University Of Massachusetts Amherst

NSF Awards · FY 2025 · 2025-09

This project makes foundation grantmaking data more accessible and understandable to scientists and the public by building an open access database tracking foundation grants to nonprofit organizations in five U.S. metropolitan areas over time. Philanthropic foundations play a vital role in supporting essential programs in areas such as human services, health, education, and the arts, yet the low amount of research on foundation grantmaking is largely due to limited access to reliable, structured grants data. By showing where foundation dollars go, who benefits, and what issues receive support over time, this project enables interdisciplinary research and advances public understanding of philanthropic behavior and its societal impact. The project addresses a notable gap in the scientific understanding of foundation grantmaking through two activities. First, it curates a Longitudinal Foundation Grants Database (LFGD) by extracting and structuring data from foundation tax filings, focusing on grantmaking in five major U.S. metropolitan areas from 2020 to 2023. While the release of Form 990 makes foundation tax data publicly available, its nested XML format and the complexity of funder-nonprofit grants render it difficult to parse and access for research. This project stands out not only for the scale and longitudinal depth of its dataset, but also for its novel use of large language models (LLMs) to classify unstructured grant descriptions into structured variables such as purpose, issue area, and target population. Second, this project applies both descriptive and inferential network models, respectively, to map and analyze the structure and evolution of funder-nonprofit grants networks over time. Drawing on theoretical frameworks from organizational science, the project investigates how factors like organizational status, organizational attributes, and institutional environments shape grantmaking networks and how foundations respond differently to institutional pressures. The resulting comprehensive open-access database will include detailed grant-level data, LLM-generated insights from grant descriptions, organizational characteristics of foundation funders and nonprofit grantees, and network data capturing relational dynamics in grantmaking over time. This project creates a publicly accessible database that serves as a resource for examining the role foundations play in advancing the public good. 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 · 2025-09

How do people derive meaning from sentences? In what situations does the language comprehension process break down? Artificial intelligence (AI) language models such as ChatGPT, which appear to understand and use language as proficiently as humans do, might seem poised to provide potential answers to this question—answers that could not only enrich our scientific understanding, but also help address language processing deficits. But for AI systems to fully realize this potential, they need to process language in a similar way to humans. Many distinct lines of research show that this is not the case. One area where the discrepancy between humans and AI is particularly pronounced concerns temporary semantic ambiguity in language: cases where the first few words of the sentence are consistent with multiple interpretations, and only later in the sentence is it clear which of the interpretations is the correct one. Whereas human readers can encounter significant difficulty when they are required to change their interpretation of a sentence, AI models generally do not. The goal of this project is to better understand the reason for this misalignment between humans and AI models, and explore ways of modifying AI architectures to bring them more in line with how humans process language. In this project, the researchers will benchmark success in their model development by comparing how the models process language to how humans process language using a variety of psycholinguistic measurements. By better aligning human and AI language processing, this research will open up new directions to address long-standing limitations of current AI models, such as their need to train on far more data than human language learners do. In more technical terms, this proposal explores the idea that one key difference between human and machine language processing is that humans: (i) entertain only a small number of semantic interpretations of the input at a time; and, (ii) treat incremental semantic inference as a key goal in language comprehension. This is pursued through three interrelated aims. First, the proposed work will explore the unexpectedly positive correlation between a model’s perplexity and its ability to explain human reading times: put plainly, the better the model is at predicting the next word, the less similar its predictability estimates are to those of humans. Second, it explores whether the human-model misalignment can be alleviated by adopting semantic training objectives and leveraging causal intervention techniques to focus the model’s internal representations of semantic context on a small number of possible interpretations of the input. Finally, human experiments will be conducted to test the predictions of the models on novel psycholinguistic stimuli, with the goal of determining if the proposed modifications successfully bring the models more in line with human language processing. 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 · 2025-09

PROJECT ABSTRACT Protein nanopores are powerful sensors that enable a new class of single-molecule measurements, with applications ranging from fundamental science to commercial DNA sequencing. Our focus is on developing and applying novel nanopore approaches to tackle challenges in protein analysis, ultimately improving disease diagnostics and therapeutics. In this project, we are creating innovative nanopore tweezers capable of real-time tracking kinase movements triggered by ligand or inhibitor binding at a 100-microsecond resolution for tens of minutes at the single-molecule level. This work will generate comprehensive energetic maps of the conformational states of disease-associated kinases during substrate binding, catalytic activity, allosteric regulation, and inhibition. Overall, this study will introduce new tools and opportunities for studying structural dynamics, single-molecule enzymology, drug discovery, and personalized precision medicine.

NSF Awards · FY 2025 · 2025-09

Non-technical Abstract Molecules can arrange to form larger structures, a process that is key to both complex living tissues and new, advanced materials. For example, scientists have long studied how specific sequences of amino acids fold to create proteins that act as tiny machines. Similarly, surfactants (e.g., the molecules in soap) can assemble into spheres, layers, and tubes. In both cases, the assembled structure is important for their practical use. For example, long, tube-shaped surfactant structures help to thicken shampoos while also cleaning hair. However, the ability to form this tube-like structure is usually related to shape of the surfactant molecule itself. This project seeks to learn from the ways in which long, charged molecules with protein-like sequences attract oppositely-charged surfactants, and form materials with desired structures. This effort uses both experiments and computation and will benefit society and the U.S. by establishing a versatile class of biology-inspired materials for use across chemical, agricultural, and industrial applications. The research will also involve the interdisciplinary training of researchers with broad expertise in chemistry, engineering, and physics, via both student mentorship and engagement with K-12 students. Technical Abstract This project will establish how sequence-controlled polymers can be used for the rational design of surfactant-containing materials. This effort will leverage sequence-defined polypeptides to modulate the assembly of surfactants into a variety of different nano-scale structures. The resulting materials will be evaluated by optical and electron microscopy, as well as scattering and rheological methods, to determine the relationship between polypeptide sequence and assembled structure. These experimental aspects will be integrated with a modeling effort that connects molecular simulation, colloid science, and polymer field theory to obtain predictions of assembly in polyelectrolyte-surfactant complexes. The overarching goal is to establish a fundamental understanding of how sequenced polypeptides can be used to manipulate the nano-scale structure of bioinspired surfactant-based assemblies. 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 · 2025-09

Artificial intelligence (AI) is revolutionizing scientific discovery by enabling researchers to analyze large and complex datasets that are otherwise beyond the reach of traditional methods. However, AI is not without limitations—it can introduce systematic errors, make unacceptable mistakes, and often lacks the statistical guarantees that scientists require. This project aims to unlock the full potential of AI in scientific research by developing techniques that integrate imperfect AI models with human-in-the-loop feedback and rigorous statistical estimation. These methods will enable high-throughput, high-precision scientific measurements with quantified uncertainty from large-scale datasets. The work will be grounded in four high-impact applications spanning environmental science and sensing—using data from satellite imagery, radar, acoustics, and sonar. By systematically evaluating the approach in real-world settings, this project will help expand scientists’ ability to use AI responsibly and effectively for discovery and decision-making in areas such as environmental monitoring and disaster response. The project will support interdisciplinary training for PhD students and undergraduate researchers by incorporating its research themes into undergraduate and graduate courses, designing course projects that address real-world challenges in deploying AI, and organizing community workshops that amplify the impact of AI in scientific domains. The project will develop a new framework called active measurement, which combines imperfect AI predictions with targeted human feedback to produce unbiased, statistically grounded estimates of scientific quantities. Active measurement integrates Monte Carlo estimation of a scientific quantity into the full life-cycle of interactive AI model development. Unlike previous approaches, active measurement is fully adaptive: it can dynamically select samples for humans to label and update the underlying models when resources are available. The research will investigate new methods for improving computer vision models and adapting them to novel domains using limited human input, including (1) actively constructing validation sets to improve domain adaptation, and (2) a general-purpose vision model that can be efficiently adapted to specific measurement tasks using examples and text-based guidance. The approach will be deployed and evaluated in collaboration with experts from academia, government agencies, and NGOs, across four domain applications: ecological monitoring, habitat mapping, acoustic wildlife detection, and disaster impact assessment. These efforts will validate the statistical rigor, scalability, and generalizability of the methods for scientific and policy-relevant use cases. 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 · 2025-09

Healthcare is a universal concern, yet many challenges persist in hospitals and clinics, such as preventing medical errors, designing efficient patient care plans, and ensuring quality care. These issues have far-reaching effects influencing patient safety, healthcare costs, and the overall well-being of communities. The National Science Foundation Research Traineeship (NRT) award to the University of Massachusetts Amherst creates a new program that combines nursing’s hands-on patient care expertise with engineering’s technical problem-solving skills to tackle some of healthcare’s toughest challenges. The project anticipates training 28 PhD students, including 13 funded trainees, drawn from both fields. By working in hospital settings and partnering with industry, the trainees will learn to identify and understand healthcare problems at the point of patient care, create practical solutions that benefit patients and healthcare workers, and bring these innovations to the bedside as quickly and safely as possible. Through joint mentorship and immersive projects, the program will produce healthcare leaders who can work across different fields to transform healthcare delivery and improve patient outcomes. The trainees will engage in convergence research that unites advanced engineering methods with clinical and contextual knowledge of nurses. By analyzing healthcare workflows, designing medical devices, and examining factors that affect patient outcomes, trainees will gain skills to develop and deploy effective interventions in real-world clinical environments. These efforts will be supported by an interdisciplinary curriculum and enriched by collaborations with healthcare providers and industry experts. Projects will aim to streamline healthcare delivery from hospital to home, leverage automation and robotics to ease clinician workload while enhancing safety, and make healthcare devices easier to use and connect to support caregivers and patients. By blending hands-on research experiences with leadership and communication training, this traineeship will cultivate professionals who champion nurse–engineer partnerships and drive innovations in healthcare. The NSF Research Traineeship (NRT) Program is designed to encourage the development and implementation of bold, new potentially transformative models for STEM graduate education training. The program is dedicated to effective training of STEM graduate students in high priority interdisciplinary or convergent research areas through comprehensive traineeship models that are innovative, evidence-based, and aligned with changing workforce and research needs. 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 · 2025-09

This three-year REU Site: Research Experiences for Undergraduates in Transportation Engineering will provide undergraduate students an immersive and interdisciplinary educational experience in community engaged research. Improving the transportation experience for all communities is the focus for this project. REU students will develop as well-rounded researchers by learning to design transportation systems that better serve communities; to formulate transportation policies and regulations that lead to more improved and accessible transportation outcomes; and to help bridge communication gaps across disciplines, academia, transportation professionals, and communities of interest. Ten undergraduate students each year will engage in professional development activities about how to persist in STEM and to pursue graduate education and related careers in transportation engineering fields. Participants will be recruited from a variety of institutions nationwide including R1 institutions and community colleges. This project will help build the workforce in a key strategic area – transportation - helping to increase the economic competitiveness of the U.S. by providing opportunities for promising students in community engaged research, design, and planning. The objectives of this REU Site include engaging undergraduate students in transportation research, educating students on convergent research that affects their communities’ transportation decisions, encouraging participants to pursue career pathways in transportation related fields, and enriching the undergraduate student experience through lifelong mentoring. Students will participate in a variety of mentored research projects that require data collection and analysis, such as interviews, focus groups, surveys, and secondary data analysis. In addition to working with interdisciplinary researchers, students will also engage with community partners to collect data and disseminate the results. Outside of the research activities, students will participate many activities over the summer including professional development and communication seminars, technical seminars, field trips, working lunches, broader impacts/technical writing projects, and an end-of-summer poster session. 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 · 2025-09

Non-technical abstract: This project lays a foundation for fully synthetic, lab-made membranes that can mimic the uniquely useful functions performed by the membranes of living cells. Instead of being powered and driven by active proteins, however, these new membranes are powered and driven by light: they are photoswitchable. In the future, these membranes could form the basis of artificial cells with organelle-like compartments to carry out designed biochemical reactions, release or take in chemical reagents and products, stir to enhance reaction rates, and even move in response to stimuli - all driven by light. Synthetic materials based on this science might lead to new products for skin care and for self-cleaning or protective coatings. This project uses lipid molecules that resemble those found in cells, with the crucial addition of an azobenzene group in one of the oily tails of each molecule. When exposed to light, the tails isomerize, i.e., change from an approximately straight to a sharply bent shape. Exposure to light of a different color shifts the tails back to their original shape. This bending causes the lipids to push or pull against one another, leading to transient compression or tension in the membrane. The light-induced molecular forces thereby cause dramatic changes of the membrane’s shape, drive fluid flow, and briefly allow exchange of molecules across the membrane. This three-year project includes fabricating membranes, exciting them with light, and using optical microscopy to observe their shape, permeability, elasticity, and other properties. Membranes that are fluid in the plain (like cell membranes) are compared to membranes that contain solid regions, which tend to form creases (like crumpled paper). Patterned light illumination is used to develop ways to drive membrane motion or fluid flow in a chosen direction. The project includes theoretical modeling, leading to the design rules for future materials. College and graduate students learn skills and gain experience for careers in research and development. The project also provides formal courses in the topic of soft and bio-inspired materials and engages middle-school students from a nearby school in the excitement of research. Technical abstract: This project creates new basic science toward the goal of active, bio-inspired responsive materials based on photoisomerizing lipid molecules assembled into a membrane. Photoisomerization of these lipids causes a reversible bending of one of the lipid tails. This bending in turn creates transient compressive or tensile stress in the membrane as lipids push or pull one another to reach their new equilibrium spacing. Depending on the rate of excitation, photo-induced stresses can excite undulations, multi-lobed shapes, finger-like pseudopods, or topological changes, and can also switch membrane permeability. The project’s first aim focuses on in-plane stresses and flows generated in fluid membranes. These studies show the mechanism leading to transient deformation modes and the cross-over to a spontaneously localized pseudopod-like shape. The second aim focuses on membranes that contain solid (gel-phase) domains. Experiments investigate how domain morphology determines the photo-induced response, ranging from plastic crumpling to inward endocytosis-like budding. The third aim uses spatially patterned light intensity to induce a programmed set of membrane shapes, creating fluid flows that enhance diffusive mixing of nearby particles or cause directional displacement of the vesicles. Results will inform continuum-scale theory to show how molecular conformations lead to non-equilibrium macroscopic changes. The project provides graduate and undergraduate students with training for careers in research and development. Teaching in formal classes and annual summer schools expose students to leading topics in soft and biological materials. Activities include the UMass Summer School on Soft Solids and Complex Fluids; two new forms of training undergraduates in bio-inspired materials; and co-hosting an annual field trip for 8th graders at a nearby school. 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 · 2025-09

This project aims to develop a deeper understanding of the underlying mysteries of four-dimensional spaces, which are mathematical objects locally modeled on the space-time. These spaces also arise in physics, through classical mechanics, string theory, and quantum field theory. The project explores similarities and differences among such spaces when equipped with additional geometric structures, such as smooth, symplectic, and complex structures, using a mix of topological, geometric, analytical, combinatorial, and algebraic methods. Many of the central questions are translated into hands-on problems involving algebraic relations between curves on surfaces, offering accessible entry points for graduate and undergraduate students. The project includes broader community contributions through the creation of training opportunities for students and early-career mathematicians, such as organizing workshops and summer schools—and through work on new problems and updates to longstanding open problems in the field. The research lies in low-dimensional topology and geometry, particularly concerning the topology of smooth and symplectic 4-manifolds and contact 3-manifolds. Key goals include constructing exotic smooth structures on previously inaccessible families of 4-manifolds, developing new examples with definite or signature-zero intersection forms, and producing symplectic analogues of complex surfaces such as fake projective planes and ball quotients. The project also explores smooth mapping class groups and equivariant embeddings of surfaces in the 4-sphere. A variety of methods from gauge theory, geometric group theory, and symplectic and contact topology will be used to advance these investigations and uncover new structural results. 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 · 2025-09

Understanding how energy moves through nonlinear systems is essential for progress in many areas of science and engineering, including fluid dynamics, neuroscience, and the design of advanced materials. This project studies a mathematical model known as a nonlinear oscillator chain, where interactions between neighboring components can create complex, cascading flows of energy between different scales. Such systems serve as simplified yet powerful representations of more complicated physical processes, such as ocean turbulence or signal propagation in the brain. This project supports fundamental research in probability and applied dynamical systems, as well as the development of new computational tools for analyzing high-dimensional stochastic systems that also inform coupled neuronal oscillators and machine learning algorithms. Through student training activities, this work will help build a capable STEM workforce, contributing to national priorities in scientific advancement and education. Recent breakthroughs have drawn new connections between nonlinear dispersive equations and wave kinetic equations (WKE), with particular interest in understanding how energy cascades through scales in weakly nonlinear regimes. A central object in this theory is the Kolmogorov–Zakharov (KZ) spectrum, a formal steady-state solution of the WKE that reflects how energy transfers across modes. This project investigates a class of nonlinear oscillator chains—called energy cascade systems—that are derived from nonlinear dispersive equations and serve as finite-dimensional approximations to wave turbulence. The principal goal is to rigorously study the nonequilibrium steady states (NESS) of these systems and their connection to the KZ spectrum. Building on recent success of proving the stochastic stability of NESS in short cascade chains using a newly developed Feynman-Kac-Lyapunov method, this work will extend these results to longer chains, addressing a key open problem in the field. Complementing this analytical work, the project will extend the principal investigator's earlier development of deep learning-based Fokker-Planck solver to genuinely high-dimensional systems and to Fokker–Planck eigenfunction problems. The numerical work will support the study of oscillator chains and enable broader applications in coupled neuronal systems and machine learning. These combined efforts will advance the mathematical understanding of energy cascades, nonequilibrium phenomena, and high-dimensional stochastic dynamics. 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 · 2025-09

Extreme temperatures on land frequently occur, especially in the tropics. East Africa is particularly vulnerable because a large portion of the population relies on subsistence agriculture and the region has experienced dangerous heatwaves with consequences for human health, flooding/droughts, and famine, and associated implications for societal unrest, emigration, and regional security. Currently, the relationship between temperature and precipitation in East Africa is not well understood, and it is unknown if this region will become wetter or drier in the future. This project will examine a geological record to generate new long-term reconstructions of temperature and rainfall for East Africa, thereby providing the observations needed for future trends in temperature, rainfall and drought to be more reliably predicted, and offering a technical basis for water management programs. This project will train graduate and undergraduate students and provide summer workshops for middle and high school teachers, helping to strengthen U.S. STEM education and train the future STEM workforce. This study will apply organic geochemical and isotopic techniques to a previously collected drill core from Lake Malawi to generate reconstructions of temperature, precipitation and vegetation spanning the past 1.38 million years. These records will be examined in conjunction with existing data to investigate the roles of tectonic, oceanic and atmospheric forcings on East African temperature and rainfall variability on different timescales. Data produced by this study will provide information on the natural drivers of continental temperature and rainfall variability over a range of tectonic and environmental conditions, providing a particularly valuable dataset, unique for the African continent, to improve the next generation of atmospheric circulation models. Hands-on field, classroom and laboratory teaching activities developed during teacher training workshops will be made available online allowing educators anywhere to access and download these modules. 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 · 2025-09

Warfighters must maintain agility and performance in extreme conditions such as navigating rugged terrain, carrying heavy loads, and enduring prolonged exertion, often while facing unpredictable threats. Wearable technologies like robotic exoskeletons and advanced footwear have the potential to enhance warfighter performance and reduce injury risk. However, current design methods often rely on one-size-fits-all approaches and fail to account for how individuals adapt to these devices in real-world settings. This project addresses that gap by developing Digital Twins of agile locomotion in the form of personalized, data-driven simulations that model the complex and dynamic interaction between human movement, wearable technology, and the environment. By integrating real-time physiological and biomechanical data, these models enable better design, training, and deployment of active wearable technology to improve human agility. In addition to advancing national defense and security, this work has broad societal benefits to public health as the mathematical modeling techniques developed can also be used to improve wearable technology design for other user populations, such as those with motor impairment. The overarching goal of this project is to develop mathematical methods enabling an advanced Digital Twin model of human agile locomotion, aimed at optimizing the design of advanced footwear technology to enhance human agility and mobility. In order to accomplish this, this project will advance the state of the art in statistical surrogate modeling, which currently is focused on vector-valued parameters, to accommodate parameters which are functions. This will require significant mathematical and methodological innovation as the parameter spaces are thus infinite dimensional. The investigators will develop an approach which searches within a manifold of finite but increasing dimension to find candidate functions to test. This new methodology will be developed using data from human locomotion when using wearable technologies in a lab setting. The investigators will first deploy this methodology to develop a novel active model reduction method which searches for parameter settings which are not accommodated by the current reduced model. Next, they will extend sequential design for data-efficient predictive modeling to the acquisition of functions, enabling a model of human adaptation in response to wearable devices. Finally, they will develop an infinite-dimensional extension of the Active Subspace method for dimension reduction to enable interpretable optimization of wearable devices. Taken together, this work will lead to a general-purpose framework for building Digital Twins of systems parameterized by functions, as well as a specific implementation of a Digital Twin for the complex system of a human bearing wearable technology. 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 · 2025-08

PROJECT SUMMARY Age-related mobility loss is a major public health concern due to its profound negative effect on health and quality of life. An increased energetic cost to perform activities such as walking has been well-documented in older adults and may be a primary contributor to age-related mobility loss. However, the mechanisms that lead to increased energy cost of walking (CoW) remain unclear. The overall aim of this proposal is to determine how age-related changes in gait strategy combine with contractile and bioenergetic deficits in 3 major locomotory muscle groups to increase CoW. Our working hypothesis is that proximal extensor muscle weakness in older adults requires relatively greater muscle activation and thus greater use of these muscles to produce the joint moments needed for walking. This greater “neuromechanical demand” would increase muscle energy needs, translating to a greater CoW. Robust evidence indicates older muscles are generally smaller and thus weaker than young muscles, but there is also evidence that declines in muscle strength may exceed the loss of muscle size in old age. We will address missing knowledge on relative changes in specific torque (strength/size) of 3 locomotory muscle groups, the consequences on neuromechanical demand and CoW, and the potential effects of biological sex on these outcomes (Aim 1). The gait strategy used by older adults may exacerbate the problem of greater CoW. However, comprehensive studies are lacking about the impact of joint work distribution (e.g., gait strategy) on the neuromechanical demands of proximal muscles, which may experience a larger decrease in specific torque, and the subsequent consequences for CoW (Aim 2). Finally, exacerbation of greater CoW in old may occur due to an intrinsic muscle bioenergetic deficit, quantified as muscle metabolic economy (energy used for a given contraction relative to muscle size). We expect lower metabolic economy in older than younger muscle, and consequently even greater CoW (Aim 3). Data will be collected for 30 young (25-40 y) and 60 older (65-85 y) community-dwelling adults. Groups will be balanced by sex to assess sex as a biological variable and habitual physical activity will be quantified using accelerometry. We will address these mechanistic hypotheses by measuring: neural (electromyography) and mechanical (gait analysis, dynamometry) demand during gait, muscle specific torque (dynamometry, magnetic resonance imaging), and metabolic economy (magnetic resonance spectroscopy, dynamometry) in all 3 major locomotory muscle groups (hip extensor, knee extensor, plantar flexor). We will also quantify the distributions of joint work in walking (gait analysis) and energy CoW (indirect calorimetry). The planned approach builds on our preliminary data, is novel in its assessment of both bioenergetics and biomechanics, and tightly integrates rigorous biomechanical and physiological methods to arrive at a mechanistic understanding of greater CoW in aging. The problem addressed- the role of gait mechanics in the increased CoW in older adults- tackles stated goals of the NIA, as described in this RFA.

NIH Research Projects · FY 2025 · 2025-08

Project Summary The rapid advancement of single-cell spatial imaging technologies, such as Spatial Transcriptomics, Imaging Mass Cytometry (IMC), and CODEX (CO-Detection by Indexing), has revolutionized our understanding of tissue microenvironments by offering unprecedented insights into the spatial organization and interactions of diverse cell types within tissues. These technologies generate large, complex datasets that capture the heterogeneity and dynamic behaviors of tissues at single-cell resolution. However, there remains a significant need for advanced mathematical models that can fully leverage these datasets to interpret the complex biological processes involved. This project seeks to bridge that gap by developing sophisticated mathematical models, particularly using partial differential equations (PDEs), to investigate the spatial interactions between cells and molecules within tissues. Specifically, the models will aim to capture tissue-specific biochemical and biomechanical characteristics to uncover the pathways and mechanisms that influence various diseases and immune responses. Understanding these key factors is essential for revealing how tissue microenvironments regulate disease progression and immune interac- tions. For example, modeling interactions in bone tissue can elucidate mechanisms responsible for conditions like aging, bone deformation, autoimmune diseases such as rheumatoid arthritis (RA), and cancer. By integrating both mechanical and biochemical dynamics into our models, we will develop a comprehensive framework for understanding multi-scale interactions in disease contexts. These models will also serve as predictive tools to simulate how different interventions may influence disease progression and immune responses in various tissues. The spatial dependence introduced in the PDEs will help model the movement of cells, cytokines, and other signaling molecules within tissue environments, a critical factor for processes such as inflammation, immune responses, and tissue remodeling. Ultimately, this project will contribute to the discovery of novel therapeutic targets and the development of personalized treatment strategies by providing a deeper understanding of the pathways and mechanisms that drive complex diseases across different tissues.

NSF Awards · FY 2025 · 2025-08

This award provides support to U.S. researchers participating in a project competitively selected by a 55-country initiative on global change research through the Belmont Forum. The Belmont Forum is a consortium of research funding organizations focused on support for transdisciplinary approaches to global environmental change challenges and opportunities. It aims to accelerate delivery of the international research most urgently needed to remove critical barriers to sustainability by aligning and mobilizing international resources. Each partner country provides funding for their researchers within a consortium to alleviate the need for funds to cross international borders. This approach facilitates effective leveraging of national resources to support excellent research on topics of global relevance best tackled through a multinational approach, recognizing that global challenges need global solutions. Working together in this Collaborative Research Action, the partner agencies have provided support to foster global transdisciplinary research teams of natural, health and social scientists and stakeholders from across the globe to improve understanding of climate, environment and health pathways to protect and promote health. The projects will provide crucial new understanding into the health implications arising from the impacts of climate change and variability on; 1) decision-science approaches to adaptation and implementation, 2) food, environment, and biological security and 3) risks to ecosystems and populations. This award provides support for the U.S. researchers to cooperate in consortia that consist of partners from at least three of the participating countries to increase our knowledge of the complex linkages and pathways between the climate, environment and health to help solve complex challenges that face societies. The YAKU project seeks to study the connections between water governance and health by investigating child mortality, which represents a key marker of the overall health of a society. Unsafe drinking water, lack of proper sanitation and presence of stagnant water rank among the main causes of under-5 children mortality. Adequate water and sanitation management systems are instrumental in countering the spread of infectious vector borne and water-borne diseases responsible for a large proportion of child mortality around the world. The YAKU team will identify effective cross-sectoral strategies that can significantly reduce mortality rates by improving water governance models. Specifically, the team will examine water governance models in the sectors related to safe water supply, sanitation, and wastewater management in rural and urban community settings in Ecuador, Peru, Chile, Morocco and Tunisia to elucidate their potentially differential impacts on under-5 child mortality. This information will help improve population health, especially for approaches to water governance and child health across different geographies, including the United States. The project will provide information on water governance adaptation and resilience mechanisms needed to confront environmental risks, in relation to child health which can be used to enhance water governance/management systems to reduce child mortality due to infectious diseases, and in turn, improve the overall health of their societies. 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

This project explores an affordable and energy-efficient technology to remove inorganic nutrients discharged from wastewater treatment plants. Nitrogen and phosphorus discharges are a main cause for harmful algal blooms. This technology uses oxygenic photogranules (OPGs), which are dense, naturally forming clusters of algae and bacteria. The OPGs grow in sunlight to remove nutrients from wastewater. OPGs settle naturally due to their compact structure, eliminating the need for costly separation technologies. OPGs can operate in simple tank systems with low energy and space requirements. This project will test and optimize the use of OPGs for removing low levels of nutrients in wastewater after traditional treatments. The project will investigate how OPGs grow under these conditions, how the microbes inside them respond to stress, and how reactor design choices like light intensity, mixing patterns, and retention time affect their ability to remove nitrogen and phosphorus. This technology could significantly lower operational costs of wastewater treatment and reduce water bills for communities and households. The project will also provide hands-on research opportunities for students and support workforce development. Nutrient pollution from municipal wastewater effluent continues to degrade surface water quality across the United States, driving eutrophication, harmful algal blooms, and ecological decline in rivers, lakes, and estuaries. While conventional secondary treatment processes — such as activated sludge, aerated lagoons, and membrane bioreactors — reduce organic matter and nutrients, they often cannot meet increasingly stringent nitrogen (N) and phosphorus (P) discharge limits, particularly in ecologically sensitive watersheds. Advanced treatment options like chemical precipitation, enhanced biological nutrient removal (BNR), and photobioreactor systems provide improved nutrient removal but require high capital investment, intensive energy use, and operational complexity. This project will develop and optimize a low-energy, phototrophic wastewater treatment system based on oxygenic photogranules (OPGs) — dense microbial aggregates composed of cyanobacteria, algae, and heterotrophic bacteria that naturally self-immobilize and remove nutrients via photosynthesis and microbial metabolism. OPGs settle passively, eliminating the need for mechanical or chemical separation, and can be operated in tank-based systems with low light and mixing requirements. Unlike suspended algal systems, OPGs form compact, structurally stable granules that enable high-density biomass retention and simplified harvesting, significantly reducing the energy and space footprint of treatment infrastructure. The project will investigate the performance and resilience of OPGs in post-secondary applications using sequencing batch reactors (SBRs) treating nutrient-limited secondary effluent. The project has three specific aims: (1) demonstrate the structural integrity and stable growth of photogranules under low-nutrient conditions by evaluating granule formation, size distribution, density, porosity, and shear resistance; (2) assess microbial physiological responses through extracellular polymeric substance (EPS) profiling, enzymatic activity assays, and RT-qPCR to evaluate expression of key genes involved in ammonia oxidation, denitrification, and phosphorus uptake; and (3) optimize key reactor parameters, including light intensity, mixing patterns, hydraulic retention time (HRT), solids retention time (SRT), and shear stress, to enhance nutrient removal kinetics. The anticipated outcomes include a deeper mechanistic understanding of OPG function under nutrient-limited conditions and the development of a scalable, cost-effective post-secondary treatment process that achieves high nitrogen and phosphorus removal with minimal energy and chemical input. This work contributes to environmental engineering by advancing knowledge of phototrophic biofilm systems, biogranule formation, and low-energy nutrient recovery. It aligns with national priorities in sustainable infrastructure and water quality protection. The project will support hands-on training for graduate and undergraduate students, collaboration with municipal wastewater operators, and engagement with students and global partners through Engineers Without Borders. Together, these efforts aim to promote innovation, knowledge transfer, and improved access to sustainable water technologies. 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

Materials with nanoscale cavities are used in many industrial applications, enabling efficient chemical production and energy generation. Their performance relies upon precisely matching the nanocavity shape to the target application, similar to matching a lock to a key. However, identifying optimal structures among millions of possibilities through traditional experiments and physics-based simulations can be impractical or prohibitively labor- and resource-extensive. This project will develop new machine learning tools capable of rapidly predicting structure-performance relationships for nanoporous materials that have very small pore sizes, with a focus on complex molecules. The developments will accelerate the discovery of nanoporous materials for a diverse array of emerging applications for clean energy and sustainability, including gas storage (e.g., for clean fuel vehicles), membrane separations (traditional separations account for more than 10% of global energy consumption), solid-state batteries, and plastic waste upcycling. Additionally, the cross-disciplinary collaboration provides unique educational and outreach opportunities to train a broad, AI-literate next-generation workforce while bridging the gap between computer science and engineering research communities. With the ever-expanding use of machine learning for materials discovery over the past decade, graph neural networks have emerged as the predominant choice for representing molecules and materials. Graph models are appealing as atoms and bonds intuitively map to nodes and edges, and it is relatively straightforward to ensure the invariance of input features with respect to translation and rotation. However, our preliminary tests indicate that graph models perform poorly in capturing confinement effects in nanoporous zeolites, which are effects controlled by the precise positioning of atoms in 3D space to create nanoscale channels and cages in these materials. In contrast, convolutional neural networks (ConvNets) operating on 3D volumetric grids offer the most efficient and accurate representation, essentially viewing materials structures as 3D analogues of images. Building on these preliminary results, the interdisciplinary research team will work together to develop scalable group-equivariant ConvNets to exploit the symmetry and invariance of the underlying materials structures, investigate unsupervised learning and multi-tasking to obtain transferable representations, and integrate ConvNets with graph representations to enable zero-shot learning for arbitrary host-guest systems. A successful outcome of the proposed work may lead to a general representation framework that can accurately describe subtle noncovalent interactions in extended 3D materials, which can broadly benefit a range of material systems. 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

This program will create computer models of the very first galaxies and black holes to form in the Universe. New observations have discovered extremely distant galaxies, some of these galaxies appear more massive and evolved than expected by current models. Through computer simulations, this program will make an important contribution to establishing the new models needed to properly explain the recent observations of the most distant galaxies. To make the scientific results of this program available to everyone, the PIs will design and fabricate dozens of 3D-printed models from various simulated galaxies and any interesting structures within them. These 3D-printed models will be displayed in outreach events and art exhibits. This collaborative proposal will deliver theoretical and observational predictions for galaxy and Super-Massive Black Hole (SMBH) formation and growth during Cosmic Dawn with a particular focus on galaxies with overly massive black holes. Specifically, this proposal will investigate if galaxies in the early universe with and without a black hole seed can be distinguished observationally. Using the exascale-capable code Enzo-E, this program will carry out simulations of high-redshift galaxy formation, accounting for SMBH fueling, feedback models and their impact on the circumgalactic medium. The principal goal of this work is to determine the observational signatures of a galaxy that has hosted a massive black hole seed. This proposal will provide a basis for training astrophysical simulations enhanced by Artificial Intelligence. This proposed work will provide a breadth of research opportunities for the professional development of undergraduate and graduate students. 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

Modern drones and robots are increasingly being used as intelligent agents in critical real-world tasks such as search and rescue operations, environmental monitoring, and disaster response. These agents often need to work together in fast-changing and uncertain environments. However, current systems often struggle with planning efficient air or ground paths and making safe, real-time decisions. This project addresses this challenge by developing a new framework called AERIAL (AI-Embedded Responsive Intelligent Agents with Trajectory-Induced Digital Twin Learning). AERIAL will enable drones and robots to collaborate intelligently by combining advanced mathematics with artificial intelligence. It also leverages a virtual simulation of the real world (a digital twin) to help the agents plan and adapt their paths as situations evolve. This project aims to improve the safety, speed, and efficiency of drone missions that support public safety and national resilience. The project also includes hands-on educational opportunities for both college and high school students. To achieve these goals, the research introduces a new AI-driven mathematical model called the "trajectory-induced graph," which captures how drone flight paths and communication networks evolve over time. These graphs support a new class of AI tools powered by graph neural networks, enabling drones to interpret and respond to their environment effectively. The project centers on two main thrusts: (1) developing the mathematical foundations for trajectory-induced graphs, (2) applying them to real-time missions using novel machine learning and reinforcement learning methods. In addition, the system will be optimized so that low-cost drones with limited battery life and computing power can operate efficiently. The approach will be deployed and validated through real-world scenarios, including drone-based search and rescue missions. The outcomes will advance fundamental areas of cyber-physical systems, including autonomy, machine learning, control, real-time systems, and networking. 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 goal of this project is to understand how microbial activity in the root zone of the crop plant sorghum impacts the plant’s ability to grow and utilize nitrogen. Results of this work may be used to engineer plant-associating microbial communities to enhance crop yield, plant hardiness, and for efficient cultivation practices. This project will enhance U.S. national security and economic competitiveness by analyzing a promising system for improving crop yield and crop resiliency. This project will also train and help build the future STEM workforce by providing scientific education and research training to early career scientists and students. This project aims to elucidate homoserine lactone-mediated interbacterial signaling in the rhizosphere and its effects on bacterial community structure and function that impact nitrogen cycling in the soil to support plant growth and health. This project will study a sorghum plant system with growth-promoting bacterial communities. Experiments will utilize a multi-omics approach, bacterial strains with controllable signaling, and synthetic bacterial communities to study these microbial interactions in the root zone of sorghum during its growth. This project combines approaches from synthetic biology and systems biology to improve understanding of quorum sensing in the rhizosphere. Key research objectives are to understand the response of the root-associated microbial communities to diverse homoserine lactone signals, decipher the ability for rhizobacteria to communicate via diverse homoserine lactones, and characterize the effects of this interbacterial communication in the rhizosphere for plant growth conditions. This knowledge will provide the foundation for studying and predicting the breadth of bacterial intercellular communication in the rhizosphere and its effects on the rhizomicrobiome, plant growth, and nutrient availability in the soil. 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 · 2025-08

Trypanosomatid parasites infect diverse species of insects, animals, and plants, but are best known as causative agents of the Neglected Tropical Diseases human African trypanosomiasis, Chagas disease and Leishmaniasis, some of which are fatal without intervention. New, selective, short-course trypanocidals are needed for human and animal infections. Trypanosomes have one of the most complex mitochondrial genomes in nature called kinetoplast DNA (kDNA) that is a catenated network composed of maxicircles and minicircles. The kDNA structure and replication mechanism are divergent from all other eukaryotes, and is essential for parasite survival and life cycle completion. Prominent features of the kDNA replication model include release and attachment of minicircles, and remarkably, persistent nicks and gaps in the newly replicated molecules that are not repaired until the segregation of daughter networks. This suggests that the DNA damage tolerance response during kDNA replication is essential and therefore notably different in trypanosomatids. Lastly, while most eukaryotes rely on one essential mitochondrial DNA polymerase (Pol), T. brucei uses three family A DNA Pol paralogs (POLIB, POLIC, POLID) that are independently essential to maintain the kDNA network suggesting these proteins have evolved specialized functions. Our long-term goal of determining how multiple DNA Pols are used to maintain the kDNA network is key to gaining a deeper understanding of how to selectively interfere with this essential process. This project will clarify the roles of POLIB and POLID in kDNA replication and evaluate essential functional domains responsible for responding to replication stress. We address the functions of POLIB and POLID using a system with multiple inducers that allow independent, tunable, and temporal regulation of gene expression for detailed mechanistic studies. Aim 1 will define roles of POLIB and POLID in kDNA replication through RNAi complementation with a focus on maxicircle replication and proofreading replicative roles. Aim 2 will establish a novel triple control induction system to induce kDNA-specific replication stress and subsequently evaluate the roles of POLIB and POLID under replication stress conditions through RNAi complementation. Our findings will clarify the specialized roles of POLIB and POLID paving the way to elucidate the division of labor among the Pol paralogs, a feature that has been enigmatic in the long-held kDNA replication model. The Pol paralogs are preserved across kinetoplastids, therefore a mechanistic understanding of POLIB and POLID in T. brucei has the potential for a broad-based anti-trypanocidal, and to provide a rare opportunity to study features that have evolved for Family A DNA Pols.

NIH Research Projects · FY 2025 · 2025-08

Project Summary In the U.S., over one million individuals live with severe visual impairments, a figure projected to double in the next 30 years. Guide dogs offer independent, natural, and safe mobility assistance, allowing handlers to walk at speeds comparable to sighted peers and navigate dynamic environments safely. However, only 2% of this population has access to guide dogs, primarily due to the limited supply of these highly trained animals. The significant cost and time required to train and deploy guide dogs (approximately $50,000 USD and 2 years) and their relatively short working span (less than 10 years) are major barriers. Additionally, guide dog handlers must provide continuous care, including medical attention, feeding, and daily exercise. Despite their compelling benefits, these challenges make guide dogs a less scalable and sustainable solution for the wider visually impaired community. Motivated by recent progress in quadruped robots and their potential for mass production and long-term sustainability, we are determined to create a practical guide-dog robot as an additional solution for navigation assistance for blind or low-vision (BLV) people. We are dedicated to aligning our development process with the authentic needs of end-users, adopting a human-centered design philosophy that involves key stakeholders throughout every stage of the process, including: 1) defining the robot’s specifications, 2) developing algorithms and integrating them into a single robotic system, and 3) conducting evaluations, identifying unforeseen issues, and iteratively refining the hardware and algorithms. Our proposed research is grounded in our comprehensive qualitative study, which included semi-structured interviews with 23 guide-dog handlers, five trainers, seven ob- servation sessions, and two blindfolded guide-dog walking experiences. Our prior work revealed the limitations of existing quadruped robots for the BLV population and pinpointed critical areas for development. Rooted in our extensive understanding of the interaction between guide dogs and their handlers, we offer technological inno- vations such as: 1) new quadruped robot hardware developed through a human-centered approach, featuring compactness, portability, extended operation, and multi-modal sensing (e.g., tactile, audio, and vision), 2) a novel co-optimization framework for robot hardware and controllers to create a robotic navigation assistant optimally designed for BLV people, and 3) a robust navigation algorithm that adapts to scene variations by creating a novel self-supervised learning framework. Our core innovation lies in introducing a human-centered development approach to creating a guide-dog robot, addressing the significant open problem spanning hardware configuration to navigation algorithm development. The developed technology will be consolidated into a single quadruped robot, which will undergo rigorous evalua- tions by guide-dog handlers and white cane users. The successful completion of this project will yield an effective navigation system for BLV individuals and trigger a paradigm shift in the field of guide-dog robot research.

NIH Research Projects · FY 2025 · 2025-08

PROJECT SUMMARY Ribonucleoprotein complexes (RNPs) are essential for cellular function and their dysregulation has been implicated in various cancers and neurological disorders. These complexes are dynamic assemblies formed by interactions between RNAs and RNA-binding proteins (RBPs), which regulate numerous fundamental processes, including RNA biogenesis, RNA editing, stress response, and genome stability maintenance. Recent research has shown that RNPs can form membraneless organelle condensates through liquid-liquid phase separation for their functional output. However, the molecular mechanisms underlying the formation, disassembly, and regulation of these RNP condensates are poorly understood. The overall goal of my research program is to elucidate how RNP condensates contribute to cellular function and disease pathogenesis. We develop and apply novel methods to unravel the dynamics and composition of RNP condensates. We recently developed a new approach for readily tracking RNA dynamics in living cells and a novel method for identifying the RBPs of modular RNA motifs within RNPs. In this application, we propose to utilize and further expand these methods to address three major challenges in RNP condensate research, including: (1) Accurately identifying RBP composition of RNPs ─ A major challenge in understanding RNP condensate formation and regulation is a lack of robust methods to accurately identify the RBPs that trigger these changes. This proposal aims to address this major problem by developing and using an RNA tag with ultrahigh affinity for accurately identifying RBPs in RNPs, thus accelerating our understanding in the composition of RNP condensates; (2) Artifact-free characterizing RBP function in RNPs ─ Dynamic RBP-RNA interactions are essential for the formation and disassembly of RNP condensates. Being able to selectively induce and disrupt these interactions are critical to dissect the functional role of RBPs. However, current methods for RBP characterization lack temporal control and are prone to overexpression artifact. This proposal aims to address this limitation by using a small molecule- regulated RNA-protein tethering approach to functionally characterize RBPs in living cells; (3) Perturbation- free tracking RNP dynamics ─ A major question in RNP research is to understand how intermolecular RNA- RNA interactions contribute to condensate assemblies in response to cellular cues. This proposal seeks to address this longstanding question by applying a fluorogenic RNA imaging system to understand how long noncoding RNA-mRNA interactions contribute to RNP condensate formation during stress response. Together, the proposed research will provide an unprecedented view of the composition and dynamics of RNPs, thus laying the foundation for uncovering novel mechanism governing health and disease.

NSF Awards · FY 2025 · 2025-08

This project investigates how language comprehenders use syntactic information during real-time language comprehension. Syntactic information refers to the abstract, hierarchical organization of linguistic elements within a sentence, and has long been recognized as central to the interpretation of human language. For example, in a sentence such as “The neighbors’ kids playfully teased each other,” the interpretation of “each other” must be linked to “kids”, not “neighbors”, because of the syntactic position each of these occupies. However, relatively little is known about how such information is used in real-time language comprehension. This project tests the use of syntactic relations in real-time linguistic processing. The project employs eye-tracking methodology, a highly time-sensitive technique, to monitor the time course of online comprehension. The researchers will determine the relative importance of hierarchical syntactic organization and simple linguistic features like number in guiding attention in the earliest moments of language comprehension. In addition to training a graduate student, the findings from this project will provide insight into the cognitive mechanisms involved in language comprehension. Moreover, these findings will inform the continued development of computational language models, which continue to struggle with the hierarchical organization of human language despite achieving impressive performance on many linguistic tasks. This project explores how hierarchical relations are represented and used to guide retrieval within the working memory systems that support language comprehension. These questions are examined in two eye-tracking experiments investigating the time course of anaphor comprehension. Anaphors, such as reflexives (himself, herself) and reciprocals (each other), are elements whose meaning is dependent on their referent, which must stand in a hierarchically relevant position relative to the anaphor. The first experiment aims to determine the mechanism used to encode hierarchical information in short-term or working memory representations. Two theoretical hypotheses will be evaluated: that comprehenders keep hierarchically relevant items in active attention, rendering them immediately available in language comprehension, or that comprehenders are able to specifically reactivate only hierarchically accessible items when needed for interpretation. These hypotheses will be tested using a visual world study that allows for direct investigation into the time course of referent access. In this task, comprehenders will listen to sentences while viewing a visual display containing relevant referents, and their eye movements on the display will be recorded. The second experiment investigates whether hierarchical relations constrain predictive processes. This study will employ an eye-tracking-while-reading paradigm, where participants read sentences silently while their eye movements are recorded. If hierarchical relations are rapidly employed, hierarchically unavailable referents should not influence reading behavior, whereas hierarchically available ones should. Together, these two experiments aim to shed light on the mental representations and operations that language comprehenders rely on when using hierarchical information to constrain interpretation in real-time processing. 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

String theory asserts that our universe has ten dimensions: the usual three spatial dimensions and time, together with six microscopic dimensions which form a geometric object at each point called a Calabi--Yau manifold. The geometry of the Calabi--Yau manifold encodes the interactions of subatomic particles in a way that is compatible with the theory of gravity. There are millions of different types of Calabi--Yau manifolds known, but it is conjectured by Miles Reid that they are all related by simple "surgery" operations. The physical theory changes in a continuous way under these surgeries, so Reid's conjecture implies that the physical theories associated to different Calabi--Yau manifolds are all connected. The mirror symmetry phenomenon posits that Calabi--Yau manifolds come in pairs X and Y which determine equivalent physical theories. leading to an intricate but mysterious relation between the geometries of X and Y. In this project, the PI will study the mirror symmetry phenomenon and its relation to Reid's conjecture and the classification of Calabi--Yau manifolds. The project also provides research training opportunities for graduate students. The mirror symmetry phenomenon of string theory implies that Calabi--Yau manifolds come in pairs X and Y such that the complex geometry of X is related to the symplectic geometry of Y, and vice versa. More precisely, the homological mirror symmetry (HMS) conjecture of Kontsevich asserts that the derived category of holomorphic vector bundles on X is equivalent to the Fukaya category of Lagrangian submanifolds of Y. The related Strominger--Yau--Zaslow (SYZ) conjecture asserts that X and Y admit dual Lagrangian torus fibrations over a common base, and the HMS equivalence is obtained via an analogue of the Fourier transform. The PI and a collaborator will establish new cases of the HMS conjecture for Calabi--Yau manifolds of complex dimensions 2 and 3, guided by the SYZ heuristic. The PI will use mirror symmetry to study moduli spaces of Calabi--Yau 3-folds and Reid's conjecture, including the case of non-Kahler Calabi--Yau 3-folds which plays a key role in the conjecture. The PI will pursue several related projects with undergraduate students, graduate students, and postdocs, as follows: mirror symmetry for smoothings of triangle singularities, classification of Calabi--Yau 3-folds fibered in abelian surfaces, HMS for non-compact analogues of Enriques surfaces, HMS for Fano varieties and their Kuznetsov components, and Kollar's conjecture on deformations of rational surface singularities. 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.