Massachusetts Institute Of Technology

NSF Awards · FY 2024 · 2024-12

This grant supports students and postdocs (12 Travel Awards) to attend the "MCELS: Advances in Basic Research and Translational Opportunities" scientific meeting which will take place in Hilton Head, South Carolina, 26-28 March 2025, including registration fees and travel accommodations. The grant additionally provides funding for event space rental, poster boards, and the conference website. These students join researchers from diverse backgrounds, including biology, engineering, modeling, ethics, medicine, and government, facilitating multidisciplinary projects and collaborations necessary to reach the potential of M-CELS. They will have access to invited speakers, technical talks, posters and other meetings over two days. The specific objectives of this conference are: 1) to help define the M-CELS vision, foster a collective capacity for ethical deliberation, and chart a course to realize the vision, and 2) to promote interdisciplinary collaborations and career development and networking opportunities. Multi-cellular Engineered Living Systems (M-CELS) utilize emergence arising from the behavior of integrated clusters of living cells and the interactions of groups of clusters to accomplish a goal. Engineering systems of living cells comprised of cell clusters with distinct functional units are capable of, for example, sensing, information processing, actuation, regulated protein expression, and transport. Their collective functionality can meet a wide range of critical societal needs in human health, disease modeling, and drug screening. This meeting will bring together scientists and investigators from diverse backgrounds to facilitate multidisciplinary projects and collaborations necessary to reach the potential of M-CELS. A satellite program engaging the general public will allow development of new understandings, which could be used in societal contexts. This conference will help define that vision, foster a collective capacity for ethical deliberation, and chart a course to realize the vision. The results of the meeting will be disseminated as a collection of articles in a special issue. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NIH Research Projects · FY 2026 · 2024-11

PROJECT SUMMARY Autism spectrum disorder (ASD) is a debilitating neurodevelopmental disease that severely affects cognition and communication in patients. Genome-wide association studies of ASD have revealed complex polygenic variation in synapse-associated proteins. The normal development and function of neuronal circuits rely critically on the spatially controlled expression and regulation of synaptic proteins, their messenger RNAs (mRNAs), and their interactions. Thus, understanding how penetrant genetic variations associated with neurodevelopmental diseases such as ASD impact neuronal synapse development, homeostasis, plasticity, and activity is crucial for the identification of new therapeutics to treat patients. Toward this end, single-cell fluorescence imaging of neuronal synapse activity integrated with in situ measurement of synapse protein and mRNA levels and localizations offers the potential to identify convergent synaptic phenotypes in ASD. The present project builds on prior research in which we applied highly multiplexed fluorescence imaging of synaptic proteins to fixed neuronal samples to identify convergent synaptic phenotypes associated with ASD and SCZ risk genes. Specifically, we build on this technique by integrating high-throughput molecular characterization of mRNA expression levels and localizations in intact neurons together with characterization of neuronal activity in the same cells. Live-cell imaging of glutamate and calcium levels is performed followed by fixation and multiplexed fluorescence imaging of both multi-protein and mRNA levels and localizations including mRNA translational state. CRISPRi will be used to characterize how high confidence ASD-associated gene deletions affect neuronal synapse activities and their multiprotein-mRNA interaction networks. Bayesian network analysis will identify convergent networks and pathways by which ASD-associated genetic variations impact neuronal function both in rodent and human iPSC-derived neuronal cultures, in collaboration with the Stanley Center at the Broad Institute of MIT and Harvard. Development of this multi-modal synapse characterization approach will pave the way towards the discovery of new therapeutics for ASD, as well as other neurodevelopmental disorders such as SCZ using patient samples in future work.

NSF Awards · FY 2024 · 2024-11

Many geoscientific models—such as those used to study air pollution or climate—are computationally intensive to run, and this limits their usefulness. Often, limited availability of computational resources limits scientific progress; additionally, these models are not usable by scientists without access to high-performance computing clusters. This work aims to increase the computational speed of these models by creating simpler versions of each model component via machine-learned (ML) “surrogate models”. This will allow improved tradeoffs to be made between accuracy and computational cost in geophysical modeling, resulting in more accurate and efficient virtual models of Earth. At the same time, results of the project will greatly decrease the computational expertise and resources required of new model users and developers, increasing the number of people able to engage with geoscientific modeling. Removing barriers to model use in educational and policy settings will increase the fraction of the population familiar with the workings of geoscientific models, improving public trust and perception of the transparency of models and their outputs. Project research will be organized in three Thrusts. Thrust 1 will develop surrogate models for atmospheric chemistry—the most computationally intensive component in models of atmospheric composition—and will also develop improved dimensionality reduction methods for these systems. Thrust 2 will use the same methods to develop ML models for wildfire plume rise, which is a key determinant of wildfire smoke transport (which is in turn an increasingly important determinant of public health) but is not well characterized in current models. Thrust 3 of the proposed project will develop an “equation-based” platform for atmospheric chemical transport modeling which expands the state of the science in performance, modularity, and differentiability for geoscientific modeling, thus allowing improved integration between physics-based and ML modeling components. This platform will also remove barriers to the broader use of geoscientific models by making models easier to use, understand, and develop. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2024 · 2024-11

This project will exploit high resolution plasma line observations made by the Arecibo Radar to significantly increase the number of photoelectron observations available to the community. The Earth’s ionosphere is created by the Sun’s radiation, which breaks up particles in the upper atmosphere to create plasma. One product of this ionization process is a high energy electron called a photoelectron. Photoelectrons are constantly created during the day and are important for sustaining and heating the ionosphere. Measurements of photoelectrons are rare and difficult to make. Archived experiments from the Arecibo Observatory are one of few datasets that can be used to create meaningful photoelectron measurements. This work will further our understanding of the interaction between photoelectrons and plasma in the ionosphere. The primary output of this research is altitude resolved measurements of the photoelectron distribution which will be made available to the community. The team is diverse in gender and career stages, including early career researchers. This project will support a graduate student and undergraduate students at an MSI (NJIT). Furthermore, reports and presentations of this research will broaden the community and public’s understanding of Arecibo’s legacy as a unique, world-class instrument. Measurements of photoelectrons in the ionosphere are rare and difficult to make, particularly in situ. This project will significantly increase the number of photoelectron observations available to the community by exploiting high resolution plasma line observations made by the Arecibo radar. The team will use a combination of experiment, kinetic plasma theory, and data science to answer the following questions: 1. Does a given photoelectron distribution provide a unique set of plasma line observations? 2. Do asymmetries in the photoelectron distribution create asymmetries in the upshifted versus downshifted plasma lines? and 3. What is the effect of photoelectrons on the frequency of the plasma line? The work will improve our understanding of the photoionization process, and the pathway photoelectrons take to heat the ionosphere and resolve the anisotropy of the photoelectron distribution and assess the relative importance of local production and vertical transport of high energy electrons. This work will also create a catalogue and archive of past plasma line experiments at Arecibo, making this unique dataset more accessible to the community through the Madrigal database. The Geospace Facilities (GF) Program cofunds this project. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2024 · 2024-10

A long-standing goal of computing research is to create "tools for thought", in which computers extend our abilities to think and communicate in both work and social contexts. Used carefully, large language models (LLMs) -- and their remarkable ability to process and generate text -- can contribute to this goal. Already, people use LLMs to generate and organize ideas, summarize documents, support writing, plan events or meals, generate computer programs, and analyze data. However, current LLM usage prioritizes conversational "chat" interactions involving a single person and one-at-a-time responses, whereas creative work requires considering a variety of possibilities and may include multiple collaborators. The goal of this project is to leverage and evaluate LLMs as "tools for thought" that support creative, open-ended, and collaborative work. The main aims are to (1) integrate LLMs into larger, interactive systems while safeguarding LLM output quality, (2) help people generate and consider diverse, relevant ideas, and (3) support collaborative work involving multiple people and LLMs interacting together. This project looks beyond current chat-based interactions to leverage LLMs to support people's everyday work in a reliable and effective manner. More specifically, this project develops novel methods, evaluations, and applications to better leverage LLMs as tools for thought in both single-user and cooperative scenarios. The main approach is to scaffold LLM-powered systems to provide higher control and reliability, while focusing on a key step of open-ended information work: "divergent" phases of generating diverse yet relevant candidate ideas, followed by "convergent" phases in which one navigates, selects, and synthesizes the most promising ideas. The first objective of this project is to develop a design space and guidance for building more reliable and controllable LLM workflows, drawing upon over a decade of crowdsourcing research and documenting the adaptations necessary to build effective workflows and evaluate LLM capabilities. The second objective is to enable cycles of divergent and convergent work: developing robust operations for generating diverse yet relevant candidates -- whether they be writing suggestions, brainstorming ideas, or salient quotes to extract from a text -- alongside methods for choosing among and combining responses. The third objective expands this focus to cooperative projects, enabling hybrid multi-user/LLM workflows and investigating how LLMs could improve awareness and coordination among collaborators. In support of these objectives, the project will develop and evaluate user-facing applications for tasks such as scientific writing, text analysis, and design ideation, providing practical examples of LLM-supported "tools for thought". This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2024 · 2024-10

Place-based economic development approaches have a strong potential for revitalizing lagging regional economies. Successful strategies, however, will need to consider the decision-making of private sector entities, including firms, workers, entrepreneurs, and innovators. To complement existing work on the geography of innovation, this project aims to understand clustering patterns among firms in different industries and how proximate technology infrastructure coupled with geographic clustering can affect job growth, job quality, and social and economic opportunity in a regional economy. The project will use high-quality data on establishments, employment, and payroll for almost 1,000 industries to measure industry concentration patterns in tech- and innovation-driven industries. These data will document the degree of geographic concentration in specific sectors and trends over time, providing local and regional policymakers and practitioners with an understanding of the difficulty of creating new centers of tech- and innovation-driven industry activity. These data will be augmented with information on characteristics of jobs, workers, and living wages to provide insight into the benefits of successfully creating and scaling new centers of tech activity. Overall, the project will generate nearly 75 million data points illuminating agglomeration dynamics and their evolution over time and space. This will be highly relevant to regional economic development practitioners and policymakers tasked with scaling tech-based industries over the following decades. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2024 · 2024-10

Over recent years, there has been remarkable progress in providing algorithms with provable guarantees for various fundamental machine learning problems. These problems are often of an unsupervised flavor (i.e., are given unlabeled data and look for patterns and insights without any explicit guidance), and the samples come from some unknown but fixed distribution. Yet, there are important problems coming from signal processing, control theory, and natural language processing that do not fit into this mold because the data arrives in a sequence with a rich dependency structure. The goal of this project is to design better algorithms for such problems by building the appropriate bridges to the tools and perspectives in more classic settings. This project will also involve training the next generation of graduate students and equipping them with the technical tools to work at the cutting edge of theoretical machine learning. The investigator will also revise his free online graduate textbook with material from recent progress related to this project. This project explores learning problems for linear dynamical systems, graphical models, and hidden Markov models. The team will prove rigorous guarantees for methods like prefiltered least squares as well as study what happens when our observations are intermittent and the usual algebraic structure is unavailable. They will also show how learning from the Glauber dynamics makes it possible to circumvent known computational lower bounds for learning higher-order graphical models. And, finally, the team will study how hidden Markov models can be learned using a conditional sampling oracle. As a byproduct, this project will export technical ideas from theoretical computer science into areas where there are currently wide gaps in our algorithmic understanding. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2024 · 2024-10

Clustering algorithms are one of the most important modern tools for understanding data. Given data on various entities, clustering algorithms group entities into sets or "clusters" such that similar entities are likely to end up in the same cluster while dissimilar entities tend to end up in different clusters. For example, clustering algorithms can be used to group images together according to the contents of the image. However, modern datasets are so large that many existing clustering algorithms cannot be feasibly used. This project aims to systematically address this situation by way of new clustering algorithms that scale to massive datasets with billions of entities. Clustering is widely used by scientists, companies, and government agencies. The toolkit developed in the project will be open-sourced and will make scalable, high-performance clustering more broadly accessible to scientists and practitioners by improving the efficiency and programming productivity of their clustering tasks. Results from the project will be integrated into courses that the investigators teach, and the researchers will recruit undergraduate students to participate in the project. This three-institution collaborative project investigates a new approach for clustering pointsets by constructing sparse graphs that preserve relevant properties of the pointset. By carefully leveraging high-quality near-linear work graph clustering algorithms, very large datasets can be clustered in time that is nearly linear to the number of objects in the input with high accuracy. Particular attention will be paid to new algorithms for graph clustering and construction that utilize structure observed in practice, exploit parallelism, and enable dynamism with provable accuracy guarantees. A major contribution of the project will be an end-to-end clustering toolkit for graphs and pointsets that enables clustering to be scaled to inputs with billions of objects. The investigators will collaborate through regular remote meetings and seminars, student visits, joint publications, and annual in-person workshops. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2024 · 2024-10

The next generation of technologies that take advantage of the unique aspects of quantum mechanics promise revolutionary advancements in cryptography, secure communications, and scientific discovery. Devices such as quantum computers or networks will need to execute quantum algorithms that typically use thousands of error-corrected qubits made up of millions of individually controlled physical qubits. A major outstanding problem is scaling existing technology to the required millions of qubits. This NSF project aims to develop a scalable, semiconductor-based quantum system that leverages the advancements in semiconductor manufacturing achieved in the past half-century that has enabled fabrication of billions of transistors, sustaining Moore’s Law and beyond. New design tools, systems-integration software, and semiconductor hardware will be produced to meet the scaling challenges. This project will also include an extensive outreach effort to local K-12 and community colleges to build up the next generation of scientists in important industries such as semiconductor manufacturing, systems, and quantum information science. This NSF research project will focus on the co-design of silicon-based application-specific integrated circuits and atom-control photonic integrated circuits specifically for large-scale control of color-center qubits in diamond. The ultimate goal is to construct a high-performance prototype quantum system able to perform complex operations, programmed completely by the electronic and photonic control chips. We divide our research approach into the following tasks: develop cryo-compatible CMOS integrated circuits for embedded color centers; develop large-scale integration of color centers, encompassing tens of thousands of individually addressable qubits; systems integration of piezoelectrically actuated atom-control photonic integrated circuits; and understand how errors propagate in our diamond color center qubits. Combining the four tasks together, we aim to demonstrate that the assembled qubits with the CMOS-fabricated electronic and photonic controls satisfy the DiVincenzo criteria: precise qubits, initialization to a known state, sustained coherence, a universal quantum gate set, and individual qubit measurability without disruption. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2024 · 2024-10

One common way to help people understand data is through visualizations. These visual representations of data can support analysis, lead to new insights, and communicate important ideas. However, visualizations are less useful for the many millions of people who are blind or have low-vision (BLV). Researchers and designers have worked to develop sound- and touch-based versions of visualizations to better serve BLV people, but these efforts tend to be both limited to specific kinds of visualization and provide minimal control to BLV users. This project investigates methods and systems that BLV users can use to build richer, customized representations of data that span multiple modes (i.e., text, verbal and non-verbal audio, and tactile) simultaneously. The goal is to allow users to say what they would like the representation to depict, letting the underlying system create the needed code to produce a working multisensory data representation. Further, these representations will be made interactive so users can rapidly explore multiple slices and representations of the data, increasing the chance they can learn from it. Through this work, this project will empower BLV people to engage in data analysis in ways as rich and interactive as methods that sighted people enjoy today. To facilitate this impact, project outcomes will be made available as open-source software, and project personnel will lead educational and outreach efforts. This project aims to develop abstractions akin to those found in visualization grammars such as ggplot2 or Vega-Lite but for multisensory data representations. Through a mix of qualitative methods including contextual inquiry and in-field ethnography, the research team will first study how expert BLV scientists and data analysts work with and communicate about data. The goal will be to learn their mental models and existing approaches for constructing non-visual data representations, and how they discuss these representations with their sighted colleagues. The researchers will then host a series of co-design workshops with BLV participants to elicit expectations and populate the design space of multisensory data representations. Results across both threads of work will then inform the design of computational abstractions that will be reified either through a textual language or through an interactive structured editor. Project contributions will be evaluated through summative and comparative user studies conducted with BLV participants. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2024 · 2024-10

Robots that can interact well with humans need social and emotional intelligence, especially in domains such as healthcare and companionship. The project team will repurpose a pool of Jibo robots and make them a community resource for research on human-robot interaction. To create such a platform, the team must engage with different research communities. First, this project will gather scientists and researchers to understand their needs. Then, the research team will make a list of requirements to define new tools and resources. The requirements will include a social robot research platform and AI algorithms. The project will also find ways to make these research resources accessible. It will include websites and forums where researchers can learn from each other. The research platform will be available for many different researchers and institutions. This will promote education and inclusivity. This project can make positive impacts on our society in many ways. First, it will benefit society by improving human-AI interactions. AI and robots will be more useful and easier to use. Second, the platform will support AI education for K-12 and higher education. It will help diverse students learn about AI and/or learn with it. They can learn about AI's responsible and ethical design and get hands-on experiences. Students can design, deploy, and test their own social AI solutions. Lastly, this project will also foster collaboration between academia and industry. This partnership is a key to advancing AI research for community social good. This project involves a four-step community participatory design process. First, the project will share the Jibo research platform with research labs for hands-on experiences. The second step involves completing a comprehensive survey to gather community needs. During the third step, the project team will host workshops to show the platform to the community. From these workshops they will request feedback from different research communities. The last step involves analyzing the data. Then the project team will refine the platform development, distribution, and sustainment guidelines. The project team will lead the discussion for the scope of the infrastructure. This will include: the development of a social intelligence taxonomy and benchmark datasets. The final project outcome will be to develop a living-lab framework for large-scale and longitudinal studies. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2024 · 2024-10

Formal verification has made a significant impact on hardware verification. Even though hardware formal verification tools available through EDA (Electronic Design Automation) companies and open source are widely used in industrial and research practice, these tools face significant scalability and usability challenges. Specifically, there is no framework that makes these techniques available to architects and hardware designers for direct use and subsequent integration with existing hardware verification flows. The project's novelties are to address these challenges by leveraging architectural insights in a systematic way to make formal verification scalable and usable by computer architects. The project's impacts are the advancement of both functional and security verification for contemporary architectures, and formal verification techniques for synthesis of invariants and information leakage verification via abstraction-refinement. The overarching theme of the project is the use of architectural insights in lifting important formal verification techniques to be directly usable by computer architectures. Specifically, the project involves four tasks: 1) developing architecture-driven abstractions, component interfaces, and invariants for functional verification of complex processors using modular-refinement-based techniques; 2) leveraging architectural insights to derive shadow logic (monitors) and abstraction/refinement schemes for taint analysis, for security verification of software-hardware contracts; 3) developing new formal verification methods for synthesis of architecture-driven invariants and information-leakage verification via abstraction-refinement; 4) developing an open-source prototype framework with the above techniques built-in to be integrated with existing hardware verification flows and tools. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NIH Research Projects · FY 2025 · 2024-09

Visual attention is our ability to prioritize vision as a sensory modality, and our ability to extract discrete information from a visual scene. The prefrontal cortex (PFC) is considered a source of feedback modulation to the visual cortex– guiding, biasing or modulating activity in the visual cortex, to produce visual attention. The visual cortex of the mouse receives monosynaptic input from two discrete PFC subregions, the anterior cingulate cortex (ACA) and the orbitofrontal cortex (ORB), that contribute to diverse aspects of cognition. However, it is currently unknown how these distinct PFC subregions contribute to different aspects of visual attention. Equipped with precise understanding of distinct forms of PFC feedback modulation on visual processing, we will be in a better position to strategically intervene in disorders of visual processing and attention. The proposed work therefore aims to bridge the long-standing hypothesis of PFC feedback modulation with changes in population activity in the visual cortex, how this activity change alters visual processing, and ultimately what effect this has on goal-directed visual attention. To achieve this goal, I will quantitively and qualitatively compare the activity of ACA and ORB axons in the visual cortex during different behavioral epochs of visual attention (Aim 1), probing each areas contribution to different aspects of vision. In following, perturbating the activity of each discrete PFC output pathway, allows me to observe changes in population activity in the visual cortex, how this activity change alters visual processing, and ultimately what effect this has on goal-directed visual attention (Aim 2). Finally, I will target PFC output pathways with a distinct connectivity profile, to understand how these projections modulate the activity of the visual cortex, and if they represent a distinct functional module in regulating sensory processing (Aim 3). Understanding how discrete subregions of the PFC influence the activity of the visual cortex, and its behavioral consequences, will provide key insights towards the cellular underpinnings of visual processing in the brain. The proposed work will be conducted at the Brain and Cognitive Sciences Department, MIT, with the direct mentorship of Prof. Mriganka Sur (MIT), from which I have already benefitted experimental training in numerous techniques including two-photon microscopy, visual cortex neurophysiology, circuit anatomy, and behavior. In addition, I will receive training and guidance on optogenetic tools in combination with two-photon imaging, and PFC physiology from Prof. Ofer Yizhar (WI), and computational methods in population activity analysis from Prof. Mehrdad Jazayeri (MIT). The outlined research, training, and mentorship will facilitate my long-term goal of establishing an independent researcher leveraging cutting-edge optical technologies available in mice to understand how PFC feedback modulation optimizes goal-directed sensory processing in the cortex.

NIH Research Projects · FY 2025 · 2024-09

Project Summary/Abstract Technologies for capturing multi-faceted neural signals underlying brain communication stand to improve our understanding of these complex pathways, which can be leveraged to better diagnose and treat neurological disorders and diseases. Such signals may be electrical or chemical in nature, originate in single neurons and propagate through entire networks, and occur on sub-millisecond timescales yet persist for days to weeks. To maximize downstream clinical impact, effective neuromonitoring tools should offer multimodal sensing and stim- ulation capabilities with high spatiotemporal resolution, while chronically recording from large neuronal popula- tions, and minimally perturbing animal physiology and behavior. This proposal seeks to fulfill these needs by equipping thin, polymer-based multifunctional fibers with optical imaging capabilities and coupling them to wireless recording devices. Existing endoscopic optical imaging tools, which use implanted lenses to visualize neural activity via genetically-encoded fluorescent indicators, can record from greater numbers of spatially-distinct neurons than electrophysiological methods, and detect complementary information to neuronal firing, such as neurotransmitter release. However, these tools lack direct electrical and chemical stimulation and recording abilities, and may provoke foreign body response, limiting long-term use in vivo, especially in deep brain circuits. Alternatively, multifunctional fibers for electrical, chemical, and optical interrogation of localized brain regions exhibit stronger materials compatibility with tissue due to their softer sub- strates and smaller diameters, enabling chronic usage. Although these devices have previously only offered opportunities for bulk optical recordings, this work will integrate polymer fiber waveguide bundles to achieve spatially-resolved images, while preserving small device footprint, low stiffness, and multifunctionality. We will leverage light field signal processing to transform fiber bundle images into 3D volumes, captured by a head- mounted device featuring hardware for dual-wavelength imaging and fully wireless data transmission and real- time control. We will deploy our fully-untethered devices to study firing and neurotransmitter dynamics in re- sponse to social interactions in the mesolimbic pathway, a deep brain circuit implicated in stress, motivation, and social dysfunction. These experiments will highlight our ability to complementarily expand the aspects of neural activity able to be captured, as well as the experimental paradigms under which such recordings are feasible. This work will benefit strongly from the multidisciplinary training environment at the Massachusetts Institute of Technology through access to key technical resources provided by the Materials Science, Electrical Engineering, and Brain and Cognitive Science departments, which will be critical to developing the proposed devices. Addi- tional intellectual and career development resources offered by mentored and independent training programs will further strengthen technical foundations and offer necessary preparation for future independence in this field.

NSF Awards · FY 2024 · 2024-09

There are uncountably many possible shapes in the world, and computers cannot store, represent, and display them all. Instead, one typically discretizes a shape. This means that a shape is constructed of many smaller, simpler sub-shapes, for example, triangles. A computer can easily store a triangle by storing the locations of each of its corners; a large complex shape can then be represented as a collection of triangles. Traditional discretization approaches like this—named discretization because they turn a shape into a discrete collection of triangles—are powerful in their simplicity, but they have critical drawbacks. Finding a discretization of a shape in terms of triangles is a difficult and computationally-intensive process, and it is easy to accidentally create an invalid collection of triangles (for example, because it has holes) that is invalid for computational use. Moreover, such a discretization will always be a mere approximation of a true shape, and computations performed on these discretizations can suffer from artifacts introduced by the discretization process. Finally, discretizations are often hard to use in modern machine learning applications based on neural networks, because the discretization process is hard to differentiate, an integral step of training a neural network. This project will overcome these problems by developing discretization-free methods for processing shapes on computers. New methods for the animation of computer graphics characters will be developed that circumvent the traditional step of discretizing the interior of a character before an animation can be computed. The project will also develop discretization-free interpolation methods—when information is given at certain points on a shape (for example, climate readings on isolated weather stations), these methods will be able to interpolate this data over an entire shape for visualization and computation purposes. Lastly, the project will develop discretization-free representations of vector fields, which model data such as hair on a character, wind on the surface of the planet, or electric fields. The primary outcome of this research will be the development of discretization-free methods that will enable smart geometry methods of the future. Furthermore, these awards will fund the education of graduate students at the Massachusetts Institute of Technology and the University of Southern California. A broad variety of mathematical, engineering, and application-oriented challenges will be tackled in the course of carrying out this research. In particular, design of robust algorithms for geometry processing requires solution of partial differential equations (PDEs) as well as PDE-constrained optimization problems on curved domains, with nonlinear objective terms and constraints coupling together multiple unknown functions. The key hypothesis in this work is that neural function representations are well-suited to geometry processing applications, since they are smooth, capable of representing a broad variety of functions, easily differentiable, and compatible with modern machine learning representations, but they will need to be tailored to the needs of this application by making them conform to input geometries and constraints of geometry processing problems. To accomplish this broad goal, the project is divided into three thrusts reflecting applications described in the previous paragraph. As a model problem for animation problems, custom fields will be used to optimize for skinning weights on volumes, a key computational challenge in pipelines for 3D deformation. Extending to cage-based animation, more complex constraints will then be added to the neural fields for geometry processing by considering the problem of optimizing for generalized barycentric coordinates, whose reproduction property is not well-captured by standard machine learning architectures. Finally, non-scalar problems in geometry processing such as frame field design and geometric flows will be considered for which conventional mesh-based algorithms are numerically stiff. Each thrust of the project centers around practical open problems in computer graphics. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NIH Research Projects · FY 2025 · 2024-09

Summary Conventional chemotherapy regimens induce complete remission in the majority of patients with acute myeloid leukemia (AML) and results in >95% reduction in tumor burden. However, the persistence of minimal or measurable residual disease (MRD) is considered to be a key determinant of relapse and poor patient prognosis in AML. The current clinical strategy at complete remission with MRD positivity is, depending on the level, to either provide additional dose(s) of the initial treatment or switch to a new therapy at the time of relapse. Our long-term goal is to instead target MRD by selecting a more optimal therapy before relapse as there are many advantages to treating patients with MRD-only disease rather than waiting for clinical relapse. This would be a transformative clinical advance for patients with persistent MRD if selective targeting of MRD either forestalled or completely obviated clinical relapse. We hypothesize that an optimal assay performed at the time of complete remission would not only predict therapeutic response but also identify relevant heterogeneity that is present within the patient’s MRD. We are proposing to achieve this assay by integrating Deepcell’s high-content label-free brightfield imaging and AI powered sorting with MIT’s high precision biophysical measurements and DFCI’s clinical resources and expertise. We expect to deliver a novel platform to enable real-time functional assessment of AML MRD therapeutic vulnerabilities with meaningful clinical impact. Furthermore, technical advances and experience gained here will facilitate future application of a similar device to the MRD state in other cancers.

NIH Research Projects · FY 2025 · 2024-09

PROJECT SUMMARY/ABSTRACT Fusion-positive alveolar rhabdomyosarcoma (FP-RMS) remains a poorly understood but highly fatal cancer of childhood. The pathognomonic fusion oncoprotein of ARMS is PAX3-FOXO1 (i.e., PAX3::FOXO1, P3F), a transcription factor fusion driving ARMS progression through dysregulation of gene expression and chromatin state. Major obstacles to the development of therapies for FP-RMS and other oncogenic fusion proteins include the high degree of conformational plasticity of the fusion protein drivers and the lack of knowledge about protein partners or suitable mechanisms for inhibiting or clearing the fusions. To solve these gaps, we need a comprehensive, coordinated approach to discover and develop chemical agents that clarify the relevance of various molecular mechanisms capable of inhibiting PAX3-FOXO1-mediated tumorigenesis. We assembled a team of chemical biologists, medicinal chemists and RMS-focused physician-scientists whose unique and complementary expertise in high-throughput screening, chemoproteomics, quantitative molecular interaction analyses, transcriptional biology, medicinal chemistry, pharmacology and RMS biology will be applied to the discovery and development of small molecules that impact PAX3-FOXO1 function in FP-RMS. A key goal of the Center is to explore multiple mechanisms of action for targeting PAX3-FOXO1 and efforts will emphasize 1) identifying direct small-molecule binders of PAX3-FOXO1 or nearest neighbor proteins that may be pursued as direct functional modulators or silent binders for degrader development, 2) development of PAX3-FOXO1 degraders, including heterobifunctional proteolysis targeting chimeras (PROTACs) and molecular glues that degrade a target by hijacking the ubiquitin-proteasome system, and 3) indirect targeting of PAX3-FOXO1 by targeting transcriptional network collaborators that modulate the function of the fusion or that control transcription of the fusion. We propose three complementary Projects supported by four Research Groups, including the High-Throughput Screening Group (HTS), the Molecular & Cellular Mechanism Group (MCM), the Medicinal Chemistry Group (Med Chem), and the Pharmacology Group (Pharm). The Specific Aims involve all four Research Groups and include: 1) Ligand Discovery for PAX3-FOXO1 (Project 1); 2. Discovery and Development of Targeted Protein Degraders for PAX3-FOXO1 (Project 2); and 3. Targeting the PAX3-FOXO1-associated core regulatory transcriptional complex (Project 3). While the aims to explore multiple pharmacologic modes of perturbing PAX3-FOXO1 activities are ambitious, our combined expertise and experience in balancing foraging and focusing in drug discovery or development will enable our goal of accelerating the identification of therapies for PAX3-FOXO1-positive FP-RMS. Our unique strengths will also inform the TFCC Network, resulting in knowledge generalizable to targeting of fusion oncoproteins in childhood cancer and accelerating advances in clinical care.

NIH Research Projects · FY 2025 · 2024-09

Collagen is the most abundant protein in animals, constituting up to one-third of total protein in humans. As the major proteinaceous component of tissues ranging from bone and skin to cartilage and basement mem- branes, it constitutes the molecular scaffold for animal life. This ubiquitous protein is uniquely challenging for cells to produce, requiring highly coordinated intracellular processes of synthesis, folding, assembly, and qual- ity control. Owing to the hierarchical nature of collagenous extracellular matrices, the physical and biochemical properties of such tissues are fundamentally defined by these upstream, intracellular processes. Defects, whether genetic or otherwise, that are detrimental to any aspect of collagen proteostasis can impact the health or function of collagen-producing cells and also propagate to extracellular matrices, leading to diseases known as the collagenopathies. Unfortunately, these diseases almost universally lack effective, disease-modifying therapies. Current therapeutic approaches to the collagenopathies focus on regenerative interventions, efforts to strengthen the extracellular matrix itself, or palliative care. None of these strategies aims to address the up- stream issue leading to disease: a failure to properly fold and quality control collagen molecules themselves. If the breakdown of collagen proteostasis could be effectively addressed, the downstream symptoms targeted by current clinical strategies would be alleviated. Indeed, proteostasis enhancement has proven remarkably effi- cacious in many other genetic disorders, including cystic fibrosis, but it has yet to make serious inroads in the collagenopathies. One obstacle is inadequate understanding of the critical decision points in the collagen pro- teostasis network. Another issue is the challenge of pre-clinical testing of proteostasis-targeted interventions in a disease that requires robust, yet biochemically amenable, tissue model systems for discovery efforts. This R01 proposal seeks to address these knowledge gaps, both identifying and elucidating key mech- anisms of intracellular folding and quality control, and assessing the therapeutic potential of proteostasis net- work-targeted interventions in the collagenopathies. In Aim 1, functions of the highly conserved procollagen N- glycan in promoting folding, enabling quality control, and identifying when folded procollagens are ready for secretion will be elucidated, via comprehensive work both in cells and in vivo that will reveal the molecular mechanisms of collagen glycoproteostasis. The expectation is to demonstrate that the long-ignored procolla- gen N-glycan is actually the critical fulcrum of collagen proteostasis. In Aim 2, a state-of-the-art, 3D cartilage- in-a-dish model system is deployed to enable robust testing of proteostasis network-targeted therapies for the collagenopathies. Combined with mechanistic studies to elucidate the biochemistry of dysregulated collagen proteostasis, work in this Aim will provide a strong foundation for a new, proteostasis-focused perspective on treating the collagenopathies.

NSF Awards · FY 2024 · 2024-09

Public Abstract: Traditional bioelectronic implants have paved the way for groundbreaking advancements in medical diagnostics, therapeutics, and research. However, their reliance on invasive surgical procedures poses significant limitations. In this paradigm-shifting proposal, we introduce a novel method that eliminates the need for surgery altogether. Our primary focus lies in the development of bioelectronic brain implants that hold the promise of identifying target brain regions and autonomously implanting themselves. Once implanted, these devices will facilitate high-resolution brain stimulation, offering unprecedented control over neural activity. This pioneering approach not only revolutionizes the field of neural stimulation but also opens doors to a myriad of possibilities in neuroscience and medical intervention. By obviating the need for invasive surgery, our proposed method promises safer, more accessible, and more precise interventions for neurological disorders and brain research. Technical Abstract: Bioelectronic implants provide a versatile platform for diagnosis, therapeutics as well as basic research but require invasive surgery. Here, we propose a paradigm shift: wherein intravenously introduced ultra-small bioelectronic devices, circulate through vasculature to implant in target regions, without the need for surgery. Specifically, in this proposed work, we aim to develop the bioelectronic brain implants that can recognize target brain regions and self-implant overcoming one of the body’s strictest biological barriers: the blood-brain barrier without any surgery, and enable brain stimulation with high spatio-temporal resolution, leading to the first non-surgical brain implant for neural stimulation. Accomplishing this requires innovations in diverse fields of applied physics, electrical- and bio-engineering and we are uniquely enabled due to our expertise in not only physics and solid-state nanoelectronic devices but also in bioelectronics, synthetic biology and neural engineering. The proposed technology, to our knowledge, is a radical departure from all existing bioelectronic interfaces and if successful can lead to a new field of self-implanting biomedical devices. It is not only nearly non-invasive but it can also be extended to applications where surgery may not be even possible (such as intricate body parts challenging to access surgically, or patients not suited for invasive procedures). Specifically, the proposed first non-surgical brain implant for neuromodulation overcomes the challenges of existing brain stimulation technologies by enabling spatio-temporally precise targeted neuromodulation without any surgery and creates pathways for novel brain therapies. This technology is modular and beyond brain, it can have vast applications in numerous other arenas including cardiac pacing, therapeutic modulation of peripheral nervous system, tissue engineering and regenerative medicine to name a few. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NIH Research Projects · FY 2025 · 2024-09

Project Summary Proper gene expression regulation is pivotal for cellular function, and its dysregulation contributes to disease. Transcription factors (TFs) initiate gene expression by identifying binding sites in enhancers and promoters and recruiting the transcriptional machinery. However, the nucleus's crowded nature and the abundance of non-specific sites make it extremely challenging for TFs to efficiently search for and binding correct DNA binding sites. This project addresses the fundamental question: How do TFs efficiently locate specific sites amidst numerous non-specific ones inside a crowded nucleus? Innovatively, this study employs a new advanced microscopy technique that overcomes the low spatiotemporal resolution associated with camera-based single-molecule tracking. This approach enables precise tracking of TFs in live human and mouse cells with unprecedented spatial (~2-4 nm) and temporal (one hundred microseconds) precision. This transformative approach represents a substantial advancement over traditional methods, facilitating the investigation of TF search mechanisms. By comprehensively tracing TFs' 3D diffusion, DNA interactions, and target site discrimination, we will resolve the TF target search mechanism. First, we will optimize and validate the proposed tracking method and develop novel computational methods for handling and analyzing these new types of tracking data. Second, we will apply this technology to understand how TFs involved in pluripotency and genome structure find their target sites and elucidate how individual protein domains affect the target search mechanisms. Third, we will apply this technology to uncover the oncogenic potential of fusion TFs in several cancers. Fourth, we will leverage these studies to understand how “search domains” in TFs regulate the target search mechanism and efficiency towards the rational design of synthetic TFs with tunable search properties. Taken together, this proposal will reveal how TFs find their target sites with applications to synthetic biology and cancer biology.

NSF Awards · FY 2024 · 2024-09

This Research Advanced by Interdisciplinary Science and Engineering (RAISE) award is made in response to Dear Colleague Letter 23-109, as part of the NSF-wide Clean Energy Technology initiative. Plastics are ubiquitous, they can be found in products ranging from clothes to food packaging. Both manufacturing them and disposing of them are significant sources of greenhouse gas emissions. Additionally, the majority of plastics are made from fossil fuel-derived feedstocks, which are non-renewable resources. Nearly half of plastics are single-use, and almost 80% of plastic waste ends up in landfills or in the environment. Its presence in the environment further damages ecosystems, and as plastic physically degrades, it is converted into microplastics (microscopic particles) that enter soil, water sources, and the food chain. Recycling can extend the usable life of plastic based on the recycling method, but only a small percentage of plastic is currently recycled, due in part to challenges with mixed materials and energy intense recycling methods. Incineration remains the most prevalent method of degradation because of its ease and tolerance for highly complex input mixtures. Research efforts have focused on decreasing the energy requirements and improving product selectivity of recycling processes, but they remain insufficient to solve the plastic waste problem. This project focuses on the development of low-energy strategies to degrade plastics by combining biological and electrochemical engineering approaches. The principal investigators study new strategies for sustainable recycling of plastics with a net-zero approach. Experiments are combined with techno-economic analysis and life cycle assessments to determine the impacts of these technologies as compared to existing approaches. Educational and workforce training are integrated through programs to bring undergraduate and high school students into the researchers’ laboratories at the Massachusetts Institute of Technology. This project involves the integration of polymer degradation with microbial bioenergy production to achieve the goals of critical materials recycling and net-zero energy. The team will engineer enzymes to enable polymer deconstruction into monomers, subsequently combine this approach with electroactive microbe engineering and systems optimization to utilize the plastic degradation products as feedstock for bioenergy production. The project represents a unique integration of the disparate fields of microbial and electrochemical engineering to achieve unprecedented efficiency in plastic waste remediation. Importantly, the components of the project’s workflow are modular and based on platform technologies that are readily adapted to other environmental contaminants for conversion into feedstocks for bioenergy production and use. The study is a fundamental shift in approach towards sustainable engineering to achieve environmental remediation and emissions reduction goals and represents an integration of diverse fields of study as well as a novel strategy to integrate microbial and electrochemical engineering to controllably generate bioenergy from polymers via bio-electrochemical degradation. The project aims to develop microbial mixtures to degrade polymers and use the degradation products as carbon sources to generate bio-electricity. Importantly, both the electrochemical and microbial proposed technologies are platforms that can be adapted for the degradation and utilization of additional contaminants. Further, the fundamental insights resulting from coupling genetic engineering with high-throughput screening of electroactive microbes are expected move their utilization into the mainstream. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2024 · 2024-09

In the data-driven world that we live in, sharing digital information is a key component underpinning a vast body of technologies. Low-latency, high fidelity access to information is central to algorithms that impact how we work, are entertained, how we travel, and our healthcare. Systems that, in particular, rely on wireless communication to deliver their services have become ubiquitous. With the increase in data transmitted over the air, however, the central resource that they depend on, spectrum, can become congested with multiple communications overlapping and negatively impacting each other. This project brings together diverse researchers from Northeastern University (NU), the Massachusetts Institute of Technology (MIT) and Boston University (BU) to develop methods that improve communication performance in shared, congested, and contested spectrum bands. The presence of interference in communications detrimentally impacts the throughput and reliability of systems. Interference and noise are often used interchangeably as they are commonly lumped together as general deleterious effects that corrupt communications. Interference, however, has a more structured form than noise. Central to this project is developing new means to leverage that structure to improve communication systems. By enabling more efficient use of scarce resources, more services can reliably co-exist, advancing national health, prosperity and welfare. By developing techniques that are receiver-only, it allows both backward compatibility and graceful adoption paths. Interference management motivates substantial engineering effort at all levels, from hardware design, to signal processing, to error correction, to retransmission, and resource allocation protocols. A traditional approach to managing interference is to consider its impact as being part of noise. This project aims to do more, leveraging the structure of interference to improve performance through receiver-side approaches only, thus circumventing barriers to technological adoption. When a modulated communications signal experiences interference that arises from other modulated communications, those characteristics can be taken into account. Even when an interferers' modulation may not be discerned, the interference can influence the noise experienced by a receiver in semi-predictable ways that can be exploited by a receiver. When interference is due to the presence of other communication systems where individual interferers' modulation can be detected but the signal not decoded, unlike in a multiple user system, this project proposes an approach that takes into account both noise and the restricted forms the interference can take. When channel and modulation may not be available at the receiver, interference will still have characteristics that are different from, e.g., Gaussian noise. The statistical characteristics of such interference can be used to improve forward error correction decoding, enabling reliable communication with less overhead, which this project explores. When interference is due to signals that vary more slowly than the communication, such as from electronic devices, the receiver cannot rely on knowledge of the structure of the interference, other than the fact that it will exhibit a slowly varying profile. In that case, this project aims to discover post-decoding the interference experienced by some signals and use it as a starting point to remove pre-emptively at least partially that interference from other signals that are proximate in time, and thus subject to a similar interference. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2024 · 2024-09

The detection of gravitational waves (GW) has opened a new window for astronomy. GW astronomy holds the potential for helping reconstruct a picture of the universe at energy scales much higher (and at times much earlier) than the picture obtained using the cosmic microwave background (CMB). A research program between Carnegie-Mellon University (CMU) and Massachusetts Institute of Technology (MIT) will use gravity as a tool to understand more about high energy physics and cosmology, as well as numerical simulations of magnetohydrodynamic processes in the early universe. The project also includes a broad range of education and outreach activities. There will be training of graduate and undergraduate students at both CMU and MIT, along with a vigorous public outreach program, including programs with local middle and high schools, an astronomy training unit with local disabled American veterans, a summer Teacher Training Program, and direct community engagement. The investigators will continue to invite visual artists, astrophotographers, videographers, game designers, and writers to share their cultural arts network for the promotion of science. The LIGO/Virgo detection of gravitational waves has ignited interest in the future direction of GW astronomy, including the search for intriguing signals of stochastic backgrounds from early-universe physics. The NANOGrav collaboration announced detection of a stochastic GW background that can be understood as possibly including a signal from the early universe, such as GWs from and shortly after inflation, cosmic strings and domain walls, phase transitions, turbulence and magnetic fields. The detection of such GWs is challenging due to their small amplitudes, the specific range of the characteristic frequencies, and astrophysical foregrounds. The focus of this research is a study of the GWs from turbulent sources possibly presented at (or around) the quantum (QCD) energy scale. The team will evaluate detection prospects of these GWs, with particular interest in modeling parity violating (chiral) sources that might explain matter-antimatter asymmetry in the universe and investigating the range of the QCD epoch cosmological parameters that can be tested through the pulsar timing arrays. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2024 · 2024-09

This research program will deepen our understanding of the final stages of stellar evolution, specifically focusing on red giants, which play a crucial role in enriching our Galaxy with elements essential for forming new stars and planets. The investigators will study how these dying stars shed mass through molecular "masers”. Masers are the radio wavelength equivalent of lasers, and they arise from molecules in the atmospheres of some red giants owing to the specific combination of gas density, temperature, and velocity. The presence of masers provides a unique tool to study the gas motions and physical conditions within these stars. This investigation will employ the Very Long Baseline Interferometry (VLBI), extending observations to wavelengths as short as 1 millimeter. This research program will provide training opportunities for undergraduate students and a postdoctoral researcher, fostering the next generation of scientific talent. Additionally, collaboration with science educators will produce accessible podcast episodes that communicate project findings to the public, enhancing scientific literacy and engagement. The project addresses critical gaps in our understanding of late-stage stellar mass loss, particularly focusing on the mechanisms driving winds from red supergiants and asymptotic giant branch stars. These winds are major contributors to the enrichment of galaxies with heavy elements and dust. The study will utilize VLBI observations across multiple frequencies, including unprecedented measurements above 200 GHz, enabling spatial resolutions as fine as 10–500 microarcseconds. This high resolution is essential for mapping the regions where stellar winds are launched, particularly using SiO maser lines. Methodologically, the project will develop advanced data processing techniques and calibration methods tailored for high-frequency VLBI observations. These innovations will be shared with the wider scientific community, enhancing the capability to study complex astrophysical phenomena. Coupled with ongoing research on evolved star atmospheres, this effort promises to deliver some of the most comprehensive views to date of red giant dynamics. In addition to its scientific contributions, the project will produce two podcast episodes aimed at broadening public understanding of stellar evolution and its implications. These episodes will feature project updates and interviews with team members, contributing to public engagement with astronomy and astrophysics. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2024 · 2024-09

As human-emitted greenhouse gas pollution warms the planet and changes the dynamics of the climate system, losses due to weather extremes and their impacts on human life and property has become a significant and costly challenge. Reliance on historical records with outdated climate states, coarse model resolution, and incongruency between the spatiotemporal scale of impacts exacerbates the problem and presents serious difficulties for the insurance and finance sectors that rely on accurate assessment of natural perils and the corresponding uncertainties around their frequency, intensity, and duration. This knowledge is required to cover climate disaster related losses that, annually, reach well into tens to hundreds of billions of dollars. To address this challenge, three institutions: George Mason University, the Massachusetts Institute of Technology, and the City University of New York have come together to plan an industry-university cooperative research center that addresses the critical, high priority needs of the insurance and finance sectors, both of which are wrestling with uncertainties in assessing risks and damages due to climate-related disasters. Center research thrusts include: (1) improving climate predictions at spatiotemporal scales needed by the insurance and finance industries; (2) modeling the catastrophic impacts of natural perils to critical infrastructure systems; and (3) quantifying how the local environment modifies the frequency, intensity, and impact of weather-related natural perils on people and property. Broader impacts of the Center would include increased national economic stability by providing better and more reliable tools for assessing climate risk; training the next generation of climate science, engineering, and policy professionals able to tackle the challenges that a changing climate poses to the nation; and broadening the diversity of underrepresented groups in climate disaster modeling field. Research conducted by the Center for Climate Risk Applications, now in the planning phase, will focus on addressing existing gaps on the impact of climate change on a range of natural perils by analyzing state-of-the-art climate model ensembles, improving existing models, and advancing the science of integration between climate modeling and asset-scale risks. Research will analyze and improve the output of climate models at the actionable spatial and temporal scales required by the insurance and finance sectors of the economy. The Center will also develop new methods for downscaling hazard information to asset-scale granularity, while quantifying uncertainties of year-to-decadal climate predictions. Additional work will address the sensitivity of interconnected infrastructure systems to a changing landscape of natural perils and the potential for disruption of critical services and supply/value chains. Natural disasters impact people, not just infrastructure; thus, the Center, presently in the planning stage will also focus on how public policy and regulation impacts the insurance of properties, as well as how existing frameworks for decision-making around these perils inform resilience efforts in the private and public sectors. The Center's education and outreach activities will help enable and maintain healthy insurance and reinsurance markets to promote economic stability and growth in the face of severe threats from climate change to life and property. It will also develop a diverse, knowledgeable, and capable workforce necessary to quantify risks of climate change for those owning assets that need protection as well as the need to improve their ability to understand and predict risks and create policies, standards, and incentives that reduce the risks of loss due to climate change. The role of the Massachusetts Institute of Technology's role will be the contribution of multi-sector and cascading disaster dynamics, policy, and climate science. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.