University Of Illinois At Urbana-Champaign

NIH Research Projects · FY 2026 · 2026-02

The ovary is a particularly important reproductive organ because it is essential for the production of oocytes and sex steroid hormones. Unfortunately, exposure to environmental endocrine disrupting chemicals (EDCs) can damage the ovary. EDC-induced ovarian damage leads to female reproductive dysfunction, which cannot be prevented or treated by eliminating EDC exposures. It is extremely important to understand the mechanisms by which EDCs damage the ovary so that we can develop strategies to prevent and/or treat EDC-induced reproductive toxicity. Towards this end, the overall goals of the proposed RIVER program are to: 1) unravel the intricate mechanisms underlying EDC-induced ovarian damage and female reproductive dysfunction, 2) decode the multigenerational effects of EDCs on ovarian function and female reproductive capacity, and 3) bridge the gap to human health by elucidating how EDC exposure is associated with ovarian function and reproductive aging in a prospective cohort of midlife women. To address these urgent clinical and public health needs, we will use single cell RNAseq to identify novel pathways of EDC-induced toxicity at the single cell level in the ovary as well as other female reproductive organs, spatial transcriptomics to map EDC-induced changes in gene activity while preserving spatial context, advanced 3-D ovarian follicle culture techniques to uncover the direct effects of EDCs on the ovary in a controlled environment, pioneering in vitro and in vivo experiments that include environmentally relevant individual EDCs and mixtures of EDCs, state-of-the-art LC-MS techniques to detect the concentrations of EDCs that reach the female reproductive organs and determine the ability of the ovary to detoxify or bioactive EDCs, high resolution LC-MS/MS techniques to conduct quantitative global and targeted proteomics, whole genome methylome analysis or reduced-representation bisulfite sequencing (RRBS) to identify the effects of EDCs on cell-type specific DNA methylation patterns in the ovary, spatial epigenome- transcriptome co-profiling to localize EDC-induced changes in specific cell types in the ovary and determine the interaction between EDC-induced methylation and gene expression changes, CRISPR-Cas 9 technology to correct EDC-induced DNA methylation errors in cells, LC-MS techniques at the forefront of the field to measure selected EDCs and biomarkers of reproductive function and aging in a prospective cohort of midlife women, and leading edge statistical models to assess associations between EDC mixtures and selected biomarkers/outcomes. The applicant is uniquely qualified to successfully lead the RIVER program. The applicant served as PI on 24 NIH-funded awards and her research produced over 325 peer-reviewed publications. The applicant has demonstrated a broad vision, conducted ground-breaking research, and made seminal contributions to the understanding of the impacts of EDCs on the ovary and female reproduction. The flexible and sustained RIVER support will help the applicant to continue pioneering and impactful research, mentoring, and leadership in environmental health sciences.

NSF Awards · FY 2026 · 2026-02

Part 1: Non-technical summary Electrochemical energy storage plays a central role in modern technologies. In recent years, highly concentrated electrolytes have gained growing attention in energy storage research, in large part because they can expand the operating range and improve the stability of rechargeable batteries. At the same time, commonly used lithium-based batteries are approaching fundamental performance limits and rely on materials that face cost and supply constraints. As a result, alternative battery chemistries based on more abundant elements, such as sodium and potassium, are increasingly important for future energy technologies. A major challenge limiting the development of these next-generation batteries is the incomplete understanding of how protective layers - known as the solid electrolyte interphase (SEI) - form at the electrode surface during battery operation. The SEI plays a critical role in determining battery safety, lifetime, and efficiency. An ideal SEI layer should be chemically stable, electronically insulating, and ionically conductive. Its structure and properties are sensitive to the chemical environment near the electrode, but details regarding how and why remain poorly understood, especially in highly concentrated electrolytes. With support from the Solid State and Materials Chemistry Program in the Mathematical and Physical Sciences Directorate, this project aims to uncover the fundamental mechanisms that control the formation, stability, and evolution of the SEI by combining laboratory experiments with modeling. With a primary focus on super-concentrated electrolytes, the project studies water-in-salt electrolytes (WiSEs) with sodium and potassium, as well as mixtures that incorporate ionic liquids (WiSILs). By identifying how electrolyte composition influences interfacial behavior, this work informs the design of more efficient, safer, and cost-effective materials for next-generation battery systems. Beyond advancing fundamental knowledge, this project serves the national interest by supporting economic growth and technological innovation, while improving societal welfare through enhanced public safety. Broader impacts include training two graduate students at the intersection of materials and interfacial science and electrochemistry, and the involvement of undergraduate students in the principal investigator’s laboratory, helping to strengthen the STEM workforce pipeline. Part 2: Technical summary With support from the Solid State and Materials Chemistry Program in the Mathematical and Physical Sciences Directorate, this project addresses fundamental gaps in the understanding of solid electrolyte interphase (SEI) formation in super-concentrated electrolytes beyond Li-based systems. The central hypothesis is that interfacial reactivity, SEI nucleation, and growth cannot be inferred solely from bulk electrolyte properties but are instead governed by ion aggregation and solvent organization within the electrical double layer (EDL) at the electrode-electrolyte interface. The research further explores how incorporating ionic liquids into water-in-salt electrolytes can introduce not only tunability but also the emergence of new fundamental phenomena that influence SEI formation. The project thus focuses on sodium- and potassium-based water-in-salt electrolytes (WiSEs) and related water–in–salt–in–ionic liquids (WiSILs), leveraging distinct nanostructures to test hypotheses and elucidating composition-structure-property relationships for SEIs in these super-concentrated electrolytes and establishing new pathways for controlling SEI properties. Experimental research is complemented by collaboration with experts in molecular dynamics simulations of highly concentrated electrolytes to provide molecular-level insight into experimentally observed phenomena. The results are expected to advance fundamental understanding of interfacial chemistry in complex electrolytes, with broad implications for solid-state and materials chemistry, electrochemistry, and the design of next-generation battery 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.

NIH Research Projects · FY 2026 · 2026-02

PROJECT SUMMARY The increasing frequency of respiratory infectious diseases, such as influenza and COVID-19, which can spread rapidly and lead to severe outbreaks, necessitates that we re-envision our approaches to monitor pathogen exposures in the indoor environments. Current surveillance methods mostly depend on syndromic data from hospital admissions, clinic visits, and school absenteeism rates. However, these approaches can lead to underestimation and delay in disease surveillance due to no reporting of mild or asymptomatic cases, lack of access to healthcare, and time-consuming lab and diagnostic processes. A proactive approach in combating airborne diseases requires early detection of target pathogens. Here, we propose to innovate an intelligent system capable of real-time, efficient, and cost-effective monitoring of airborne pathogens in the environment. We will build upon our preliminary success in automated bioaerosol sampling and pathogen detection, to create an Airborne Pathogen Sensing (APS) system. This goal will be achieved by focusing on two specific aims. First, we will create a novel class of modular whole-cell biosensors for sensitive, rapid, and robust detection of multiple critical airborne pathogens. The pathogen detection will be achieved by creating quenchbody (Q-body) display biosensors, where target specific quenchbody is expressed and displayed on the surface of microbial host cells. When the Q-body binds to its antigen (target pathogen), the fluorescence intensity substantially increases via the antigen-dependent removal of the quenching effect on the fluorophore. Second, we will design and build a portable and automated bioaerosol sampling device that can be coupled to the biosensing and signal detection systems. To this end, we will evaluate and optimize two sampling devices: mist chamber and biosampler, and choose the device with the highest bioaerosol capture efficiency. Finally, we will integrate the biosensing component with the bioaerosol sampling device and an automated flow-through fluorescence detection system to achieve automated real-time monitoring of airborne pathogens.

NIH Research Projects · FY 2026 · 2026-02

Project Summary/Abstract Premature infants are vulnerable to long-term social, cognitive, and behavioral difficulties. However, not all premature infants go on to have developmental delays. Moderate to late preterm infants (born after 32 weeks) represent a missed clinical population as they do not automatically qualify for federally provided health screening and intervention programs. Therefore, the investigation into the neural mechanisms underlying heterogeneities in behavioral development is a promising approach to improve our ability to identify infants in need of additional services and inform treatment responses. This is also an important public health mission and a NICHD funding priority. The proposed K99 study will chart brain development using electroencephalography (EEG) during well-baby visits (4, 9, and 12 months) at a primary care clinic and a follow-up lab visit (24 months). Participants (N = 720 target recruitment) will be drawn from an ongoing R01 (PI Nelson: 5R01NS120986) study that examines if EEG is feasible in the context of a primary care facility and aids in the early identification of children who go on to receive an autism diagnosis. To this end, I will assess the longitudinal trajectories of brain development in preterm and full term infants across the first two years of life (aim 1). Next, I will determine the link between brain development and development delays (aim 2). Finally, in the R00 phase, I will recruit and longitudinally follow a new sample of preterm (1 month chronological, 1 month corrected, 4 months chronological, and 4 months corrected) and full term infants (1 month chronological and 4 months chronological). Using a multimethod neuroimaging approach (simultaneous functional Near- Infrared Spectroscopy and EEG) I will explore when disruptions in cerebral blood flow responses and brain activity first emerge and their associations with developmental delay. Overall, understanding neurodevelopmental processes before and when delays first emerge provides a tractable approach to improving psychological outcomes and well-being across the lifespan. The training and experience gained during the award period will support my transition to becoming an independent investigator and will contribute to my long-term research goals of investigating infant brain mechanisms underlying heterogeneities in social and cognitive development.

NSF Awards · FY 2026 · 2026-01

This project seeks to provide a deeper understanding of how major biogeochemical cycles that support all living marine resources will respond to climate warming in a changing ocean environment. It will train one postdoctoral researcher and three graduate students, and provides research training opportunities for undergraduate students in microbial physiology and ecology, bioinformatics, trace metal biogeochemistry, and oceanography. Project personnel also conduct K-12 education and outreach activities. All data is freely available through the Biological and Chemical Oceanographic Data Management Office (BCO-DMO). This project investigates how climate warming will interact with the unique trace metal requirements of marine nitrifying microorganisms (nitrifiers) to affect ammonia and nitrite oxidation pathways in the rapidly changing ocean. Four investigators with diverse expertise in microbial global change physiology, nitrogen and trace metal biogeochemistry, and mechanistic transcriptomics and proteomics combine their efforts, using well-controlled pure culture-based laboratory studies along with field incubation experiments with natural communities to systematically investigate 1) thermal effects on iron (Fe) and copper (Cu) requirements and use efficiencies in isolated cultures and natural populations of marine ammonia-oxidizing archaea and bacteria (AOA, AOB) and nitrite-oxidizing bacteria (NOB), 2) the underlying molecular and biochemical mechanisms that facilitate such thermally-driven adaptive responses, and 3) system-level feedbacks between global change, trace metal biogeochemistry, and marine nitrifiers and their associated microbial communities in diverse marine environments. Together, these studies enhance our understanding of the marine nitrogen cycle and trace metal biogeochemistry, and ultimately contribute to a more detailed understanding of the impact of rapid ocean warming on critical major nutrient and micronutrient cycles. 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 2026 · 2026-01

Cyclones occur in both the tropics (as hurricanes) and middle latitudes (as, e.g., Nor'easters), posing significant threats to lives and economies. While weather forecasts have improved overall, the evolution and hazards of some of these high-impact storms remain poorly forecast and understood, partly because different types of storms are often studied in isolation. However, real-world storms can transform and interact with other weather systems, as seen with Hurricanes Sandy (2012) and Helene (2024). This project addresses this challenge by using advanced Artificial Intelligence (AI) to study the full life-cycle of cyclones, viewing them not as separate categories but as an interconnected spectrum of weather systems. The ultimate goal is to uncover the early warning signs of dangerous storms, leading to more accurate and trustworthy long-range predictions. This will give communities more time to prepare, potentially saving lives and reducing economic damage. The project will also train a new generation of scientists at the cutting edge of atmospheric science and AI, and by making its tools and findings openly available, it will help accelerate improvements in forecasting for all. This project aims to advance fundamental understanding of the long-range predictability and environmental dependency of cyclones using new observational datasets and AI tools. The research leverages an interdisciplinary approach to: 1) Delineate the predictability limits of high-impact cyclones using newly developed AI weather forecasting models and identify the initial conditions that control their development; 2) Adapt AI algorithms based on robust representation learning to automatically flag atmospheric precursors that influence long-range forecasts; 3) Conduct and co-develop physical and physics-informed AI simulations to test hypotheses about the factors controlling cyclone evolution; and 4) Use AI and statistical models to probe the contributions of ocean, sea ice, and land properties to the predictability of cyclone activity. This work will generate new insights into cyclone dynamics and provide a robust framework for improving weather and climate prediction 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 · 2025-11

Grassland wildfires increasingly threaten human life and property in regions of the USA and worldwide. Managing fuels is a key way to reduce grassland fire risk. However, risk management strategies developed for forests do not translate to grassland systems, which are not as well studied. Grassland fuels vary across the year, vary from year to year, and change dramatically across the landscape. The critical knowledge gaps around how grassland fuel structures vary across space and through time make it difficult to evaluate methods for effectively reducing wildfire risk. In addition, approaches that allow scaling on-the-ground fuel measurements to characterize landscape-scale parcels are needed to assess risk and prioritize mitigation. In this project, a collaborative network of researchers, land managers, and fire practitioners work to fill these knowledge gaps and build the capacity to coordinate across organizations and regions in the Southern Great Plains of the United States. This project aims to fill several critical gaps in understanding grassland wildfire risk. The project forms and coordinates a collaborative network of partners interested in grassland wildfire that range from researchers to land managers and fire practitioners across regions of the Southern Great Plains. In addition, this project organizes coordinated data collection on grassland fuel variation and how the attributes of fuel influence fire behavior. This dataset enables effective scaling from on-the-ground fuel characteristic measurements to landscape scales important for land management planning and risk assessment. This network of field sites provides the ability to test patterns in grassland fuel characteristics across different climates, plant communities, and cultural fire contexts. Together, these advances enable better representation of grassland fuels in large scale fuel models and ultimately improve fire behavior modeling and risk assessments. 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 · 2025-10

Synthetic membranes facilitate the selective separation of specific molecules from mixtures. For example, membranes enable the production of clean water and safe pharmaceuticals. Unfortunately, the industrial manufacturing process used to produce membranes relies on the use of toxic solvents. In contrast, biological membranes leverage components that self-assemble to create multi-scale and hierarchical barriers that maintain unprecedented selectivity, controlling what and when molecules enter and exit cells. This Designing Materials to Revolutionize and Engineer our Future (DMREF) project seeks to harness biology-like performance and the chemical versatility of synthetic polymers. This looks to be achieved by mixing lipids, which are the building blocks of biological membranes, with charged polymers that can be synthesized to exhibit specific chemistries or charge patterns. These mixtures will spontaneously form nanoscale, ordered structures, that can form the basis for high-precision membranes. A major challenge is to design both lipids and polymers, which can have countless variations of chemical and physical features, to yield membranes for a given application. This project aligns with DMREF and the Materials Genome Initiative by combining materials synthesis and characterization with multi-scale molecular simulation and machine learning as the experiments will inform new computational models, which will then be used to expedite materials discovery to design new membranes. This interdisciplinary effort will bring together academic researchers and scientists from the Air Force Research Laboratory (AFRL) who have combined expertise regarding the making and characterization of membranes, molecular simulation and machine learning, and the physics of lipid assembly. The effort looks to harness nanoscale structure to achieve separations capabilities seen in biology, which will benefit society and the US by establishing a versatile class of membranes with broad applications in biological, chemical, agricultural, and industrial separations. The research will also involve the interdisciplinary training of researchers with broad expertise spanning chemistry, engineering, and physics, via both student mentorship and educational outreach to K-12 students. This project seeks to establish rational design of co-assembling lipids and polyelectrolytes, using patterned polyelectrolytes and judicious choice of chemical features to target specific nanostructures that can be used in separation membranes. This effort looks to harness expertise in polymer and lipid characterization and synthesis, using sequence-defined polyelectrolytes to modulate nano-scale assembly that will be evaluated by scattering. This will be coupled with a multi-scale modeling effort that connects atomistic simulations with coarse-grained models and polymer field theory to yield predictions of charge-driven assembly. Physical insights from this combined experimental and modeling approach intend to inform machine learning tools to predict structures relevant for separation membranes, which will be tested experimentally. The overarching goal is to establish a versatile molecular design protocol capable of integrating bioinspired lipid-based assemblies with complex-forming polymers to rationally engineer permeable membranes with desired selectivity. 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 · 2025-10

This project aims to serve the national interest by developing a skilled workforce capable of deploying robotic additive manufacturing in the construction industry—an emerging technology that is critical for increasing productivity and addressing persistent labor shortages in infrastructure development. Additive manufacturing in construction, commonly known as 3D printing, utilizes robotics and advanced materials to fabricate building components and, in some cases, entire structures. Despite its growing relevance, current construction professionals and tradespeople rarely have opportunities to gain hands-on experience with these advanced technologies. To bridge this gap, the project will implement an immersive experiential training program that enables experienced workers to pivot into careers in construction automation and advanced manufacturing. Participants will gain practical knowledge in robotic 3D printing technology, including equipment operation, safety procedures, design for additive manufacturing, and quality control of printed construction elements. Through partnerships with construction firms, robotic printer manufacturers, labor unions, and workforce development agencies, the project will support national efforts to modernize the construction workforce, broaden participation in STEM fields, and accelerate the adoption of emerging technologies in the built environment. The project will develop and evaluate the Robotic Additive Manufacturing Experiential Immersive Learning (RAMEIL) platform, a web-based immersive training environment enhanced with artificial intelligence to support personalized learning. A nationwide needs assessment will identify the critical competencies required for robotic 3D printing in construction, including calibration, material handling, and jobsite safety. These findings will inform the development of interactive, immersive modules grounded in experiential learning and cognitive apprenticeship frameworks. The platform will feature real-time performance tracking and adaptive feedback to support individualized progression. Upon completing the immersive component, participants will engage in a two-week, hands-on lab training using educational 3D printing equipment, followed by a four-week micro-internship with partnering industry firms. By integrating classroom instruction, immersive virtual training, laboratory practice, and real-world industry experience, the project offers a comprehensive pathway for upskilling the construction workforce in advanced manufacturing techniques. The research team will employ a mixed-methods evaluation strategy—combining pre- and post-assessments, usability testing, and field performance metrics—to evaluate learning outcomes and job readiness. Through the integration of theoretical instruction and applied practice, the project aims to advance scalable, data-driven training strategies for reskilling professionals in automation-intensive sectors. Close collaboration with major construction companies (including general contractors and technology suppliers) and local trade organizations will ensure the training remains responsive to industry needs and that successful participants have clear pathways to employment in emerging roles such as robotic construction machine operators and additive manufacturing specialists. Ultimately, the project will contribute to evidence-based training models that support career transitions into emerging technology fields and enhance the nation’s capacity for innovation in construction. The ExLENT Program, supported by the NSF TIP and EDU Directorates, seeks to support experiential learning opportunities for individuals to increase their interest in and access to career pathways in emerging technology fields. 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 · 2025-10

The web-based application Blast helps astronomers understand the connection between transient events, such as supernovae, and the galaxies in which they live. This program will expand the capabilities of Blast to match the challenges created by the vast amount of data that will be generated by the new generation of telescopes, such as Rubin (NSF-DOE) and Roman (NASA). This program will also work with Zooniverse, a citizen science platform with more than 2.7 million registered users. With Zooniverse this program will engage the public in a variety of research projects related to transients and their host galaxies. Over the last several years, the wealth of transient data has increased dramatically and with it, the discovery potential. This program focuses on the ways in which the physics of astrophysical transients are fundamentally linked with the properties of the host galaxies in which their progenitor stars form and evolve. Understanding the stellar populations that give rise to these transients plays a key role in our understanding of the transients themselves, including constraining the progenitor systems of core-collapse supernovae, correcting Type Ia supernova distances, and probabilistically classifying transients with galaxy data. This program will support a major upgrade to Blast, a web application for host-galaxy inference, which provides real-time spectral energy distribution fitting from ultraviolet to infrared wavelengths for every astrophysical transient using the Prospector Bayesian inference framework. Among several outreach initiatives in Hawaii and Illinois, the PIs will support the Institute for Astronomy’s Hawaii Student/Teacher Astronomy Research program, which has trained astronomy-enthusiastic high school students in research skills for over a decade. 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 · 2025-10

This project addresses urgent safety, efficiency, and cost challenges in the roof inspection industry by developing a robotic platform that combines the mobility of drones with the terrain adaptability of legged robots. Roof inspection remains one of the most hazardous construction tasks in the United States, with a significant portion of injuries and fatalities attributed to falls and unstable surfaces. By equipping aerial robots with legged mobility and advanced perception capabilities, this project enables safe, detailed inspection on sloped roofs, reducing risk to human inspectors while improving access and inspection quality in challenging environments. The hybrid aerial-hexapod robot autonomously conducts detailed inspections by integrating visual, tactile, and light-detection and ranging (LiDAR) data to detect structural anomalies, surface degradation, and moisture intrusion. The resulting robot can seamlessly switch between flight mode and legged mode to navigate multi-layered and irregular roof structures, supporting scalable and task-specific operations. The project also offers impactful educational and outreach opportunities, including summer STEM workshops for K-12 students and teachers, as well as open-access datasets for robotics and artificial intelligence education. The research team collaborates with industry partners to ensure the system addresses real-world operational needs and facilitates technology transfer. This research addresses the scientific challenge of enabling detailed, autonomous roof inspection using a hybrid robotic platform capable of operating both in flight and on the ground. The project’s goals are threefold: (1) to develop an integrated robot with dual-mode mobility and multimodal perception capabilities; (2) to design algorithms that can interpret sensory data in real time for autonomous navigation and condition assessment; and (3) to validate the system’s performance through extensive experimental evaluation in both laboratory and real-world settings. To achieve these goals, the research team designs a lightweight legged mobility system that attaches to a quadrotor platform, enabling the robot to transition seamlessly between flight and stable ground locomotion. A modular sensor suite – including an RGB-D camera, a LiDAR scanner, and footpad-embedded tactile sensors – is developed to enable multimodal perception for the robot. Each sensing modality is selectively activated based on the complexity of the inspection task, enabling energy-efficient operation across diverse inspection scenarios. The team develops artificial intelligence (AI)-based algorithms to fuse data across modalities, build a unified representation of the inspection environment, and extract high-level semantic and geometric features for roof condition assessment. The research further explores intelligent control strategies to leverage these features for real-time decision-making and affordance-driven control to enable safe and efficient navigation and inspection on complex roof structures. Experimental evaluation follows a multi-phase strategy that includes high-fidelity simulations, controlled laboratory tests, and field deployments on residential and commercial roof structures. Finally, the project aims to advance foundational knowledge in robotics by pushing the state of the art in sensor fusion, multimodal perception, and robotic mobility. 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 · 2025-10

Cyberattacks on computer networks pose growing threats to critical infrastructure, businesses, and personal data across the United States. Computer security systems that monitor network traffic to detect suspicious activity are essential for protecting against these attacks, but they face a significant challenge: detecting sophisticated attacks often requires analyzing data from multiple organizations, devices, or locations simultaneously. However, sharing network data raises serious privacy concerns because this information can reveal sensitive details about individuals, businesses, and government operations. This project addresses this challenge by developing advanced privacy protection methods that allow organizations to work together to detect cyberattacks without exposing sensitive information. This work serves the national interest by strengthening cybersecurity defenses across critical infrastructure, supporting economic competitiveness through improved data protection, advancing national security through enhanced threat detection capabilities, and enabling compliance with privacy regulations while maintaining robust cyber defenses. This project develops privacy-preserving techniques for graph-based intrusion detection systems that model network traffic and device relationships as interconnected graphs. The research activities include developing specialized cryptographic protocols for essential operations such as sparse matrix multiplications that are fundamental to graph-based analysis. The project will utilize different data partitioning strategies and computational models to perform most operations locally on unencrypted data, minimizing the computational overhead of cryptographic protocols. The team will implement selective revelation of intermediate computational results under differential privacy protection to improve system efficiency while maintaining privacy guarantees. The research extends these protocols to protect the complete training process of graph convolutional neural networks used in intrusion detection, providing comprehensive privacy protection with enhanced computational efficiency. Additionally, the project will support privacy-preserving data provenance in graph-traversal-based detection systems by modeling graph traversal algorithms as matrix operations implemented through the specialized cryptographic protocols. The team will validate these approaches using real-world network datasets and evaluate their effectiveness in collaborative intrusion detection scenarios while measuring privacy preservation and computational performance across diverse network environments. 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 · 2025-10

Most of the digital technology around us processes and stores information in a sequential, well-ordered manner. For example, a computer hard drive stores data bits in a structured way that allows them to be retrieved in order. Cell phone systems transmit and receive data as sequences of bits coded to allow the receiver to reconstruct the information in the same order it was sent. Recent technological advances such as DNA sequencing technologies, however, defy this ordered information paradigm. Such technologies generate data consisting of many short, out-of-order fragments. Processing this data is akin to assembling a jigsaw puzzle, where the desired information is only conveyed by the final assembled picture. Developing powerful algorithms for these tasks is important for several applications in the field of genomics and for the development of emerging molecular data storage technologies. The goal of this project is to extend techniques from the ordered digital world - codes, algorithms, and an information-theoretic framework - to these emerging out-of-order settings. This should enable new data storage paradigms to be deployed and lead to the development of new computational methods to analyze genomics data. The project will seek to extend Information Theory techniques to out-of-order information scenarios and to characterize how much information can be reliably conveyed by an unordered set of data fragments. The research will be organized along three thrusts with important practical applications. Motivated by tasks in immunogenomics and resistomics, the first thrust will focus on the problem of recovering a set of similar-looking sequences (genes, in most applications) from a set of unordered fragments (the DNA sequencing reads). This will lead to new algorithms to characterize the presence of antimicrobial resistance genes in a microbial community. The second thrust will address the problem of reordering a set of unordered fragments given a noisy reference. This has applications in reference-based genome assembly (when the genome of a related species is available) and in the problem of aligning out-of-order data across two databases. Motivated by molecular data storage and its potential for addressing ever-increasing data storage demands, the third thrust will focus on fundamental limits and coding strategies for out-of-order channels. This includes the development of near-capacity-achieving codes for molecular storage, the analysis of the combined effects of fragmentation and symbol-level noise, and the design of efficient codes that minimize synthesis costs. 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 · 2025-10

The NSF ECR Building Capacity in STEM Education Research (BCSER) program contributes to the NSF mission (42 U.S. Code Chapter 16) by building the capacity of the US STEM education research workforce to design, propose, and implement high quality STEM education research. The BCSER Institutes for Methods and Practices in STEM Education Research (IMP) track supports institutes that provide participants with training and support to advance the participants' knowledge, skills, and competencies in STEM education research including in the use of cutting-edge methodological techniques. Institute participants include investigators at any stage in their career development. This institute's focus is on building capacity in STEM education research by sustaining and expanding a novel, problem-led, and quality-focused approach to interpretive research design. This project extends the impact of the first ProQual institute by training 48 scholars and providing web-accessible case study examples of key elements of the ProQual approach. The ProQual approach reconceptualizes research design as a structured, design-based process, helping STEM scholars overcome epistemological and methodological barriers in educational research. This BCSER IMP project is providing training to approximately 48 STEM faculty interested in retooling to become STEM education researchers during the lifetime of the institute. The participants engage in a suite of activities to learn how to approach STEM education research as a design problem and to gain qualitative and mixed-methodology skills to undertake their own research project. Through an innovative 4-step program, participants develop research competence while engaging in a community of practice that fosters long-term knowledge exchange. The incorporation of "ProQual-in-a-Box" resources further extends these benefits beyond direct participants, enhancing dissemination and adoption. This expansion of the first ProQual Institute will strengthen STEM education by increasing the quality of interpretive STEM research that is designed and conducted by faculty and postdocs with technical backgrounds in STEM fields. 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 · 2025-10

This project will develop new computational tools to infer physical parameters such as object shape and optical properties of a scene from measured images such as photographs. These tools are essential for building digital twins of real-world objects and will enable new applications in a wide array of fields including computer vision, computational imaging, robotics, and virtual/augmented reality (VR/AR). Unlike many existing methods that are purely data-driven, this research will develop inference techniques that leverage a simulator of how light propagates. This simulator will be differentiable, meaning that it is possible to smoothly relate its control parameters to its decisions, offering a new level of generality and physical accuracy for recovering parameters reliably under complex scenarios such as illumination from reflected light. Project outcomes have the potential for broad impact by creating new areas in computer graphics and computational photonics. Additional broad impact will derive from the PI's commitment to promoting STEM education for underrepresented minorities, and from the project facilitating UCI’s outreach programs at the undergraduate and high school levels including lab visits to allow hands-on experience with software development. This research will enable differentiable and inverse rendering that is efficient, physically accurate, and sufficiently general to handle arbitrary scene parameters under a wide variety of light transport phenomena. The work will make the following four core contributions: first, devising new mathematical tools to describe how infinitesimal changes in a virtual scene affect the distribution of light, supporting a variety of light transport models including steady state, polarized, and transient; second, introducing physics-based differentiable rendering algorithms that enjoy the generality of the new formulations while providing low-variance derivative estimates; third, leveraging these algorithms to build differentiable rendering software systems capable of efficiently handling complex real-world configurations; and, lastly, utilizing the new algorithms and systems to introduce physics-based inverse rendering pipelines that offer a new level of generality and accuracy benefiting a wide array of 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 2025 · 2025-10

When one watches a movie with computer-generated special effects, plays a video game, or sees a robot assemble parts in a factory, one is witnessing visual computing in action. The technology allows computers to create, analyze, and interact with images and videos. Despite their widespread use, these technologies currently lack rigorous quality guarantees. This means that a medical imaging system might produce misleading visualizations, a digital twin used for industrial simulation might fail to detect collisions, or a robot arm might misjudge the position of objects it needs to grasp. This project addresses this critical gap by applying formal methods, the mathematical techniques that prove computer systems behave correctly, to visual computing systems. The project's novelties are the development of mathematical frameworks that can provide assurance about the quality and correctness of visual computing systems. The project's impacts include creating foundations for visual systems that can be mathematically proven to work correctly in autonomous vehicles, augmented reality and virtual reality (AR/VR) environments, and industrial control, while making these advanced techniques accessible to developers and students through education programs and open-source tools. The research establishes connections between two previously separate communities, formal methods researchers who develop mathematical techniques for proving system correctness and visual computing specialists who create graphics, vision, and simulation systems, through three integrated thrusts. First, the investigators develop abstract rendering algorithms that propagate uncertainties through neural scene representations to compute guaranteed pixel-level error bounds, enabling verification of perception-based controllers in simulation environments. Second, they handle neural signed distance functions using neural network verification techniques, allowing for provable geometric reasoning. Third, they develop certifiable physics simulation and video generation techniques to create end-to-end guarantees for video synthesis. These advances support the inclusion of visual systems in formal verification pipelines, addressing a significant limitation in current safety verification approaches. 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 · 2025-10

Non-technical Description: The mass production of integrated circuits (commonly known as ‘microchips’ or simply ‘chips’) is a key driver for modern computational advances. Chip manufacturing requires a process called photolithography to template the intricate patterns of electronic components. This process uses patterns of light to selectively pattern a material known as a photoresist. New extreme ultraviolet (EUV) based lithography methods are poised to enable more powerful chips than ever before by packing higher volumes of smaller electronic components onto a single chip, making new photoresists essential to reaching the desired small features sizes. This Designing Materials to Revolutionize Our Future (DMREF) project combines chemistry, processing, and computation to design new photoresists to enable high-volume EUV lithography for chip manufacturing. This will be achieved by understanding how the local molecular structure of polymer-based photoresists defines the patterning at nanoscale dimensions, and how this translates to manufacturing outcomes. This interdisciplinary effort will bring together scientists and engineers from academia and the Air Force Research Lab with expertise in synthesizing materials, characterizing their physical properties, modeling their behavior with simulations, and predicting new materials with improved properties using AI. The new materials and patterning methodologies developed in this project will broadly benefit the US by enabling advanced manufacturing of next-generation computer chips with applications ranging from personal electronics and health care monitoring to supercomputers and generative AI. This research will further be combined with K-12 outreach and student training to prepare the next generation STEM workforce. Technical Description: This project will integrate combined expertise in polymer chemistry, physics, computation, and advanced manufacturing into a closed loop process to enable the design and implementation of crosslinkable polymeric photoresists for EUV lithography. Theory, molecular simulation, and data science will be combined with polymer chemistry and advanced metrology to understand how the sequence-specific molecular structure of copolymers translates to local patterning heterogeneity. Additionally, this effort will be combined with data science-enabled proxy measurements to rapidly and efficiently traverse an enormous chemical space for materials discovery. The ultimate goal of this work is to develop candidate chemistries that produce patterns with appropriate dimensions and fidelity under industrially-relevant EUV exposures. More broadly, workflows to aid the discovery-to-translation timeline of EUV lithography resists will be developed. Additionally, this research will be integrated with education and workforce development efforts to train students who can effectively communicate across the materials development continuum and contribute to the semiconductor industry. 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 · 2025-10

Graphs, also known as networks, are used to represent many kinds of relationships between pairs of entities. For example, a graph may describe pairs of networking devices directly connected together or pairs of roadway intersections connected by a stretch of road. Many useful tasks, such as understanding the reliability of a computer network or finding the fastest route between two locations, can be performed by doing computations on these graphs. When a graph has certain properties such as being drawable on a piece of paper without crossings between its connections, it becomes possible to do these types of computations much more quickly than if the properties were not present. Many of these faster computations rely on important results from topology, the mathematical study of what properties geometric objects maintain after certain types of deformations. This project seeks to better understand the role topology can take both in performing fast computations on graphs and explaining what graph properties are necessary for these fast computations. The project aims to make substantial topology based additions to the toolkit used in graph computations. These additions should make new computational tasks possible and greatly simplify established tasks. The project involves a substantial education component as well that includes educating students on the known connections between topology and computer science and providing undergraduate minority students their first opportunities to participate in the research process. The research activities have three components. The first involves generalizing known planar graph algorithms for many fundamental problems in computer science to graphs embeddable in low complexity surfaces. The second component explores how topological intuitions and tools created during the first component can be used to solve difficult problems back in the setting of planar graphs. The third component involves pushing these tools to their limits in the creation of algorithms for more general families of graphs than those embeddable in low complexity surfaces with a focus on the so-called H-minor-free graphs. The types of problems studied for all three components include the computations of optimal network flows, minimum cuts, and shortest paths. Student education will take place through the development of a new course in computational topology at the awardee institution that focusses on graph algorithms and topological data analysis. Activities for undergraduate minority students will consist primarily of the implementation and experimental analysis of algorithms designed during the primary research 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 · 2025-10

Error-controlled lossy compressors are widely used to manage the large amount of data produced by scientific applications. Still, they may produce undesired compression artifacts that distort both raw and post hoc data analytics. This project aims to bridge the gap by developing a novel learning-driven framework to mitigate artifacts produced by scientific lossy compressors. The success of this project is expected to improve the integrity and quality of lossy-compressed scientific data significantly, thus facilitating the use of existing lossy-compression frameworks for efficient data storage, transmission, and analytics in scientific applications. This contributes to scientific discoveries in a broad range of domains, including climatology, cosmology, fusion energy science, and X-ray ptychography, as well as multiple aspects of research and education in advanced cyberinfrastructure. This project addresses the artifact issue by leveraging recent scientific data compression and deep-learning advancements. In-depth investigations are conducted to generically characterize the compression artifacts produced by scientific compressors on both raw data and post-hoc analysis. This aims to improve the understanding of data quality and establish a benchmark for artifact mitigation. Next, deep learning models are designed to tackle artifact mitigation on both raw data and features of interest, with specifically designed transfer learning to reduce training costs. The quality of the recovered data is improved by fusing model outputs tailored to preserve different features. Finally, the quality of the recovered data is validated through tailored uncertainty quantifications, and the performance of the framework is investigated through careful optimization and parallelization. Integration into state-of-the-art error-controlled lossy compressors and incorporation with real-world scientific applications are expected to advance multiple scientific data management tasks, including data storage, I/O, and transmission. 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 · 2025-10

This BRITE Pivot research project aims to develop new engineering tools to better understand how biological signals (like gene and protein expression) interact with mechanical signals (like tissue structure and stiffness) in the body. These tools will allow scientists to combine and visualize biological and mechanical data at the cellular and subcellular levels, offering new mechanobiology insights into how these signals work together. The research focuses on the uterus, specifically investigating how exposure to endocrine-disrupting chemicals (EDCs), such as propylparaben (PP)—a common preservative found in everyday products like lotions and cosmetics—may affect fertility and pregnancy. Growing evidence suggests that long-term exposure to EDCs may trigger inflammation and fibrosis (the buildup of collagen), which can interfere with embryo implantation, a crucial step in pregnancy. Tools that look to be developed could also be used to study inflammation and fibrosis in other organs, including the lung, kidney, and liver. By advancing engineering tools and applying them to urgent public health challenges, this research seeks to advance the fields of mechanobiology and mechanics and contributes more broadly to understanding tissue dysfunction across multiple organ systems. This research aims to bring powerful and emerging tools in molecular and cellular biology—spatial omics and multiplexed protein profiling—into the field of mechanobiology. The goal is to integrate and co-register these complex biological data with mechanical and imaging data obtained by second harmonic generation (SHG), atomic force microscopy (AFM), and nanoindentation. This effort seeks to create a new multi-scale framework for understanding mechanobiology by mapping spatial relationships among molecular signals, tissue microstructure (e.g., collagen fiber organization), and mechanical properties at cellular and subcellular resolution. The project focuses on uterine tissue, which exhibits complex signaling and remodeling, to investigate how exposure to endocrine-disrupting chemicals (EDCs), specifically propylparaben (PP), alters tissue structure and mechanics. While the uterus serves to set up the framework, the experimental and computational approaches that look to be developed can be broadly applicable to other heterogeneous tissues influenced by mechanical and molecular disruption, such as the lung, liver, and kidney. 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 · 2025-10

Bats play critical roles in ecosystems globally. However, key aspects of bat biology, from the causes and consequences of population declines to their ability to transmit viruses to people, remain poorly understood. This AccelNet project establishes the Global Union of Bat Diversity Networks (GBatNet) to fill key knowledge gaps and create an international structure to accelerate discoveries across disciplines and borders. The network of networks fosters new avenues for global research exchange through coordination of joint research, education, and outreach. GBatNet links 14 regional and global networks with a shared vision to address pressing questions in bat biology of direct relevance to ecosystem and human health. GBatNet will advance understanding of mechanisms that govern the ecology and evolution of bats, and address NSF’s Big Idea Understanding the Rules of Life. The network of network forges novel connections among experts in paleontology, evolution, morphology, ecology, virology, genomics, and conservation. GBatNet will address 3 broad questions : (1) eco-evolutionary dynamics – what are the feedbacks between ecological function, evolutionary adaptation, and rapidly changing environments?, (2) metabolic homeostasis – how do individuals maintain metabolic homeostasis, and what are the evolutionary contributions and ecological consequences for populations and species?, and (3) tree of sex – what are the evolutionary and ecological consequences of genomic rearrangement, especially in sex chromosomes? Annual meetings, interdisciplinary synthesis sessions, international engagement in bat diversity hotspots will foster coordination and preparation of the next generation of professionals. In addition to producing research syntheses and developing public outreach materials, the project will synthesize existing datasets to create interdisciplinary tools and protocols to gain insights to complex systems. GBatNet will build and test predictive models of species vulnerability to ongoing habitat change, emerging infectious diseases, and climate change. GBatNet will provide unparalleled collaborative opportunities for bat research and conservation worldwide, and the U.S. scientific community will gain expanded research opportunities in global bat diversity hotspots and with networks across diverse disciplines. The Accelerating Research through International Network-to-Network Collaborations (AccelNet) program is designed to accelerate the process of scientific discovery and prepare the next generation of U.S. researchers for multiteam international collaborations. The AccelNet program supports strategic linkages among U.S. research networks and complementary networks abroad that will leverage research and educational resources to tackle grand scientific challenges that require significant coordinated international efforts. Co-funding for this award is being provided by the Directorates for Biological Sciences from the Population and Community Ecology Program (BIO/DEB) and the Physiological Mechanisms and Biomechanics Program (BIO/IOS). 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 · 2025-10

Multipartite entanglement is a crucial resource for quantum communication and computation, enabling exciting applications of quantum information science such as efficient algorithms to factor large integers or simulate complex physical systems. However, the exponential scaling of the dimension of multipartite quantum systems and the presence of environmental noise make a precise mathematical characterization of entanglement challenging. The goal of this project is to study the behavior of multipartite quantum correlations under the influence of environmental noise through the lens of symmetries. Understanding the interplay of correlations and noise is crucial to analyzing the performance of quantum communication protocols and informs the design of error-correcting codes that protect quantum information from decoherence. This project will develop versatile methods to tightly characterize the fundamental limits of faithful quantum communication and provide tools to analyze the asymptotic behavior of multipartite entanglement in large systems undergoing noisy quantum evolution. The research efforts of this project are complemented by the development of an online summer course at the undergraduate level teaching advanced concepts in linear algebra that are needed to start research in quantum information theory. The publicly available course will be specifically aimed at undergraduate students transferring from community colleges to research-active institutions and students interested in undergraduate research, in order to broaden the participation in quantum information science. In this project, the effects of noise on multipartite quantum correlations are studied from an information-theoretic point of view in which environmental noise is modeled as a quantum channel. The task of protecting correlations from noise is cast as a communication task using this channel, with the optimal rates of faithful information transmission characterized by quantum channel capacities. Coding theorems express these capacities as the solutions of optimization problems of entropic quantities, which measure the amount of information that can be stored in or processed by a system. These entropic optimization problems are typically hard to solve due to the exponential scaling of the state space of multipartite quantum systems, and the resulting complicated structure of quantum correlations. However, the natural symmetries in this information-theoretic framework can be used to simplify the corresponding optimization problems using tools from representation theory. The first thrust of this project proposes a general framework for leveraging the structure implied by these symmetries to obtain efficient methods of approximating quantum channel capacities. This approach includes and generalizes many known examples of tight channel capacity characterizations and will lead to both new bounds on capacities and a better understanding of their fundamental behavior. The second thrust exploits symmetries in relevant noise models to analyze the asymptotic behavior of quantum correlations in large systems, shedding new light on the noise robustness of multipartite entanglement in such systems. The methods developed in this project will likely find applications in other parts of quantum information theory, entanglement theory, and learning theory. 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 · 2025-10

This Pathways to Enable Open-Source Ecosystems (POSE) project centers on the development of a self-sustaining open-source ecosystem (OSE) for security and privacy assurance in Internet of Things (IoT) standards and their implementations. Modern IoT systems are integral to daily life, supporting safety, health, energy, and convenience across residential, commercial, and infrastructure settings. However, inconsistent designs and development practices across IoT devices have led to significant gaps in security and compliance. This project seeks to address these challenges by creating shared tools and infrastructure for continuous security verification—making it easier for developers to build secure-by-design devices and comply with emerging standards. The resulting OSE will promote trust in connected technologies, reduce risks for consumers, and contribute to safer, more reliable systems. It will also support hands-on educational opportunities that prepare the next generation of security and software engineers with practical skills in verification, threat modeling, and secure software design and development. Consumers, developers, educators, and researchers will benefit from the ecosystem’s collaborative and transparent approach, which aims to improve technological understanding and long-term resilience in connected environments. This POSE project establishes the Formal and Human-Centered Security Verification in IoT Standards (FHS-IoT), a non-profit organization dedicated to providing formally verified security guarantees for IoT standards and to improving compliance assurance for consumer IoT products. The FHS-IoT will develop a Continuous Integration and Continuous Delivery/Deployment (CI/CD) infrastructure to support formal and human-centered verification tools and transition to an open-source ecosystem. This project focuses on three areas: 1) ecosystem justification and user engagement; 2) establishment of FHS-IoT governance models; and 3) content developer engagement and community building. The anticipated outcomes include a metric-based justification for the FHS-IoT OSE, creation of an open-source CI/CD pipeline for continuous security verification of IoT standards and standardization implementations, and the formation of a user and contributor community with resources and onboarding instructions. 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 · 2025-10

Non-technical Description: Energy storage technologies such as batteries critically require safe and thermally stable ion-conducting materials. Lithium-ion batteries are pervasive in modern society, but safety concerns have prompted the development of new solid-state ion-conducting materials. To date, nearly all solid polymer ion conductors are comprised of synthetic materials that lack precisely defined structures. In contrast, biological macromolecules such as peptides have precisely defined sequences, allowing for control over three-dimensional molecular structure, such as helical elements. This project aims to understand the role of peptide helices on enhanced ion conduction, focusing on: (1) the ability to arrange ion-conducting groups in controlled ways and (2) the presence of a macrodipole along the backbone that grows with helix length. This project will enable the development of a new class of helical peptide ion conductors for enhanced energy storage applications. A wide range of peptide chemistries will be designed and synthesized using an AI-guided discovery approach to understand the role of chemistry, sequence, helical character, and arrangement of ion-conducting groups on the performance of energy storage materials. A key outcome of this project is to understand how the molecular structure of peptides affects ion transport for the development of next-generation energy storage materials. Technical Description: This project will address key challenges in developing new materials for enhanced ion conduction by efficiently exploring a vast chemical space using a machine learning (ML)-guided approach, together with complementary materials synthesis and characterization methods. The team focuses on developing a new class of helical peptide-based polymers for enhanced ion conduction by controlling the macrodipole inherent to peptide-based helices and the spatial arrangement of ion-conducting motifs away from the backbone. To this end, this project will leverage the ability to control several key properties of peptide electrolytes such as: (1) helicity via introduction of amino acids of opposite chirality; (2) molecular weight via controlled ring-opening polymerizations of cyclic amino-acid monomers; (3) ion conducting motifs and linkages to polymer backbones using non-natural amino acids; (4) monomer sequence via solid-phase peptide synthesis; and (5) alignment of helices via materials processing, e.g., hot-pressing at different temperatures and pressures. PIs and graduate students will engage in outreach activities geared towards hands-on demonstrations of scientific concepts for middle and high school 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.

NIH Research Projects · FY 2025 · 2025-09

PROJECT SUMMARY Heart failure (HF) will affect one in four Americans during their lifetime. It is associated with a 50% 5-year mortality, disproportionately burdening individuals who experience accelerated decline in cardiac function. In most patients, cardiac dysfunction starts with an adaptive asymptomatic stage, before eventually progressing to symptomatic HF. Although current methods are excellent to identify population level HF risk, they are limited in identifying if or when any given individual develops HF symptoms. Extra-cardiac comorbidities are likely important contributors to heterogeneity observed in HF susceptibility and clinical presentation. Critically, traditional non-invasive measures of cardiac function perform poorly in discriminating HF patients from their comorbidity-matched symptom-free counterparts. The lack of effective approaches to identify individuals at risk of HF is a critical barrier to identifying and preventing HF. There is, thus, an urgent need to develop personalized methods to understand the natural history and progression to overt HF on an individual level. Our goal is to study the effect of an individual’s comorbidity profile on their cardiac structure and function in early HF using explainable machine learning. Our central premise is that individual comorbidity profiles differentially modify pathophysiologic mechanisms of HF, which can be detected using a virtual digital heart, a “digital twin”. Leveraging rich data from 4,483 participants from the longitudinal NHLBI-funded Atherosclerosis Risk in Communities (ARIC) study free of HF, we will characterize cardiac performance at an individual level using primary echocardiographic images, quantitative echocardiographic measures, and arterial pulse wave velocity. We will then use domain-guided mechanistic reinforcement learning to simulate cardiac adaptation, conditioning on known HF-related comorbidities including body composition, obesity, renal function, pulmonary function, skeletal muscle strength, and frailty. Additionally, we will use circulating serum protein biomarkers to evaluate biological mechanisms of HF. We will leverage the longitudinal NHLBI-funded Atherosclerosis Risk in Communities (ARIC) study. Our specific aims are to (1) define individualized comorbidity-driven risk of HF in asymptomatic older adults; and (2) to identify echocardiographic patterns in asymptomatic older adults that predict future HF onset. These studies will yield a novel personalized model of HF progression, providing critical insights into cardiac dysfunction at an asymptomatic stage and its trajectory. This study will be performed by Pranav Dorbala, an MD/PhD student at the University of Illinois Urbana Champaign pursuing a PhD in machine learning. His sponsor has extensive expertise in machine learning for health care applications and his co-sponsor is an established ARIC investigator with extensive experience in cardiovascular epidemiology, quantitative echocardiography, and high dimensional data. Completion of the proposed studies will serve to train the student in the fundamentals of becoming a physician-scientist in the field of personalized medicine.