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    2023-24 Takeda Fellows: Advancing research at the intersection of AI and health

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    The School of Engineering has selected 13 new Takeda Fellows for the 2023-24 academic year. With support from Takeda, the graduate students will conduct pathbreaking research ranging from remote health monitoring for virtual clinical trials to ingestible devices for at-home, long-term diagnostics.

    Now in its fourth year, the MIT-Takeda Program, a collaboration between MIT’s School of Engineering and Takeda, fuels the development and application of artificial intelligence capabilities to benefit human health and drug development. Part of the Abdul Latif Jameel Clinic for Machine Learning in Health, the program coalesces disparate disciplines, merges theory and practical implementation, combines algorithm and hardware innovations, and creates multidimensional collaborations between academia and industry.

    The 2023-24 Takeda Fellows are:

    Adam Gierlach

    Adam Gierlach is a PhD candidate in the Department of Electrical Engineering and Computer Science. Gierlach’s work combines innovative biotechnology with machine learning to create ingestible devices for advanced diagnostics and delivery of therapeutics. In his previous work, Gierlach developed a non-invasive, ingestible device for long-term gastric recordings in free-moving patients. With the support of a Takeda Fellowship, he will build on this pathbreaking work by developing smart, energy-efficient, ingestible devices powered by application-specific integrated circuits for at-home, long-term diagnostics. These revolutionary devices — capable of identifying, characterizing, and even correcting gastrointestinal diseases — represent the leading edge of biotechnology. Gierlach’s innovative contributions will help to advance fundamental research on the enteric nervous system and help develop a better understanding of gut-brain axis dysfunctions in Parkinson’s disease, autism spectrum disorder, and other prevalent disorders and conditions.

    Vivek Gopalakrishnan

    Vivek Gopalakrishnan is a PhD candidate in the Harvard-MIT Program in Health Sciences and Technology. Gopalakrishnan’s goal is to develop biomedical machine-learning methods to improve the study and treatment of human disease. Specifically, he employs computational modeling to advance new approaches for minimally invasive, image-guided neurosurgery, offering a safe alternative to open brain and spinal procedures. With the support of a Takeda Fellowship, Gopalakrishnan will develop real-time computer vision algorithms that deliver high-quality, 3D intraoperative image guidance by extracting and fusing information from multimodal neuroimaging data. These algorithms could allow surgeons to reconstruct 3D neurovasculature from X-ray angiography, thereby enhancing the precision of device deployment and enabling more accurate localization of healthy versus pathologic anatomy.

    Hao He

    Hao He is a PhD candidate in the Department of Electrical Engineering and Computer Science. His research interests lie at the intersection of generative AI, machine learning, and their applications in medicine and human health, with a particular emphasis on passive, continuous, remote health monitoring to support virtual clinical trials and health-care management. More specifically, He aims to develop trustworthy AI models that promote equitable access and deliver fair performance independent of race, gender, and age. In his past work, He has developed monitoring systems applied in clinical studies of Parkinson’s disease, Alzheimer’s disease, and epilepsy. Supported by a Takeda Fellowship, He will develop a novel technology for the passive monitoring of sleep stages (using radio signaling) that seeks to address existing gaps in performance across different demographic groups. His project will tackle the problem of imbalance in available datasets and account for intrinsic differences across subpopulations, using generative AI and multi-modality/multi-domain learning, with the goal of learning robust features that are invariant to different subpopulations. He’s work holds great promise for delivering advanced, equitable health-care services to all people and could significantly impact health care and AI.

    Chengyi Long

    Chengyi Long is a PhD candidate in the Department of Civil and Environmental Engineering. Long’s interdisciplinary research integrates the methodology of physics, mathematics, and computer science to investigate questions in ecology. Specifically, Long is developing a series of potentially groundbreaking techniques to explain and predict the temporal dynamics of ecological systems, including human microbiota, which are essential subjects in health and medical research. His current work, supported by a Takeda Fellowship, is focused on developing a conceptual, mathematical, and practical framework to understand the interplay between external perturbations and internal community dynamics in microbial systems, which may serve as a key step toward finding bio solutions to health management. A broader perspective of his research is to develop AI-assisted platforms to anticipate the changing behavior of microbial systems, which may help to differentiate between healthy and unhealthy hosts and design probiotics for the prevention and mitigation of pathogen infections. By creating novel methods to address these issues, Long’s research has the potential to offer powerful contributions to medicine and global health.

    Omar Mohd

    Omar Mohd is a PhD candidate in the Department of Electrical Engineering and Computer Science. Mohd’s research is focused on developing new technologies for the spatial profiling of microRNAs, with potentially important applications in cancer research. Through innovative combinations of micro-technologies and AI-enabled image analysis to measure the spatial variations of microRNAs within tissue samples, Mohd hopes to gain new insights into drug resistance in cancer. This work, supported by a Takeda Fellowship, falls within the emerging field of spatial transcriptomics, which seeks to understand cancer and other diseases by examining the relative locations of cells and their contents within tissues. The ultimate goal of Mohd’s current project is to find multidimensional patterns in tissues that may have prognostic value for cancer patients. One valuable component of his work is an open-source AI program developed with collaborators at Beth Israel Deaconess Medical Center and Harvard Medical School to auto-detect cancer epithelial cells from other cell types in a tissue sample and to correlate their abundance with the spatial variations of microRNAs. Through his research, Mohd is making innovative contributions at the interface of microsystem technology, AI-based image analysis, and cancer treatment, which could significantly impact medicine and human health.

    Sanghyun Park

    Sanghyun Park is a PhD candidate in the Department of Mechanical Engineering. Park specializes in the integration of AI and biomedical engineering to address complex challenges in human health. Drawing on his expertise in polymer physics, drug delivery, and rheology, his research focuses on the pioneering field of in-situ forming implants (ISFIs) for drug delivery. Supported by a Takeda Fellowship, Park is currently developing an injectable formulation designed for long-term drug delivery. The primary goal of his research is to unravel the compaction mechanism of drug particles in ISFI formulations through comprehensive modeling and in-vitro characterization studies utilizing advanced AI tools. He aims to gain a thorough understanding of this unique compaction mechanism and apply it to drug microcrystals to achieve properties optimal for long-term drug delivery. Beyond these fundamental studies, Park’s research also focuses on translating this knowledge into practical applications in a clinical setting through animal studies specifically aimed at extending drug release duration and improving mechanical properties. The innovative use of AI in developing advanced drug delivery systems, coupled with Park’s valuable insights into the compaction mechanism, could contribute to improving long-term drug delivery. This work has the potential to pave the way for effective management of chronic diseases, benefiting patients, clinicians, and the pharmaceutical industry.

    Huaiyao Peng

    Huaiyao Peng is a PhD candidate in the Department of Biological Engineering. Peng’s research interests are focused on engineered tissue, microfabrication platforms, cancer metastasis, and the tumor microenvironment. Specifically, she is advancing novel AI techniques for the development of pre-cancer organoid models of high-grade serous ovarian cancer (HGSOC), an especially lethal and difficult-to-treat cancer, with the goal of gaining new insights into progression and effective treatments. Peng’s project, supported by a Takeda Fellowship, will be one of the first to use cells from serous tubal intraepithelial carcinoma lesions found in the fallopian tubes of many HGSOC patients. By examining the cellular and molecular changes that occur in response to treatment with small molecule inhibitors, she hopes to identify potential biomarkers and promising therapeutic targets for HGSOC, including personalized treatment options for HGSOC patients, ultimately improving their clinical outcomes. Peng’s work has the potential to bring about important advances in cancer treatment and spur innovative new applications of AI in health care. 

    Priyanka Raghavan

    Priyanka Raghavan is a PhD candidate in the Department of Chemical Engineering. Raghavan’s research interests lie at the frontier of predictive chemistry, integrating computational and experimental approaches to build powerful new predictive tools for societally important applications, including drug discovery. Specifically, Raghavan is developing novel models to predict small-molecule substrate reactivity and compatibility in regimes where little data is available (the most realistic regimes). A Takeda Fellowship will enable Raghavan to push the boundaries of her research, making innovative use of low-data and multi-task machine learning approaches, synthetic chemistry, and robotic laboratory automation, with the goal of creating an autonomous, closed-loop system for the discovery of high-yielding organic small molecules in the context of underexplored reactions. Raghavan’s work aims to identify new, versatile reactions to broaden a chemist’s synthetic toolbox with novel scaffolds and substrates that could form the basis of essential drugs. Her work has the potential for far-reaching impacts in early-stage, small-molecule discovery and could help make the lengthy drug-discovery process significantly faster and cheaper.

    Zhiye Song

    Zhiye “Zoey” Song is a PhD candidate in the Department of Electrical Engineering and Computer Science. Song’s research integrates cutting-edge approaches in machine learning (ML) and hardware optimization to create next-generation, wearable medical devices. Specifically, Song is developing novel approaches for the energy-efficient implementation of ML computation in low-power medical devices, including a wearable ultrasound “patch” that captures and processes images for real-time decision-making capabilities. Her recent work, conducted in collaboration with clinicians, has centered on bladder volume monitoring; other potential applications include blood pressure monitoring, muscle diagnosis, and neuromodulation. With the support of a Takeda Fellowship, Song will build on that promising work and pursue key improvements to existing wearable device technologies, including developing low-compute and low-memory ML algorithms and low-power chips to enable ML on smart wearable devices. The technologies emerging from Song’s research could offer exciting new capabilities in health care, enabling powerful and cost-effective point-of-care diagnostics and expanding individual access to autonomous and continuous medical monitoring.

    Peiqi Wang

    Peiqi Wang is a PhD candidate in the Department of Electrical Engineering and Computer Science. Wang’s research aims to develop machine learning methods for learning and interpretation from medical images and associated clinical data to support clinical decision-making. He is developing a multimodal representation learning approach that aligns knowledge captured in large amounts of medical image and text data to transfer this knowledge to new tasks and applications. Supported by a Takeda Fellowship, Wang will advance this promising line of work to build robust tools that interpret images, learn from sparse human feedback, and reason like doctors, with potentially major benefits to important stakeholders in health care.

    Oscar Wu

    Haoyang “Oscar” Wu is a PhD candidate in the Department of Chemical Engineering. Wu’s research integrates quantum chemistry and deep learning methods to accelerate the process of small-molecule screening in the development of new drugs. By identifying and automating reliable methods for finding transition state geometries and calculating barrier heights for new reactions, Wu’s work could make it possible to conduct the high-throughput ab initio calculations of reaction rates needed to screen the reactivity of large numbers of active pharmaceutical ingredients (APIs). A Takeda Fellowship will support his current project to: (1) develop open-source software for high-throughput quantum chemistry calculations, focusing on the reactivity of drug-like molecules, and (2) develop deep learning models that can quantitatively predict the oxidative stability of APIs. The tools and insights resulting from Wu’s research could help to transform and accelerate the drug-discovery process, offering significant benefits to the pharmaceutical and medical fields and to patients.

    Soojung Yang

    Soojung Yang is a PhD candidate in the Department of Materials Science and Engineering. Yang’s research applies cutting-edge methods in geometric deep learning and generative modeling, along with atomistic simulations, to better understand and model protein dynamics. Specifically, Yang is developing novel tools in generative AI to explore protein conformational landscapes that offer greater speed and detail than physics-based simulations at a substantially lower cost. With the support of a Takeda Fellowship, she will build upon her successful work on the reverse transformation of coarse-grained proteins to the all-atom resolution, aiming to build machine-learning models that bridge multiple size scales of protein conformation diversity (all-atom, residue-level, and domain-level). Yang’s research holds the potential to provide a powerful and widely applicable new tool for researchers who seek to understand the complex protein functions at work in human diseases and to design drugs to treat and cure those diseases.

    Yuzhe Yang

    Yuzhe Yang is a PhD candidate in the Department of Electrical Engineering and Computer Science. Yang’s research interests lie at the intersection of machine learning and health care. In his past and current work, Yang has developed and applied innovative machine-learning models that address key challenges in disease diagnosis and tracking. His many notable achievements include the creation of one of the first machine learning-based solutions using nocturnal breathing signals to detect Parkinson’s disease (PD), estimate disease severity, and track PD progression. With the support of a Takeda Fellowship, Yang will expand this promising work to develop an AI-based diagnosis model for Alzheimer’s disease (AD) using sleep-breathing data that is significantly more reliable, flexible, and economical than current diagnostic tools. This passive, in-home, contactless monitoring system — resembling a simple home Wi-Fi router — will also enable remote disease assessment and continuous progression tracking. Yang’s groundbreaking work has the potential to advance the diagnosis and treatment of prevalent diseases like PD and AD, and it offers exciting possibilities for addressing many health challenges with reliable, affordable machine-learning tools.  More

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      3 Questions: A new PhD program from the Center for Computational Science and Engineering

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      This fall, the Center for Computational Science and Engineering (CCSE), an academic unit in the MIT Schwarzman College of Computing, is introducing a new standalone PhD degree program that will enable students to pursue research in cross-cutting methodological aspects of computational science and engineering. The launch follows approval of the center’s degree program proposal at the May 2023 Institute faculty meeting.

      Doctoral-level graduate study in computational science and engineering (CSE) at MIT has, for the past decade, been offered through an interdisciplinary program in which CSE students are admitted to one of eight participating academic departments in the School of Engineering or School of Science. While this model adds a strong disciplinary component to students’ education, the rapid growth of the CSE field and the establishment of the MIT Schwarzman College of Computing have prompted an exciting expansion of MIT’s graduate-level offerings in computation.

      The new degree, offered by the college, will run alongside MIT’s existing interdisciplinary offerings in CSE, complementing these doctoral training programs and preparing students to contribute to the leading edge of the field. Here, CCSE co-directors Youssef Marzouk and Nicolas Hadjiconstantinou discuss the standalone program and how they expect it to elevate the visibility and impact of CSE research and education at MIT.

      Q: What is computational science and engineering?

      Marzouk: Computational science and engineering focuses on the development and analysis of state-of-the-art methods for computation and their innovative application to problems of science and engineering interest. It has intellectual foundations in applied mathematics, statistics, and computer science, and touches the full range of science and engineering disciplines. Yet, it synthesizes these foundations into a discipline of its own — one that links the digital and physical worlds. It’s an exciting and evolving multidisciplinary field.

      Hadjiconstantinou: Examples of CSE research happening at MIT include modeling and simulation techniques, the underlying computational mathematics, and data-driven modeling of physical systems. Computational statistics and scientific machine learning have become prominent threads within CSE, joining high-performance computing, mathematically-oriented programming languages, and their broader links to algorithms and software. Application domains include energy, environment and climate, materials, health, transportation, autonomy, and aerospace, among others. Some of our researchers focus on general and widely applicable methodology, while others choose to focus on methods and algorithms motivated by a specific domain of application.

      Q: What was the motivation behind creating a standalone PhD program?

      Marzouk: The new degree focuses on a particular class of students whose background and interests are primarily in CSE methodology, in a manner that cuts across the disciplinary research structure represented by our current “with-departments” degree program. There is a strong research demand for such methodologically-focused students among CCSE faculty and MIT faculty in general. Our objective is to create a targeted, coherent degree program in this field that, alongside our other thriving CSE offerings, will create the leading environment for top CSE students worldwide.

      Hadjiconstantinou: One of CCSE’s most important functions is to recruit exceptional students who are trained in and want to work in computational science and engineering. Experience with our CSE master’s program suggests that students with a strong background and interests in the discipline prefer to apply to a pure CSE program for their graduate studies. The standalone degree aims to bring these students to MIT and make them available to faculty across the Institute.

      Q: How will this impact computing education and research at MIT? 

      Hadjiconstantinou: We believe that offering a standalone PhD program in CSE alongside the existing “with-departments” programs will significantly strengthen MIT’s graduate programs in computing. In particular, it will strengthen the methodological core of CSE research and education at MIT, while continuing to support the disciplinary-flavored CSE work taking place in our participating departments, which include Aeronautics and Astronautics; Chemical Engineering; Civil and Environmental Engineering; Materials Science and Engineering; Mechanical Engineering; Nuclear Science and Engineering; Earth, Atmospheric and Planetary Sciences; and Mathematics. Together, these programs will create a stronger CSE student cohort and facilitate deeper exchanges between the college and other units at MIT.

      Marzouk: In a broader sense, the new program is designed to help realize one of the key opportunities presented by the college, which is to create a richer variety of graduate degrees in computation and to involve as many faculty and units in these educational endeavors as possible. The standalone CSE PhD will join other distinguished doctoral programs of the college — such as the Department of Electrical Engineering and Computer Science PhD; the Operations Research Center PhD; and the Interdisciplinary Doctoral Program in Statistics and the Social and Engineering Systems PhD within the Institute for Data, Systems, and Society — and grow in a way that is informed by them. The confluence of these academic programs, and natural synergies among them, will make MIT quite unique. More

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      J-WAFS announces 2023 seed grant recipients

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      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

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      Researchers develop novel AI-based estimator for manufacturing medicine

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      When medical companies manufacture the pills and tablets that treat any number of illnesses, aches, and pains, they need to isolate the active pharmaceutical ingredient from a suspension and dry it. The process requires a human operator to monitor an industrial dryer, agitate the material, and watch for the compound to take on the right qualities for compressing into medicine. The job depends heavily on the operator’s observations.   

      Methods for making that process less subjective and a lot more efficient are the subject of a recent Nature Communications paper authored by researchers at MIT and Takeda. The paper’s authors devise a way to use physics and machine learning to categorize the rough surfaces that characterize particles in a mixture. The technique, which uses a physics-enhanced autocorrelation-based estimator (PEACE), could change pharmaceutical manufacturing processes for pills and powders, increasing efficiency and accuracy and resulting in fewer failed batches of pharmaceutical products.  

      “Failed batches or failed steps in the pharmaceutical process are very serious,” says Allan Myerson, a professor of practice in the MIT Department of Chemical Engineering and one of the study’s authors. “Anything that improves the reliability of the pharmaceutical manufacturing, reduces time, and improves compliance is a big deal.”

      The team’s work is part of an ongoing collaboration between Takeda and MIT, launched in 2020. The MIT-Takeda Program aims to leverage the experience of both MIT and Takeda to solve problems at the intersection of medicine, artificial intelligence, and health care.

      In pharmaceutical manufacturing, determining whether a compound is adequately mixed and dried ordinarily requires stopping an industrial-sized dryer and taking samples off the manufacturing line for testing. Researchers at Takeda thought artificial intelligence could improve the task and reduce stoppages that slow down production. Originally the research team planned to use videos to train a computer model to replace a human operator. But determining which videos to use to train the model still proved too subjective. Instead, the MIT-Takeda team decided to illuminate particles with a laser during filtration and drying, and measure particle size distribution using physics and machine learning. 

      “We just shine a laser beam on top of this drying surface and observe,” says Qihang Zhang, a doctoral student in MIT’s Department of Electrical Engineering and Computer Science and the study’s first author. 

      Play video

      A physics-derived equation describes the interaction between the laser and the mixture, while machine learning characterizes the particle sizes. The process doesn’t require stopping and starting the process, which means the entire job is more secure and more efficient than standard operating procedure, according to George Barbastathis, professor of mechanical engineering at MIT and corresponding author of the study.

      The machine learning algorithm also does not require many datasets to learn its job, because the physics allows for speedy training of the neural network.

      “We utilize the physics to compensate for the lack of training data, so that we can train the neural network in an efficient way,” says Zhang. “Only a tiny amount of experimental data is enough to get a good result.”

      Today, the only inline processes used for particle measurements in the pharmaceutical industry are for slurry products, where crystals float in a liquid. There is no method for measuring particles within a powder during mixing. Powders can be made from slurries, but when a liquid is filtered and dried its composition changes, requiring new measurements. In addition to making the process quicker and more efficient, using the PEACE mechanism makes the job safer because it requires less handling of potentially highly potent materials, the authors say. 

      The ramifications for pharmaceutical manufacturing could be significant, allowing drug production to be more efficient, sustainable, and cost-effective, by reducing the number of experiments companies need to conduct when making products. Monitoring the characteristics of a drying mixture is an issue the industry has long struggled with, according to Charles Papageorgiou, the director of Takeda’s Process Chemistry Development group and one of the study’s authors. 

      “It is a problem that a lot of people are trying to solve, and there isn’t a good sensor out there,” says Papageorgiou. “This is a pretty big step change, I think, with respect to being able to monitor, in real time, particle size distribution.”

      Papageorgiou said that the mechanism could have applications in other industrial pharmaceutical operations. At some point, the laser technology may be able to train video imaging, allowing manufacturers to use a camera for analysis rather than laser measurements. The company is now working to assess the tool on different compounds in its lab. 

      The results come directly from collaboration between Takeda and three MIT departments: Mechanical Engineering, Chemical Engineering, and Electrical Engineering and Computer Science. Over the last three years, researchers at MIT and Takeda have worked together on 19 projects focused on applying machine learning and artificial intelligence to problems in the health-care and medical industry as part of the MIT-Takeda Program. 

      Often, it can take years for academic research to translate to industrial processes. But researchers are hopeful that direct collaboration could shorten that timeline. Takeda is a walking distance away from MIT’s campus, which allowed researchers to set up tests in the company’s lab, and real-time feedback from Takeda helped MIT researchers structure their research based on the company’s equipment and operations. 

      Combining the expertise and mission of both entities helps researchers ensure their experimental results will have real-world implications. The team has already filed for two patents and has plans to file for a third.   More

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      Meet the 2022-23 Accenture Fellows

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

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

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

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

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

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      Celebrating open data

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      The inaugural MIT Prize for Open Data, which included a $2,500 cash prize, was recently awarded to 10 individual and group research projects. Presented jointly by the School of Science and the MIT Libraries, the prize recognizes MIT-affiliated researchers who make their data openly accessible and reusable by others. The prize winners and 16 honorable mention recipients were honored at the Open Data @ MIT event held Oct. 28 at Hayden Library. 

      “By making data open, researchers create opportunities for novel uses of their data and for new insights to be gleaned,” says Chris Bourg, director of MIT Libraries. “Open data accelerates scholarly progress and discovery, advances equity in scholarly participation, and increases transparency, replicability, and trust in science.” 

      Recognizing shared values

      Spearheaded by Bourg and Rebecca Saxe, associate dean of the School of Science and John W. Jarve (1978) Professor of Brain and Cognitive Sciences, the MIT Prize for Open Data was launched to highlight the value of open data at MIT and to encourage the next generation of researchers. Nominations were solicited from across the Institute, with a focus on trainees: research technicians, undergraduate or graduate students, or postdocs.

      “By launching an MIT-wide prize and event, we aimed to create visibility for the scholars who create, use, and advocate for open data,” says Saxe. “Highlighting this research and creating opportunities for networking would also help open-data advocates across campus find each other.” 

      Recognizing researchers who share data was also one of the recommendations of the Ad Hoc Task Force on Open Access to MIT’s Research, which Bourg co-chaired with Hal Abelson, Class of 1922 Professor, Department of Electrical Engineering and Computer Science. An annual award was one of the strategies put forth by the task force to further the Institute’s mission to disseminate the fruits of its research and scholarship as widely as possible.

      Strong competition

      Winners and honorable mentions were chosen from more than 70 nominees, representing all five schools, the MIT Schwarzman College of Computing, and several research centers across MIT. A committee composed of faculty, staff, and a graduate student made the selections:

      Yunsie Chung, graduate student in the Department of Chemical Engineering, won for SolProp, the largest open-source dataset with temperature-dependent solubility values of organic compounds. 
      Matthew Groh, graduate student, MIT Media Lab, accepted on behalf of the team behind the Fitzpatrick 17k dataset, an open dataset consisting of nearly 17,000 images of skin disease alongside skin disease and skin tone annotations. 
      Tom Pollard, research scientist at the Institute for Medical Engineering and Science, accepted on behalf of the PhysioNet team. This data-sharing platform enables thousands of clinical and machine-learning research studies each year and allows researchers to share sensitive resources that would not be possible through typical data sharing platforms. 
      Joseph Replogle, graduate student with the Whitehead Institute for Biomedical Research, was recognized for the Genome-wide Perturb-seq dataset, the largest publicly available, single-cell transcriptional dataset collected to date. 
      Pedro Reynolds-Cuéllar, graduate student with the MIT Media Lab/Art, Culture, and Technology, and Diana Duarte, co-founder at Diversa, won for Retos, an open-data platform for detailed documentation and sharing of local innovations from under-resourced settings. 
      Maanas Sharma, an undergraduate student, led States of Emergency, a nationwide project analyzing and grading the responses of prison systems to Covid-19 using data scraped from public databases and manually collected data. 
      Djuna von Maydell, graduate student in the Department of Brain and Cognitive Sciences, created the first publicly available dataset of single-cell gene expression from postmortem human brain tissue of patients who are carriers of APOE4, the major Alzheimer’s disease risk gene. 
      Raechel Walker, graduate researcher in the MIT Media Lab, and her collaborators created a Data Activism Curriculum for high school students through the Mayor’s Summer Youth Employment Program in Cambridge, Massachusetts. Students learned how to use data science to recognize, mitigate, and advocate for people who are disproportionately impacted by systemic inequality. 
      Suyeol Yun, graduate student in the Department of Political Science, was recognized for DeepWTO, a project creating open data for use in legal natural language processing research using cases from the World Trade Organization. 
      Jonathan Zheng, graduate student in the Department of Chemical Engineering, won for an open IUPAC dataset for acid dissociation constants, or “pKas,” physicochemical properties that govern how acidic a chemical is in a solution.
      A full list of winners and honorable mentions is available on the Open Data @ MIT website.

      A campus-wide celebration

      Awards were presented at a celebratory event held in the Nexus in Hayden Library during International Open Access Week. School of Science Dean Nergis Mavalvala kicked off the program by describing the long and proud history of open scholarship at MIT, citing the Institute-wide faculty open access policy and the launch of the open-source digital repository DSpace. “When I was a graduate student, we were trying to figure out how to share our theses during the days of the nascent internet,” she said, “With DSpace, MIT was figuring it out for us.” 

      The centerpiece of the program was a series of five-minute presentations from the prize winners on their research. Presenters detailed the ways they created, used, or advocated for open data, and the value that openness brings to their respective fields. Winner Djuna von Maydell, a graduate student in Professor Li-Huei Tsai’s lab who studies the genetic causes of neurodegeneration, underscored why it is important to share data, particularly data obtained from postmortem human brains. 

      “This is data generated from human brains, so every data point stems from a living, breathing human being, who presumably made this donation in the hope that we would use it to advance knowledge and uncover truth,” von Maydell said. “To maximize the probability of that happening, we have to make it available to the scientific community.” 

      MIT community members who would like to learn more about making their research data open can consult MIT Libraries’ Data Services team.  More

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      New leadership at MIT’s Center for Biomedical Innovation

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      As it continues in its mission to improve global health through the development and implementation of biomedical innovation, the MIT Center for Biomedical Innovation (CBI) today announced changes to its leadership team: Stacy Springs has been named executive director, and Professor Richard Braatz has joined as the center’s new associate faculty director.

      The change in leadership comes at a time of rapid development in new therapeutic modalities, growing concern over global access to biologic medicines and healthy food, and widespread interest in applying computational tools and multi-disciplinary approaches to address long-standing biomedical challenges.

      “This marks an exciting new chapter for the CBI,” says faculty director Anthony J. Sinskey, professor of biology, who cofounded CBI in 2005. “As I look back at almost 20 years of CBI history, I see an exponential growth in our activities, educational offerings, and impact.”

      The center’s collaborative research model accelerates innovation in biotechnology and biomedical research, drawing on the expertise of faculty and researchers in MIT’s schools of Engineering and Science, the MIT Schwarzman College of Computing, and the MIT Sloan School of Management.

      Springs steps into the role of executive director having previously served as senior director of programs for CBI and as executive director of CBI’s Biomanufacturing Program and its Consortium on Adventitious Agent Contamination in Biomanufacturing (CAACB). She succeeds Gigi Hirsch, who founded the NEW Drug Development ParadIGmS (NEWDIGS) Initiative at CBI in 2009. Hirsch and NEWDIGS have now moved to Tufts Medical Center, establishing a headquarters at the new Center for Biomedical System Design within the Institute for Clinical Research and Health Policy Studies there.

      Braatz, a chemical engineer whose work is informed by mathematical modeling and computational techniques, conducts research in process data analytics, design, and control of advanced manufacturing systems.

      “It’s been great to interact with faculty from across the Institute who have complementary expertise,” says Braatz, the Edwin R. Gilliland Professor in the Department of Chemical Engineering. “Participating in CBI’s workshops has led to fruitful partnerships with companies in tackling industry-wide challenges.”

      CBI is housed under the Institute for Data Systems and Society and, specifically, the Sociotechnical Systems Research Center in the MIT Schwarzman College of Computing. CBI is home to two biomanufacturing consortia: the CAACB and the Biomanufacturing Consortium (BioMAN). Through these precompetitive collaborations, CBI researchers work with biomanufacturers and regulators to advance shared interests in biomanufacturing.

      In addition, CBI researchers are engaged in several sponsored research programs focused on integrated continuous biomanufacturing capabilities for monoclonal antibodies and vaccines, analytical technologies to measure quality and safety attributes of a variety of biologics, including gene and cell therapies, and rapid-cycle development of virus-like particle vaccines for SARS-CoV-2.

      In another significant initiative, CBI researchers are applying data analytics strategies to biomanufacturing problems. “In our smart data analytics project, we are creating new decision support tools and algorithms for biomanufacturing process control and plant-level decision-making. Further, we are leveraging machine learning and natural language processing to improve post-market surveillance studies,” says Springs.

      CBI is also working on advanced manufacturing for cell and gene therapies, among other new modalities, and is a part of the Singapore-MIT Alliance for Research and Technology – Critical Analytics for Manufacturing Personalized-Medicine (SMART CAMP). SMART CAMP is an international research effort focused on developing the analytical tools and biological understanding of critical quality attributes that will enable the manufacture and delivery of improved cell therapies to patients.

      “This is a crucial time for biomanufacturing and for innovation across the health-care value chain. The collaborative efforts of MIT researchers and consortia members will drive fundamental discovery and inform much-needed progress in industry,” says MIT Vice President for Research Maria Zuber.

      “CBI has a track record of engaging with health-care ecosystem challenges. I am confident that under the new leadership, it will continue to inspire MIT, the United States, and the entire world to improve the health of all people,” adds Daniel Huttenlocher, dean of the MIT Schwarzman College of Computing. More

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      MIT welcomes eight MLK Visiting Professors and Scholars for 2022-23

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      From space traffic to virus evolution, community journalism to hip-hop, this year’s cohort in the Martin Luther King Jr. (MLK) Visiting Professors and Scholars Program will power an unprecedented range of intellectual pursuits during their time on the MIT campus. 

      “MIT is so fortunate to have this group of remarkable individuals join us,” says Institute Community and Equity Officer John Dozier. “They bring a range and depth of knowledge to share with our students and faculty, and we look forward to working with them to build a stronger sense of community across the Institute.”

      Since its inception in 1990, the MLK Scholars Program has hosted more than 135 visiting professors, practitioners, and intellectuals who enhance and enrich the MIT community through their engagement with students and faculty. The program, which honors the life and legacy of MLK by increasing the presence and recognizing the contributions of underrepresented scholars, is supported by the Office of the Provost with oversight from the Institute Community and Equity Office. 

      In spring 2022, MIT President Rafael Reif committed to MIT to adding two new positions in the MLK Visiting Scholars Program, including an expert in Native American studies. Those additional positions will be filled in the coming year.  

      The 2022-23 MLK Scholars:

      Daniel Auguste is an assistant professor in the Department of Sociology at Florida Atlantic University and is hosted by Roberto Fernandez in MIT Sloan School of Management. Auguste’s research interests include social inequalities in entrepreneurship development. During his visit, Auguste will study the impact of education debt burden and wealth inequality on business ownership and success, and how these consequences differ by race and ethnicity.

      Tawanna Dillahunt is an associate professor in the School of Information at the University of Michigan, where she also holds an appointment with the electrical engineering and computer science department. Catherine D’Ignazio in the Department of Urban Studies and Planning and Fotini Christia in the Institute for Data, Systems, and Society are her faculty hosts. Dillahunt’s scholarship focuses on equitable and inclusive computing. She identifies technological opportunities and implements tools to address and alleviate employment challenges faced by marginalized people. Dillahunt’s visiting appointment begins in September 2023.

      Javit Drake ’94 is a principal scientist in modeling and simulation and measurement sciences at Proctor & Gamble. His faculty host is Fikile Brushett in the Department of Chemical Engineering. An industry researcher with electrochemical energy expertise, Drake is a Course 10 (chemical engineering) alumnus, repeat lecturer, and research affiliate in the department. During his visit, he will continue to work with the Brushett Research Group to deepen his research and understanding of battery technologies while he innovates from those discoveries.

      Eunice Ferreira is an associate professor in the Department of Theater at Skidmore College and is hosted by Claire Conceison in Music and Theater Arts. This fall, Ferreira will teach “Black Theater Matters,” a course where students will explore performance and the cultural production of Black intellectuals and artists on Broadway and in local communities. Her upcoming book projects include “Applied Theatre and Racial Justice: Radical Imaginings for Just Communities” (forthcoming from Routledge) and “Crioulo Performance: Remapping Creole and Mixed Race Theatre” (forthcoming from Vanderbilt University Press). 

      Wasalu Jaco, widely known as Lupe Fiasco, is a rapper, record producer, and entrepreneur. He will be co-hosted by Nick Montfort of Comparative Media Studies/Writing and Mary Fuller of Literature. Jaco’s interests lie in the nexus of rap, computing, and activism. As a former visiting artist in MIT’s Center for Art, Science and Technology (CAST), he will leverage existing collaborations and participate in digital media and art research projects that use computing to explore novel questions related to hip-hop and rap. In addition to his engagement in cross-departmental projects, Jaco will teach a spring course on rap in the media and social contexts.

      Moribah Jah is an associate professor in the Aerospace Engineering and Engineering Mechanics Department at the University of Texas at Austin. He is hosted by Danielle Wood in Media Arts and Sciences and the Department of Aeronautics and Astronautics, and Richard Linares in the Department of Aeronautics and Astronautics. Jah’s research interests include space sustainability and space traffic management; as a visiting scholar, he will develop and strengthen a joint MIT/UT-Austin research program to increase resources and visibility of space sustainability. Jah will also help host the AeroAstro Rising Stars symposium, which highlights graduate students, postdocs, and early-career faculty from backgrounds underrepresented in aerospace engineering. 

      Louis Massiah SM ’82 is a documentary filmmaker and the founder and director of community media of Scribe Video Center, a nonprofit organization that uses media as a tool for social change. His work focuses on empowering Black, Indigenous, and People of Color (BIPOC) filmmakers to tell the stories of/by BIPOC communities. Massiah is hosted by Vivek Bald in Creative Media Studies/Writing. Massiah’s first project will be the launch of a National Community Media Journalism Consortium, a platform to share local news on a broader scale across communities.

      Brian Nord, a scientist at Fermi National Accelerator Laboratory, will join the Laboratory for Nuclear Science, hosted by Jesse Thaler in the Department of Physics. Nord’s research interests include the connection between ethics, justice, and scientific discovery. His efforts will be aimed at introducing new insights into how we model physical systems, design scientific experiments, and approach the ethics of artificial intelligence. As a lead organizer of the Strike for Black Lives in 2020, Nord will engage with justice-oriented members of the MIT physics community to strategize actions for advocacy and activism.

      Brandon Ogbunu, an assistant professor in the Department of Ecology and Evolutionary Biology at Yale University, will be hosted by Matthew Shoulders in the Department of Chemistry. Ogbunu’s research focus is on implementing chemistry and materials science perspectives into his work on virus evolution. In addition to serving as a guest lecturer in graduate courses, he will be collaborating with the Office of Engineering Outreach Programs on their K-12 outreach and recruitment efforts.

      For more information about these scholars and the program, visit mlkscholars.mit.edu. More

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      Computational modeling guides development of new materials

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      Metal-organic frameworks, a class of materials with porous molecular structures, have a variety of possible applications, such as capturing harmful gases and catalyzing chemical reactions. Made of metal atoms linked by organic molecules, they can be configured in hundreds of thousands of different ways.

      To help researchers sift through all of the possible metal-organic framework (MOF) structures and help identify the ones that would be most practical for a particular application, a team of MIT computational chemists has developed a model that can analyze the features of a MOF structure and predict if it will be stable enough to be useful.

      The researchers hope that these computational predictions will help cut the development time of new MOFs.

      “This will allow researchers to test the promise of specific materials before they go through the trouble of synthesizing them,” says Heather Kulik, an associate professor of chemical engineering at MIT.

      The MIT team is now working to develop MOFs that could be used to capture methane gas and convert it to useful compounds such as fuels.

      The researchers described their new model in two papers, one in the Journal of the American Chemical Society and one in Scientific Data. Graduate students Aditya Nandy and Gianmarco Terrones are the lead authors of the Scientific Data paper, and Nandy is also the lead author of the JACS paper. Kulik is the senior author of both papers.

      Modeling structure

      MOFs consist of metal atoms joined by organic molecules called linkers to create a rigid, cage-like structure. The materials also have many pores, which makes them useful for catalyzing reactions involving gases but can also make them less structurally stable.

      “The limitation in seeing MOFs realized at industrial scale is that although we can control their properties by controlling where each atom is in the structure, they’re not necessarily that stable, as far as materials go,” Kulik says. “They’re very porous and they can degrade under realistic conditions that we need for catalysis.”

      Scientists have been working on designing MOFs for more than 20 years, and thousands of possible structures have been published. A centralized repository contains about 10,000 of these structures but is not linked to any of the published findings on the properties of those structures.

      Kulik, who specializes in using computational modeling to discover structure-property relationships of materials, wanted to take a more systematic approach to analyzing and classifying the properties of MOFs.

      “When people make these now, it’s mostly trial and error. The MOF dataset is really promising because there are so many people excited about MOFs, so there’s so much to learn from what everyone’s been working on, but at the same time, it’s very noisy and it’s not systematic the way it’s reported,” she says.

      Kulik and her colleagues set out to analyze published reports of MOF structures and properties using a natural-language-processing algorithm. Using this algorithm, they scoured nearly 4,000 published papers, extracting information on the temperature at which a given MOF would break down. They also pulled out data on whether particular MOFs can withstand the conditions needed to remove solvents used to synthesize them and make sure they become porous.

      Once the researchers had this information, they used it to train two neural networks to predict MOFs’ thermal stability and stability during solvent removal, based on the molecules’ structure.

      “Before you start working with a material and thinking about scaling it up for different applications, you want to know will it hold up, or is it going to degrade in the conditions I would want to use it in?” Kulik says. “Our goal was to get better at predicting what makes a stable MOF.”

      Better stability

      Using the model, the researchers were able to identify certain features that influence stability. In general, simpler linkers with fewer chemical groups attached to them are more stable. Pore size is also important: Before the researchers did their analysis, it had been thought that MOFs with larger pores might be too unstable. However, the MIT team found that large-pore MOFs can be stable if other aspects of their structure counteract the large pore size.

      “Since MOFs have so many things that can vary at the same time, such as the metal, the linkers, the connectivity, and the pore size, it is difficult to nail down what governs stability across different families of MOFs,” Nandy says. “Our models enable researchers to make predictions on existing or new materials, many of which have yet to be made.”

      The researchers have made their data and models available online. Scientists interested in using the models can get recommendations for strategies to make an existing MOF more stable, and they can also add their own data and feedback on the predictions of the models.

      The MIT team is now using the model to try to identify MOFs that could be used to catalyze the conversion of methane gas to methanol, which could be used as fuel. Kulik also plans to use the model to create a new dataset of hypothetical MOFs that haven’t been built before but are predicted to have high stability. Researchers could then screen this dataset for a variety of properties.

      “People are interested in MOFs for things like quantum sensing and quantum computing, all sorts of different applications where you need metals distributed in this atomically precise way,” Kulik says.

      The research was funded by DARPA, the U.S. Office of Naval Research, the U.S. Department of Energy, a National Science Foundation Graduate Research Fellowship, a Career Award at the Scientific Interface from the Burroughs Wellcome Fund, and an AAAS Marion Milligan Mason Award. More