Trustees of Boston University

NSF Awards · FY 2025 · 2025-08

The Langlands program is a far-reaching framework in modern mathematics connecting two seemingly unrelated types of objects. These are Galois representations, which encode the symmetries of polynomials, and certain analytic functions known as automorphic forms. The relationship between these objects has traditionally been studied using techniques from representation theory. Recently, however, a groundbreaking geometric perspective introduced by Fargues and Scholze has opened up new avenues of investigation, allowing mathematicians to apply powerful tools from algebraic geometry. Despite its elegance, this geometric approach remains inexplicit and only partially understood in relation to classical representation-theoretic results. In this project, the PI will develop a new and explicit theory, of cuspidal vector bundles, that will provide a means to connect these two approaches and enhance understanding of each. Beyond advancing mathematical knowledge, the project will provide training opportunities for graduate and undergraduate students and contribute to the mathematical community through workshops and conferences. The formulation of the categorical local Langlands conjecture of Fargues and Scholze was a major breakthrough in the Langlands program for p-adic groups. However, this theory is quite inexplicit and its connections to classical representation-theoretic results are not well understood. The PI will investigate the representation-theoretic consequences of the geometric formulation of the local Langlands correspondence, as developed by Fargues and Scholze. The first main objective is to construct a category of cuspidal sheaves on the stack of L-parameters. The PI will prove this category is semisimple, with simple objects given by irreducible cuspidal vector bundles. This will yield geometric interpretations and generalizations of the combinatorial data appearing in Moeglin's work on supercuspidal enhanced Langlands parameters for classical groups, the conjectures of Aubert–Moussaoui–Solleveld, and Lusztig’s classification of cuspidal local systems. The second major goal is to construct the Galois-side analogues of Hecke eigensheaves associated to Arthur parameters. These eigensheaves are conjecturally related to the cohomology of local Shimura varieties. Drawing on techniques from linear Koszul duality, the PI will define these eigensheaves and propose new local-global compatibility conjectures linking them to the cohomology of Igusa stacks. Additionally, a detailed study of their stalks will extend the PI’s prior work with Oi on endoscopic character identities for generalized L-packets. 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 2025 · 2025-08

Geomagnetic storms significantly impact satellite-based communication systems, such as the Global Positioning System (GPS). Research into GPS position errors is a crucial aeronomy and space weather research topics for improving the accuracy and reliability of societal applications that rely on GPS technology, including agriculture and autonomous vehicles. Investigations into mid-latitude GPS performance are particularly important due to the dense populations in these regions. The proposed research on quantifying position errors and identifying mitigation methods will increase reliance on GPS, contributing to economic prosperity and national security. The project will also provide research opportunities for students and postdoctoral scholars, contributing to the development of the American workforce. The knowledge gained through this project, including signal processing techniques and scientific applications, will be incorporated into undergraduate courses to enhance undergraduate education. The proposal will support continued service to society by answering public questions and disseminating knowledge through lectures, contributing to improved public scientific literacy and engagement. The project's goal is to understand GPS positioning errors at mid-latitudes in North America. The specific research objectives are: (1) How do GPS position errors evolve during severe storms, and how are these errors related to TEC structures? (2) How do GPS positioning errors depend on storm progression, locations, and seasons? (3) What physical and technical processes control these positioning errors? GPS receiver data from the NSF-supported GAGE facility will be used to compute GPS signal fluctuations and position errors. Investigations of GPS data during severe storms will address the evolution of plasma structures and position errors. GPS position errors at various storm levels will be analyzed to examine how these errors depend on storm strength. By using various input conditions, the project will determine how position errors change by modifying the input data and calculation algorithms. 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 2025 · 2025-08

Individuals can vary greatly in their response to the same infection. This variation can be caused by many factors, including genetics, life experiences, and environmental conditions. By using fruit flies (Drosophila) as a model, researchers can control for many of these factors, allowing them to focus on the roles of genetics and other factors in infection outcomes. However, due to technical limitations, traditional infection studies typically rely on measurements of the infection progression at a single time point, making it difficult to track the infection’s dynamic progression in an individual over time. This project aims to solve that problem by developing new light-based tools to monitor infections in living animals. By using glowing bacteria and glowing proteins that show an animal’s immune response, researchers will measure both bacterial growth and the host’s immune response in individual flies. This will allow them to identify genetic and other factors that influence infection outcomes in different animals. This research will introduce new biotechnology i.e., innovative imaging techniques, that will contribute to the bioeconomy and could reshape our understanding of disease progression, potentially revealing immune genes conserved across species. The project also provides hands-on training for students and postdoctoral researchers in genetics, computational modeling, and imaging. Additionally, the team will create educational materials and outreach programs to inspire the next generation of American scientists. By advancing both research and education, this work has the potential to improve our ability to predict, understand, and ultimately control infections. This research aims to uncover genetic and stochastic factors that influence infection survival by tracking microbial dynamics and host responses in individual animals. Using the Drosophila model system, researchers will develop bioluminescence imaging (BLI) tools for real-time, longitudinal monitoring of infection. This approach will allow direct observation of infection progression and help identify novel immune genes. The project has two main goals. Goal 1: Uncover the genetic drivers of infection resistance, tolerance, and outcome. Different fly genotypes show significant variation in survival, but infection resistance at a single time point does not always predict survival. This may be because immune responses unfold over time and immune tolerance mechanisms also influence survival. By monitoring infection dynamics in genetically diverse individuals, researchers aim to identify genetic factors that affect resistance, tolerance, and overall survival. Goal 2: Uncover the stochastic contributions to infection outcome. Current technologies do not allow observation of microbial growth dynamics and host responses in a single animal. To address this void, researchers will develop orthogonal luciferase-luciferin pairs that report on microbial load and host gene expression. A simple model of bacterial growth and host response will be compared to those with more complex dynamics, including feedback and fluctuations, to find which fit the data best. Such work will reveal the role of fluctuations in microbial growth and host response to infection outcome, providing potentially novel strategies for infection control. 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 2025 · 2025-08

Most stars exist in “binary systems”, where two stars orbit one another, rather than as single stars like the Sun. If the two stars in a binary system have different masses, over time the higher mass star will expand into a giant star before the lower mass star. This often results in a short phase where the lower mass star orbits within the larger star, resulting in a dramatic exchange of energy and ejection of mass. The process results in the two stars coming closer together with only the core of the high mass star remaining. The phase is poorly understood and has never been observed in real-time. For this project, the investigators will use telescopes and computer simulations to study the process. The investigators will also provide research and training opportunities for undergraduate and graduate students. The investigators will observationally determine the mapping between the initial and final conditions of common envelope (CE) evolution by characterizing a benchmark set of detached post-common envelope binaries located in stellar clusters. They recently completed a systematic search for post-CE white dwarf and main-sequence binary systems in 299 Milky Way open star clusters, yielding 52 high-probability post-CE systems, three of which they classify as confirmed. The researchers will characterize and model these 52 high-probability systems, thereby creating a unique set of benchmarks where the initial and final conditions of common envelope evolution are known. Directly relating post-CE systems to their pre-CE binary parameters will provide critical constraints on stellar evolution and population synthesis models. 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 2025 · 2025-08

In this NSF project, funded by the Chemical Mechanism, Function, and Properties Program of the Chemistry Division, Professor Eric Cueny of the Department of Chemistry and Professor Maria Kamenetska of the Departments of Chemistry and Physics at Boston University are using single molecule conductance measurements via the scanning tunneling microscope break junction (STMBJ) technique to evaluate the strength of interactions between gold ions within molecules, known as aurophilicity. Molecules containing two gold ions, dinuclear gold complexes, often act as catalysts for reactions necessary to produce societally important products or as light sources. While, aurophilicity between the gold ions in the molecule is commonly invoked as the key factor in the catalytic and luminescent properties of dinuclear gold complexes, a general method for quantifying aurophilic interactions is lacking. Here the research team at Boston University propose to develop a much-needed, general experimental technique for studying factors that promote aurophilicity. The ultimate goal is to establish the chemical principles for strengthening aurophilic interactions in dinuclear gold complexes in order to engineer more effective gold complexes as photocatalysts. Dinuclear gold complexes bearing a variety of ligands have been synthesized and studied for catalytic and luminescent applications. The aurophilic interactions in these dinuclear gold complexes are credited for the desirable catalytic and luminescent properties. While computations have been used to evaluate aurophilicity, direct experimental measurements of aurophilic interactions are lacking. Resonance Raman spectroscopy has been used in the direct quantification of such aurophilic interactions; however, this measurement requires Raman silent ligands precluding a more general analysis of aurophilic interactions. By measuring the conductance of dinuclear gold complexes in an STMBJ, the research team can correlate the conductance with the strength of aurophilic interactions. In this proposal they will 1) establish single molecule conductance signatures of aurophilic interactions 2) use single molecule conductance measurements to quantify aurophilicity in dinuclear gold complexes and 3) use single molecule conductance to probe the aurophilicity and reactivity of dinuclear gold complexes under homocoupling reaction conditions. Ultimately, they aim to establish the chemical principles behind strengthening aurophilic interactions. Guided by these principles, the research team seek to improve the photochemical activity of these dinuclear gold complexes. 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 2025 · 2025-08

The lower ionosphere, located between 85 and 140 kilometers above Earth's surface, is a dynamic region where the atmosphere transitions from cold neutral air into a plasma-rich environment. Here, high-speed winds, rapid density changes, strong electric currents (called electrojets), and aurora borealis all interact to create irregularities—disturbances in the plasma density. These impact electrical currents flow, the plasma temperatures, and, perhaps most importantly, how radio travel moves through the upper atmosphere, sometimes disturbing GPS and a variety of communications. The broader impacts of this work extend beyond ionospheric science. This project has strong educational and training components. It will include mentoring undergraduate and graduate students in cutting-edge plasma physics and space science. The tools and simulation techniques developed can be applied to other areas of plasma research, and the open availability of these resources will support a wide community of scientists. Ultimately, this research will contribute to better models of Earth's upper atmosphere, improve our ability to interpret remote sensing (such as from radars) and rocket data, as well as train the next generation of space physicists. This research project aims to better understand the physical processes behind these plasma irregularities by using high-resolution computer simulations, theory, and comparisons with observations. These simulations will model conditions in the ionosphere more realistically than previous efforts, accounting for vertical variations in conductivity and the turbulent effects of space weather events such as geomagnetic storms. This research will combine simulations with theoretical models to better predict how these irregularities evolve over time and impact the larger ionosphere. A key goal is to improve how scientists interpret radar and satellite data that monitor Earth's space environment. By refining both computer and theoretical models, the research will support more accurate forecasting of how the upper atmosphere responds to external forces like solar storms. This is particularly timely, as new missions such as NASA’s Electrojet Zeeman Imaging Explorer (EZIE) and a forthcoming international campaign in Peru will be gathering detailed observations of these electrojets. 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 2025 · 2025-08

A quantitative and predictive understanding of gene regulation – how, when, and why specific genes are expressed as proteins - is crucial to fully deciphering cellular function which has profound consequences for biology, medicine, and biotechnology. Understanding and harnessing gene regulation is central to numerous applications in biofuels, biopharmaceuticals, and biotechnology. Moreover, many diseases including cancer, genetic disorders, and autoimmune conditions, result from misregulated gene expression. Gene regulation is driven by biophysical interactions between different regulatory proteins. These interactions can be quantified. But due to technical limitations, quantitative models of these interactions are only available for a handful of regulatory proteins. Advances in genomics and machine learning are providing the potential to address this shortcoming. The researchers have developed a novel framework, called BoltzNet, that combines the computational power of modern neural networks with the analytical power of biophysical models to translate genomic data directly into quantitative and predictive biophysical models of regulatory interactions. With this award, the researchers will extend BoltzNet to quantitatively model the regulatory interactions required to understand and predict gene regulation. The resources and algorithms developed in this research will have utility for molecular biologists and microbiologists seeking to quantitatively and mechanistically interpret genomic data, for synthetic biologists to predictively engineer new biotechnology applications, and for computational biologists seeking to develop interpretable and biophysically motivated neural networks. The project will extend BoltzNet to use ChIP-Seq and RNA-Seq genomic data to quantitatively and predictively model allosteric transcription factor (TF) binding to DNA, interactions between TFs, regulated binding of RNA polymerase to DNA, and ultimately transcriptional regulation. The resulting algorithms will enable the interpretation of genomic data in fundamental biophysical terms for diverse regulated promoters involving TF interactions; no such tool currently exists. The algorithms will also facilitate the quantitative design of new regulatory interactions for synthetic biology applications. The researchers will develop a publicly available BoltzNet web server to allow the scientific community to run BoltzNet algorithms on their own data sets. The web server will also host the results of analyzing publicly available bacterial ChIP-Seq data resulting in a compendium of quantitative biophysical models of transcriptional regulators as a resource for the scientific community. 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 2025 · 2025-08

Understanding dynamical processes/systems by learning the coefficients of partial differential equations (PDEs) is a fundamental problem in theoretical and applied sciences. In the statistical literature, recovering unknown parameters of PDEs from noisy observations belongs to a class of problems known as inverse problems. Researchers have long been developing methodologies for solving inverse problems, driven by their fundamental importance across a wide range of scientific and engineering domains. Recently, physics-informed neural networks (PINNs) have gained popularity for simultaneously solving partial differential equations (PDEs) and estimating their parameters from noisy observations. Despite their empirical successes, the statistical properties of these estimators remain poorly understood. In particular, due to the complexity of neural networks and the non-parametric function estimation involved, PINNs often produce biased estimates of PDE parameters. Such an estimation bias in this context can lead to inaccurate inference about the physical parameter of interest, with potentially serious implications for downstream applications. This project aims to develop a rigorous statistical framework to draw reliable inferences about the parameters learned by PINNs. The PIs will develop a novel debiasing technique to remove the bias of estimators obtained from PINNs, thus facilitating inference. The method is quite general and can be extended in multiple directions with real-world applications. This project contributes to advancing the literature on the squared-root-rate estimation of finite-dimensional functionals in semiparametric models, especially in the context of PDE learning via neural networks. Current methods rely on undersmoothing the nonparametric component and cannot be applied directly to deep neural network models and PINNs. The debiasing method to be developed allows researchers to bypass this important challenge for PINN models, which is also easy to implement. Further important extensions to Physics-informed neural operators (PINO), where the nonparametric component is an operator, will be developed. This research also advances the literature on high-dimensional statistics. In many applications, the exact form of the observed PDE may be unknown, leading to the PDE discovery problem, a uniquely challenging version of sparse high-dimensional regression. By leveraging techniques from sparse high-dimensional regression literature combined with the debiasing method, this research will develop a framework for solving the PDE discovery problem with high probability, along with reliable statistical guarantees. Finally, on the applied side, this research also aims to contribute to atmospheric science. Using the developed PINN methodologies, the PIs aim to obtain a more precise understanding of the dynamics of the intertropical convergence zone (ITCZ) in West Africa, which will ultimately improve the ability to predict rainfall under the tropics on a seasonal scale. 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 2025 · 2025-07

Artificial Intelligence (AI) and accessible computing have tremendous potential to improve the quality of life for users. Despite significant advancements in AI, the needs and preferences of people who have low vision (e.g., older adults and those who are blind and visually impaired) are not appropriately considered. A multimodal AI model may misinterpret critical directional information in navigation tasks, causing potential safety hazards. When users attempt to provide detailed feedback, the model often fails to follow the request. The model may respond with long and inaccurate descriptions. It may not recognize the needs of the user and focus on irrelevant information. This research highlights a critical gap in AI’s ability to quickly recognize and adapt to the specific interaction needs of users with varying abilities. One of the results of this research is to create the first publicly available, large-scale multi-modal dataset with labeled preferences from users with low vision. This work aligns the data with the goal of AI working effectively and safely with all users. The research also emphasizes and supports the importance of scalable multimodal systems for goal-oriented language generation and real-world decision-making tasks. Through pre-training and generalized feedback from users of these multimodal large language models, it will make these AI-based systems more usable. The methods and results will support those with low vision but can be adapted to address other disabilities. This will allow AI-based systems to better serve the different needs and contexts of users with different abilities. This project addresses a critical gap in computing associated with artificial intelligence (AI) by embedding comprehensive knowledge of accessibility including user preferences into interactive, goal-oriented AI-based agents. The results from Aim 1 will create the first publicly available, large-scale multimodal dataset, AccessBench, with labeled preferences from low vision users. This can be extended to people with different abilities. AccessBench facilitates the training and evaluation of multi-modal large language AI models tailored to user needs in different contexts. The system aligns the data with the goal of collaborating effectively and safely with end-users with different abilities. The approach includes detailed feedback from expert users and people with lived experiences of low vision to identify limitations in the existing reward modeling and instruction generation methods. This will allow for better AI models and system designs, which will provide for preferences and create more helpful systems at scale. Aim 2 will result in pre-trained and fine-tuned multimodal large language models (MLLMs), known as AccessAgent. This will result in a feedback-based framework for instilling usability knowledge into MLLMs, facilitating diverse downstream HCI and usability tasks for various users and contexts. The outcome will be a large-scale preference benchmark derived from Aim 1 in collaboration with industry partners. The project will revisit standard MLLM training pipelines to explore and address robustness issues in the context of preferences and user abilities throughout the development pipeline. A key outcome from this project is the release of a novel pre-trained, fine-tuned, and aligned MLLM at various capacities and sizes as an open-source project for the community to use. 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 2025 · 2025-06

Technological advances have made it possible to collect massive datasets in many scientific applications. A major challenge is to create algorithms that can analyze these datasets efficiently while also providing guarantees on the quality of the analysis. This project focuses on iterative algorithms that start with an initial guess and then refine it until they reach a solution of the desired quality. Prior work has made it possible to design efficient iterative algorithms with excellent performance guarantees, under the assumption that all of the data is processed synchronously, without any losses or errors. However, large datasets often need to be distributed across multiple servers, leading to asynchronous updates, partial losses, and errors. The main contribution of this project is a novel framework for the design and analysis of iterative algorithms that process very large datasets in a distributed fashion. This framework naturally captures many of the phenomena that arise in distributed data processing, and can be used to design strategies that are more efficient than requiring the servers to maintain perfect synchronization. Furthermore, this research will train undergraduate and graduate students to be experts on distributed processing of massive datasets. It will also lead to the creation of educational materials, such as tutorial articles, focused on the statistical analysis of massive datasets. From a technical perspective, this project builds on the approximate message passing framework, which is an established methodology for precise probabilistic characterizations of iterative inference algorithms, such as matrix estimation and linear regression, in the high-dimensional setting. The project will expand the approximate message passing framework to scenarios with distributed, dynamic, and stochastic data processing. This framework will be used to create iterative algorithms that can handle partial updates, distributed computation, and dynamic data, as well as to maximize the efficiency of these algorithms, in terms of the number of iterations or the overall compute budget. The project will also explore a new proof technique based on Gaussian coupling in order to provide non-asymptotic guarantees on the performance of approximate message passing with long-term memory, non-separable denoising functions, and very high iteration counts. 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 2025 · 2025-06

The computing ecosystem continues to grow at a breakneck pace and consumes a substantial portion of the world’s electricity. Artificial Intelligence's (AI) recent successes have further exacerbated this computing-driven surge in energy demand. As a result, there is a growing need to integrate various energy sources in the energy grid. However, volatility of some energy sources creates new challenges for the power grid operators, who must continuously balance electricity supply and demand. Achieving the energy efficient data centers would not only help stabilize the power grid but also ensure that AI’s rapid growth remains supported. This project aims to realize that vision by integrating AI data centers into emerging smart grid programs. To this end, the project will develop a data center control framework to regulate data center power consumption with power grid and carbon constraints, all while providing necessary performance guarantees to data center users. By doing so, the project will help stabilize the grid and provide electricity cost incentives for data centers. This project aims to improve fundamental knowledge in AI data center management and optimization. While there is a rich body of research in large-scale computing system management and recent advancements in computing demand response, the state-of-the-practice is substantially lagging due to science and engineering challenges. To enable AI data centers to perform “flexible computing", this project will innovate in the following three directions: (1) designing AI data center planning and runtime optimization policies that provide performance or quality-of-service guarantees to user jobs while following dynamic power signals, (2) building an optimization framework where multiple data centers interact with grid operators and with each other so as to improve a global social cost metric, and (3) addressing key challenges in cyber-infrastructure implementation so that the designed server and data-center level policies are impactful in real-world scenarios. The project will provide validation of ideas through experiments on servers and simulations, and via deployment of prototype implementations on real-world clusters. 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 2025 · 2025-04

Microorganisms can be engineered to express proteins that direct the synthesis of valuable products. However, the resources used to accomplish this take away from resources devoted to maintaining normal cell functions, which can often result in reduced cell growth. This project will explore using light to control metabolic pathways with the goal of balancing production of valuable products and normal cell growth. The outcomes of this research could lead to advances in sustainable production by enabling light-controlled strategies for optimizing biosynthesis in real time. The research program is complemented by educational outreach. Most notable are efforts partnering with the STEM Pathways program to host an event at the Cambridge Science Festival. The project will also provide research training opportunities for undergraduate students. Designing and testing protein-level optogenetic regulation for metabolic engineering is promising and challenging. As a test case, the researchers will focus on octanoic acid, used in production of fuels, plastics, surfactants, and other products. Octanoic acid can be produced in Escherichia coli via expression of heterologous thioesterases. Production of octanoic acid is metabolically taxing, motivating the need for dynamic strategies to shift cellular resources between growth and production. First, the researchers will develop a novel method for generating light-inducible proteins. They will use a domain insertion approach to construct libraries of proteins containing a modular photodomain and will subject these libraries to selection to generate novel light-responsive thioesterases. Second, the project will comprehensively test light induction strategies to map the impact of temporal parameters associated with light exposure to production outcomes. This approach will provide insight into the differences between alternative optogenetic implementations and their ultimate impact on production. Third, the researchers will implement real-time feedback control to dynamically regulate growth and production. By implementing closed-loop control, the researchers will design autonomous methods for regulating bioproduction that are responsive to current conditions. The strategy is designed to capitalize on the unique benefits of reversibility and tunability that light affords to balance the trade-off between production and growth. 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 2025 · 2025-04

This three-year REU Site: Translational Biophotonics will engage ten undergraduate students each year in the science and engineering of using light to measure cells, tissues, and organs for the purpose of improving human health. The program will work to cultivate a new generation of researchers well-versed in the principles and applications of biophotonic technologies. These efforts will enable REU students to contribute to advancements in the field and their translation into practical solutions for human health. The Site will focus on recruiting underrepresented students, including students from undergraduate institutions with limited research opportunities. Students will engage in a range of state-of-the-art biophotonic technologies. They will be introduced to the process of clinical and commercial technology translation (a process by which new technologies are brought to clinical-care setting and eventually to the market). Students will learn written and oral communications skills, how to work effectively on diverse and interdisciplinary teams, and the principles of biomedical and engineering ethics. Participants will conduct lab-based research and attend technical seminars and panels on careers. This REU Site will focus on developing a range of state-of-the-art biophotonic technologies and methods that are opening doors in basic and translational research. The three main research thrusts of the program are methods development, neuroimaging, and clinical methods. An uncommon feature of the program is that students will be introduced to the process of clinical and commercial technology translation, and training in identifying unmet needs, regulatory pathways, market analysis, entrepreneurship, and commercialization. As an important cross-cutting element of the program, students will learn principles of biomedical and engineering ethics. Participants will present their research to the Boston University community. The project will showcase how biophotonic technologies can drive rapid innovation and advancements in medicine and human health. 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 2025 · 2025-04

Coastal ecosystems provide numerous benefits to people. While much research has focused on the coastal water column, less attention has been given to the importance of the seafloor or sediments of coastal systems. The sediments, however, can recycle nutrients to the water column and support the growth of primary producers such as phytoplankton. In turn these phytoplankton provide food for nutritionally and commercially important shellfish and finfish. The sediments also store carbon and filter excess nutrients from the water column, thereby improving water quality. This project is focused on bringing together a variety of data sources - from observations to modeling results for the Northeast United States. This region was chosen because it is relatively data rich compared to other areas and plays an important role in the US blue economy. A literature review and synthesis will be conducted to gather relevant data, and these data will be evaluated to find patterns of variation. Ocean models will then be used to assess changes across this region that could happen on the short and long-term. Sediment data will also be compared to water column data to see how they are connected, and if their connection is changing over time. This study will engage the scientific community to develop best practice guidelines for sediment data collection and develop community driven priorities for future sediment research studies. The project will provide training for a graduate student and a postdoctoral researcher and support workshops for both scientists and stakeholders. The seafloor plays a major role in influencing atmospheric carbon dioxide and oxygen concentrations, low-oxygen zones in the ocean, and ocean acidity, and represents the only geologic-scale storage of oceanic carbon. The sediments in coastal areas are particularly important as it is estimated that they account for ~70% of ocean carbon burial. Coastal sediments also recycle nutrients to the water column, fueling future water column primary production, and they can improve water quality via nutrient removal. Despite their importance, coastal sediments are poorly sampled relative to the water column, with large spatiotemporal gaps in datasets of nutrients and biogenic gas fluxes. The paucity of coastal sediment flux data leads to incomplete estimates for carbon, oxygen, and nutrient budgets in the ocean. Synthesizing disparate datasets of benthic variables therefore addresses an urgent need to improve the understanding of the role of sediments in the carbon and nutrient cycling in coastal regions at multiple timescales. This effort would further the understanding of mechanisms and environmental conditions influencing benthic dynamics, and consequently the role they play in driving pelagic biogeochemical cycles. The project seeks to do this by focusing on the Northeast US (NE-US), which is a relatively data-rich region. Specifically, existing long-term sediment and water column datasets from the NE-US, model output, and re-analyses will be combined to evaluate the role of different environmental conditions on benthic fluxes of carbon, oxygen, and nutrients. The project will characterize flux patterns in this region, help interpret the larger context these fluxes were observed in, co-locate different benthic fluxes and related variables in space and time to evaluate their relationship to changing environmental conditions, and develop community driven guidelines for data use and future observations. This project will support an early career scientist, one Ph.D. student, and one postdoctoral researcher. Results from this study will be disseminated through publications, presentations at scientific conferences, workshops with stakeholders and public outreach events. 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 2025 · 2025-04

This project introduces HoloStream, a novel system that enables the efficient analysis of large data streams on different types of computing platforms. The project’s novelties include (i) a customizable design that can be tailored to various deployment settings, (ii) mechanisms to tune system configuration according to workload changes, and (iii) techniques to manage resources automatically. The project's broader significance and importance are the potential to make data streaming tools more accessible to everyday users, enabling new applications in areas like smart cities, healthcare, personalized recommendations, and tracking disease outbreaks. The project involves three sets of tasks that address challenges in adaptive resource management and efficient reconfiguration of streaming applications on dataflow systems. First, the investigator designs an adaptive distributed runtime system that can be tailored to the diverse workload characteristics of streaming applications and achieve practical performance on heterogeneous deployments. Second, she introduces online adaptation mechanisms to achieve consistent and correct reconfiguration of streaming applications without downtime. Third, the investigator develops a heterogeneity-aware optimization framework to enable self-management of streaming applications. Project results have the potential to lower the deployment costs of streaming technology for users and providers alike, and inform future research on self-management policies for long-running applications, beyond streaming analytics. 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 2025 · 2025-03

Nontechnical summary This award supports theoretical and computational research and education into quantum mechanical systems of interacting particles far from equilibrium. The importance of quantum physics in technology is rapidly growing. Quantum mechanics combines both particle and wave concepts leading to unintuitive but very robust effects used in modern technology. In recent years, the focus of quantum research has shifted to non-equilibrium phenomena including but not limited to quantum computation and simulation in complex circuits, various high-precision metrological applications, and designing quantum materials with desired properties by driving them away from thermal equilibrium. This project focuses on two major themes: i) understanding, characterizing and controlling chaos in interacting many-particle systems and ii) developing formalism for describing systems which have both quantum and classical degrees of freedom interacting with each other. Most systems around us are chaotic. On the one hand chaos reduces our ability to predict and control the behavior of various systems. On the other, it leads to long time stability of matter. Colloquially, the situation is like with tornadoes: they appear because of chaos leading to instabilities in the atmosphere and they disappear also because of chaos eventually relaxing to a homogeneous state dictated by the laws of equilibrium statistical mechanics, which would not exist without chaos. The P.I. and his group recently developed a new framework of defining and characterizing both quantum and classical chaotic systems using a geometric approach. In the current project, the P.I. will focus on universal (model independent) aspects of chaos focusing on least studied intermediate and long-time behavior of such systems. A special focus will be on finding similarities and differences between quantum and classical chaotic systems. The second thrust is focused on the interplay between fast quantum and slow classical degrees of freedom. A colloquial example is a bucket with water. When classical bucket quickly rotates, the quantum water molecules form a new equilibrium state where water stays in the bucket even when it is upside down. This happens because quantum and classical degrees of freedom cannot be easily separated from each other forming a joint synchronized motion. While this example with rotations is of course well understood, for more complex motions like crystal vibrations, the existing theoretical framework, known as the Born-Oppenheimer approximation (BOA), is insufficient. The PI and collaborators have just developed a new general approach allowing one to systematically go beyond the BOA and describe such systems. In the current project PI plans to apply this approach to specific setups relevant to materials and other experimental platforms. The main broader impact is a commitment to finish a new graduate level textbook on quantum mechanics with the expected completion date in mid-2026. The book (co-authored with M. Rigol and P. Claeys) gives a new perspective on quantum physics shifting focus from understanding atomic orbitals to concepts relevant for understanding modern technology. The PI, also actively continues training students at all levels and performing extra curriculum teaching at various US and international short-term schools. Technical summary This award supports theoretical and computational research and education into quantum mechanical systems of interacting particles far from equilibrium. The research part of this proposal has two main themes: i) understanding quantum and classical chaos and ergodicity through adiabatic transformations and ii) systematic expansion of dynamics of systems with joint classical and quantum degrees of freedom beyond Born-Oppenheimer approximation (BOA). As it was shown by PI and collaborators adiabatic transformations connect into a single framework many seemingly different phenomena like emergence of local conservation laws, Schrieffer-Wolff transformations, Floquet Hamiltonians, short and long-time dynamics and operator growth, quantum chaos and ergodicity, design of fast and efficient quantum annealing protocols and quantum thermal machines. There also emerged new ideas, which will be central to this proposal like connecting quantum and classical chaos/ergodicity into a single framework, universal dynamics of systems close to integrability, and a systematic approach allowing one to go beyond the BOA for describing systems with quantum and classical degrees of freedom like molecules or electron-phonon systems. Using this approach PI also plans to study emergent nonequilibrium steady states as states approximately equilibrating in the moving frame and, in particular, to understand how these non-perturbative states affect macroscopic physical observables like transport coefficients. PI will continue close interactions with experimental groups to test these theoretical ideas in various setups such as cold atoms, trapped ions, NV-centers, superconducting qubits, and various other quantum simulators. The main broader impact is a commitment to finish a new graduate level textbook on quantum mechanics with the expected completion date in mid-2026. The book (co-authored with M. Rigol and P. Claeys) gives a new perspective on quantum physics, shifting focus from understanding atomic orbitals to concepts relevant for understanding modern technology. The PI also actively continues training students at all levels and performing extra curriculum teaching at various US and international short-term schools. 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 2025 · 2025-02

NONTECHNICAL ABSTRACT This award supports computational research and education focused on understanding the interaction of light with ordered assemblies of carbon-based organic molecules, with the goal of realizing advanced solar energy conversion materials. Solar energy conversion refers to the process of harnessing solar energy and converting it to electricity or chemical energy, a promising technology for addressing the challenges associated with the projected future growth in energy needs. This technology requires new materials, designed specifically to achieve more efficient and inexpensive solar energy conversion devices. In contrast to the traditional inorganic materials used in solar energy conversion devices, organic materials are abundant and extensively tunable by virtue of the mature field of synthetic organic chemistry. To utilize and improve these materials for solar energy conversion, it is necessary to understand their fundamental physical properties. However, this understanding is hindered by the challenges in characterizing the behavior of atoms and electrons at nanometer length scales. By utilizing and developing upon state-of-the-art simulation methods, the PI and the research team will investigate the fundamental properties that govern the behavior of electrons within organic assemblies, in the presence of light, and develop physically intuitive models of the influence of molecular structure on electronic properties. The ultimate result of this research will be to provide new design rules to efficiently convert solar energy within organic assemblies. This project will integrate education and outreach with research in two ways. The PI will integrate the simulation methods developed in this research project into the graduate level course “Computational Materials Science”. Additionally, in collaboration with the Boston University Outreach and Diversity Program, the research team will deploy activity kits to museums across the world, introducing the scientific concepts to the broader community. TECHNICAL ABSTRACT This award supports computational studies aimed at understanding the behavior of optically excited states (excitons) in organic molecular assemblies. Organic materials are a highly tunable class of optically active materials that are promising as components in solar cells and photocatalysts. To make the use of organic materials in such applications feasible, we must develop an intuitive understanding of how to improve the efficiency and lifetime of the component materials. The field of organic chemistry is mature enough such that molecular assemblies can be artificially constructed with a great degree of precision; however, there is still a lack of understanding regarding the design of these systems for efficient energy transfer, necessitating theory and computation to provide deeper physical intuition about their excited states. This project focuses on the role of long-range order on the optical properties of organic molecules in the condensed phase. The PI and the research team will employ first-principles electronic structure theory to better understand how inter-molecular electronic and vibrational interactions modify the electronic structure and evolution of the excited-state within organic molecules in the solid state. The ultimate result of this research will be to provide new design rules to efficiently direct optical excitations along molecular assemblies. This project will integrate education and outreach with research in two ways. The PI will integrate the simulation methods developed in this research project into the graduate level course “Computational Materials Science”. Additionally, in collaboration with the Boston University Outreach and Diversity Program, the research team will deploy activity kits to museums across the world, introducing the scientific concepts to the broader community. 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 2025 · 2025-01

Human beings constantly change the way they speak. Discovering how and why people change their speech is key to understanding knowledge of language. Previous research has shown that people quickly change their way of speaking when they talk to one another, in a phenomenon known as phonetic accommodation. People may also quickly change the way they speak and listen to their first language (L1) when they learn a second language (L2), in a process called phonetic drift. While these two processes of speech change may be connected, they have not yet been studied in tandem. This doctoral dissertation project investigates both how bilingual speakers adjust their L2 when exposed to a person who speaks a different variety of the L2, and how this experience affects the bilingual speaker’s L1. Because previous research has largely focused on native speakers or on nonnative speakers who learned their L2 later in life, this project instead examines nonnative speakers who learned their L2 early in life – early bilinguals. Early bilinguals are of scientific interest because they show similarities to native speakers but also to nonnative speakers. This project gathers experimental data on speech production and perception in early bilinguals during interactions with another speaker. The findings contribute to a scientific understanding of how sound changes occur in bilingual individuals and have implications for research on the bilingual brain. This project also benefits society by providing educational opportunities and workforce development skills for students. Bilingual people who learn a second language (L2) early and use it regularly in daily life raise key questions about what shapes bilingual speech. Some early bilingual speakers, despite their similarities to native speakers, show differences in how their first language (L1) influences their L2. This doctoral dissertation project tests theories of L2 production and perception by examining within-speaker language and dialect change across two bilingual populations, each with a distinct L1 background, living in different language environments. The project advances three aims: (1) to analyze how bilingual speakers modify their L2 speech production when exposed to a foreign variety of that language (i.e., L2 phonetic accommodation), (2) to identify short-term changes in L1 speech production following L2 phonetic accommodation (i.e., L1 production drift), and (3) to investigate short-term changes in L1 speech perception following L2 phonetic accommodation (i.e., L1 perceptual drift) along with the relationship between perception and production changes. By comparing groups with different L1 backgrounds and language environments, the project provides insight into the roles of linguistic background and ambient language context in L2 phonetic accommodation and L1 phonetic drift. Participants are early bilingual speakers in two distinct environments. Demographic data comes from a background questionnaire, and acoustic and perceptual data from a series of production and perception tasks (e.g., picture naming, shadowing, forced-choice identification). The results contribute to a broader understanding of bilingual phonetics, the dynamics of cross-linguistic influence, and the factors shaping language change in bilingual populations. 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 2025 · 2025-01

This CAREER award focuses on designing mechanisms that allocate resources in a way that maximizes "consumer utility," defined as the value of the allocation received minus the payments made. This is particularly important in social services, or generally when charging monetary payments is infeasible. In such cases, to ration demand or prove user need, a non-monetary cost is imposed on consumers, such as waiting in line or filling out labor-intensive paperwork. The project explores trade-offs between efficient allocation and the burdens imposed on individuals, aiming to develop simple, explainable mechanisms guiding principles on when to use these mechanisms in practice. Through this project, the investigator will also (1) build research communities locally with workshops and online via reading groups, (2) increase the participation in computing through mentoring, awareness, and recruitment, and (3) develop educational materials based on this project's research. This project will pioneer the field of multidimensional algorithmic utility maximization when ordeals are payments. With an objective of expected (Bayesian) social welfare minus revenue, this optimization problem introduces tension between efficiently allocating and using payments to do so. Allocating multiple items only complicates the problem, as reducing an item's allocation can sometimes yield more utility than charging payments. The investigation is organized into three thrusts: (1) characterize utility-optimal mechanisms, proving their intractability and solving for optimal mechanisms at the frontier of tractability, (2) approximate optimal utility by upper-bounding the quantity and designing simple, practical mechanisms with provable guarantees, and (3) apply the theory of utility maximization and ordeals to practical domain-specific questions. 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 2025 · 2025-01

This three-year renewal REU Site: Integrated Nanomanufacturing is hosted by Boston University. Ten students each summer will engage in research in interdisciplinary labs pursuing advances in nanotechnology: the science and engineering of structures with important features measuring much smaller than a micrometer. The project will target students who come from institutions with limited research or technology translation opportunities. The goal of this project is to increase participants’ access to valuable learning opportunities and to build a foundation for career pathways in the field of integrated nanomanufacturing. REU participants will be mentored and coached through an immersive research experience with a focus on developing research independence. Participants will share in mentored research and discovery, exploring nanoscale systems, fabricating novel devices, and engineering new materials. Students will receive training and access to state-of-the-art equipment (photolithography, 3D printing, scanning electron microscopy, transmission electron microscopy, and nanofabrication). The project will feature how integrated nanotechnology systems can drive rapid innovation in societally and economically important application areas of electronics, computing, and medicine. The REU Site will educate members of the next generation of engineers and motivate them to pursue STEM graduate degrees and careers. Each summer, ten students will join a vibrant research community to conduct mentored research and be paired with BU faculty and graduate student mentors, with some students placed at labs of startup companies at the Business Innovation Center. This project focuses on recruiting underrepresented minorities and women from 2- and 4-year colleges with limited research opportunities. Four thematic interdisciplinary and interconnected research areas represented are nanobiosystems, nanofabrication, nanomaterials, and nanophotonics. In addition, the Site will engage innovative nanotech companies in the Photonics Center’s Business Innovation Center to serve as mentor hosts for a select group of REU participants who express a preference for work involving nanotechnology translation. Participants will engage in technical training, professional development workshops, and community building activities. 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 2025 · 2025-01

The Urban Biodiversity Futures Research Coordination Network (UBF RCN) brings together scholars and practitioners to address the interlinked challenges of biodiversity conservation, climate resilience, and social justice. Urban areas are increasingly recognized as important sites for biodiversity conservation, and cities are investing in urban green spaces for their ecological benefits as well as their benefits for human well-being. At the same time, urban green spaces are not distributed evenly across or within cities and climate change is creating an emerging challenge for urban biodiversity efforts. The UBF RCN is nurturing transdisciplinary conversations that engage scholars from diverse disciplines along with urban practitioners to synthesize knowledge and build a research agenda that captures the range of questions, hopes, and concerns at the intersection of urban biodiversity, equity, and climate change. In addition, an important goal of the project is to train the next generation of scholars who can work across disciplines and collaborate with community partners. The project is providing educational and professional development opportunities for upwards of 20 graduate students and early career scholars. While there is a mature body of research in both the natural and social sciences that interrogates urban biodiversity in increasingly nuanced ways, there are few transdisciplinary collaborations that deeply examine what biodiversity in the city means, how it functions for different groups of humans and nonhumans, and what it may take to produce equitable, just urban conservation in a climate-changed future. The UBF RCN responds to this need by building a multi-disciplinary community of scholars and practitioners to reconceptualize urban biodiversity, envision a transdisciplinary research agenda to understand and support just, resilient urban biodiversity conservation, and equip scholars and practitioners with the language and skills for interdisciplinary collaboration. To do so, the investigators are hosting place-based workshops annually, supporting the coordination of interdisciplinary conference symposia and collaborations, providing training and mentorship for graduate students, and developing a web-based community. The findings and data are being disseminated in and beyond the four host cities and to multiple scholarly communities. 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 2025 · 2025-01

A focus of modern computer engineering is designing customized computer processors (accelerators) in order to meet the timing and power needs of emerging applications, and one critical application space that can benefit from customized accelerators is autonomous systems, e.g., robots that can walk and grasp objects. Robots must complete heavy computational workloads fast enough to keep up with changes in the world around them, especially if they are interacting closely with people (e.g., assistive technology, elder care), because they must react quickly to guarantee safety. However, a key challenge in designing accelerators for autonomous systems is the enormous diversity of deployment scenarios (variations in robot shape, weight, power, task, environment, etc.), leading to an explosion of the computing design space. This project will enable efficient navigation of this large, diverse design space by identifying common computational patterns that are shaped by the physical characteristics of the robot deployment scenario and encoding this physical information into the accelerator design process. This will enable the design of accelerators to increase the capabilities of autonomous systems, including unlocking faster control to help robots safely interact with people. Additionally, the educational and outreach activities associated with this project will prepare students for careers in advanced interdisciplinary computing design. Specifically, this project targets the application of motion planning for autonomous systems (i.e., calculating motion trajectories), with three main technical tasks: (1) Developing a library of hardware design flows for commonly-used motion-related computations; (2) Establishing intermediate representations, interfaces, and domain-specific languages to encode real-world parameters, and combining the low-level hardware flows with high-level algorithmic-choice optimizations; and (3) Creating runtime systems that tune both the algorithmic and hardware parameters adaptively during the runtime of a robot motion planning task. This work complements existing hardware compiler and high-level synthesis (HLS) techniques, providing richer information to guide design optimization, and enabling agile deployment of accelerators without ongoing intervention from domain experts or computer engineers. 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 · 2024-12

Coral symbioses are comprised of animal, algal, and microbial members. The primary association, between the coral animal and its photosynthetic algal symbiont, is crucial for the coral host; most tropical corals are highly dependent on their algal symbionts and will bleach and subsequently die if this symbiosis is disrupted by poor environmental conditions, e.g. warming ocean waters. The Northern Star Coral, Astrangia poculata, a common species found in temperate coastal waters of eastern North America, exhibits a flexible symbiosis with its algal symbiont. Adjacent colonies, subjected to the same environmental conditions, can be found with few to many algal symbionts; some colonies appear stark white, seemingly bleached, whereas others are brown in color, containing photosynthesizing algal symbionts, and yet both types of colonies can be healthy. This unique aspect of the biology of A. poculata with its algal symbiont, Breviolum psygmophilum, will be used to examine how symbiosis affects the energy corals devote towards the basic tasks required of all organisms: development, growth, body maintenance, and reproduction. Given rapidly deteriorating ocean conditions for most corals worldwide, understanding how corals acquire and use energy with and without their symbionts, in different environmental conditions, is imperative. The project will support the education and research training of numerous undergraduate students from diverse backgrounds, and will support outreach efforts to engage the public in temperate coral symbiosis and conservation. While tropical corals exhibit one of the classic examples of mutualisms in biology, the fitness costs, benefits, and tradeoffs of coral-algal symbioses are challenging to explicitly test because their symbiosis is obligate. Astrangia poculata, a common subtidal coral found in temperate coastal waters of eastern North America, may permit this type of testing because it exhibits a facultative symbiosis with its algal symbiont, Breviolum psygmophilum. By their very nature, facultative symbioses are evolutionary, ecological, and physiological puzzles that are intriguing at many levels of biological inquiry, and their energetic ramifications may be best disentangled using integrative and comparative approaches. There are certainly fitness costs, benefits, and tradeoffs to different symbiotic states, but unless the benefits are perfectly balanced across states, one would predict convergence towards or away from symbiosis. In A. poculata however, multiple symbiotic states persist in nature, immediately adjacent to each other in physical space. This study will examine symbiosis from an energetic perspective, quantifying the effects of symbiotic state on reproductive investment and larval performance, important life history characters and measures of fitness. The investigators will also construct, parameterize, and validate a comprehensive dynamic energy budget model for A. poculata - B. psygmophilum, the first such model in any system to explore energetic acquisition, expenditure, and needs. Broader impacts will include coordination with existing institutional programs to facilitate the transition into research labs of underrepresented first-year undergraduate students, empowering women in science, and outreach efforts to engage the public in coral symbiosis and conservation. 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

The broader impact of this I-Corps project is based on the development of advanced dementia assessment tools designed to enhance the efficiency of clinical trial patient recruitment and screening processes, thereby accelerating dementia drug development. These tools will enable more precise patient recruitment and reduce screening durations that can currently extend up to 2 years. These advances could lead to a reduction in both the time and cost associated with developing new dementia therapies. This project aims to provide a scalable, robust assistive tool that enables healthcare professionals to assess cognitive health with greater accuracy. Primary care physicians and other healthcare providers can utilize this tool to efficiently and confidently evaluate patients' cognitive status across three major stages and ten etiologies of dementia. By addressing the challenges posed by the shortage of neurological healthcare professionals, the tool facilitates earlier diagnosis and intervention, allowing for more tailored and effective care based on the specific type and stage of dementia. This I-Corps project utilizes experiential learning coupled with a first-hand investigation of the industry ecosystem to assess the translation potential of the technology. The solution is based on the development of a deep learning framework that underpins leading large language models to provide comprehensive probability assessments across various stages of cognitive decline, including normal cognition, mild cognitive impairment, and dementia, as well as across 10 major etiologies, such as Alzheimer's, vascular, and Lewy body diseases. The model leverages a wide range of routine standard-of-care data, including demographic information, patient medical history, neurological tests, images, and genetic data. Beyond its cutting-edge predictive capabilities, this technology incorporates explainable results, enhancing transparency and allowing for the identification of key features influencing each prediction on an individual basis. The solution's diagnostic accuracy meets or exceeds that of experienced clinicians, representing a significant advancement in medical artificial intelligence applications. 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

Harnessing the value of data into applications with great societal value raises significant security and privacy concerns that are likely to stifle progress and decrease the return-on-investment of an AI-powered market economy. The goal of this GCR proposal is to accelerate the development of trusted, low-overhead tools that enable computation directly on encrypted data such that, for example, confidential data can be shared with an untrusted party who can extract insights from the data without having access to the unencrypted data. Such a capability, which is currently unavailable for application to large scale problems, would have the broader impact of greatly increasing the public's trust in modern AI tools and create new opportunities for data-powered, socially responsible innovation. The success of this project hinges critically on the interplay and synergy of ideas and state-of-the-art techniques and methodologies from physics, mathematics, and computer science. The paradigm shift and the powerful practical tools proposed can be realized and implemented only by the integration of expertise and the strong interdisciplinary interactions stimulated and supported by this project. Apart from their significant practical implications, the novel convergent ideas driving this project are also likely to raise new questions and stimulate new ways of thinking and new directions of research in each of the disciplines: physics, mathematics, and computer science. This project brings together state of the art tools from theoretical physics, mathematics, and computer science to: (a) explore a novel paradigm for circuit obfuscation, a fundamental tool in modern cryptography; and (b) to establish the security and efficiency of a recently proposed scheme for computation on encrypted data, referred to as Encrypted Operator Computing (EOC). The conceptual elements that drive both the new approach to circuit obfuscation and the EOC scheme are inspired by the project team's experience with the fundamental physics of complex quantum and classical systems. In particular, the obfuscation of circuits is related to ``local thermalization” of circuits implemented through ``gate collisions,” a novel concept in the context of gate-based computational circuits, which the proposal connects with relators of group presentations in geometric group theory. The connection with geometric group theory provides a natural mathematical framework for formalizing the notion of ``circuit thermodynamics.” Moreover, the critical design elements of the EOC emerge from an exact mapping of reversible classical computation (via circuits of universal reversible classical gates) into the dynamics of strings of Pauli matrices in the space of Pauli strings, a formulation which highlights many useful parallels (as well as differences) between classical and quantum computation. In this context, the quality of encryption by classical ciphers can be characterized by tools that quantify scrambling of information, entropy production, and irreversibility and chaos in the space of Pauli strings, tools commonly used in modern quantum information science. The goal of this project is to scrutinize these physics-inspired ways of thinking with state-of-the-art tools of modern cryptography, mathematics, and statistical mechanics; and to leverage the proposed collaboration and strong multi-disciplinary expertise to establish the proposed paradigm for circuit obfuscation and the EOC scheme for classical computation on encrypted data as trusted practical cryptographic 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.