George Mason University

NSF Awards · FY 2026 · 2026-07

The ACM Conference on Computer and Communications Security (CCS) is the flagship annual conference of the ACM Special Interest Group on Security, Audit and Control (SIGSAC) and is widely recognized as one of the top four venues in computer and information security. Since its inception in 1993, CCS has served as a premier international forum for researchers, practitioners, and policymakers to present and discuss advances across the full spectrum of cybersecurity, including software and systems security, network security, applied cryptography, privacy, and emerging areas such as machine learning security and cyber-physical systems. With a highly selective acceptance rate (15–20%) and global participation, CCS consistently showcases cutting-edge, high-impact research that shapes the future of secure computing. The conference directly aligns with U.S. national priorities in cybersecurity and privacy by fostering innovation, advancing trustworthy and resilient systems, and strengthening the nation's capacity to address evolving cyber threats that impact critical infrastructure, economic security, and national defense. This project requests NSF support to provide travel grants for approximately 15 U.S.-based graduate students to attend ACM CCS 2026. The funding will be used to offset travel and participation costs, enabling students, particularly those with limited institutional resources, to engage in this premier research venue. Participation will allow students to present their work, gain exposure to state-of-the-art research, and interact with leading experts, thereby enhancing their technical expertise, professional development, and integration into the cybersecurity research community. The project helps to strengthen the domestic talent pipeline in cybersecurity and contribute to workforce development. 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 · 2026-07

This award supports US-based participants of the conference "Moduli Spaces, Quantization and Poisson Geometry" at the SwissMAP Research Station at Les Diablerets, Switzerland, August 23-28, 2026. The study of moduli spaces parametrizing mathematical structures has a long and rich history, and remains a highly active area of investigation, with close ties to several areas of mathematics, including symplectic geometry, complex geometry, algebraic geometry, representation theory, and mathematical physics. The conference will provide a significant opportunity for researchers at various career stages to interact, fostering research collaborations between research groups located in the US and abroad, and engaging with recent developments in the field. Moduli spaces frequently carry rich geometric structures, including symplectic or Poisson structures and their relatives. Quantization of these moduli spaces links several different areas of mathematics and theoretical physics. A cross-cutting theme of the meeting is the interplay of higher geometric structures, moduli spaces of geometric structures on surfaces, and their quantizations. Topics to be discussed at the conference include: character varieties and moduli spaces of flat connections on surfaces; Teichmuller theory and moduli spaces of geometric structures on surfaces; Symplectic, Poisson and Dirac geometry; Lie groupoids and Lie algebroids; Differentiable stacks, gerbes, and higher structures; Quantization; Twisted K-theory; Loop groups, affine Lie algebras, Virasoro algebra. More information about the conference can be found at: https://swissmaprs.ch/events/moduli-spaces-quantization-and-poisson-geometry/ 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 · 2026-06

Personalized social media feeds and emerging AI chatbot technologies influence what adolescents see, how they receive social feedback, and how they interpret themselves and others. While these systems can provide information, entertainment, and connection, they also shape attention and can gradually steer interests, preferences, and how adolescents see themselves. This project examines how adolescents experience algorithmic influence and explores how attention can be reclaimed as a resource for agency and self-understanding in highly personalized digital environments. By helping adolescents recognize and reflect on algorithmic influence and strengthen attentional autonomy, the project develops new ways to support healthy development and emotional well-being in increasingly automated media environments. Tools developed in this project can be used independently by adolescents and implemented in schools, libraries, and youth organizations to help mitigate the negative impacts of AI personalization. This project advances the "Resilience Framework," a sociotechnical model that explains how adolescents maintain a sense of self that is coherent and self-directed in environments that utilize algorithmic personalization. The research integrates qualitative, quantitative, and participatory design methods. In the foundational phase, interviews, diary studies, and other situated qualitative activities will examine how adolescents interpret and respond to recommender systems and generative artificial intelligence chatbots, including their perceived influence on self concept, emotional experiences, and resistance strategies. Survey research will measure relationships linking algorithmic shaping, attention disruption, emotional regulation, and self-concept clarity. In the second phase, participatory co-design activities will develop a paper-based intervention, the "Algorithmic Self Defense Toolkit," designed to help adolescents reflect on algorithmic influence through structured exercises focused on attention, self reflection, and emotional distancing. The third phase is a twelve-week longitudinal study that investigates whether the intervention strengthens adolescents' self-concept clarity, the primary outcome and a proxy for resilience. Additional analyses will examine how reflective disengagement and emotional regulation shape self-concept development over time. The project will produce new theory, validated measurement instruments, and design tools and guidance for technologies and educational interventions that support youth development in algorithmically mediated environments. Through educational activities, including undergraduate curriculum and a youth summer institute, this project embeds self-concept resilience as a core developmental concept throughout its educational and mentoring activities. It aims to cultivate a generation of students and youth collaborators who can critically engage with the psychosocial consequences of algorithmic environments and to design interventions that support adolescent agency, coherence, and well-being. 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 · 2026-06

This NSF CAREER project aims to ensure the safety, reliability, and efficiency of modern interconnected autonomous systems, such as robot swarms, intelligent transportation networks, and smart manufacturing. The project will bring transformative change to how these complex systems share limited resources, including communication networks, computer processors, and shared physical space, by preventing the "traffic jams" of data or physical collision that cause catastrophic physical accidents or energy waste. This will be achieved by creating new mathematical methods that allow networked systems to smartly schedule actions and design controls that compensate for time delays in real time. The intellectual merit of the project includes establishing a unified theoretical framework that links complex timing delays with physical stability guarantees, enabling safe resource management for large-scale autonomous technologies. The broader impacts of the project include contributing to national prosperity through potential economic savings from mitigated traffic congestion and improved manufacturing efficiency. Furthermore, the project cultivates a highly skilled engineering workforce by integrating these research concepts into the Airborne Robotics Competition (ARC), an accessible, low-cost national robotic blimp competition where K-12 students gain hands-on experience with the critical importance of networked real-time robotic systems. The fundamental technical challenge in large-scale networked control systems is "correlated resource contention," where simultaneous demands for shared resources create complex non-linear timing dynamics. Traditional periodic or centralized methods fail to predict when these unpredictable scheduling delays will destabilize the physical system or determine how to scale up safely. To resolve these issues, this research develops a decentralized real-time scheduling and control co-design framework. First, novel models are formulated to accurately capture timing dynamics caused by multi-layered resource competition. Next, the project designs timing-aware event-triggered control mechanisms that only consume network resources when necessary. A key contribution of this work is establishing rigorous analytical tools to certify "schedu-stability," which is a joint guarantee of scheduling deadline feasibility and control system stability. Finally, the project creates a computationally efficient decentralized optimization framework to solve previously intractable co-design problems for large-scale systems. The optimal solutions and verified timing models resulting from this framework can be used to generate high-quality training data for methods such as imitation learning or reinforcement learning, for even larger system scales, where real-time computation of optimality is infeasible. All theoretical advancements will be integrated into an open-source software toolbox, lowering the barrier for researchers to analyze complex timing behaviors in cyber-physical 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 2026 · 2026-06

This award supports the participation of students, postdoctoral scholars, and early-career researchers in the International Chinese Statistical Association (ICSA) 2026 Applied Statistical Symposium, which will be held in Arlington, Virginia, from June 14 to June 17, 2026. Centered on the theme “Integrating Statistics and AI for Responsible Innovation and Trustworthy Decision-Making,” this symposium will bring together researchers and practitioners from statistics, biostatistics, applied mathematics, computer science, and data science. As artificial intelligence and data-driven technologies become more prevalent in critical sectors such as healthcare and finance, it is increasingly important to develop statistical methods that ensure these tools are reliable, fair, and transparent. The symposium provides a collaborative environment for experts from academia, industry, and government to address these complex challenges together. By fostering these professional connections and supporting junior participants, the project strengthens the statistical foundations of modern technology and prepares a highly skilled scientific workforce to navigate the evolving landscape of modern data science. The symposium will serve as a forum for advancing the integration of statistics, applied mathematics, data science, and AI, with a focus on developing rigorous, interpretable, and scalable methodologies that support responsible innovation and trustworthy data-driven systems. It will foster cross-disciplinary exchange, connecting theoretical advances in statistical inference, optimization, numerical analysis, and uncertainty quantification with practical challenges in AI reliability, interpretability, and decision science. By convening researchers, practitioners, and educators across academia, industry, and government, the symposium will highlight emerging developments that integrate statistical rigor and mathematical modeling with AI-driven methods, emphasizing methodological innovation, computational scalability, numerical stability, and reproducibility. The four-day program will feature three keynote lectures, short courses, and over 100 invited sessions, including many focused on AI foundations and applications. Furthermore, the symposium’s location in Arlington enables direct engagement with federal agencies, policy institutions, and regulatory bodies, alongside strong representation from pharmaceutical companies and technology firms, enhancing the dialogue on fairness, accountability, and numerical stability in high-stakes data-driven systems. The symposium website is accessible at https://symposium2026.icsa.org/. 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 · 2026-06

Acute nutrient pollution, such as raw sewages spills into waterways, represent one of the most pervasive and ecologically damaging stressors to freshwater and estuarine systems in the U.S., with recovery timescales often spanning decades. Changes in water quality and biodiversity immediately and soon after a pollution event are rarely studied in real time and are proposed to have outsized effects on the long-term trajectory of water systems. In January 2026, the Potomac Interceptor sewer outside of Washington DC collapsed, causing one of the largest raw sewage spills in history until it was capped in March 2026. This discharge released into the river a mixture of nutrients, pathogens, heavy metals, pharmaceuticals, and per- and polyfluoroalkyl substances, including E. coli concentrations reaching up to 10,000 times above recreational water quality limits. This project documents an intensive investigation of ecosystem response, from tidal freshwater to brackish water regions of the Potomac River Estuary, following the sewage spill. Data will be collected monthly at eleven sites for six months. This data will be examined in the context of 3 months of rapid-response monitoring as well as baseline data since 1984, which allows it to contextualize impacts on biodiversity, community structure, and ecosystem function. These initial and near-term changes may later be used to understand longer term ecological trajectories and consequences. This project will generate policy-relevant, publicly accessible data to directly inform human health advisories, recreational use decisions, and adaptive management strategies for the Potomac River, while developing a framework useful for future sewage spill events nationally. It engages community members and students in data collection and labwork, as well as includes results in on-going education programs. The Potomac Interceptor spill introduced a massive and discrete nutrient and contaminant pulse into an ecologically important estuarine system, creating a natural experiment for testing foundational ecological theory, including the intermediate disturbance hypothesis and alternative stable state dynamics, in a real-world context. Through monthly data collection, in conjunction with historical data and immediate post-pollution data, this project will document early responses that represent a transient disturbance signature that cannot be reconstructed once the system begins to reorganize. The project will quantify biodiversity and functional composition of bacteria, phytoplankton and zooplankton, macroinvertebrates, and fish across eleven sites, using eDNA metabarcoding and whole organism taxonomic identification. By tracking contaminant redistribution across water column, sediment, and biological compartments simultaneously, this study will generate mechanistic understanding of recovery trajectories, ecological thresholds, and the cascading trophic consequences of large-scale disturbance that is broadly applicable to impaired river systems nationwide. 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 · 2026-05

HIV infection and nicotine addiction are significant global health issues, particularly among young adults, with increasing co-occurrence. Despite successful antiretroviral therapy (ART), 30–60% of people living with HIV (PLWH) develop HIV-associated neurocognitive disorders (HAND). Nicotine negatively impacts HIV⁺ individuals, potentially contributing to disease progression by facilitating neuroinvasion through CD4⁺ T cells that cross the blood-brain barrier (BBB). These cells, once in the central nervous system (CNS), release neurotoxic particles and inflammatory cytokines, leading to synaptic damage. Chronic nicotine activates nicotinic acetylcholine receptors (nAChRs) on T cells, enhancing their proliferation and cytokine release. Our hypothesis is that chronic nicotine enhances HIV⁺ CD4⁺ T cell neuroinvasion through convergent chemotactic signaling involving HIV gp120 and nicotine receptor activation. We will test this hypothesis through three aims. Aim 1 is to investigate how HIV infection promotes chemotaxic signaling in CD4⁺ T cell neuroinvasion, utilizing an in vitro BBB transmigration assay and measuring the phosphorylation of cytoskeletal regulators. Aim 2 is to assess chronic nicotine's effects on HIV⁺ CD4⁺ T cell proliferation and pro-inflammatory responses, focusing on nAChR expression and activation of chemotactic signaling pathways. Aim 3 is to evaluate the effects of chronic nicotine and HIV infection on CD4⁺ T cell behavior in an HIV humanized mouse model, monitoring inflammatory cytokines, viral reservoirs, and neuroinvasion in brain regions susceptible to T cell infiltration. This study is proposed by a strong team of co-PIs with complementary expertise in HIV signaling, nicotine research, T cell immunology, and drug abuse animal models, ensuring a comprehensive approach to understanding the interplay between HIV and nicotine addiction in neuroinvasion.

NSF Awards · FY 2026 · 2026-05

The goal of this NSF CAREER award is to establish a rigorous scientific understanding of how autonomous robots can communicate using only their physical motion, without relying on explicit verbal or wireless communication. As autonomous systems increasingly operate alongside humans and other machines, their movements are continuously observed and interpreted by others. Successful completion of the project will lead to a transformative improvement in the capability of robots to work alone or in teams by unifying game theory, machine learning, and robotics to enable characterization of when and how motion can influence the decisions of observers, whether they are cooperative partners or adversaries. This will be achieved by modeling interactions between moving robots and observers as strategic games in which some information, such as their intent or capabilities, is hidden. The intellectual merit of the project includes new theoretical tools for analyzing these interactions and integrating them with machine learning to extend these insights to larger and more complex multi-agent systems. The broader impacts of the project include improving the safety and security of autonomous systems in applications such as parcel delivery robots, transportation, and security operations; informing the design of infrastructure and policies that reduce vulnerabilities to adversarial behavior; and advancing engineering education by integrating game theory into hands-on robotics curricula, workshops, and open-source competition platforms that broaden participation in STEM. The research objective of this project is to characterize the feasibility, forms, and effectiveness of motion-based signaling in multi-agent systems. Analyzing interactions between mobile agents and an observing agent is a major challenge because the observer’s interpretation and response to motion are typically unknown. To address this challenge, these interactions will be modeled using incomplete-information games, including dynamic Bayesian games and partially observable stochastic games, which capture both strategic reasoning and uncertainty about other agents’ private information. New solution methods that leverage structural properties of games embedded in physical environments will be introduced, enabling more tractable analysis of signaling behaviors that arise through motion. To extend these methods to more complex multi-agent scenarios, machine learning will be integrated with analytical game-theoretic models to accelerate the discovery of coordinated strategies while maintaining interpretability and performance guarantees. The theory will be validated using simulation platforms and a modular robotic testbed supporting controlled experimentation and human-in-the-loop studies. By bridging formal analysis, learning, simulation, and physical experimentation, the project establishes a rigorous foundation for signaling-aware robot motion control in complex multi-agent 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 Health-related social needs (HRSNs), such as food security, housing quality, reliable transportation, and social support, are important contributors to health. However, unmet HRSNs are prevalent in Medicare. Recently, Medicare embarked on an unprecedented large-scale initiative to address those unmet needs through the Medicare Advantage (MA) program. MA is a private alternative to Traditional Medicare (TM). MA plans can provide supplemental benefits not covered in TM. MA supplemental benefits were traditionally limited to Primarily Health-Related benefits, such as dental or vision care, that incur direct medical costs. In 2020, in an attempt to address HRSNs, this restriction was lifted and MA plans were allowed to offer non-medical supplemental benefits, such as food/produce, pest control, and living supports (e.g., utility coverage). Policy makers expect these benefits to help maintain or improve the health and functioning of chronically ill patients and thereby reduce use of costly acute care, such as hospitalizations. However, limited evidence is available to support this expectation. MA plans have rapidly adopted non-medical benefits. Adoption of these benefits is particularly high among dual-eligible special needs plans (D-SNPs), a type of MA plan that serves only individuals with both Medicare and Medicaid benefits (dual-eligible enrollees). Over 80% of D-SNPs offered at least one non-medical benefit in 2025. Despite this development, little is known about the use and value of MA’s non-medical benefits. It is also unclear whether the rapid adoption of non-medical benefits by D-SNPs improves health for dual-eligible enrollees, who often receive fragmented care. This proposed study will address these critical gaps by examining the relations between MA’s non-medical benefits, health care utilization, and patients’ health outcomes. It will also assess whether D-SNPs amplify impacts of non-medical benefits for dual-eligible beneficiaries. We will leverage MA plans’ staggered adoption of non-medical benefits over time and use a quasi-experimental design to obtain causal estimates of benefit impacts. This study will provide critical insights into an important policy question whether addressing HRSNs through private health insurers brings the intended outcomes. It will provide national evidence to help refine policy options to address HRSNs through MA’s non-medical supplemental benefits.

NSF Awards · FY 2025 · 2025-10

This REU Site award to the Smithsonian-Mason School of Conservation, located at the Smithsonian’s National Zoo and Conservation Biology Institute in Front Royal, VA, will support the training of 10 students for 10 weeks during the summers of 2026- 2028.  Undergraduate students will have the opportunity to conduct hands-on conservation research and gain career-ready skills to develop the best management practices for the conservation of threatened species, with the goal of helping to alleviate species extinction globally.  The REU connects students with the greater conservation community as they meet and network with researchers in the field, develop career skills, and share results on how their research can inform and impact species conservation.  Student projects will aid in their development of field and lab research methods, data collection, management, and analysis, all of which are necessary skills for future careers in science.  Students will enhance their communication skills by presenting the results of their work at a final symposium and other scientific conferences. Students should apply to the REU site using NSF ETAP (Education and Training Application) at https://etap.nsf.gov. Students will work with a mentor to develop an original inquiry-based research project within one of three conservation focal areas: 1) assessing and monitoring species in the wild, 2) understanding ecological integrity and species resilience to environmental threats, and 3) improving captive management of threatened species.  This REU takes advantage of the resources offered by the Smithsonian-Mason School of Conservation, located at the Smithsonian’s National Zoo and Conservation Biology Institute, which provides opportunities to learn and conduct research onsite in laboratories and at nearby field sites (forests, wetlands, and meadows).  Students will be trained in the responsible and ethical conduct of research and participate in professional development and skills-building seminars.  They will also attend colloquium talks given by experts in conservation while interacting with peers to develop teamwork skills and networks.  Successful applicants will be paired with a research mentor based on their interest in this REU’s thematic areas and the best fit of the REU experience for the student’s academic and/or professional or career goals. A list of available projects and associated mentors can be found here: https://smconservation.gmu.edu/nsf-reu/. 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 improving undergraduate education to better prepare future engineering and computing professionals to use and develop artificial intelligence (AI). The increasing integration of AI-enabled technologies across domains creates workforce opportunities for students as well as providing mechanisms for improving human well-being. The project's significance lies in its innovative use of situated case studies to help students understand AI complexity, differing stakeholder requirements, and to develop critical reasoning about AI applications. Through early exposure in first-year courses, the project aims to develop transferable mindsets and skills that students can apply throughout their careers, advancing their understanding of how to prepare a workforce capable of AI innovation and supporting the nation’s economic well-being. The project goals include developing and implementing six case studies that focus on familiar AI applications such as career preparedness, campus sustainability, autonomous vehicles, and mental health systems using a Situated AI Literacy framework. The scope encompasses implementation across first-year engineering and computing courses at Youngstown State University and George Mason University, serving over 500 students during the project period, with additional dissemination through faculty development workshops reaching ten external institutions. The methodology employs role-play case study discussions integrating three key competencies- complex systems cognition, perspectival understanding, and critical thinking. The project plans to use mixed-methods evaluation, including pre- and post-surveys, concept maps, discussion transcripts, and focus groups to assess student learning gains across these elements. The research investigates how the case studies support the development of multi-level AI understanding, stakeholder perspective-taking, and critical assessment of AI benefits and limitations. The NSF IUSE: EDU Program supports research and development projects to improve the effectiveness of STEM education for all students. Through the Engaged Student Learning track, the program supports the creation, exploration, and implementation of promising practices and tools. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2025 · 2025-10

Today's quantum circuit designs are akin to classical circuits in their early stages, which were designed by hand and manually laid out, while the power of classical computing hardware was not fully unleashed until the emergence of Electronic Design Automation (EDA) in the 1950s, enabling the scalable design of integrated circuits. Although quantum computing holds great promise to dramatically speed up many chemical, financial, cryptographic, and machine-learning applications, we are witnessing that the existing quantum computing design workflow significantly relies on human designs, such as manually implementing and verifying quantum circuits on the gate level for quantum algorithms. As such, domain experts from other fields without a sufficient fundamental understanding of quantum operations can hardly leverage the power of quantum computers for their domain applications, and more importantly, they lack toolkits to test the correctness of an ad-hoc designed quantum circuit. Furthermore, since quantum computing has a fundamentally different computing scheme, which relies on superposition and entanglement, the traditional EDA techniques cannot be directly applied to quantum circuits. To close the gap between quantum hardware (in physics) and quantum algorithms (in computer science), we envision the necessity of a quantum EDA framework, which will play a role similar to that of EDA in revolutionizing classical Silicon-based hardware design. Beyond the technical impact, the fundamentals of the design automation tools can help beginners understand how a quantum system is designed and how it works, which are compiled in the education activities in this project for public access. To carry out pilot research on the quantum EDA, this project proposes to develop an automated framework, namely SPV, to efficiently synthesize, profile, and verify quantum circuits, which include a set of quantum EDA tools: (1) We develop an automated quantum circuit construction toolset to optimize quantum circuit design in modern quantum processors. The toolset supports end-to-end quantum circuit design, including both quantum state preparation and function synthesis using available quantum gates. (2) We develop both formal and simulation-based approaches to verify quantum circuits at scale. Specifically, we utilize the widely adopted ZX calculus to optimize quantum circuits for equivalence checking, and we develop a scalable, simulation-based verification methodology tailored for larger circuits. Moreover, it will comprise methodologies to verify quantum circuits in the presence of quantum error correction (QEC). And (3) we build a benchmark test platform with circuit property profiling and performance validation. To address the shortage of QEC designs in existing benchmarks for quantum verification, we integrate a set of state-of-the-art QEC code designs into the benchmark tool. After all the synthesis, profiling, verification, and benchmarking tools are developed, we integrate them into a holistic quantum design automation toolchain. With a completed toolchain, SPV can benefit researchers in deploying and testing domain-specific quantum algorithms on available quantum computers. 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

Software vulnerabilities, which are flaws or weaknesses in code that can be exploited by attackers, pose significant risks to computing infrastructures across industry, government, and academia. Current research on vulnerability detection and remediation faces several key challenges, including keeping pace with rapidly evolving software, enabling data-driven methods (e.g., artificial intelligence-based techniques), and integrating various types of vulnerability-related metadata. To address these gaps, this planning project will lead to the construction of a robust, community-supported infrastructure and shared dataset that advance software vulnerability research, ultimately enhancing the security of diverse computing systems critical to national defense and prosperity. The project will also develop accessible security training resources for students and professionals. This project will plan an infrastructure featuring a continuous collection framework that captures scalable and multimodal data to facilitate high-impact software vulnerability research through a series of planning activities. First, the project team will conduct surveys and interviews with the security, software engineering, and human-computer interaction communities to understand researchers’ practical needs and how an infrastructure and dataset can reduce barriers in their work. Second, the project team will host workshops to gather feedback and share best practices on the initial infrastructure design. Third, the project team will conduct summative surveys and form a working group to assess, refine, and improve the design. By identifying community needs and priorities, the project will inform the infrastructure design that benefits and accelerates research on software vulnerability detection and remediation. Long-term collaboration with participants will also be fostered to support the establishment of the new infrastructure. 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 Leading Engineering for America's Prosperity, Health, and Infrastructure (LEAP-HI) Grant Opportunities for Academic Liaison with Industry (GOALI) project addresses the NSF Big Ideas of Understanding the Rules of Life and Harnessing the Data Revolution in targeting the need to provide food, fiber and fuel for a growing population using fewer resources (land, water, pesticides and fertilizers) in uncertain and rapidly changing environments. It is widely recognized that current agricultural technologies, from crop genetic improvement to field crop production, will not meet future agricultural demands, due to their heavy reliance on expensive, time-consuming, trail and error field trials to develop improved plant breeds. Emerging mathematical optimization and machine learning methods for analyzing high-dimensional data provide opportunities to speed up plant breeding to achieve rapid and efficient adaptation of crops to changing environments. The approaches in this project will take advantage of engineering techniques that have been used to remarkably improve the efficiency and resiliency of communication, manufacturing, transportation and energy systems. The research requires the synthesis of multiple disciplines, including agronomy, crop modeling, machine learning, operations research, optimization and plant breeding and aims to demonstrate the leadership role of engineering in addressing agricultural challenges. Three technical issues, which represent a small but highly visible subset of agronomic systems, will be addressed: (1) accurately predicting plant phenotypes based on genetic, agronomic management and environmental data and their interactions; (2) design of genetic improvement systems to efficiently develop cultivars with superior phenotypes; and (3) design of crop management strategies to assure that crops achieve superior phenotypes under changing environments, while balancing reward, time, and risk in the decision-making process. The research team will first translate the technical issues into engineering objectives and then identify existing methods and design new ones to achieve the objectives. The corresponding engineering objectives are: (1) identify a small subset of variables associated with synergistic effects in addition to their additive effects; (2) design a set of algorithms for genomic selection, which is a special type of nonlinear, non-convex, high-dimensional, and dynamic optimization problem constrained by resource availability and laws of reproductive biology; and (3) create a set of multi-objective and multi-level optimization models and algorithms for balancing reward, time, and risk, subject to genetic, environmental, and logistical constraints. Achieving these objectives will demonstrate the power of engineering approaches in improving the efficiency and resiliency of agronomic systems, with the aim of establishing plant breeding as an engineering discipline. 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

Evacuation and public sheltering move people from harm’s way and are common life-saving strategies in response to severe weather such as flooding and hurricanes. However, some citizens may exhibit lower propensities to evacuate and seek public shelter due to transportation challenges, past experiences, risk perceptions, and concerns about the availability of critical services at shelters. From the planning perspective of emergency management, choosing which shelters to open and when based on risks to infrastructure, optimizing resource allocation in operating public shelters, and estimating shelter demand present challenges. Current decision support systems rely primarily on weather forecasts, flood risk assessments, retrospective knowledge of shelter usage, and past public behavior. However, such data inputs are unable to fully account for the dynamic nature of evolving needs and movement behavior of the public, as well as failure risks of infrastructure necessary to run shelter operations due to their uncertain and dynamic interdependencies like transportation and power. This research will fill this gap in current decision support systems to perform continual risk analysis for shelter planning to facilitate optimal decision-making under rapidly evolving events. This project advances the well-being of citizens by reducing risk and helping communities increase resilience to severe emergency events. This project proposes to design and test an Artificial Intelligence (AI)-assisted adaptive decision support system for shelter planning called PCExplorer (Physical & Citizen Sensing Exploration tool), in collaboration with the Virginia Beach Office of Emergency Management. The project team will first develop a novel dynamic knowledge graph using probabilistic graphical models to represent and integrate heterogeneous, dynamic data. This will enable risk prediction modeling for sheltering-related infrastructure by incorporating physical sensing data, citizen movement behavior, and complex interdependencies and vulnerabilities of infrastructure. It will then develop a novel neurosymbolic AI-based planning framework for adaptive, tractable, and explainable decision-making, with the ability to ingest symbolic safety constraints and instructions from emergency managers and explain decisions in natural language for resource allocation. The personalized, responsive messaging to citizens for available shelters enabled by the resulting PCExplorer system will increase the propensity to seek shelter and facilitate a feedback loop to provide dynamic information on citizen actions back to the system. In addition, the project outcomes and open-sourced PCExplorer will contribute to education and research across multiple disciplines (computing, infrastructure engineering, and emergency management) and teach students to experience the process of developing applications for addressing community-centric challenges. 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 ExLENT Pivots track project aims to serve the national interest by expanding experiential learning pathways in emerging hardware technologies that are driven by nanofabrication. It focuses on increasing workforce opportunities in Northern Virginia, a region positioned for growth in emerging technology sectors. As this dynamic area continues to demand more advanced skill sets, developing a skilled workforce becomes a critical priority, one that calls for innovative and responsive training models. George Mason University (GMU) is well positioned to lead these efforts through its Innovation District initiative centered at the university's SciTech campus. Importantly, the project offers a three-phase curriculum that combines virtual instruction, hands-on training at the university, and paid apprenticeships in high-tech industry settings. This approach is designed to serve a wide range of learners, including non-STEM professionals and adults seeking to reskill or upskill. The project draws on a new nanofabrication facility at the SciTech campus and fosters cross-sector partnerships among academia, industry, and government. Through this high-impact effort, the project aims to deepen understanding of how to design experiential learning models that support career transitions into technology fields where nanofabrication expertise is in high demand. The project pursues three interconnected goals: 1) to develop and iteratively refine an innovative, community-engaged experiential learning and training model that prepares participants to pivot into emerging technology areas, 2) create a nanofabrication-specific hands-on training curriculum for the benefit of communities around the Northern Virginia and the greater Washington DC metropolitan area, and 3) enhance GMU’s capacity for innovative research and workforce development programs in STEM. To achieve these goals, the initiative integrates a three-phase experience designed to immerse participants in a multi-component, nanofabrication-centered learning pathway that aligns with workforce needs in areas such as AI, quantum, energy storage, and cybersecurity. Participants begin with foundational theories of nanofabrication and explore real-world examples of its role in technological innovation. They then engage in hands-on training at GMUs research labs and nanofabrication facilities. The final phase connects participants with on-site industry apprenticeships that support career transitions into high-demand fields. Evaluation efforts include a longitudinal study that captures both formative feedback and summative outcomes, enabling continuous refinement of the training model and its long-term impact. Findings and materials are dissemination through regional and national workforce development networks, and academic workshops and conferences. Through this integrated approach, the project contributes new insights into how discipline-specific, experiential learning models can support career mobility and expand access to emerging technology sectors. The NSF ExLENT Program, supported by the NSF TIP and EDU Directorates, seeks to support experiential learning opportunities for individuals to increase their interest in and their 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.

NIH Research Projects · FY 2025 · 2025-09

PROJECT SUMMARY Exposure to war, persecution, and forced migration put FDPs at disproportionately high risk for IPV and mental health problems. Many FD women, including from the Democratic Republic of Congo (DRC), arrive from countries where IPV is prevalent, and face increased risk before, during, and after displacement. Foreign- born women who experience IPV are more likely to be severely injured or killed, and report worse mental health than US-born women. Financial and social stressors, and persistent gender inequities in the US can exacerbate both IPV and poor mental health outcomes among FDPs. Yet, US-based programs primarily focus on connecting survivors to services once IPV occurs, overlooking strategic opportunities to prevent IPV and poor mental health among FDPs. In contrast to the US context, there is a rapidly evolving evidence-base of interventions developed globally that show promise in reducing IPV among FDPs. The EA$E (Economic And Social Empowerment) intervention (developed by IRC and MPI Wachter), has shown promise in reducing IPV in West Africa in research led by MPI Gupta. The International Rescue Committee (IRC), a global humanitarian organization, developed EA$E to address two underlying drivers of IPV in crisis settings--household financial strain and gender inequity in decision-making--innovatively diminishing risk factors associated with IPV. EA$E leads couples engaged in economic activities through an 8-session discussion series on household financial wellbeing, budgeting, spousal communication, and alternatives to violence. US-based programs for FDPs are in need of primary prevention strategies to address IPV in this health disparity population. Our overall objective is to advance the nascent science of primary IPV prevention among FDPs in the US. Our interdisciplinary team (public health, social work) is uniquely positioned to conduct the proposed study based on our combined expertise in IPV and research and practice with FDPs across settings and longstanding partnership with the IRC. Guided by the ADAPT-ITT framework, and Cascade Implementation Framework, we will develop EA$E-US, examine preliminary effectiveness on IPV, and intervention and intermediary outcomes, and asses implementation outcomes. This is aligned with the US National Plan to End GBV, which identifies refugees as a priority population.

NIH Research Projects · FY 2025 · 2025-09

PROJECT SUMMARY Exposure to war, persecution, and forced migration put FDPs at disproportionately high risk for IPV and mental health problems. Many FD women arrive from countries where IPV is prevalent, and face increased risk before, during, and after displacement. Foreign-born women who experience IPV are more likely to be severely injured or killed, and report worse mental health than US-born women. Financial and social stressors, and persistent sex-based inequities in the US can exacerbate both IPV and poor mental health outcomes among FDPs. Yet, US-based programs primarily focus on connecting survivors to services once IPV occurs, overlooking strategic opportunities to prevent IPV and poor mental health among FDPs. In contrast to the US context, there is a rapidly evolving evidence-base of interventions developed globally that show promise in reducing IPV among FDPs. The EA$E (Economic And Social Empowerment) intervention (developed by IRC and MPI Wachter), has been shown to reduce IPV and PTSD symptoms in a randomized controlled trial (RCT) conducted in West Africa led by MPI Gupta. The International Rescue Committee (IRC), a global humanitarian organization, developed EA$E to address two underlying drivers of IPV in crisis settings--household financial strain and sex-based inequity in decision-making--innovatively diminishing risk factors associated with IPV. EA$E addresses IPV at the individual and interpersonal levels by leading couples engaged in economic activities through an 8-session discussion series on household financial wellbeing, budgeting, spousal communication, and alternatives to violence. US-based programs for FDPs are in need of evidence-based curricula to address IPV and mental health. Our overall objective is to advance the nascent science of IPV prevention among FDPs in the US. Our interdisciplinary team (public health, social work, and clinical psychology) is uniquely positioned to conduct the proposed study based on our combined expertise in IPV and mental health research and practice with FDPs across settings and longstanding partnership with the IRC. Guided by the ADAPT-ITT framework, Cascade Implementation Framework, and the NIMH experimental therapeutics approach, we will develop EA$E-US, examine preliminary effectiveness on intervention and intermediary outcomes, and assess implementation outcomes via a hybrid type 2 pilot study design.

NIH Research Projects · FY 2025 · 2025-09

Family caregivers of persons living with Alzheimer’s disease and related dementias (ADRD) experience high levels of psychosocial distress due to prolonged and intensive caregiving. Persistent challenges remain among dementia caregivers, with some groups experiencing poorer health outcomes. Of particular concern are caregivers with limited English proficiency such as Chinese Americans who face barriers in communication, service navigation, and care. These challenges include misperception of dementia, limited caregiving skills, lack of social support, and restricted access to social services. To date, little research has been focused on this population. Accordingly, the goal of this R01 application is to conduct a Stage 2 efficacy trial of Wellness Enhancement for Caregivers (WECARE), a personalized digital health intervention designed for Chinese American dementia caregivers. Based on behavioral theories and extensive preliminary studies, the 7-week WECARE program offers multimedia content, quiz-embedded games, personalized feedback, location-specific resources, and social networks, all designed to enhance caregiving mastery and psychosocial wellbeing of the target users. A total of 160 participants will be recruited for an RCT with data collected at baseline and 3, 6 and 12 months. The efficacy of WECARE will be evaluated with depressive symptoms as the primary outcome. The implementation process will be assessed using mixed methods to identify barriers to and facilitators of WECARE’s adoption and sustained use. This application is directly responsive to NIA’s priority of effective interventions for ADRD-affected populations. If proven effective, the WECARE intervention will inform the development of personalized interventions for dementia caregivers.

NSF Awards · FY 2025 · 2025-09

As quantum information science continues to advance, it will lead to radical technological changes that require changes to the STEM education system. Significant resources have been invested in workforce development to ensure the world is prepared for the growth of the quantum industry, yet relatively little work has focused on K-12 education. This project will address the challenge of effectively engaging K-12 students in this new area and teaching them complex quantum science concepts by developing a toolkit of K-12 quantum frameworks that will serve as a guide for building student understanding of quantum concepts over time. This project will identify the alignment of content across grade levels required for teaching quantum within the disciplines of chemistry, physics, mathematics, and computer science. The research team will also identify multidisciplinary connections that will allow students to develop a richer understanding of quantum concepts. This project will engage ten teachers in quantum professional learning, curriculum curation, and curriculum design, and will directly impact more than 2000 K-12 students during the project's duration. To advance research in this area, the research team will work with elementary, middle, and high school teachers to identify learning progressions in quantum within and across STEM disciplines. These progressions will be used to illustrate how quantum concepts should be scaffolded across grade levels to optimize student learning from elementary to high school, and will inform curriculum development and instructional practices. Teachers will engage in iterative design cycles as they teach and revise quantum lessons and engage in lesson study with other teachers and the research team to identify and refine quantum progressions within their discipline. Data collection will be multifaceted, utilizing teacher interviews, classroom observations, and assessments of student learning. Validated measures will be used to examine student interest and classroom engagement. Data analysis methods will include statistical and qualitative analysis. Project results will be disseminated at national conferences for teachers and education researchers, through the project website and peer-reviewed publications, and at a quantum summit for regional teachers and administrators. This project is co-funded by NSF's DRK-12 and ITEST programs. The Discovery Research preK-12 program (DRK-12) seeks to significantly enhance the learning and teaching of science, technology, engineering, and mathematics (STEM) by preK-12 students and teachers, through research and development of innovative resources, models, and tools. Projects in the DRK-12 program build on fundamental research in STEM education and prior research and development efforts that provide theoretical and empirical justification for proposed projects. The Innovative Technology Experiences for Students and Teachers (ITEST) program supports projects that build understandings of practices, program elements, contexts and processes contributing to increasing students' knowledge and interest in science, technology, engineering, and mathematics (STEM) and information and communication technology (ICT) careers. 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

One of the biggest unknowns in projecting future sea level is how fast the Antarctic Ice Sheet will melt in response to continued warming. An increase in high-latitude snowfall may offset some ice sheet melt due to warming of surrounding ocean and atmosphere, though it is not yet known how effective this compensating mechanism is, or under what timescales or conditions it might be important. To better understand these competing processes, researchers are investigating moisture-driven mechanisms of ice sheet growth during a past interval in Earth’s history where the climate was warm (the Miocene Climate Optimum, about 17 to 14.8 million years ago). During this time, Earth was warmer than today, yet geologic records hint at episodes where Antarctica was gaining ice. This project brings together an interdisciplinary team of experts across three institutions to investigate the potential for moisture-driven ice growth using a combination of advanced Earth system models and geologic data, while providing hands-on interdisciplinary geoscience training for graduate and undergraduate students. Researchers will use isotope-enabled climate and ice sheet models to test a suite of hypothesized mechanisms for precipitation-driven Antarctic ice growth during the Miocene Climate Optimum. Each model simulation tracks the oxygen isotopic concentration of ice, generating a modeled oxygen isotope signal that can be compared directly against deep-sea isotopic records. To evaluate model simulations, the team will generate a new high-resolution record of Antarctic Ice Sheet volume using paired benthic foraminiferal oxygen isotopes and Mg/Ca measurements from a deep-sea sediment core from 17-15 Ma, providing a key dataset for model validation alongside a synthesis of published geologic records spanning this time. Data-model comparisons will evaluate how well each modeled mechanism can explain the observed ice volume and oxygen isotope changes recorded in deep sea sediments. Specifically, investigators will explore the ice-growth potential of local polar mechanisms (such as ice-proximal ocean warmth and sea ice cover), as well as global hemispheric processes (such as CO2 and orbital forcing) that influence the heat and moisture transport to the ice sheet. Miocene data and model output will contribute to international community synthesis efforts, and project results will provide critical context for understanding long-term trajectories of global sea level. 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

Recent advances in genomic sequencing technologies have made it possible to examine the behavior of individual cells at unprecedented scale and resolution. These technologies generate massive amounts of complex biological data, especially from emerging single-cell studies that are revolutionizing our understanding of tissue function, disease mechanisms and therapeutic responses. However, current computer-based methods often fall short in analyzing these large datasets accurately and efficiently, limiting the pace of scientific discovery. This project introduces a new approach using quantum computing, a cutting-edge technology that uses the principles of quantum mechanics to solve certain types of problems more efficiently than classical computers. By applying quantum computing to single-cell omics data, this research aims to build faster and more powerful tools for advancing data analysis and studying how cells behave, interact and respond to treatments. The project also includes public sharing of software tools and educational resources to help train the next generation of scientists at the intersection of biology, computer science and quantum technology. This project will develop a suite of novel quantum algorithms specifically designed for analyzing single-cell omics data. These algorithms will address complex computational tasks such as optimal cell clustering, comparative analysis across biological conditions, and modeling of cellular dynamics responses to drug combinations. The work will formulate these problems as quadratic unconstrained binary optimization models and solve them using quantum annealing approaches on D-Wave machines. In addition, gate-based quantum algorithms will be implemented and tested on IonQ platforms, alongside hybrid classical-quantum approaches. The algorithms will be applied to real single-cell transcriptomic datasets from the mouse brain and targeted studies of drug response in multiple myeloma and ovarian cancer, demonstrating the advantages of quantum-enabled analysis. A central deliverable will be the creation of QOTBox, a scalable quantum computing platform tailored for single-cell data analysis. All algorithms and code will be openly shared, with educational materials including online tutorials and interactive notebooks to support adoption across the scientific community. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2025 · 2025-09

The aim of this project is to extend the theory of calculus to more complex geometric settings, particularly those relevant to theoretical physics. Traditionally, calculus is defined on flat spaces like lines or planes, while modern differential geometry expands these concepts to smoothly curved spaces—such as spheres or donuts—and their higher-dimensional analogues, called manifolds. This theory plays a central role in physics, from Einstein’s description of gravity as the curvature of spacetime to the Standard Model of particle physics. The algebraic process of solving equations corresponds geometrically to the intersection of graphs, a principle that extends naturally to manifolds. However, intersections of manifolds are not always manifolds themselves, rendering differential geometry insufficient. The PI has made significant contributions to derived differential geometry (DDG), an advanced framework designed to handle such non-smooth intersections. Yet, integration—a cornerstone of calculus—has not been fully developed in this setting. The first aim of the project is to fill in this gap by building a robust theory of integration in DDG, with particular relevance to the computation of Feynman path integrals in physics. The second aim is to generalize geometric quantization—a powerful method traditionally used to describe the transition from classical mechanics to quantum mechanics—to more sophisticated systems such as classical field theories. Classical mechanics describes the motion of point particles, while field theories govern the behavior of extended objects, such as electromagnetic fields, and arise from purely mathematical constructions. Concrete outcomes of this project will include the development of new mathematical formalisms for integration and quantization over derived stacks, which can be used for computations in quantum field theory, such as path integrals and quantum invariants arising from topological field theories. These tools are expected to be applicable in both physics and mathematics, and the project will also foster interdisciplinary education by supporting the design of joint coursework in geometry, topology, and field theory for students in both disciplines. The project consists of two complementary components. The first involves constructing a comprehensive theory of geometric integration applicable to quasi-smooth derived higher stacks within derived differential supergeometry. This will be accomplished by developing a six-functor formalism, identifying dualizing complexes as Berizinians of cotangent complexes, and defining integration through the co-unit of exceptional inverse and direct image functors. The second aim of this project is to develop a notion of geometric quantization for shifted symplectic derived smooth stacks whose output is a fully extended topological field theory with values in higher categorical vector spaces. The resulting program will be a refinement of the shifted geometric quantization program of Safranov, appropriately adapted to the smooth setting, and will build on work of Calaque-Haugseng-Scheimbauer on TQFTs. The functorial field theories constructed using this method should yield new quantum invariants. By the cobordism hypothesis, such a theory is determined by what it assigns the point, and this should correspond to the underlying higher vector space of polarized sections of a higher prequantum line bundle. The PI proposes to prove that the fully extended framed TQFT associated to Chern-Simons theory is determined by a certain linear 2-category of representations of the string 2-group. 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

The aim of this project is to advance disaster decision making, risk assessment, and management science by incorporating access to shelters and the ability to seek shelter into the national disaster risk assessment. Management decisions on resource allocation and emergency preparedness planning are hindered when disaster risk is not well understood. The existing tools and resources available to emergency managers often lack measures of shelter accessibility and the ability to seek shelter, which can escalate natural disasters into human disasters. By advancing existing measures, this translational project equips emergency managers with the knowledge and tools needed to cope proactively with disasters such as wildfires, hurricanes, tornadoes, and coastal floods. The potential societal benefits in this project include engaging emergency directors, planners, and operators to minimize redundancy and tool fatigue and ensure that outcomes align with their needs, and improving the well-being and survival of populations by identifying gaps in shelter access and prioritizing the allocation of shelter and mobility resources. This effort is guided by a vision of improving disaster risk understanding within a framework that integrates community resources and capabilities into risk assessment, management, and decision making. The research team achieves this goal through three research activities, co-produced in close collaboration with emergency managers across the nation. First, the research team develops a comprehensive, risk-based national shelter accessibility model to advance the state of the art in shelter accessibility measurement by accounting for both the availability and accessibility of shelters, as not all shelters remain functional during disasters. Second, the research augments existing national measures by integrating shelter accessibility and evacuation capabilities to enhance both short-term and long-term emergency management decisions. Third, the researchers create a science-informed decision-making tool to test risk perception and decision making in emergency management, enabling emergency directors, planners, and operators to explore how short-term and long-term strategies can provide evacuees with a better chance to survive. 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

At the end of the Devonian period the Earth experienced a uniquely cool climate, the first record of limbed vertebrates, and one of Earth’s “Big Five” mass extinctions. For this study, researchers are collecting and analyzing data from rock and fossil samples from surface exposures and from the subsurface (rock cores) to test the link between environmental extremes and vertebrate habitats during the late Devonian ~360 million years ago. This work will advance understanding of connections between biological, chemical, and physical processes in Earth’s past. This collaborative and multidisciplinary project will support the education and development of eight students and two early career faculty. It will involve rural community public outreach to share knowledge of Devonian geology, which underlies much of the rural landscape of central Pennsylvania, including educational field events and the development of an interactive display at a highly trafficked rural zoo. Engaging with the public through this research will promote awareness of Earth science career paths, and the significance of such knowledge in understanding our planet’s past and future. Evidence of late Famennian (~361-359 Ma) glaciation along the eastern margin of North America is spatially limited and controversial yet calls for abrupt and anomalous cold climates globally that are unrecognized in existing models for the earliest stages of the Late Paleozoic Ice Age (LPIA). This project examines the Upper Devonian rock record in Pennsylvania and Ohio (Appalachian Basin) along a continental-to-marine transect, acquiring abundant new geochemical, sedimentological, and paleontological data to 1—establish an age framework for broad correlation, 2—scrutinize the (long-debated) glaciogenic nature of these deposits, and 3—elucidate dramatic paleoenvironmental changes in vertebrate habitats that hosted the fins-to-limbs transition. This is an archetypal region to assess the impact of environmental change on aquatic ecosystems, but modern quantitative analytical data are lacking to date. 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.