Grants

Subterms

    Latest story

    75 Shares159 Views

    Forging climate connections across the Institute

    by

    Climate change is the ultimate cross-cutting issue: Not limited to any one discipline, it ranges across science, technology, policy, culture, human behavior, and well beyond. The response to it likewise requires an all-of-MIT effort.

    Now, to strengthen such an effort, a new grant program spearheaded by the Climate Nucleus, the faculty committee charged with the oversight and implementation of Fast Forward: MIT’s Climate Action Plan for the Decade, aims to build up MIT’s climate leadership capacity while also supporting innovative scholarship on diverse climate-related topics and forging new connections across the Institute.

    Called the Fast Forward Faculty Fund (F^4 for short), the program has named its first cohort of six faculty members after issuing its inaugural call for proposals in April 2023. The cohort will come together throughout the year for climate leadership development programming and networking. The program provides financial support for graduate students who will work with the faculty members on the projects — the students will also participate in leadership-building activities — as well as $50,000 in flexible, discretionary funding to be used to support related activities. 

    “Climate change is a crisis that truly touches every single person on the planet,” says Noelle Selin, co-chair of the nucleus and interim director of the Institute for Data, Systems, and Society. “It’s therefore essential that we build capacity for every member of the MIT community to make sense of the problem and help address it. Through the Fast Forward Faculty Fund, our aim is to have a cohort of climate ambassadors who can embed climate everywhere at the Institute.”

    F^4 supports both faculty who would like to begin doing climate-related work, as well as faculty members who are interested in deepening their work on climate. The program has the core goal of developing cohorts of F^4 faculty and graduate students who, in addition to conducting their own research, will become climate leaders at MIT, proactively looking for ways to forge new climate connections across schools, departments, and disciplines.

    One of the projects, “Climate Crisis and Real Estate: Science-based Mitigation and Adaptation Strategies,” led by Professor Siqi Zheng of the MIT Center for Real Estate in collaboration with colleagues from the MIT Sloan School of Management, focuses on the roughly 40 percent of carbon dioxide emissions that come from the buildings and real estate sector. Zheng notes that this sector has been slow to respond to climate change, but says that is starting to change, thanks in part to the rising awareness of climate risks and new local regulations aimed at reducing emissions from buildings.

    Using a data-driven approach, the project seeks to understand the efficient and equitable market incentives, technology solutions, and public policies that are most effective at transforming the real estate industry. Johnattan Ontiveros, a graduate student in the Technology and Policy Program, is working with Zheng on the project.

    “We were thrilled at the incredible response we received from the MIT faculty to our call for proposals, which speaks volumes about the depth and breadth of interest in climate at MIT,” says Anne White, nucleus co-chair and vice provost and associate vice president for research. “This program makes good on key commitments of the Fast Forward plan, supporting cutting-edge new work by faculty and graduate students while helping to deepen the bench of climate leaders at MIT.”

    During the 2023-24 academic year, the F^4 faculty and graduate student cohorts will come together to discuss their projects, explore opportunities for collaboration, participate in climate leadership development, and think proactively about how to deepen interdisciplinary connections among MIT community members interested in climate change.

    The six inaugural F^4 awardees are:

    Professor Tristan Brown, History Section: Humanistic Approaches to the Climate Crisis  

    With this project, Brown aims to create a new community of practice around narrative-centric approaches to environmental and climate issues. Part of a broader humanities initiative at MIT, it brings together a global working group of interdisciplinary scholars, including Serguei Saavedra (Department of Civil and Environmental Engineering) and Or Porath (Tel Aviv University; Religion), collectively focused on examining the historical and present links between sacred places and biodiversity for the purposes of helping governments and nongovernmental organizations formulate better sustainability goals. Boyd Ruamcharoen, a PhD student in the History, Anthropology, and Science, Technology, and Society (HASTS) program, will work with Brown on this project.

    Professor Kerri Cahoy, departments of Aeronautics and Astronautics and Earth, Atmospheric, and Planetary Sciences (AeroAstro): Onboard Autonomous AI-driven Satellite Sensor Fusion for Coastal Region Monitoring

    The motivation for this project is the need for much better data collection from satellites, where technology can be “20 years behind,” says Cahoy. As part of this project, Cahoy will pursue research in the area of autonomous artificial intelligence-enabled rapid sensor fusion (which combines data from different sensors, such as radar and cameras) onboard satellites to improve understanding of the impacts of climate change, specifically sea-level rise and hurricanes and flooding in coastal regions. Graduate students Madeline Anderson, a PhD student in electrical engineering and computer science (EECS), and Mary Dahl, a PhD student in AeroAstro, will work with Cahoy on this project.

    Professor Priya Donti, Department of Electrical Engineering and Computer Science: Robust Reinforcement Learning for High-Renewables Power Grids 

    With renewables like wind and solar making up a growing share of electricity generation on power grids, Donti’s project focuses on improving control methods for these distributed sources of electricity. The research will aim to create a realistic representation of the characteristics of power grid operations, and eventually inform scalable operational improvements in power systems. It will “give power systems operators faith that, OK, this conceptually is good, but it also actually works on this grid,” says Donti. PhD candidate Ana Rivera from EECS is the F^4 graduate student on the project.

    Professor Jason Jackson, Department of Urban Studies and Planning (DUSP): Political Economy of the Climate Crisis: Institutions, Power and Global Governance

    This project takes a political economy approach to the climate crisis, offering a distinct lens to examine, first, the political governance challenge of mobilizing climate action and designing new institutional mechanisms to address the global and intergenerational distributional aspects of climate change; second, the economic challenge of devising new institutional approaches to equitably finance climate action; and third, the cultural challenge — and opportunity — of empowering an adaptive socio-cultural ecology through traditional knowledge and local-level social networks to achieve environmental resilience. Graduate students Chen Chu and Mrinalini Penumaka, both PhD students in DUSP, are working with Jackson on the project.

    Professor Haruko Wainwright, departments of Nuclear Science and Engineering (NSE) and Civil and Environmental Engineering: Low-cost Environmental Monitoring Network Technologies in Rural Communities for Addressing Climate Justice 

    This project will establish a community-based climate and environmental monitoring network in addition to a data visualization and analysis infrastructure in rural marginalized communities to better understand and address climate justice issues. The project team plans to work with rural communities in Alaska to install low-cost air and water quality, weather, and soil sensors. Graduate students Kay Whiteaker, an MS candidate in NSE, and Amandeep Singh, and MS candidate in System Design and Management at Sloan, are working with Wainwright on the project, as is David McGee, professor in earth, atmospheric, and planetary sciences.

    Professor Siqi Zheng, MIT Center for Real Estate and DUSP: Climate Crisis and Real Estate: Science-based Mitigation and Adaptation Strategies 

    See the text above for the details on this project. More

    More stories

    • 113 Shares139 Views

      in Data Management & Statistics

      Supporting sustainability, digital health, and the future of work

      by

      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

    • 75 Shares99 Views

      in Data Management & Statistics

      Artificial intelligence for augmentation and productivity

      by

      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

    • 100 Shares189 Views

      in Data Management & Statistics

      MIT-Pillar AI Collective announces first seed grant recipients

      by

      The MIT-Pillar AI Collective has announced its first six grant recipients. Students, alumni, and postdocs working on a broad range of topics in artificial intelligence, machine learning, and data science will receive funding and support for research projects that could translate into commercially viable products or companies. These grants are intended to help students explore commercial applications for their research, and eventually drive that commercialization through the creation of a startup.

      “These tremendous students and postdocs are working on projects that have the potential to be truly transformative across a diverse range of industries. It’s thrilling to think that the novel research these teams are conducting could lead to the founding of startups that revolutionize everything from drug delivery to video conferencing,” says Anantha Chandrakasan, dean of the School of Engineering and the Vannevar Bush Professor of Electrical Engineering and Computer Science.

      Launched in September 2022, the MIT-Pillar AI Collective is a pilot program funded by a $1 million gift from Pillar VC that aims to cultivate prospective entrepreneurs and drive innovation in areas related to AI. Administered by the MIT Deshpande Center for Technological Innovation, the AI Collective centers on the market discovery process, advancing projects through market research, customer discovery, and prototyping. Graduate students and postdocs supported by the program work toward the development of minimum viable products.

      “In addition to funding, the MIT-Pillar AI Collective provides grant recipients with mentorship and guidance. With the rapid advancement of AI technologies, this type of support is critical to ensure students and postdocs are able to access the resources required to move quickly in this fast-pace environment,” says Jinane Abounadi, managing director of the MIT-Pillar AI Collective.

      The six inaugural recipients will receive support in identifying key milestones and advice from experienced entrepreneurs. The AI Collective assists seed grant recipients in gathering feedback from potential end-users, as well as getting insights from early-stage investors. The program also organizes community events, including a “Founder Talks” speaker series, and other team-building activities.   

      “Each one of these grant recipients exhibits an entrepreneurial spirit. It is exciting to provide support and guidance as they start a journey that could one day see them as founders and leaders of successful companies,” adds Jamie Goldstein ’89, founder of Pillar VC.

      The first cohort of grant recipients include the following projects:

      Predictive query interface

      Abdullah Alomar SM ’21, a PhD candidate studying electrical engineering and computer science, is building a predictive query interface for time series databases to better forecast demand and financial data. This user-friendly interface can help alleviate some of the bottlenecks and issues related to unwieldy data engineering processes while providing state-of-the-art statistical accuracy. Alomar is advised by Devavrat Shah, the Andrew (1956) and Erna Viterbi Professor at MIT.

      Design of light-activated drugs

      Simon Axelrod, a PhD candidate studying chemical physics at Harvard University, is combining AI with physics simulations to design light-activated drugs that could reduce side effects and improve effectiveness. Patients would receive an inactive form of a drug, which is then activated by light in a specific area of the body containing diseased tissue. This localized use of photoactive drugs would minimize the side effects from drugs targeting healthy cells. Axelrod is developing novel computational models that predict properties of photoactive drugs with high speed and accuracy, allowing researchers to focus on only the highest-quality drug candidates. He is advised by Rafael Gomez-Bombarelli, the Jeffrey Cheah Career Development Chair in Engineering in the MIT Department of Materials Science and Engineering. 

      Low-cost 3D perception

      Arjun Balasingam, a PhD student in electrical engineering and computer science and a member of the Computer Science and Artificial Intelligence Laboratory’s (CSAIL) Networks and Mobile Systems group, is developing a technology, called MobiSee, that enables real-time 3D reconstruction in challenging dynamic environments. MobiSee uses self-supervised AI methods along with video and lidar to provide low-cost, state-of-the-art 3D perception on consumer mobile devices like smartphones. This technology could have far-reaching applications across mixed reality, navigation, safety, and sports streaming, in addition to unlocking opportunities for new real-time and immersive experiences. He is advised by Hari Balakrishnan, the Fujitsu Professor of Computer Science and Artificial Intelligence at MIT and member of CSAIL.

      Sleep therapeutics

      Guillermo Bernal SM ’14, PhD ’23, a recent PhD graduate in media arts and sciences, is developing a sleep therapeutic platform that would enable sleep specialists and researchers to conduct robust sleep studies and develop therapy plans remotely, while the patient is comfortable in their home. Called Fascia, the three-part system consists of a polysomnogram with a sleep mask form factor that collects data, a hub that enables researchers to provide stimulation and feedback via olfactory, auditory, and visual stimuli, and a web portal that enables researchers to read a patient’s signals in real time with machine learning analysis. Bernal was advised by Pattie Maes, professor of media arts and sciences at the MIT Media Lab.

      Autonomous manufacturing assembly with human-like tactile perception

      Michael Foshey, a mechanical engineer and project manager with MIT CSAIL’s Computational Design and Fabrication Group, is developing an AI-enabled tactile perception system that can be used to give robots human-like dexterity. With this new technology platform, Foshey and his team hope to enable industry-changing applications in manufacturing. Currently, assembly tasks in manufacturing are largely done by hand and are typically repetitive and tedious. As a result, these jobs are being largely left unfilled. These labor shortages can cause supply chain shortages and increases in the cost of production. Foshey’s new technology platform aims to address this by automating assembly tasks to reduce reliance on manual labor. Foshey is supervised by Wojciech Matusik, MIT professor of electrical engineering and computer science and member of CSAIL.  

      Generative AI for video conferencing

      Vibhaalakshmi Sivaraman SM ’19, a PhD candidate in electrical engineering and computer science who is a member of CSAIL’s Networking and Mobile Systems Group, is developing a generative technology, Gemino, to facilitate video conferencing in high-latency and low-bandwidth network environments. Gemino is a neural compression system for video conferencing that overcomes the robustness concerns and compute complexity challenges that limit current face-image-synthesis models. This technology could enable sustained video conferencing calls in regions and scenarios that cannot reliably support video calls today. Sivaraman is advised by Mohammad Alizadeh, MIT associate professor of electrical engineering and computer science and member of CSAIL.  More

    • 75 Shares129 Views

      in Data Management & Statistics

      J-WAFS announces 2023 seed grant recipients

      by

      Today, the Abdul Latif Jameel Water and Food Systems Lab (J-WAFS) announced its ninth round of seed grants to support innovative research projects at MIT. The grants are designed to fund research efforts that tackle challenges related to water and food for human use, with the ultimate goal of creating meaningful impact as the world population continues to grow and the planet undergoes significant climate and environmental changes.Ten new projects led by 15 researchers from seven different departments will be supported this year. The projects address a range of challenges by employing advanced materials, technology innovations, and new approaches to resource management. The new projects aim to remove harmful chemicals from water sources, develop monitoring and other systems to help manage various aquaculture industries, optimize water purification materials, and more.“The seed grant program is J-WAFS’ flagship grant initiative,” says J-WAFS executive director Renee J. Robins. “The funding is intended to spur groundbreaking MIT research addressing complex issues that are challenging our water and food systems. The 10 projects selected this year show great promise, and we look forward to the progress and accomplishments these talented researchers will make,” she adds.The 2023 J-WAFS seed grant researchers and their projects are:Sara Beery, an assistant professor in the Department of Electrical Engineering and Computer Science (EECS), is building the first completely automated system to estimate the size of salmon populations in the Pacific Northwest (PNW).Salmon are a keystone species in the PNW, feeding human populations for the last 7,500 years at least. However, overfishing, habitat loss, and climate change threaten extinction of salmon populations across the region. Accurate salmon counts during their seasonal migration to their natal river to spawn are essential for fisheries’ regulation and management but are limited by human capacity. Fish population monitoring is a widespread challenge in the United States and worldwide. Beery and her team are working to build a system that will provide a detailed picture of the state of salmon populations in unprecedented, spatial, and temporal resolution by combining sonar sensors and computer vision and machine learning (CVML) techniques. The sonar will capture individual fish as they swim upstream and CVML will train accurate algorithms to interpret the sonar video for detecting, tracking, and counting fish automatically while adapting to changing river conditions and fish densities.Another aquaculture project is being led by Michael Triantafyllou, the Henry L. and Grace Doherty Professor in Ocean Science and Engineering in the Department of Mechanical Engineering, and Robert Vincent, the assistant director at MIT’s Sea Grant Program. They are working with Otto Cordero, an associate professor in the Department of Civil and Environmental Engineering, to control harmful bacteria blooms in aquaculture algae feed production.

      Aquaculture in the United States represents a $1.5 billion industry annually and helps support 1.7 million jobs, yet many American hatcheries are not able to keep up with demand. One barrier to aquaculture production is the high degree of variability in survival rates, most likely caused by a poorly controlled microbiome that leads to bacterial infections and sub-optimal feed efficiency. Triantafyllou, Vincent, and Cordero plan to monitor the microbiome composition of a shellfish hatchery in order to identify possible causing agents of mortality, as well as beneficial microbes. They hope to pair microbe data with detail phenotypic information about the animal population to generate rapid diagnostic tests and explore the potential for microbiome therapies to protect larvae and prevent future outbreaks. The researchers plan to transfer their findings and technology to the local and regional aquaculture community to ensure healthy aquaculture production that will support the expansion of the U.S. aquaculture industry.

      David Des Marais is the Cecil and Ida Green Career Development Professor in the Department of Civil and Environmental Engineering. His 2023 J-WAFS project seeks to understand plant growth responses to elevated carbon dioxide (CO2) in the atmosphere, in the hopes of identifying breeding strategies that maximize crop yield under future CO2 scenarios.Today’s crop plants experience higher atmospheric CO2 than 20 or 30 years ago. Crops such as wheat, oat, barley, and rice typically increase their growth rate and biomass when grown at experimentally elevated atmospheric CO2. This is known as the so-called “CO2 fertilization effect.” However, not all plant species respond to rising atmospheric CO2 with increased growth, and for the ones that do, increased growth doesn’t necessarily correspond to increased crop yield. Using specially built plant growth chambers that can control the concentration of CO2, Des Marais will explore how CO2 availability impacts the development of tillers (branches) in the grass species Brachypodium. He will study how gene expression controls tiller development, and whether this is affected by the growing environment. The tillering response refers to how many branches a plant produces, which sets a limit on how much grain it can yield. Therefore, optimizing the tillering response to elevated CO2 could greatly increase yield. Des Marais will also look at the complete genome sequence of Brachypodium, wheat, oat, and barley to help identify genes relevant for branch growth.Darcy McRose, an assistant professor in the Department of Civil and Environmental Engineering, is researching whether a combination of plant metabolites and soil bacteria can be used to make mineral-associated phosphorus more bioavailable.The nutrient phosphorus is essential for agricultural plant growth, but when added as a fertilizer, phosphorus sticks to the surface of soil minerals, decreasing bioavailability, limiting plant growth, and accumulating residual phosphorus. Heavily fertilized agricultural soils often harbor large reservoirs of this type of mineral-associated “legacy” phosphorus. Redox transformations are one chemical process that can liberate mineral-associated phosphorus. However, this needs to be carefully controlled, as overly mobile phosphorus can lead to runoff and pollution of natural waters. Ideally, phosphorus would be made bioavailable when plants need it and immobile when they don’t. Many plants make small metabolites called coumarins that might be able to solubilize mineral-adsorbed phosphorus and be activated and inactivated under different conditions. McRose will use laboratory experiments to determine whether a combination of plant metabolites and soil bacteria can be used as a highly efficient and tunable system for phosphorus solubilization. She also aims to develop an imaging platform to investigate exchanges of phosphorus between plants and soil microbes.Many of the 2023 seed grants will support innovative technologies to monitor, quantify, and remediate various kinds of pollutants found in water. Two of the new projects address the problem of per- and polyfluoroalkyl substances (PFAS), human-made chemicals that have recently emerged as a global health threat. Known as “forever chemicals,” PFAS are used in many manufacturing processes. These chemicals are known to cause significant health issues including cancer, and they have become pervasive in soil, dust, air, groundwater, and drinking water. Unfortunately, the physical and chemical properties of PFAS render them difficult to detect and remove.Aristide Gumyusenge, the Merton C. Assistant Professor of Materials Science and Engineering, is using metal-organic frameworks for low-cost sensing and capture of PFAS. Most metal-organic frameworks (MOFs) are synthesized as particles, which complicates their high accuracy sensing performance due to defects such as intergranular boundaries. Thin, film-based electronic devices could enable the use of MOFs for many applications, especially chemical sensing. Gumyusenge’s project aims to design test kits based on two-dimensional conductive MOF films for detecting PFAS in drinking water. In early demonstrations, Gumyusenge and his team showed that these MOF films can sense PFAS at low concentrations. They will continue to iterate using a computation-guided approach to tune sensitivity and selectivity of the kits with the goal of deploying them in real-world scenarios.Carlos Portela, the Brit (1961) and Alex (1949) d’Arbeloff Career Development Professor in the Department of Mechanical Engineering, and Ariel Furst, the Cook Career Development Professor in the Department of Chemical Engineering, are building novel architected materials to act as filters for the removal of PFAS from water. Portela and Furst will design and fabricate nanoscale materials that use activated carbon and porous polymers to create a physical adsorption system. They will engineer the materials to have tunable porosities and morphologies that can maximize interactions between contaminated water and functionalized surfaces, while providing a mechanically robust system.Rohit Karnik is a Tata Professor and interim co-department head of the Department of Mechanical Engineering. He is working on another technology, his based on microbead sensors, to rapidly measure and monitor trace contaminants in water.Water pollution from both biological and chemical contaminants contributes to an estimated 1.36 million deaths annually. Chemical contaminants include pesticides and herbicides, heavy metals like lead, and compounds used in manufacturing. These emerging contaminants can be found throughout the environment, including in water supplies. The Environmental Protection Agency (EPA) in the United States sets recommended water quality standards, but states are responsible for developing their own monitoring criteria and systems, which must be approved by the EPA every three years. However, the availability of data on regulated chemicals and on candidate pollutants is limited by current testing methods that are either insensitive or expensive and laboratory-based, requiring trained scientists and technicians. Karnik’s project proposes a simple, self-contained, portable system for monitoring trace and emerging pollutants in water, making it suitable for field studies. The concept is based on multiplexed microbead-based sensors that use thermal or gravitational actuation to generate a signal. His proposed sandwich assay, a testing format that is appealing for environmental sensing, will enable both single-use and continuous monitoring. The hope is that the bead-based assays will increase the ease and reach of detecting and quantifying trace contaminants in water for both personal and industrial scale applications.Alexander Radosevich, a professor in the Department of Chemistry, and Timothy Swager, the John D. MacArthur Professor of Chemistry, are teaming up to create rapid, cost-effective, and reliable techniques for on-site arsenic detection in water.Arsenic contamination of groundwater is a problem that affects as many as 500 million people worldwide. Arsenic poisoning can lead to a range of severe health problems from cancer to cardiovascular and neurological impacts. Both the EPA and the World Health Organization have established that 10 parts per billion is a practical threshold for arsenic in drinking water, but measuring arsenic in water at such low levels is challenging, especially in resource-limited environments where access to sensitive laboratory equipment may not be readily accessible. Radosevich and Swager plan to develop reaction-based chemical sensors that bind and extract electrons from aqueous arsenic. In this way, they will exploit the inherent reactivity of aqueous arsenic to selectively detect and quantify it. This work will establish the chemical basis for a new method of detecting trace arsenic in drinking water.Rajeev Ram is a professor in the Department of Electrical Engineering and Computer Science. His J-WAFS research will advance a robust technology for monitoring nitrogen-containing pollutants, which threaten over 15,000 bodies of water in the United States alone.Nitrogen in the form of nitrate, nitrite, ammonia, and urea can run off from agricultural fertilizer and lead to harmful algal blooms that jeopardize human health. Unfortunately, monitoring these contaminants in the environment is challenging, as sensors are difficult to maintain and expensive to deploy. Ram and his students will work to establish limits of detection for nitrate, nitrite, ammonia, and urea in environmental, industrial, and agricultural samples using swept-source Raman spectroscopy. Swept-source Raman spectroscopy is a method of detecting the presence of a chemical by using a tunable, single mode laser that illuminates a sample. This method does not require costly, high-power lasers or a spectrometer. Ram will then develop and demonstrate a portable system that is capable of achieving chemical specificity in complex, natural environments. Data generated by such a system should help regulate polluters and guide remediation.Kripa Varanasi, a professor in the Department of Mechanical Engineering, and Angela Belcher, the James Mason Crafts Professor and head of the Department of Biological Engineering, will join forces to develop an affordable water disinfection technology that selectively identifies, adsorbs, and kills “superbugs” in domestic and industrial wastewater.Recent research predicts that antibiotic-resistance bacteria (superbugs) will result in $100 trillion in health care expenses and 10 million deaths annually by 2050. The prevalence of superbugs in our water systems has increased due to corroded pipes, contamination, and climate change. Current drinking water disinfection technologies are designed to kill all types of bacteria before human consumption. However, for certain domestic and industrial applications there is a need to protect the good bacteria required for ecological processes that contribute to soil and plant health. Varanasi and Belcher will combine material, biological, process, and system engineering principles to design a sponge-based water disinfection technology that can identify and destroy harmful bacteria while leaving the good bacteria unharmed. By modifying the sponge surface with specialized nanomaterials, their approach will be able to kill superbugs faster and more efficiently. The sponge filters can be deployed under very low pressure, making them an affordable technology, especially in resource-constrained communities.In addition to the 10 seed grant projects, J-WAFS will also fund a research initiative led by Greg Sixt. Sixt is the research manager for climate and food systems at J-WAFS, and the director of the J-WAFS-led Food and Climate Systems Transformation (FACT) Alliance. His project focuses on the Lake Victoria Basin (LVB) of East Africa. The second-largest freshwater lake in the world, Lake Victoria straddles three countries (Uganda, Tanzania, and Kenya) and has a catchment area that encompasses two more (Rwanda and Burundi). Sixt will collaborate with Michael Hauser of the University of Natural Resources and Life Sciences, Vienna, and Paul Kariuki, of the Lake Victoria Basin Commission.The group will study how to adapt food systems to climate change in the Lake Victoria Basin. The basin is facing a range of climate threats that could significantly impact livelihoods and food systems in the expansive region. For example, extreme weather events like droughts and floods are negatively affecting agricultural production and freshwater resources. Across the LVB, current approaches to land and water management are unsustainable and threaten future food and water security. The Lake Victoria Basin Commission (LVBC), a specialized institution of the East African Community, wants to play a more vital role in coordinating transboundary land and water management to support transitions toward more resilient, sustainable, and equitable food systems. The primary goal of this research will be to support the LVBC’s transboundary land and water management efforts, specifically as they relate to sustainability and climate change adaptation in food systems. The research team will work with key stakeholders in Kenya, Uganda, and Tanzania to identify specific capacity needs to facilitate land and water management transitions. The two-year project will produce actionable recommendations to the LVBC. More

    • 125 Shares109 Views

      in Data Management & Statistics

      Four from MIT receive NIH New Innovator Awards for 2022

      by

      The National Institutes of Health (NIH) has awarded grants to four MIT faculty members as part of its High-Risk, High-Reward Research program.

      The program supports unconventional approaches to challenges in biomedical, behavioral, and social sciences. Each year, NIH Director’s Awards are granted to program applicants who propose high-risk, high-impact research in areas relevant to the NIH’s mission. In doing so, the NIH encourages innovative proposals that, due to their inherent risk, might struggle in the traditional peer-review process.

      This year, Lindsay Case, Siniša Hrvatin, Deblina Sarkar, and Caroline Uhler have been chosen to receive the New Innovator Award, which funds exceptionally creative research from early-career investigators. The award, which was established in 2007, supports researchers who are within 10 years of their final degree or clinical residency and have not yet received a research project grant or equivalent NIH grant.

      Lindsay Case, the Irwin and Helen Sizer Department of Biology Career Development Professor and an extramural member of the Koch Institute for Integrative Cancer Research, uses biochemistry and cell biology to study the spatial organization of signal transduction. Her work focuses on understanding how signaling molecules assemble into compartments with unique biochemical and biophysical properties to enable cells to sense and respond to information in their environment. Earlier this year, Case was one of two MIT assistant professors named as Searle Scholars.

      Siniša Hrvatin, who joined the School of Science faculty this past winter, is an assistant professor in the Department of Biology and a core member at the Whitehead Institute for Biomedical Research. He studies how animals and cells enter, regulate, and survive states of dormancy such as torpor and hibernation, aiming to harness the potential of these states therapeutically.

      Deblina Sarkar is an assistant professor and AT&T Career Development Chair Professor at the MIT Media Lab​. Her research combines the interdisciplinary fields of nanoelectronics, applied physics, and biology to invent disruptive technologies for energy-efficient nanoelectronics and merge such next-generation technologies with living matter to create a new paradigm for life-machine symbiosis. Her high-risk, high-reward proposal received the rare perfect impact score of 10, which is the highest score awarded by NIH.

      Caroline Uhler is a professor in the Department of Electrical Engineering and Computer Science and the Institute for Data, Systems, and Society. In addition, she is a core institute member at the Broad Institute of MIT and Harvard, where she co-directs the Eric and Wendy Schmidt Center. By combining machine learning, statistics, and genomics, she develops representation learning and causal inference methods to elucidate gene regulation in health and disease.

      The High-Risk, High-Reward Research program is supported by the NIH Common Fund, which oversees programs that pursue major opportunities and gaps in biomedical research that require collaboration across NIH Institutes and Centers. In addition to the New Innovator Award, the NIH also issues three other awards each year: the Pioneer Award, which supports bold and innovative research projects with unusually broad scientific impact; the Transformative Research Award, which supports risky and untested projects with transformative potential; and the Early Independence Award, which allows especially impressive junior scientists to skip the traditional postdoctoral training program to launch independent research careers.

      This year, the High-Risk, High-Reward Research program is awarding 103 awards, including eight Pioneer Awards, 72 New Innovator Awards, nine Transformative Research Awards, and 14 Early Independence Awards. These 103 awards total approximately $285 million in support from the institutes, centers, and offices across NIH over five years. “The science advanced by these researchers is poised to blaze new paths of discovery in human health,” says Lawrence A. Tabak DDS, PhD, who is performing the duties of the director of NIH. “This unique cohort of scientists will transform what is known in the biological and behavioral world. We are privileged to support this innovative science.” More

    • 50 Shares149 Views

      in Data Management & Statistics

      Taming the data deluge

      by

      An oncoming tsunami of data threatens to overwhelm huge data-rich research projects on such areas that range from the tiny neutrino to an exploding supernova, as well as the mysteries deep within the brain. 

      When LIGO picks up a gravitational-wave signal from a distant collision of black holes and neutron stars, a clock starts ticking for capturing the earliest possible light that may accompany them: time is of the essence in this race. Data collected from electrical sensors monitoring brain activity are outpacing computing capacity. Information from the Large Hadron Collider (LHC)’s smashed particle beams will soon exceed 1 petabit per second. 

      To tackle this approaching data bottleneck in real-time, a team of researchers from nine institutions led by the University of Washington, including MIT, has received $15 million in funding to establish the Accelerated AI Algorithms for Data-Driven Discovery (A3D3) Institute. From MIT, the research team includes Philip Harris, assistant professor of physics, who will serve as the deputy director of the A3D3 Institute; Song Han, assistant professor of electrical engineering and computer science, who will serve as the A3D3’s co-PI; and Erik Katsavounidis, senior research scientist with the MIT Kavli Institute for Astrophysics and Space Research.

      Infused with this five-year Harnessing the Data Revolution Big Idea grant, and jointly funded by the Office of Advanced Cyberinfrastructure, A3D3 will focus on three data-rich fields: multi-messenger astrophysics, high-energy particle physics, and brain imaging neuroscience. By enriching AI algorithms with new processors, A3D3 seeks to speed up AI algorithms for solving fundamental problems in collider physics, neutrino physics, astronomy, gravitational-wave physics, computer science, and neuroscience. 

      “I am very excited about the new Institute’s opportunities for research in nuclear and particle physics,” says Laboratory for Nuclear Science Director Boleslaw Wyslouch. “Modern particle detectors produce an enormous amount of data, and we are looking for extraordinarily rare signatures. The application of extremely fast processors to sift through these mountains of data will make a huge difference in what we will measure and discover.”

      The seeds of A3D3 were planted in 2017, when Harris and his colleagues at Fermilab and CERN decided to integrate real-time AI algorithms to process the incredible rates of data at the LHC. Through email correspondence with Han, Harris’ team built a compiler, HLS4ML, that could run an AI algorithm in nanoseconds.

      “Before the development of HLS4ML, the fastest processing that we knew of was roughly a millisecond per AI inference, maybe a little faster,” says Harris. “We realized all the AI algorithms were designed to solve much slower problems, such as image and voice recognition. To get to nanosecond inference timescales, we recognized we could make smaller algorithms and rely on custom implementations with Field Programmable Gate Array (FPGA) processors in an approach that was largely different from what others were doing.”

      A few months later, Harris presented their research at a physics faculty meeting, where Katsavounidis became intrigued. Over coffee in Building 7, they discussed combining Harris’ FPGA with Katsavounidis’s use of machine learning for finding gravitational waves. FPGAs and other new processor types, such as graphics processing units (GPUs), accelerate AI algorithms to more quickly analyze huge amounts of data.

      “I had worked with the first FPGAs that were out in the market in the early ’90s and have witnessed first-hand how they revolutionized front-end electronics and data acquisition in big high-energy physics experiments I was working on back then,” recalls Katsavounidis. “The ability to have them crunch gravitational-wave data has been in the back of my mind since joining LIGO over 20 years ago.”

      Two years ago they received their first grant, and the University of Washington’s Shih-Chieh Hsu joined in. The team initiated the Fast Machine Lab, published about 40 papers on the subject, built the group to about 50 researchers, and “launched a whole industry of how to explore a region of AI that has not been explored in the past,” says Harris. “We basically started this without any funding. We’ve been getting small grants for various projects over the years. A3D3 represents our first large grant to support this effort.”  

      “What makes A3D3 so special and suited to MIT is its exploration of a technical frontier, where AI is implemented not in high-level software, but rather in lower-level firmware, reconfiguring individual gates to address the scientific question at hand,” says Rob Simcoe, director of MIT Kavli Institute for Astrophysics and Space Research and the Francis Friedman Professor of Physics. “We are in an era where experiments generate torrents of data. The acceleration gained from tailoring reprogrammable, bespoke computers at the processor level can advance real-time analysis of these data to new levels of speed and sophistication.”

      The Huge Data from the Large Hadron Collider 

      With data rates already exceeding 500 terabits per second, the LHC processes more data than any other scientific instrument on earth. Its future aggregate data rates will soon exceed 1 petabit per second, the biggest data rate in the world. 

      “Through the use of AI, A3D3 aims to perform advanced analyses, such as anomaly detection, and particle reconstruction on all collisions happening 40 million times per second,” says Harris.

      The goal is to find within all of this data a way to identify the few collisions out of the 3.2 billion collisions per second that could reveal new forces, explain how dark matter is formed, and complete the picture of how fundamental forces interact with matter. Processing all of this information requires a customized computing system capable of interpreting the collider information within ultra-low latencies.  

      “The challenge of running this on all of the 100s of terabits per second in real-time is daunting and requires a complete overhaul of how we design and implement AI algorithms,” says Harris. “With large increases in the detector resolution leading to data rates that are even larger the challenge of finding the one collision, among many, will become even more daunting.” 

      The Brain and the Universe

      Thanks to advances in techniques such as medical imaging and electrical recordings from implanted electrodes, neuroscience is also gathering larger amounts of data on how the brain’s neural networks process responses to stimuli and perform motor information. A3D3 plans to develop and implement high-throughput and low-latency AI algorithms to process, organize, and analyze massive neural datasets in real time, to probe brain function in order to enable new experiments and therapies.   

      With Multi-Messenger Astrophysics (MMA), A3D3 aims to quickly identify astronomical events by efficiently processing data from gravitational waves, gamma-ray bursts, and neutrinos picked up by telescopes and detectors. 

      The A3D3 researchers also include a multi-disciplinary group of 15 other researchers, including project lead the University of Washington, along with Caltech, Duke University, Purdue University, UC San Diego, University of Illinois Urbana-Champaign, University of Minnesota, and the University of Wisconsin-Madison. It will include neutrinos research at Icecube and DUNE, and visible astronomy at Zwicky Transient Facility, and will organize deep-learning workshops and boot camps to train students and researchers on how to contribute to the framework and widen the use of fast AI strategies.

      “We have reached a point where detector network growth will be transformative, both in terms of event rates and in terms of astrophysical reach and ultimately, discoveries,” says Katsavounidis. “‘Fast’ and ‘efficient’ is the only way to fight the ‘faint’ and ‘fuzzy’ that is out there in the universe, and the path for getting the most out of our detectors. A3D3 on one hand is going to bring production-scale AI to gravitational-wave physics and multi-messenger astronomy; but on the other hand, we aspire to go beyond our immediate domains and become the go-to place across the country for applications of accelerated AI to data-driven disciplines.” More

    • 88 Shares179 Views

      in Data Management & Statistics

      Research collaboration puts climate-resilient crops in sight

      by

      Any houseplant owner knows that changes in the amount of water or sunlight a plant receives can put it under immense stress. A dying plant brings certain disappointment to anyone with a green thumb. 

      But for farmers who make their living by successfully growing plants, and whose crops may nourish hundreds or thousands of people, the devastation of failing flora is that much greater. As climate change is poised to cause increasingly unpredictable weather patterns globally, crops may be subject to more extreme environmental conditions like droughts, fluctuating temperatures, floods, and wildfire. 

      Climate scientists and food systems researchers worry about the stress climate change may put on crops, and on global food security. In an ambitious interdisciplinary project funded by the Abdul Latif Jameel Water and Food Systems Lab (J-WAFS), David Des Marais, the Gale Assistant Professor in the Department of Civil and Environmental Engineering at MIT, and Caroline Uhler, an associate professor in the MIT Department of Electrical Engineering and Computer Science and the Institute for Data, Systems, and Society, are investigating how plant genes communicate with one another under stress. Their research results can be used to breed plants more resilient to climate change.

      Crops in trouble

      Governing plants’ responses to environmental stress are gene regulatory networks, or GRNs, which guide the development and behaviors of living things. A GRN may be comprised of thousands of genes and proteins that all communicate with one another. GRNs help a particular cell, tissue, or organism respond to environmental changes by signaling certain genes to turn their expression on or off.

      Even seemingly minor or short-term changes in weather patterns can have large effects on crop yield and food security. An environmental trigger, like a lack of water during a crucial phase of plant development, can turn a gene on or off, and is likely to affect many others in the GRN. For example, without water, a gene enabling photosynthesis may switch off. This can create a domino effect, where the genes that rely on those regulating photosynthesis are silenced, and the cycle continues. As a result, when photosynthesis is halted, the plant may experience other detrimental side effects, like no longer being able to reproduce or defend against pathogens. The chain reaction could even kill a plant before it has the chance to be revived by a big rain.

      Des Marais says he wishes there was a way to stop those genes from completely shutting off in such a situation. To do that, scientists would need to better understand how exactly gene networks respond to different environmental triggers. Bringing light to this molecular process is exactly what he aims to do in this collaborative research effort.

      Solving complex problems across disciplines

      Despite their crucial importance, GRNs are difficult to study because of how complex and interconnected they are. Usually, to understand how a particular gene is affecting others, biologists must silence one gene and see how the others in the network respond. 

      For years, scientists have aspired to an algorithm that could synthesize the massive amount of information contained in GRNs to “identify correct regulatory relationships among genes,” according to a 2019 article in the Encyclopedia of Bioinformatics and Computational Biology. 

      “A GRN can be seen as a large causal network, and understanding the effects that silencing one gene has on all other genes requires understanding the causal relationships among the genes,” says Uhler. “These are exactly the kinds of algorithms my group develops.”

      Des Marais and Uhler’s project aims to unravel these complex communication networks and discover how to breed crops that are more resilient to the increased droughts, flooding, and erratic weather patterns that climate change is already causing globally.

      In addition to climate change, by 2050, the world will demand 70 percent more food to feed a booming population. “Food systems challenges cannot be addressed individually in disciplinary or topic area silos,” says Greg Sixt, J-WAFS’ research manager for climate and food systems. “They must be addressed in a systems context that reflects the interconnected nature of the food system.”

      Des Marais’ background is in biology, and Uhler’s in statistics. “Dave’s project with Caroline was essentially experimental,” says Renee J. Robins, J-WAFS’ executive director. “This kind of exploratory research is exactly what the J-WAFS seed grant program is for.”

      Getting inside gene regulatory networks

      Des Marais and Uhler’s work begins in a windowless basement on MIT’s campus, where 300 genetically identical Brachypodium distachyon plants grow in large, temperature-controlled chambers. The plant, which contains more than 30,000 genes, is a good model for studying important cereal crops like wheat, barley, maize, and millet. For three weeks, all plants receive the same temperature, humidity, light, and water. Then, half are slowly tapered off water, simulating drought-like conditions.

      Six days into the forced drought, the plants are clearly suffering. Des Marais’ PhD student Jie Yun takes tissues from 50 hydrated and 50 dry plants, freezes them in liquid nitrogen to immediately halt metabolic activity, grinds them up into a fine powder, and chemically separates the genetic material. The genes from all 100 samples are then sequenced at a lab across the street.

      The team is left with a spreadsheet listing the 30,000 genes found in each of the 100 plants at the moment they were frozen, and how many copies there were. Uhler’s PhD student Anastasiya Belyaeva inputs the massive spreadsheet into the computer program she developed and runs her novel algorithm. Within a few hours, the group can see which genes were most active in one condition over another, how the genes were communicating, and which were causing changes in others. 

      The methodology captures important subtleties that could allow researchers to eventually alter gene pathways and breed more resilient crops. “When you expose a plant to drought stress, it’s not like there’s some canonical response,” Des Marais says. “There’s lots of things going on. It’s turning this physiologic process up, this one down, this one didn’t exist before, and now suddenly is turned on.” 

      In addition to Des Marais and Uhler’s research, J-WAFS has funded projects in food and water from researchers in 29 departments across all five MIT schools as well as the MIT Schwarzman College of Computing. J-WAFS seed grants typically fund seven to eight new projects every year.

      “The grants are really aimed at catalyzing new ideas, providing the sort of support [for MIT researchers] to be pushing boundaries, and also bringing in faculty who may have some interesting ideas that they haven’t yet applied to water or food concerns,” Robins says. “It’s an avenue for researchers all over the Institute to apply their ideas to water and food.”

      Alison Gold is a student in MIT’s Graduate Program in Science Writing. More