this collaboration between M’Care and MIT has the potential to improve equitable child health care in Nigeria. “It has been so fulfilling to see how our team’s work has been able to create even the smallest positive impact in such a short period of time, and it has been amazing to work with a company like Promane and Promade Limited that is so knowledgeable and caring for the communities that they serve,” shares Elizabeth Whittier, a second-year PhD electrical engineering student at MIT. More

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    Artificial intelligence for augmentation and productivity

    The MIT Stephen A. Schwarzman College of Computing has awarded seed grants to seven projects that are exploring how artificial intelligence and human-computer interaction can be leveraged to enhance modern work spaces to achieve better management and higher productivity.

    Funded by Andrew W. Houston ’05 and Dropbox Inc., the projects are intended to be interdisciplinary and bring together researchers from computing, social sciences, and management.

    The seed grants can enable the project teams to conduct research that leads to bigger endeavors in this rapidly evolving area, as well as build community around questions related to AI-augmented management.

    The seven selected projects and research leads include:

    “LLMex: Implementing Vannevar Bush’s Vision of the Memex Using Large Language Models,” led by Patti Maes of the Media Lab and David Karger of the Department of Electrical Engineering and Computer Science (EECS) and the Computer Science and Artificial Intelligence Laboratory (CSAIL). Inspired by Vannevar Bush’s Memex, this project proposes to design, implement, and test the concept of memory prosthetics using large language models (LLMs). The AI-based system will intelligently help an individual keep track of vast amounts of information, accelerate productivity, and reduce errors by automatically recording their work actions and meetings, supporting retrieval based on metadata and vague descriptions, and suggesting relevant, personalized information proactively based on the user’s current focus and context.

    “Using AI Agents to Simulate Social Scenarios,” led by John Horton of the MIT Sloan School of Management and Jacob Andreas of EECS and CSAIL. This project imagines the ability to easily simulate policies, organizational arrangements, and communication tools with AI agents before implementation. Tapping into the capabilities of modern LLMs to serve as a computational model of humans makes this vision of social simulation more realistic, and potentially more predictive.

    “Human Expertise in the Age of AI: Can We Have Our Cake and Eat it Too?” led by Manish Raghavan of MIT Sloan and EECS, and Devavrat Shah of EECS and the Laboratory for Information and Decision Systems. Progress in machine learning, AI, and in algorithmic decision aids has raised the prospect that algorithms may complement human decision-making in a wide variety of settings. Rather than replacing human professionals, this project sees a future where AI and algorithmic decision aids play a role that is complementary to human expertise.

    “Implementing Generative AI in U.S. Hospitals,” led by Julie Shah of the Department of Aeronautics and Astronautics and CSAIL, Retsef Levi of MIT Sloan and the Operations Research Center, Kate Kellog of MIT Sloan, and Ben Armstrong of the Industrial Performance Center. In recent years, studies have linked a rise in burnout from doctors and nurses in the United States with increased administrative burdens associated with electronic health records and other technologies. This project aims to develop a holistic framework to study how generative AI technologies can both increase productivity for organizations and improve job quality for workers in health care settings.

    “Generative AI Augmented Software Tools to Democratize Programming,” led by Harold Abelson of EECS and CSAIL, Cynthia Breazeal of the Media Lab, and Eric Klopfer of the Comparative Media Studies/Writing. Progress in generative AI over the past year is fomenting an upheaval in assumptions about future careers in software and deprecating the role of coding. This project will stimulate a similar transformation in computing education for those who have no prior technical training by creating a software tool that could eliminate much of the need for learners to deal with code when creating applications.

    “Acquiring Expertise and Societal Productivity in a World of Artificial Intelligence,” led by David Atkin and Martin Beraja of the Department of Economics, and Danielle Li of MIT Sloan. Generative AI is thought to augment the capabilities of workers performing cognitive tasks. This project seeks to better understand how the arrival of AI technologies may impact skill acquisition and productivity, and to explore complementary policy interventions that will allow society to maximize the gains from such technologies.

    “AI Augmented Onboarding and Support,” led by Tim Kraska of EECS and CSAIL, and Christoph Paus of the Department of Physics. While LLMs have made enormous leaps forward in recent years and are poised to fundamentally change the way students and professionals learn about new tools and systems, there is often a steep learning curve which people have to climb in order to make full use of the resource. To help mitigate the issue, this project proposes the development of new LLM-powered onboarding and support systems that will positively impact the way support teams operate and improve the user experience. More

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    MIT at the 2023 Venice Biennale

    The Venice Architecture Biennale, the world’s largest and most visited exhibition focusing on architecture, is once again featuring work by many MIT faculty, students, and alumni. On view through Nov. 26, the 2023 biennale, curated by Ghanaian-Scottish architect, academic, and novelist Lesley Lokko, is showcasing projects responding to the theme of “The Laboratory of Change.”

    Architecture and Planning and curator of the previous Venice Biennale. “Our students, faculty, and alumni have responded to the speculative theme with innovative projects at a range of scales and in varied media.”

    Below are descriptions of MIT-related projects and activities.

    MIT faculty participants

    Xavi Laida Aguirre, assistant professor of architecture

    Project: Everlasting Plastics

    Project description: SPACES, a nonprofit alternative art organization based in Cleveland, Ohio, and the U.S. Department of State’s Bureau of Educational and Cultural Affairs are behind the U.S. Pavilion’s exhibition at this year’s biennale. The theme, Everlasting Plastics, provides a platform for artists and designers to engage audiences in reframing the overabundance of plastic detritus in our waterways, landfills, and streets as a rich resource. Aguirre’s installation covers two rooms and holds a series of partial scenographies examining indoor proofing materials such as coatings, rubbers, gaskets, bent aluminum, silicone, foam, cement board, and beveled edges.

    Yolande Daniels, associate professor of architecture

    Project: The BLACK City Astrolabe: A Constellation of African Diasporic Women

    Project description: From the multiple displacements of race and gender, enter “The BLACK City Astrolabe,” a space-time field comprised of a 3D map and a 24-hour cycle of narratives that reorder the forces of subjugation, devaluation, and displacement through the spaces and events of African diasporic women. The diaspora map traces the flows of descendants of Africa (whether voluntary or forced) atop the visible tension between the mathematical regularity of meridians of longitude and the biases of international date lines.

    In this moment we are running out of time. The meridians and timeline decades are indexed to an infinite conical projection metered in decades. It structures both the diaspora map and timeline and serves as a threshold to project future structures and events. “The BLACK City Astrolabe” is a vehicle to proactively contemplate things that have happened, that are happening, and that will happen. Yesterday, a “Black” woman went to the future, and here she is.

    Mark Jarzombek, professor of architecture

    Project: Kishkindha NY

    Project description: “Kishkindha NY (Office of (Un)Certainty Research: Mark Jarzombek and Vikramaditya Parakash)” is inspired by an imagined forest-city as described in the ancient Indian text the Ramayana. It comes into being not through the limitations of human agency, but through a multi-species creature that destroys and rebuilds. It is exhibited as a video (Space, Time, Existence) and as a special dance performance.

    Ana Miljački, professor of architecture

    Team: Ana Miljački, professor of architecture and director of Critical Broadcasting Lab, MIT; Ous Abou Ras, MArch candidate; Julian Geltman, MArch; Recording and Design, faculty of Dramatic Arts, Belgrade; Calvin Zhong, MArch candidate. Sound design and production: Pavle Dinulović, assistant professor, Department of Sound Recording and Design, University of Arts in Belgrade.

    Collaborators: Melika Konjičanin, researcher, faculty of architecture, Sarajevo; Ana Martina Bakić, assistant professor, head of department of drawing and visual design, faculty of architecture, Zagreb; Jelica Jovanović, Grupa Arhitekata, Belgrade; Andrew Lawler, Belgrade; Sandro Đukić, CCN Images, Zagreb; Other Tomorrows, Boston.

    Project: The Pilgrimage/Pionirsko hodočašće

    Project description:  The artifacts that constitute Yugoslavia’s socialist architectural heritage, and especially those instrumental in the ideological wiring of several postwar generations for anti-fascism and inclusive living, have been subject to many forms of local and global political investment in forgetting their meaning, as well as to vandalism. The “Pilgrimage” synthesizes “memories” from Yugoslavian childhood visits to myriad postwar anti-fascist memorial monuments and offers them in a shifting and spatial multi-channel video presentation accompanied by a nonlinear documentary soundscape, presenting thus anti-fascism and unity as political and activist positions available (and necessary) today, for the sake of the future. Supported by: MIT Center for Art, Science, and Technology (CAST) Mellon Faculty Grant.

    Adèle Naudé Santos, professor of architecture, planning, and urban design; and Mohamad Nahleh, lecturer in architecture and urbanism; in collaboration with the Beirut Urban Lab at the American University of Beirut

    MIT research team: Ghida El Bsat, Joude Mabsout, Sarin Gacia Vosgerichian, Lasse Rau

    Project: Housing as Infrastructure

    Project description: On Aug. 4, 2020, an estimated 2,750 tons of ammonium nitrate stored at the Port of Beirut exploded, resulting in the deaths of more than 200 people and the devastation of port-adjacent neighborhoods. With over 200,000 housing units in disrepair, exploitative real estate ventures, and the lack of equitable housing policies, we viewed the port blast as a potential escalation of the mechanisms that have produced the ongoing affordable housing crisis across the city. 

    The Dar Group requested proposals to rethink the affected part of the city, through MIT’s Norman B. Leventhal Center for Advanced Urbanism. To best ground our design proposal, we invited the Beirut Urban Lab at the American University of Beirut to join us. We chose to work on the heavily impacted low-rise and high-density neighborhood of Mar Mikhael. Our resultant urban strategy anchors housing within a corridor of shared open spaces. Housing is inscribed within this network and sustained through an adaptive system defined by energy-efficiency and climate responsiveness. Cross-ventilation sweeps through the project on all sides, with solar panel lined roofs integrated to always provide adequate levels of electricity for habitation. These strategies are coupled with an array of modular units designed to echo the neighborhood’s intimate quality — all accessible through shared ramps and staircases. Within this context, housing itself becomes the infrastructure, guiding circulation, managing slopes, integrating green spaces, and providing solar energy across the community. 

    Rafi Segal, associate professor of architecture and urbanism, director of the Future Urban Collectives Lab, director of the SMArchS program; and Susannah Drake.

    Contributors: Olivia Serra, William Minghao Du

    Project:  From Redlining to Blue Zoning: Equity and Environmental Risk, Miami 2100 (2021)

    Project description: As part of Susannah Drake and Rafi Segal’s ongoing work on “Coastal Urbanism,” this project examines the legacy of racial segregation in South Florida and the existential threat that climate change poses to communities in Miami. Through models of coops and community-owned urban blocks, this project seeks to empower formerly disenfranchised communities with new methods of equity capture, allowing residents whose parents and grandparents suffered from racial discrimination to build wealth and benefit from increased real estate value and development.

    Nomeda Urbonas, Art, Culture, and Technology research affiliate; and Gediminas Urbonas, ACT associate professor

    Project: The Swamp Observatory

    Project description: “The Swamp Observatory” augmented reality app is a result of two-year collaboration with a school in Gotland Island in the Baltic Sea, arguably the most polluted sea in the world. Developed as a conceptual playground and a digital tool to augment reality with imaginaries for new climate commons, the app offers new perspective to the planning process, suggesting eco-monsters as emergent ecology for the planned stormwater ponds in the new sustainable city. 

    Sarah Williams, associate professor, technology and urban planning

    Team members: listed here.

    Project: DISTANCE UNKNOWN: RISKS AND OPPORTUNITIES OF MIGRATION IN THE AMERICAS 

    Project description: On view are visualizations made by the MIT Civic Data Design Lab and the United Nations World Food Program that helped to shape U.S. migration policy. The exhibition is built from a unique dataset collected from 4,998 households surveyed in El Salvador, Guatemala, and Honduras. A tapestry woven out of money and constructed by the hands of Central America migrants illustrates that migrants spent $2.2 billion to migrate from Central America in 2021.

    MIT student curators

    Carmelo Ignaccolo, PhD candidate, Department of Urban Studies and Planning (DUSP)

    Curator: Carmelo Ignaccolo; advisor: Sarah Williams; researchers: Emily Levenson (DUSP), Melody Phu (MIT), Leo Saenger (Harvard University), Yuke Zheng (Harvard); digital animation designer: Ting Zhang

    Exhibition Design Assistant: Dila Ozberkman (architecture and DUSP)

    Project: The Consumed City 

    Project description: “The Consumed City” narrates a spatial investigation of “overtourism” in the historic city of Venice by harnessing granular data on lodging, dining, and shopping. The exhibition presents two large maps and digital animations to showcase the complexity of urban tourism and to reveal the spatial interplay between urban tourism and urban features, such as landmarks, bridges, and street patterns. By leveraging by-product geospatial datasets and advancing visualization techniques, “The Consumed City” acts as a prototype to call for novel policymaking tools in cities “consumed” by “overtourism.”

    MIT-affiliated auxiliary events

    Rania Ghosn, associate professor of architecture and urbanism, El Hadi Jazairy, Anhong Li, and Emma Jurczynski, with initial contributions from Marco Nieto and Zhifei Xu. Graphic design: Office of Luke Bulman.

    Project: Climate Inheritance

    Project description: “Climate Inheritance” is a speculative design research publication that reckons with the complexity of “heritage” and “world” in the Anthropocene Epoch. The impacts of climate change on heritage sites — from Venice flooding to extinction in the Galapagos Islands — have garnered empathetic attention in a media landscape that has otherwise mostly failed to communicate the urgency of the climate crisis. In a strategic subversion of the media aura of heritage, the project casts World Heritage sites as narrative figures to visualize pervasive climate risks all while situating the present emergency within the wreckage of other ends of worlds, replete with the salvages of extractivism, racism, and settler colonialism.   

    Rebuilding Beirut: Using Data to Co-Design a New Future

    SA+P faculty, researchers, and students are participating in the sixth biennial architecture exhibition “Time Space Existence,” presented by the European Cultural Center. The exhibit showcases three collaborative research and design proposals that support the rebuilding efforts in Beirut following the catastrophic explosion at the Port of Beirut in August 2020.

    “Living Heritage Atlas” captures the significance and vulnerability of Beirut’s cultural heritage. 

    “City Scanner” tracks the environmental impacts of the explosion and the subsequent rebuilding efforts. “Community Streets” supports the redesign of streets and public space. 

    The work is supported by the Dar Group Urban Seed Grant Fund at MIT’s Norman B. Leventhal Center for Advanced Urbanism.

    Team members:Living Heritage AtlasCivic Data Design Lab and Future Heritage Lab at MITAssociate Professor Sarah Williams, co-principal investigator (PI)Associate Professor Azra Aksamija, co-PICity Scanner Senseable City Lab at MIT with the American University of Beirut and FAE Technology Professor Carlo Ratti, co-PIFábio Duarte, co-PISimone Mora, research and project leadCommunity Streets City Form Lab at MIT with the American University of BeirutAssociate Professor Andres Sevtsuk, co-PIProfessor Maya Abou-Zeid, co-PISchool of Architecture and Planning alumni participants   Rodrigo Escandón Cesarman SMArchS Design ’20 (co-curator, Mexican Pavilion)Felecia Davis PhD ’17 Design and Computation, SOFTLAB@PSU (Penn State University)Jaekyung Jung SM ’10, (with the team for the Korean pavilion)Vijay Rajkumar MArch ’22 (with the team for the Bahrain Pavilion)

    Other MIT alumni participants

    Basis with GKZ

    Team: Emily Mackevicius PhD ’18, brain and cognitive sciences, with Zenna Tavares, Kibwe Tavares, Gaika Tavares, and Eli Bingham

    Project description: The nonprofit research group works on rethinking AI as a “reasoning machine.” Their two goals are to develop advanced technological models and to make society able to tackle “intractable problems.” Their approach to technology is founded less on pattern elaboration than on the Bayes’ hypothesis, the ability of machines to work on abductive reasoning, which is the same used by the human mind. Two city-making projects model cities after interaction between experts and stakeholders, and representation is at the heart of the dialogue. More

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    To improve solar and other clean energy tech, look beyond hardware

    To continue reducing the costs of solar energy and other clean energy technologies, scientists and engineers will likely need to focus, at least in part, on improving technology features that are not based on hardware, according to MIT researchers. They describe this finding and the mechanisms behind it today in Nature Energy.

    While the cost of installing a solar energy system has dropped by more than 99 percent since 1980, this new analysis shows that “soft technology” features, such as the codified permitting practices, supply chain management techniques, and system design processes that go into deploying a solar energy plant, contributed only 10 to 15 percent of total cost declines. Improvements to hardware features were responsible for the lion’s share.

    But because soft technology is increasingly dominating the total costs of installing solar energy systems, this trend threatens to slow future cost savings and hamper the global transition to clean energy, says the study’s senior author, Jessika Trancik, a professor in MIT’s Institute for Data, Systems, and Society (IDSS).

    Trancik’s co-authors include lead author Magdalena M. Klemun, a former IDSS graduate student and postdoc who is now an assistant professor at the Hong Kong University of Science and Technology; Goksin Kavlak, a former IDSS graduate student and postdoc who is now an associate at the Brattle Group; and James McNerney, a former IDSS postdoc and now senior research fellow at the Harvard Kennedy School.

    The team created a quantitative model to analyze the cost evolution of solar energy systems, which captures the contributions of both hardware technology features and soft technology features.

    The framework shows that soft technology hasn’t improved much over time — and that soft technology features contributed even less to overall cost declines than previously estimated.

    Their findings indicate that to reverse this trend and accelerate cost declines, engineers could look at making solar energy systems less reliant on soft technology to begin with, or they could tackle the problem directly by improving inefficient deployment processes.  

    “Really understanding where the efficiencies and inefficiencies are, and how to address those inefficiencies, is critical in supporting the clean energy transition. We are making huge investments of public dollars into this, and soft technology is going to be absolutely essential to making those funds count,” says Trancik.

    “However,” Klemun adds, “we haven’t been thinking about soft technology design as systematically as we have for hardware. That needs to change.”

    The hard truth about soft costs

    Researchers have observed that the so-called “soft costs” of building a solar power plant — the costs of designing and installing the plant — are becoming a much larger share of total costs. In fact, the share of soft costs now typically ranges from 35 to 64 percent.

    “We wanted to take a closer look at where these soft costs were coming from and why they weren’t coming down over time as quickly as the hardware costs,” Trancik says.

    In the past, scientists have modeled the change in solar energy costs by dividing total costs into additive components — hardware components and nonhardware components — and then tracking how these components changed over time.

    “But if you really want to understand where those rates of change are coming from, you need to go one level deeper to look at the technology features. Then things split out differently,” Trancik says.

    The researchers developed a quantitative approach that models the change in solar energy costs over time by assigning contributions to the individual technology features, including both hardware features and soft technology features.

    For instance, their framework would capture how much of the decline in system installation costs — a soft cost — is due to standardized practices of certified installers — a soft technology feature. It would also capture how that same soft cost is affected by increased photovoltaic module efficiency — a hardware technology feature.

    With this approach, the researchers saw that improvements in hardware had the greatest impacts on driving down soft costs in solar energy systems. For example, the efficiency of photovoltaic modules doubled between 1980 and 2017, reducing overall system costs by 17 percent. But about 40 percent of that overall decline could be attributed to reductions in soft costs tied to improved module efficiency.

    The framework shows that, while hardware technology features tend to improve many cost components, soft technology features affect only a few.

    “You can see this structural difference even before you collect data on how the technologies have changed over time. That’s why mapping out a technology’s network of cost dependencies is a useful first step to identify levers of change, for solar PV and for other technologies as well,” Klemun notes.  

    Static soft technology

    The researchers used their model to study several countries, since soft costs can vary widely around the world. For instance, solar energy soft costs in Germany are about 50 percent less than those in the U.S.

    The fact that hardware technology improvements are often shared globally led to dramatic declines in costs over the past few decades across locations, the analysis showed. Soft technology innovations typically aren’t shared across borders. Moreover, the team found that countries with better soft technology performance 20 years ago still have better performance today, while those with worse performance didn’t see much improvement.

    This country-by-country difference could be driven by regulation and permitting processes, cultural factors, or by market dynamics such as how firms interact with each other, Trancik says.

    “But not all soft technology variables are ones that you would want to change in a cost-reducing direction, like lower wages. So, there are other considerations, beyond just bringing the cost of the technology down, that we need to think about when interpreting these results,” she says.

    Their analysis points to two strategies for reducing soft costs. For one, scientists could focus on developing hardware improvements that make soft costs more dependent on hardware technology variables and less on soft technology variables, such as by creating simpler, more standardized equipment that could reduce on-site installation time.

    Or researchers could directly target soft technology features without changing hardware, perhaps by creating more efficient workflows for system installation or automated permitting platforms.

    “In practice, engineers will often pursue both approaches, but separating the two in a formal model makes it easier to target innovation efforts by leveraging specific relationships between technology characteristics and costs,” Klemun says.

    “Often, when we think about information processing, we are leaving out processes that still happen in a very low-tech way through people communicating with one another. But it is just as important to think about that as a technology as it is to design fancy software,” Trancik notes.

    In the future, she and her collaborators want to apply their quantitative model to study the soft costs related to other technologies, such as electrical vehicle charging and nuclear fission. They are also interested in better understanding the limits of soft technology improvement, and how one could design better soft technology from the outset.

    This research is funded by the U.S. Department of Energy Solar Energy Technologies Office. More

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    The tenured engineers of 2023

    In 2023, MIT granted tenure to nine faculty members across the School of Engineering. This year’s tenured engineers hold appointments in the departments of Biological Engineering, Civil and Environmental Engineering, Electrical Engineering and Computer Science (which reports jointly to the School of Engineering and MIT Schwarzman College of Computing), Materials Science and Engineering, and Mechanical Engineering, as well as the Institute for Medical Engineering and Science (IMES).

    “I am truly inspired by this remarkable group of talented faculty members,” says Anantha Chandrakasan, dean of the School of Engineering and the Vannevar Bush Professor of Electrical Engineering and Computer Science. “The work they are doing, both in the lab and in the classroom, has made a tremendous impact at MIT and in the wider world. Their important research has applications in a diverse range of fields and industries. I am thrilled to congratulate them on the milestone of receiving tenure.”

    This year’s newly tenured engineering faculty include:

    Michael Birnbaum, Class of 1956 Career Development Professor, associate professor of biological engineering, and faculty member at the Koch Institute for Integrative Cancer Research at MIT, works on understanding and manipulating immune recognition in cancer and infections. By using a variety of techniques to study the antigen recognition of T cells, he and his team aim to develop the next generation of immunotherapies.  
    Tamara Broderick, associate professor of electrical engineering and computer science and member of the MIT Laboratory for Information and Decision Systems (LIDS) and the MIT Institute for Data, Systems, and Society (IDSS), works to provide fast and reliable quantification of uncertainty and robustness in modern data analysis procedures. Broderick and her research group develop data analysis tools with applications in fields, including genetics, economics, and assistive technology. 
    Tal Cohen, associate professor of civil and environmental engineering and mechanical engineering, uses nonlinear solid mechanics to understand how materials behave under extreme conditions. By studying material instabilities, extreme dynamic loading conditions, growth, and chemical coupling, Cohen and her team combine theoretical models and experiments to shape our understanding of the observed phenomena and apply those insights in the design and characterization of material systems. 
    Betar Gallant, Class of 1922 Career Development Professor and associate professor of mechanical engineering, develops advanced materials and chemistries for next-generation lithium-ion and lithium primary batteries and electrochemical carbon dioxide mitigation technologies. Her group’s work could lead to higher-energy and more sustainable batteries for electric vehicles, longer-lasting implantable medical devices, and new methods of carbon capture and conversion. 
    Rafael Jaramillo, Thomas Lord Career Development Professor and associate professor of materials science and engineering, studies the synthesis, properties, and applications of electronic materials, particularly chalcogenide compound semiconductors. His work has applications in microelectronics, integrated photonics, telecommunications, and photovoltaics. 
    Benedetto Marelli, associate professor of civil and environmental engineering, conducts research on the synthesis, assembly, and nanomanufacturing of structural biopolymers. He and his research team develop biomaterials for applications in agriculture, food security, and food safety. 
    Ellen Roche, Latham Family Career Development Professor, an associate professor of mechanical engineering, and a core faculty of IMES, designs and develops implantable, biomimetic therapeutic devices and soft robotics that mechanically assist and repair tissue, deliver therapies, and enable enhanced preclinical testing. Her devices have a wide range of applications in human health, including cardiovascular and respiratory disease. 
    Serguei Saavedra, associate professor of civil and environmental engineering, uses systems thinking, synthesis, and mathematical modeling to study the persistence of ecological systems under changing environments. His theoretical research is used to develop hypotheses and corroborate predictions of how ecological systems respond to climate change. 
    Justin Solomon, associate professor of electrical engineering and computer science and member of the MIT Computer Science and Artificial Intelligence Laboratory and MIT Center for Computational Science and Engineering, works at the intersection of geometry, large-scale optimization, computer graphics, and machine learning. His research has diverse applications in machine learning, computer graphics, and geometric data processing.  More

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    How machine learning models can amplify inequities in medical diagnosis and treatment

    Prior to receiving a PhD in computer science from MIT in 2017, Marzyeh Ghassemi had already begun to wonder whether the use of AI techniques might enhance the biases that already existed in health care. She was one of the early researchers to take up this issue, and she’s been exploring it ever since. In a new paper, Ghassemi, now an assistant professor in MIT’s Department of Electrical Science and Engineering (EECS), and three collaborators based at the Computer Science and Artificial Intelligence Laboratory, have probed the roots of the disparities that can arise in machine learning, often causing models that perform well overall to falter when it comes to subgroups for which relatively few data have been collected and utilized in the training process. The paper — written by two MIT PhD students, Yuzhe Yang and Haoran Zhang, EECS computer scientist Dina Katabi (the Thuan and Nicole Pham Professor), and Ghassemi — was presented last month at the 40th International Conference on Machine Learning in Honolulu, Hawaii.

    In their analysis, the researchers focused on “subpopulation shifts” — differences in the way machine learning models perform for one subgroup as compared to another. “We want the models to be fair and work equally well for all groups, but instead we consistently observe the presence of shifts among different groups that can lead to inferior medical diagnosis and treatment,” says Yang, who along with Zhang are the two lead authors on the paper. The main point of their inquiry is to determine the kinds of subpopulation shifts that can occur and to uncover the mechanisms behind them so that, ultimately, more equitable models can be developed.

    The new paper “significantly advances our understanding” of the subpopulation shift phenomenon, claims Stanford University computer scientist Sanmi Koyejo. “This research contributes valuable insights for future advancements in machine learning models’ performance on underrepresented subgroups.”

    Camels and cattle

    The MIT group has identified four principal types of shifts — spurious correlations, attribute imbalance, class imbalance, and attribute generalization — which, according to Yang, “have never been put together into a coherent and unified framework. We’ve come up with a single equation that shows you where biases can come from.”

    Biases can, in fact, stem from what the researchers call the class, or from the attribute, or both. To pick a simple example, suppose the task assigned to the machine learning model is to sort images of objects — animals in this case — into two classes: cows and camels. Attributes are descriptors that don’t specifically relate to the class itself. It might turn out, for instance, that all the images used in the analysis show cows standing on grass and camels on sand — grass and sand serving as the attributes here. Given the data available to it, the machine could reach an erroneous conclusion — namely that cows can only be found on grass, not on sand, with the opposite being true for camels. Such a finding would be incorrect, however, giving rise to a spurious correlation, which, Yang explains, is a “special case” among subpopulation shifts — “one in which you have a bias in both the class and the attribute.”

    In a medical setting, one could rely on machine learning models to determine whether a person has pneumonia or not based on an examination of X-ray images. There would be two classes in this situation, one consisting of people who have the lung ailment, another for those who are infection-free. A relatively straightforward case would involve just two attributes: the people getting X-rayed are either female or male. If, in this particular dataset, there were 100 males diagnosed with pneumonia for every one female diagnosed with pneumonia, that could lead to an attribute imbalance, and the model would likely do a better job of correctly detecting pneumonia for a man than for a woman. Similarly, having 1,000 times more healthy (pneumonia-free) subjects than sick ones would lead to a class imbalance, with the model biased toward healthy cases. Attribute generalization is the last shift highlighted in the new study. If your sample contained 100 male patients with pneumonia and zero female subjects with the same illness, you still would like the model to be able to generalize and make predictions about female subjects even though there are no samples in the training data for females with pneumonia.

    The team then took 20 advanced algorithms, designed to carry out classification tasks, and tested them on a dozen datasets to see how they performed across different population groups. They reached some unexpected conclusions: By improving the “classifier,” which is the last layer of the neural network, they were able to reduce the occurrence of spurious correlations and class imbalance, but the other shifts were unaffected. Improvements to the “encoder,” one of the uppermost layers in the neural network, could reduce the problem of attribute imbalance. “However, no matter what we did to the encoder or classifier, we did not see any improvements in terms of attribute generalization,” Yang says, “and we don’t yet know how to address that.”

    Precisely accurate

    There is also the question of assessing how well your model actually works in terms of evenhandedness among different population groups. The metric normally used, called worst-group accuracy or WGA, is based on the assumption that if you can improve the accuracy — of, say, medical diagnosis — for the group that has the worst model performance, you would have improved the model as a whole. “The WGA is considered the gold standard in subpopulation evaluation,” the authors contend, but they made a surprising discovery: boosting worst-group accuracy results in a decrease in what they call “worst-case precision.” In medical decision-making of all sorts, one needs both accuracy — which speaks to the validity of the findings — and precision, which relates to the reliability of the methodology. “Precision and accuracy are both very important metrics in classification tasks, and that is especially true in medical diagnostics,” Yang explains. “You should never trade precision for accuracy. You always need to balance the two.”

    The MIT scientists are putting their theories into practice. In a study they’re conducting with a medical center, they’re looking at public datasets for tens of thousands of patients and hundreds of thousands of chest X-rays, trying to see whether it’s possible for machine learning models to work in an unbiased manner for all populations. That’s still far from the case, even though more awareness has been drawn to this problem, Yang says. “We are finding many disparities across different ages, gender, ethnicity, and intersectional groups.”

    He and his colleagues agree on the eventual goal, which is to achieve fairness in health care among all populations. But before we can reach that point, they maintain, we still need a better understanding of the sources of unfairness and how they permeate our current system. Reforming the system as a whole will not be easy, they acknowledge. In fact, the title of the paper they introduced at the Honolulu conference, “Change is Hard,” gives some indications as to the challenges that they and like-minded researchers face. More

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    Summer research offers a springboard to advanced studies

    Doctoral studies at MIT aren’t a calling for everyone, but they can be for anyone who has had opportunities to discover that science and technology research is their passion and to build the experience and skills to succeed. For Taylor Baum, Josefina Correa Menéndez, and Karla Alejandra Montejo, three graduate students in just one lab of The Picower Institute for Learning and Memory, a pivotal opportunity came via the MIT Summer Research Program in Biology and Neuroscience (MSRP-Bio). When a student finds MSRP-Bio, it helps them find their future in research. 

    In the program, undergraduate STEM majors from outside MIT spend the summer doing full-time research in the departments of Biology, Brain and Cognitive Sciences (BCS), or the Center for Brains, Minds and Machines (CBMM). They gain lab skills, mentoring, preparation for graduate school, and connections that might last a lifetime. Over the last two decades, a total of 215 students from underrepresented minority groups, who are from economically disadvantaged backgrounds, first-generation or nontraditional college students, or students with disabilities have participated in research in BCS or CBMM labs.  

    Like Baum, Correa Menéndez, and Montejo, the vast majority go on to pursue graduate studies, says Diversity and Outreach Coordinator Mandana Sassanfar, who runs the program. For instance, among 91 students who have worked in Picower Institute labs, 81 have completed their undergraduate studies. Of those, 46 enrolled in PhD programs at MIT or other schools such as Cornell, Yale, Stanford, and Princeton universities, and the University of California System. Another 12 have gone to medical school, another seven are in MD/PhD programs, and three have earned master’s degrees. The rest are studying as post-baccalaureates or went straight into the workforce after earning their bachelor’s degree. 

    After participating in the program, Baum, Correa Menéndez, and Montejo each became graduate students in the research group of Emery N. Brown, the Edward Hood Taplin Professor of Computational Neuroscience and Medical Engineering in The Picower Institute and the Institute for Medical Engineering and Science. The lab combines statistical, computational, and experimental neuroscience methods to study how general anesthesia affects the central nervous system to ultimately improve patient care and advance understanding of the brain. Brown says the students have each been doing “off-the-scale” work, in keeping with the excellence he’s seen from MSRP BIO students over the years. For example, on Aug. 10 Baum and Correa Menéndez were honored with MathWorks Fellowships.

    “I think MSRP is fantastic. Mandana does this amazing job of getting students who are quite talented to come to MIT to realize that they can move their game to the next level. They have the capacity to do it. They just need the opportunities,” Brown says. “These students live up to the expectations that you have of them. And now as graduate students, they’re taking on hard problems and they’re solving them.” 

    Paths to PhD studies 

    Pursuing a PhD is hardly a given. Many young students have never considered graduate school or specific fields of study like neuroscience or electrical engineering. But Sassanfar engages students across the country to introduce them to the opportunity MSRP-Bio provides to gain exposure, experience, and mentoring in advanced fields. Every fall, after the program’s students have returned to their undergraduate institutions, she visits schools in places as far flung as Florida, Maryland, Puerto Rico, and Texas and goes to conferences for diverse science communities such as ABRCMS and SACNAS to spread the word. 

    Taylor Baum

    Photo courtesy of Taylor Baum.

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    When Baum first connected with the program in 2017, she was finding her way at Penn State University. She had been majoring in biology and music composition but had just switched the latter to engineering following a conversation over coffee exposing her to brain-computer interfacing technology, in which detecting brain signals of people with full-body paralysis could improve their quality of life by enabling control of computers or wheelchairs. Baum became enthusiastic about the potential to build similar systems, but as a new engineering student, she struggled to find summer internships and research opportunities. 

    “I got rejected from every single progam except the MIT Center for Brains, Minds and Machines MSRP,” she recalls with a chuckle. 

    Baum thrived in MSRP-Bio, working in Brown’s lab for three successive summers. At each stage, she said, she gained more research skills, experience, and independence. When she graduated, she was sure she wanted to go to graduate school and applied to four of her dream schools. She accepted MIT’s offer to join the Department of Electrical Engineering and Computer Science, where she is co-advised by faculty members there and by Brown. She is now working to develop a system grounded in cardiovascular physiology that can improve blood pressure management. A tool for practicing anesthesiologists, the system automates the dosing of drugs to maintain a patient’s blood pressure at safe levels in the operating room or intensive care unit. 

    More than that, Baum not only is leading an organization advancing STEM education in Puerto Rico, but also is helping to mentor a current MSRP-Bio student in the Brown lab. 

    “MSRP definitely bonds everyone who has participated in it,” Baum says. “If I see anyone who I know participated in MSRP, we could have an immediate conversation. I know that most of us, if we needed help, we’d feel comfortable asking for help from someone from MSRP. With that shared experience, we have a sense of camaraderie, and community.” 

    In fact, a few years ago when a former MSRP-Bio student named Karla Montejo was applying to MIT, Baum provided essential advice and feedback about the application process, Montejo says. Now, as a graduate student, Montejo has become a mentor for the program in her own right, Sassanfar notes. For instance, Montejo serves on program alumni panels that advise new MSRP-Bio students. 

    Karla Alejandra Montejo

    Photo courtesy of Karla Alejandra Montejo.

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    Montejo’s family immigrated to Miami from Cuba when she was a child. The magnet high school she attended was so new that students were encouraged to help establish the school’s programs. She forged a path into research. 

    “I didn’t even know what research was,” she says. “I wanted to be a doctor, and I thought maybe it would help me on my resume. I thought it would be kind of like shadowing, but no, it was really different. So I got really captured by research when I was in high school.” 

    Despite continuing to pursue research in college at Florida International University, Montejo didn’t get into graduate school on her first attempt because she hadn’t yet learned how to focus her application. But Sassanfar had visited FIU to recruit students and through that relationship Montejo had already gone through MIT’s related Quantitative Methods Workshop (QMW). So Montejo enrolled in MSRP-Bio, working in the CBMM-affiliated lab of Gabriel Kreiman at Boston Children’s Hospital. 

    “I feel like Mandana really helped me out, gave me a break, and the MSRP experience pretty much solidified that I really wanted to come to MIT,” Montejo says. 

    In the QMW, Montejo learned she really liked computational neuroscience, and in Kreiman’s lab she got to try her hand at computational modeling of the cognition involved in making perceptual sense of complex scenes. Montejo realized she wanted to work on more biologically based neuroscience problems. When the summer ended, because she was off the normal graduate school cycle for now, she found a two-year post-baccalaurate program at Mayo Clinic studying the role a brain cell type called astrocytes might have in the Parkinson’s disease treatment deep brain stimulation. 

    When it came time to reapply to graduate schools (with the help of Baum and others in the BCS Application Assistance Program) Montejo applied to MIT and got in, joining the Brown lab. Now she’s working on modeling the role of  metabolic processes in the changing of brain rhythms under anesthesia, taking advantage of how general anesthesia predictably changes brain states. The effects anesthetic drugs have on cell metabolism and the way that ultimately affects levels of consciousness reveals important aspects of how metabolism affects brain circuits and systems. Earlier this month, for instance, Montejo co-led a paper the lab published in The Proceedings of the National Academy of Sciences detailing the neuroscience of a patient’s transition into an especially deep state of unconsciousness called “burst suppression.” 

    Josefina Correa Menendez

    Photo: David Orenstein

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    A signature of the Brown lab’s work is rigorous statistical analysis and methods, for instance to discern brain arousal states from EEG measures of brain rhythms. A PhD candidate in MIT’s Interdisciplinary Doctoral Program in Statistics, Correa Menéndez is advancing the use of Bayesian hierarchical models for neural data analysis. These statistical models offer a principled way of pooling information across datasets. One of her models can help scientists better understand the way neurons can “spike” with electrical activity when the brain is presented with a stimulus. The other’s power is in discerning critical features such as arousal states of the brain under general anesthesia from electrophysiological recordings. 

    Though she now works with complex equations and computations as a PhD candidate in neuroscience and statistics, Correa Menéndez was mostly interested in music art as a high school student at Academia María Reina in San Juan and then architecture in college at the University of Puerto Rico at Río Piedras. It was discussions at the intersection of epistemology and art during an art theory class that inspired Correa Menéndez to switch her major to biology and to take computer science classes, too. 

    When Sassanfar visited Puerto Rico in 2017, a computer science professor (Patricia Ordóñez) suggested that Correa Menéndez apply for a chance to attend the QMW. She did, and that led her to also participate in MSRP-Bio in the lab of Sherman Fairchild Professor Matt Wilson (a faculty member in BCS, CBMM, and the Picower Institute). She joined in the lab’s studies of how spatial memories are represented in the hippocampus and how the brain makes use of those memories to help understand the world around it. With mentoring from then-postdoc Carmen Varela (now a faculty member at Florida State University), the experience not only exposed her to neuroscience, but also helped her gain skills and experience with lab experiments, building research tools, and conducting statistical analyses. She ended up working in the Wilson lab as a research scholar for a year and began her graduate studies in September 2018.  

    Classes she took with Brown as a research scholar inspired her to join his lab as a graduate student. 

    “Taking the classes with Emery and also doing experiments made me aware of the role of statistics in the scientific process: from the interpretation of results to the analysis and the design of experiments,” she says. “More often than not, in science, statistics becomes this sort of afterthought — this ‘annoying’ thing that people need to do to get their paper published. But statistics as a field is actually a lot more than that. It’s a way of thinking about data. Particularly, Bayesian modeling provides a principled inference framework for combining prior knowledge into a hypothesis that you can test with data.” 

    To be sure, no one starts out with such inspiration about scientific scholarship, but MSRP-Bio helps students find that passion for research and the paths that opens up.   More