Clemson University

NSF Awards · FY 2026 · 2026-09

Artificial intelligence (AI) is rapidly expanding from centralized computing infrastructures into the physical world, where networks of distributed devices must sense, reason, and act together in dynamic and uncertain environments. This transformation gives rise to swarm AI, an emerging form of intelligent infrastructure that supports applications such as disaster response, environmental monitoring, precision agriculture, and autonomous mobility. Unlike traditional systems that rely on stable wired connections, swarm intelligence operates over wireless links that are intermittent, noisy, and resource constrained. Communication, therefore, becomes a central bottleneck that limits reliability, efficiency, and coordination. This project establishes a new wireless foundation for swarm AI by prioritizing the meaning and task relevance of transmitted information rather than raw bit accuracy alone. By strengthening how distributed agents share mission-critical information under challenging wireless conditions, the research enhances the resilience, scalability, and interoperability of next-generation intelligent systems. The project integrates research and education through curriculum development in communication-aware AI, hands-on mentoring of undergraduate and graduate students, outreach to K-12 learners, and open dissemination of research outcomes. These activities broaden participation in advanced wireless and intelligent systems research and contribute to workforce development in emerging communication and intelligent system technologies. The project addresses a fundamental gap between AI systems that assume ideal connectivity and wireless communication protocols that optimize bit-level fidelity without accounting for task intent. The scientific problem is how to design wireless architectures that are aware of semantic content, resilient to time-varying channel impairments, and adaptive to heterogeneous device capabilities in swarm settings. The research establishes a semantic-centric communication framework organized into three integrated thrusts: (i) robust semantic transceiver principles that identify and protect task-relevant information against dynamic wireless distortion, ensuring reliable semantic delivery under feature-dependent channel impairments; (ii) swarm-aware radio orchestration strategies that align spectrum allocation and scheduling with collective task objectives through utility-driven coordination; and (iii) heterogeneity-aware collaborative reasoning architectures that enable progressive semantic compression and resource-adaptive inference across devices with diverse sensing, computing, and communication constraints. The research combines theoretical analysis, algorithm design, and experimental validation to advance communication-aware intelligence across layers of the wireless stack. Together, these advances provide a principled foundation for building resilient, scalable, and interoperable swarm AI infrastructures. 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 · 2026-08

Every year, natural disasters triggered by hydrological extremes such as floods and drought cause significant loss of life and property damage in the U.S. A shift has been observed in the magnitude and timing of these extreme events. This CAREER project will use a combination of statistical analysis and hydrologic modeling to investigate the underlying reasons for this shift. The project will use the results of the analysis to develop innovative tools for identifying hazards and managing them. Open-source software for data analysis will be made available to hydrologists and water resource scientists. The project outcomes will help mitigate hazards and improve communities’ resilience. The project will support an outreach program for K-12 students and the public to learn about disaster risks and mitigation. The team will also develop short courses for extension agents who work with farmers and communities. A discernible shift in the magnitude and time of occurrence of hydrological extremes has been observed. Classical extreme value analysis, which assumes that the distribution from which the extremes have been drawn and its parameters must remain constant in time, is no longer applicable. From an operational standpoint, there is a need to understand what is driving the change in hydrologic extremes. This project will (i) quantify the historic and predicted shifts in the magnitude and timing of extremes; (ii) identify the direction of change, and (ii) determine the mechanisms generating the change. The project will develop Innovative tools to integrate this information into risk assessment frameworks. Project outcomes will provide valuable insights that can be incorporated into hazards planning and management, decision-making processes, and the formulation of science-based policies. This research will be integrated with an educational program to empower the next generation of a globally competitive STEM workforce. Through teaching, mentoring, and outreach activities, students will acquire the analytical skills to investigate the impact of hydrologic extremes in their chosen field and develop mitigation strategies to safeguard their communities, society, environment, and the economy. 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

Feeding a growing global population requires crop varieties that can withstand drought, heat, pests, and extreme weather while maintaining high yields. Developing these resilient varieties requires the phenotyping of thousands of candidate plants under real-world conditions. Despite significant improvements to sensors, imaging, and computing, in-field phenotyping remains a major bottleneck for modern plant breeding. This project focuses on a collection of open-source mobile applications called PhenoApps that leverage advances in consumer electronics, image processing, and machine learning to improve digital data collection for plant breeding and genetics research. The PhenoApps suite allows breeders to capture high-quality data at scale using apps that target specific breeding activities, including field phenotyping, tissue sampling, and controlled crossing. Opening up wide access to the tools needed to breed crops more efficiently will accelerate the delivery of improved varieties and result in a more competitive U.S. agricultural sector with strengthened public plant breeding capacity. Breeders around the world have integrated PhenoApps into their research programs and are actively using these tools to address the global challenge of food security. This project will sustain and expand this impact by establishing the organizational, technical, and community foundations required to transition PhenoApps into an open-source ecosystem that serves as the default field-based phenotypic data collection platform. Specifically, the team will 1) characterize the existing user base via download metrics, targeted surveys, and interviews to identify unmet needs, barriers to adoption, and opportunities for new contributors; 2) establish a governance framework to explore long-term sustainability mechanisms; and 3) recruit complementary contributors with expertise spanning coding, plant breeding, and community management. Transitioning PhenoApps into a community-governed, open-source ecosystem with broad participation from developers, breeders, students, and technicians will create a unified, extensible phenotyping platform that can be harnessed to accelerate genetic gain and further develop improved crop varieties. 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 · 2026-06

PROJECT SUMMARY/ABSTRACT Clemson University’s rapid growth in externally funded research has propelled it to R1 status, reflecting its expanding impact in advanced scientific inquiry. Five of Clemson’s nine academic colleges are STEM-fo- cused, encompassing over 30 academic units. In the last decade, Clemson’s NIH-funded portfolio has grown from $5.5 million in 2014 to $26 million in 2024, currently spanning 85 awards across 57 investigators. This growth is fueled by the university’s strategic investments in research infrastructure, equipment, and facilities. These actions foster a resource-rich environment capable of attracting and retaining top research talent. Three major NIH Centers of Biomedical Research Excellence (COBRE) underscore Clemson’s priorities: the Eukaryotic Pathogens Innovation Center (EPIC), the South Carolina COBRE for Translational Research Improving Muscu- loskeletal Health (SC-TRIMH), and the Clemson University Center for Human Genetics. To further strengthen Clemson’s research capabilities, we seek to acquire a Refeyn mass photometer, a transformative technology that enables single-particle mass measurements. Unlike bulk methods such as dynamic light scattering (DLS), mass photometry quantifies molecular mass, providing an unparalleled view of heterogeneity, oligomerization states, and binding interactions at the single-molecule level. This technology is valuable across disciplines and research foci on campus, from probing protein-ligand and protein-protein inter- actions that occur in eukaryotic pathogens (EPIC) to evaluating biomolecular assembly and integrity in mus- culoskeletal research (SC-TRIMH). Investigators in human genetics can rapidly assess protein-DNA/RNA interactions and more complex heterogenous multi protein-protein-DNA/RNA complexes. The Refeyn mass photometer offers remarkable advantages: (1) Minimal sample requirement—only a few microliters of material are needed; (2) Low per-sample cost, approximately $2, making it accessible to both established laboratories and student trainees; (3) Ease of operation, a simple pipetting step onto a glass slide significantly reduces technical barriers; and (4) Broad applicability, it excels in characterizing oligomeric states, monitoring antibody binding, and confirming molecular compositions in diverse sample types. By installing this mass photometer at Clemson, we will foster interdisciplinary collaboration, enrich hands- on learning opportunities, and expand the capabilities of ongoing NIH-funded projects. Moreover, this cutting- edge instrument will position Clemson researchers at the forefront of next-generation single-particle analytics, supporting a range of studies—from early discovery to late-stage translational research. Although it also inte- grates seamlessly into cryo-EM workflows, the mass photometer’s utility extends well beyond structural biology. Ultimately, this S10 instrumentation request will allow Clemson to elevate its research enterprise, amplify productivity, accelerate innovation, and strengthen the university as a leader in biomedical and research.

NSF Awards · FY 2026 · 2026-06

Modern vehicles are increasingly becoming networked computers on wheels that collect detailed information about where people go, how they drive, and how they use in-car services. These practices create a major privacy challenge because drivers often cannot tell what data is being collected, who receives it, or how to limit sharing. This project addresses that problem by developing a human-centered approach to automotive privacy that helps people understand and control how their data is used. The project’s novelties are the integration of user-centered behavioral research with large-scale technical measurement of vehicle data practices, and the design of practical tools that make privacy more transparent and actionable. The project's broader significance and importance are advancing trust in connected vehicle technologies, informing consumer protection efforts, and supporting the development of policies and systems that better safeguard personal data. The work also strengthens the cybersecurity workforce by creating privacy-focused learning modules for South Carolina high school students, expanding hands-on research opportunities for undergraduate students, and sharing findings, tools, and educational materials with researchers, industry, and the public. The project studies privacy risks in vehicles that use Android Automotive OS and related third-party applications. It combines interviews, think-aloud studies, and controlled comparison studies to characterize drivers' privacy attitudes, identify manipulative interface designs that steer users toward data sharing, and evaluate privacy-enhancing alternatives. In parallel, the project develops an automated analysis framework that maps what vehicle data applications access, analyzes outgoing encrypted traffic to infer what information is transmitted, and compares observed behavior with privacy policy disclosures to detect inconsistencies. These results inform the design and evaluation of an in-vehicle privacy dashboard that presents data practices in a clear, actionable form for drivers. The project also adapts the analysis framework into a scalable vetting tool for third-party automotive applications. Together, these activities produce methods, datasets, design guidance, and open tools for improving privacy in complex 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 · 2026-05

The accumulation of plastic waste can have negative effects on ecosystems and human health. The economic benefits or recycling many types of plastic waste are limited. This CAREER project will explore new designs for common polymers used in the packaging industry that will facilitate their biodegradation after use. The project will also find new additives to improve the properties of polymers in plastic waste so that they can be recycled into useful products. Redesigning polymer systems will enable new uses for plastic waste in high value applications. Outcomes of the project could help turn plastic waste into raw materials for new products. The project will provide educational activities for high school teachers, will incorporate research findings into an undergraduate course on sustainable packaging systems, and will conduct outreach to industry to help in transitions to more recyclable packaging products. Polyurethanes and polyesters are two highly produced plastics that have limited end-of-life options. Polyurethanes also present dangers to manufacturers and applicators due to the toxic nature of isocyanates. This CAREER project will investigate the incorporation of labile bonds from renewable sources to discover new bonding motifs and polymer properties for creating biodegradable films, foams, and adhesives, capable of entering industrial or home composting operations. Studies will be conducted to understand how different precursors can be synthesized using a non-isocyanate composition and room temperature to replicate the varied properties available for current polyurethane foam materials. Research will be conducted to understand how renewable material can be used as chain extenders and toughening agents to improve the properties of these undervalued polyester materials and find new markets for value-added and advanced manufacturing products. These research objectives are combined with educational and mentoring activities at the secondary, undergraduate, and graduate level to help train the next generation of leaders in plastic innovation. 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 · 2026-04

The 2026 ASA Section on Physical and Engineering Sciences Spring Research Conference will be held in Clemson, South Carolina from May 26 – 28, 2026. The theme of the conference is Harnessing Data for the Informative Advancement of Industry. It aims to promote the development, communication, and implementation of state-of-the-art statistical and mathematical methodology toward the effective use of data in advancing industrial processes, materials science, engineering innovation, and manufacturing. The conference will bring together statisticians, data scientists, mathematicians, and domain experts to foster technical exchange, methodological advances, and engagement between academia, industry, and government. Particular highlights of the conference will include recent developments in uncertainty quantification, digital twins, computer experiments, reliability engineering, physics-informed machine learning, and challenges arising from smart manufacturing and advanced materials, including optimal design of experiments, Bayesian optimization, and multi-fidelity modeling. The conference will feature invited sessions, panels, and contributed presentations centered around engaging with emerging statistical challenges driven by complex data sources. The organizers seek to maximize participation from students, postdoctoral fellows, and early-career researchers. Conference details may be found online at https://sites.google.com/view/src2026 . 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 · 2026-04

PROJECT SUMMARY Invasive infections related to the saprophytic mold Aspergillus fumigatus are on the rise, and drug resistance often exacerbates the problem. The mechanisms of fungal adaptation to the azole drugs are not clear. Recently, it has been shown that A. fumigatus can form persistent colonies in response to azole drugs. Long non-coding (lnc)RNAs have been associated with stress adaptation that may eventually lead to resistance. This research team has identified a lncRNA, afu-182, that acts as a negative regulator of azole tolerance. This project will now use innovative approaches to identify the role(s) of afu-182 and delineate the azole response network in A. fumigatus. Aim 1 will characterize the roles of the RTM family of proteins, a class of protein that is involved in ameliorating xenobiotic stress, and define their regulation by afu-182. Aim 2 will study the structure-function relationship of afu-182 to understand the mechanistic basis of afu-182 action. Aim 3 will study a closely related homothallic fungus, Aspergillus nidulans, with a short, defined sexual cycle in an unbiased forward-genetics approach to identify novel factors in azole response. Using a new fundamental paradigm of study, this project will determine the novel mechanisms of azole response in Aspergilli, which will eventually reveal new strategies to combat the rise of azole drug resistance.

NSF Awards · FY 2026 · 2026-03

The Meeting on Applied Algebraic Geometry (MAAG) conference series is a southeastern regional conference series focused on interactions between algebraic geometry and other fields. The venue of the conference rotates between Clemson University, Georgia Institute of Technology, and Auburn University. This award provides support for the 2026 instance of this conference, which will take place at Clemson University on April 18-19, 2026. The MAAG conference series seeks to strengthen and grow a local network of support among applied algebraic geometers. The conference is structured to involve a wide representation of fields, and it is designed to spur new cross-disciplinary research. The MAAG conference schedule includes time for short presentations from graduate students and postdocs, as well as ample time for discussions, networking, and mentoring. In the 2026 conference, presenters will discuss new developments in the quickly-evolving field of applied algebraic geometry. Research talks will be given by experts in many fields, including algebraic statistics, commutative algebra, data science, tropical geometry, algebraic combinatorics, numerical algebraic geometry, and symbolic computation. In addition, the conference will feature a poster session and lightning talks by graduate students and postdocs. The conference will also provide an introduction to and tutorials on the Macaulay2 computer algebra system, a popular software package for computations in applied algebraic geometry. For further information, see the conference website: https://klee669.github.io/maag. 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 · 2026-02

NON-TECHNICAL SUMMARY This project is building an improved understanding of how magnetic nanomaterials behave when they are placed in a magnetic field that oscillates back and forth in time. Also known as magnetic relaxation, these behaviors depend on the properties of the material, including what elements comprise the material, as well as what geometric shape the material is formed into. Intellectual merit: By changing the ratio of the elements included, as well as the length and diameter of rod-shaped magnetic particles, this project is delivering new knowledge about how these core properties affect the time-varying magnetism response in these materials. This new understanding could ultimately enable the use of these materials in technologies that can identify, image, and treat cancer and other diseases, improve detection of magnetic effects and materials in industrial applications such as advanced manufacturing, and consumer applications such as home and food health and safety. Broader Impacts: A future workforce is being trained to discover and implement technology that can improve the health and well-being of U.S. citizens. Finally, this project is explaining why these materials are so important to audiences of all ages and interests across the state of South Carolina, which already manufactures many goods whose continued improvement depends on an understanding of the raw materials that enter these factories. TECHNICAL SUMMARY Dynamic relaxation in magnetic nanostructures, especially suspended in fluids, is very difficult to measure at frequencies higher than 100’s of kHz, and as a result, researchers in this field are left using an over-simplified ratios to combine physical (Brownian) particle relaxation with magnetic (Neel) relaxation in these particles. Intellectual Merit: By measuring relaxation over a wider range of frequencies and particle number, this project will deliver an improved expression that more accurately combines these relaxation contributions. In addition, by varying both the composition of the magnetic material, and the aspect ratio of cylindrical nanorods, the contribution of particle magnetism (i.e., magnetization, anisotropy, and uniformity) will be separated from that arising from interactions between the particles, e.g. in a fluid, delivering engineers information needed to properly design and specify magnetism and size for nanostructured magnetic systems to be used in applications ranging from medical imaging, diagnostics, and treatment, to industrial optimization of manufacturing precision, relying on magnetic sensing and measurement. Broader Impacts: A future workforce skilled in magnetic nanomaterials and measurement is being trained to work in these factories and clinics to improve the health and economic security of U.S. citizens. 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 · 2026-02

This Research Infrastructure Improvement EPSCoR Research Fellows project provides a fellowship to an Assistant professor and training for a graduate student at Clemson University. This work is conducted in collaboration with National Synchrotron Light Source at Brookhaven National Laboratory. Through the fellowship, the PI will develop advanced analytical approaches to fundamentally understand the formation mechanism during the synthesis of composite electrodes, as well as the degradation mechanism and working principles for solid-state batteries. This project aims to overcome interfacial issues through the microstructural and chemical design of the composite electrodes. Through the collaboration with the national synchrotron facility, the development of multi-modal characterization techniques can be integrated into the imaging facility at Clemson University, which can serve as an anchor capability and service to be made available to the national research community. Moreover, this fellowship project will serve as a mechanism to foster and train the next generation of material scientists in energy science and advanced characterization. Rechargeable batteries play a crucial role as the leading energy storage devices and power sources for various applications. Solid-state batteries are considered promising in terms of thermal stability and safety, while the undesired interfacial reactions between solid-state electrolytes and electrodes hinder the wide adoption of the technique. The project will rationally design the solid-state batteries by elucidating the role of the solid electrolyte, cathode, and interface regarding battery performance. This project will investigate three key aspects: (1) the role of interdiffusion, phase transition, and microstructural evolution during solid state synthesis; (2) the intercalation and interfacial reactions during electrochemical degradation; and (3) the combination of synchrotron-based characterization techniques, the data-driven analysis, and the scientific interpretation with the power of artificial intelligence. This fellowship will greatly transform the career of the PI by providing unique opportunities for training, research, and establishing a sustainable partnership with collaborators. New modules on X-ray-based characterization methods will be introduced in existing undergraduate and graduate courses. This project will also contribute to the research infrastructure enhancement regarding X-ray imaging, aligning with a strategic plan for Clemson University and South Carolina. This project is supported by the EPSCoR Research Infrastructure Improvement Program: EPSCoR Research Fellows, which supports early- and mid-career investigators in eligible jurisdictions to develop collaborations at the nation’s private, government or academic research institutions. 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

Modern infrastructure faces unprecedented challenges due to urbanization and aging systems, which require innovative solutions that traditional engineering approaches alone cannot provide. This Research Experience for Undergraduates (REU) Site at Clemson University addresses a critical national need by training the next generation of civil and environmental engineers (CEE) to harness the power of machine learning (ML) and causal inference. The program will immerse 12 undergraduate students annually in an 8-week intensive research experience that tackles real-world infrastructure problems such as ensuring equitable water distribution, optimizing transportation networks to reduce congestion and emissions, and designing infrastructure systems that can withstand natural disasters. Industry partnerships will ensure that research outcomes translate into practical solutions, while outreach activities will inspire younger students to pursue STEM careers. This program supports NSF's mission to advance national prosperity and welfare through improved infrastructure resilience and efficiency by bridging the gap between traditional engineering education and cutting-edge computational methods. This REU Site will integrate machine learning and causal inference principles into civil and environmental engineering research through mentored projects spanning multiple CEE domains. The program's technical objectives include: (1) developing novel ML methodologies for infrastructure optimization, including deep learning models for traffic flow prediction and reinforcement learning for adaptive infrastructure management; (2) creating causal inference frameworks to understand complex relationships between infrastructure performance and environmental factors; (3) building open-source ML tools and curated datasets specific to CEE applications; and (4) establishing ethical guidelines for ML deployment in infrastructure projects. The research methodology combines computational modeling, data analytics, and validation through partnerships with local infrastructure firms and agencies. Each project will incorporate modules on responsible ML development to ensure that participants understand the ethical implications of algorithmic decision-making in public infrastructure. The program will produce peer-reviewed publications, open-source software tools, and a pedagogical framework for integrating ML into undergraduate CEE curricula. 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 · 2026-01

Project Summary: Cardiovascular diseases (CVDs) such as myocardial ischemia/reperfusion (I/R) injury lead to extensive cardiomyocyte death and subsequent reduced cardiac function. Due to the human adult heart’s limited regenerative capacity, there is a significant need for exogenous cardiac function restoration post-I/R. Human pluripotent stem cell-derived cardiomyocytes (hPSC-CMs) have emerged as a favorable cell source to restore myocardial function. While promising, the therapeutic potential of hPSC-CM transplantation is hampered by low cell survival and inadequate integration with host myocardium. Thus, our lab has developed nanowired human cardiac organoids composed hPSC-CM, primary human adult cardiac fibroblasts, endothelial cells, stromal cells, and electrically conductive silicon nanowires (e-SiNWs). Our in vivo data shows that organoids are capable of robustly engrafting and providing functional recovery (69% Fractional Shortening recovery) in I/R injured rat hearts, while using 10% of the cell dose (1E6, 1x106 cells/rat) that other hPSC-CM transplantation studies used (1E7, 1x107 cells/rat). Additionally, to alleviate major histocompatibility class (MHC) mismatching to provide a translational platform for hPSC-CM delivery, our lab developed isogenic cardiac organoids, composed of hPSC-CMs, -cardiac fibroblasts (hPSC-cFBs), and -endothelial cells (hPSC-ECs) from a single hiPSC cell line. The goals of this proposal are to 1) investigate the impact of e-SiNW geometry on isogenic organoid function, and 2) demonstrate the therapeutic efficacy of optimized nanowired isogenic cardiac organoids in a rat I/R injury model. The central hypothesis of this proposal is that engineered nanowire surface geometry will enhance interactions between host myocardium and transplanted hPSC-CMs within isogenic cardiac organoids, resulting in efficient engraftment and significant functional recovery. The innovation of this proposal is engineering e-SiNW surface roughness to optimize the efficiency of myocardial engraftment, and thus the functional recovery of I/R-injured hearts. My long-term goal is to leverage translational engineering and informatics to develop a clinically viable cardiac cell therapy heart repair. Accordingly, we will pursue the following two aims: 1) investigate the effects of e-SiNW geometry on isogenic cardiac organoid function, and 2) determine the therapeutic efficacy of optimized nanowired isogenic cardiac organoids to treat I/R-injured rat hearts and investigate e-SiNW graft-host interactions using spatial transcriptomics. The proposed research would provide a translational platform for cardiac repair with efficient engraftment.

NSF Awards · FY 2025 · 2025-12

This award supports a collaborative effort at West Virginia University and the University of New Hampshire to study energy conversion in weakly collisional plasmas. One of the most challenging problems in the study of plasmas - gases hot enough that electrons come apart from the atoms - is understanding how energy is converted between electromagnetic fields and the thermal energy of the plasma, which is the energy associated with random motion of the electrically charged particles. This is an important problem across many types of plasmas, including plasmas in space and the very hot plasmas that are used in fusion energy development. These plasmas, where collisions between particles are very rare, are most often far from local thermodynamic equilibrium (LTE), which means that one cannot even define a temperature in the traditional sense. This project builds on a recent result quantifying energy conversion in non-LTE plasmas to perform the first systematic study of the non-LTE energy conversion in weakly collisional plasmas. The project will employ state-of-the-art particle-in-cell (PIC) simulations and satellite data from the Magnetospheric Multiscale (MMS) mission. Parametric simulations of two-dimensional magnetic reconnection and turbulence will be used to understand the dependence of thermal energy conversion on ambient plasma parameters. Secondary islands and flux ropes will be studied in two-dimensional and three-dimensional magnetic reconnection since they are known to be sites of particle acceleration and non-LTE dynamics. Finally, the theoretical formalism will be generalized to account for energy in both random motion and bulk motion and it will be used to study energy conversion in two-dimensional reconnection and turbulence. The project will directly contribute to the study of energy conversion in eruptive flares in the solar atmosphere, geomagnetic substorms that produce aurora and space weather impacts, and the heating of the solar wind; it will also set the stage for application in other areas of plasma science, including high energy density and fusion plasmas. The collaborative award is co-funded by the Plasma Physics program in the Division of Physics and the Magnetospheric Physics program in the Division of Atmospheric and Geospace Sciences. 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 contribute to the national need for well-educated scientists, mathematicians, engineers, and technicians by supporting the retention and graduation of high-achieving, low-income students with demonstrated financial need at Clemson University and Tri-County Technical College. A total of 48 scholars pursuing Bachelor's degrees in Engineering will receive annual scholarships up to $15,000 for up to five years. Scholars will receive faculty mentoring and the project will build strong scholar cohorts through professional development activities. Additional support for scholars will include residency on the Clemson campus for Tri-County Technical College scholars and potential engagement with research courses. The overall goal of this Track 3 Scholarships in STEM project is to increase STEM degree completion of academically talented, low-income undergraduates with demonstrated financial need. There is a significant national need to grow the STEM workforce and nurture key talent that will ensure economic competitiveness and provide domestic leadership across critical sectors. This project directly speaks to this need by supporting STEM student success, which will strengthen the workforce in Engineering and other key areas of need. The project will be assessed by an experienced evaluator, and the research data collection will be informed by a Transfer Student Capital framework. The planned research will contribute to the knowledge base regarding effective strategies to support talented, low-income students in STEM. This project is funded by NSF's Scholarships in Science, Technology, Engineering, and Mathematics program, which seeks to increase the number of academically talented, low-income students with demonstrated financial need who earn degrees in STEM fields. It also aims to improve the education of future STEM workers, and to generate knowledge about academic success, retention, transfer, graduation, and academic/career pathways of low-income students. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2025 · 2025-10

This Designing Materials to Revolutionize and Engineer our Future (DMREF) joint NSF-Department of Science and Technology of India (NSF-DST) project aims to establish a transformative framework for the development of structural alloys that simultaneously achieve high strength at high temperatures and enhanced ductility at room temperature. The research focuses on a relatively new class of metallic materials known as refractory multi-principal element alloys (RMPEAs), which are recognized for their high-temperature strength but typically suffer from limited plasticity under ambient conditions. The team will develop the new alloy design paradigm through a concept called “metastability engineering,” which activates novel nano-scale deformation mechanisms by controlling dislocation dynamics and phase stability. The research integrates combinatorial synthesis, advanced in-situ experiments, atomistic and mesoscale simulations, and machine learning (ML)-guided discovery. The resulting framework will enable accelerated design of high-performance RMPEAs across broad temperature ranges. In parallel, the project will contribute to training a new generation of materials scientists in experimental, computational, and data-driven methods, while supporting outreach and international collaboration through partnerships with five US universities and Indian Institute of Technology Bombay. This project aims to establish a transformative framework for metastability engineering in refractory-type multi-principal element alloys (RMPEAs) that combines high-temperature strength with improved room-temperature ductility and strain hardenability. This project will address two key technical thrusts: (1) understanding dislocation dynamics for solid-solution strengthening at both room and high temperatures, and (2) enabling nano-scale transformation-induced plasticity (nano-TRIP) and twin-induced plasticity (nano-TWIP) mechanisms for enhancing ductility at room temperature. To navigate the vast composition and processing space, the team will integrate combinatorial synthesis, high-throughput and autonomous mechanical testing, and advanced machine learning techniques to accelerate the discovery of high performance RMPEAs. In the first thrust, the project will quantify the contributions of dislocations to high-temperature strength through autonomous nanoindentation creep testing, in situ neutron diffraction, and atomistic simulations. Advanced microscopy techniques will be used to reveal how local chemical ordering and lattice distortion affect dislocation motion. In the second thrust, the team will identify composition-processing pathways that promote metastable deformation modes using thermodynamic modeling, combinatorial deposition, and transformer-based machine learning models. These models will predict TWIP/TRIP propensity and guide multi-objective optimization across large alloy design spaces. Down-selected alloy systems will be validated through multiscale mechanical testing and simulations that span atomic to bulk scales. Collectively, the project will deliver a mechanistic foundation and data-driven design tools for metastability engineering in RMPEAs, aligning with the DMREF project’s mission to accelerate materials innovation through the integration of theory, experimentation, and data science with closed-loop design 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 2025 · 2025-10

NON-TECHNICAL SUMMARY In this project, Dr. Maria Soledad Peresin aims to unlock the potential of certain components of plant or animal biomass to design biomaterials by advancing the fundamental understanding of naturally occurring systems to address critical issues of societal concern, such as the removal of emerging contaminants from drinking water. Polymers are natural or man-made chemicals that are composed of building blocks of smaller repeating molecules, as one would picture individual Legos® within a larger structure. Natural polymers, such as cellulose (from wood, soybean hulls, cotton, etc.), chitosan (from the outer shell of shellfish) and alginates (from algae) are sustainable and renewable resources that are an essential component of a circular economy, aimed at minimizing waste. Combining properties of different natural polymers is a way to develop a new generation of products that may replace traditional, non-renewable fossil fuel-based materials. This project will focus on understanding, developing and using renewable, natural polymers to design efficient and sustainable adsorbents, which are highly porous structures for the removal of contaminants. Dr. Peresin proposes an extensive study of a variety of polymer systems to maximize their potential adsorption capacity for removing contaminants from water bodies in different environmental conditions. Adsorption capacity of the polymers’ assemblies and their performance will be assessed using three model emerging aquatic contaminants, tetracycline (an antibiotic), ibuprofen (an analgesic) and sulfamethoxazole (an antibiotic). Dr. Peresin will use this research program as a platform for education with an impactful contribution to improving science literacy in the State of Alabama while contributing to local, national and global efforts to provide a sustainable method for cleaning drinking water. Younger generations have an increased environmental concern and awareness of the need to decrease our impact on the planet. Through her research and mentorship, Dr. Peresin hopes to advance career opportunities within the forest industry for environmentally conscience students with the development of novel processes and new products that contribute to the sustainable use of resources and economic benefit of society. This project is jointly funded by the Biomaterials Program in the Division of Materials Research, and the Established Program to Stimulate Competitive Research (EPSCoR).   TECHNICAL SUMMARY The overarching goals of this CAREER plan are 1) to uncover the principles that underlie the structure-property relationship between naturally occurring polysaccharides (PS) interactions, surface properties and their assembly for the design of macroscale adsorbents, 2) to use this platform to educate students on interfacial phenomena involved in cleaning water using natural resources, and 3) to contribute to global efforts to provide clean drinking water to society. Natural polymers are renewable resources, essential for the circular economy. Combining their properties is critical for developing the new generation of functional materials on lieu of traditional fossil-based materials. However, several challenges remain in order to deploy these materials, such as cost, performance and scale. The long-term research goal of this CAREER is to advance the knowledge that will enable the rational design of PS structures based on natural polymers for effective removal of emerging contaminants from drinking water. This CAREER proposal will provide a fundamental framework for elucidating the fine interplay between the composition, surface functionality, and the supramolecular structure of natural polymers, specifically PS such as cellulose and chitin, and their effect on interfacial interactions with emerging contaminants. Polymers self-assembly and their interfacial behavior have been extensively studied, however, more work is needed for understanding the correlation between the surface properties of natural PS assembly and their impact on the interfacial adsorption phenomena. During this five year CAREER award, the following hypotheses will be addressed: 1) PS interactions can be controlled by changing environmental conditions and macromolecules intrinsic properties, 2) entropic and enthalpic contributions to binding energy of PS assemblies will affect their swelling and adsorption capacity, 3) interfacial interactions resulting from the macromolecule assembly will determine the surface energy and chemistry of the PS structures, with a direct impact on the total sorption capacity. The majority of the work will be performed on cellulose and chitin (and their derivatives) as they are ideal for this project due to their abundancy, robust chemical structural, surface area and versatile surface functionality. To test the hypothesis the PI will use a combinatory approach that involves PS assemblies and adsorption studies on 2D model surfaces. These findings then will be translated to a 3D hydrogel system, produced through a bottom-up self-assembly approach. Adsorption capacity and performance will be assessed using three model of emerging contaminants namely, tetracycline (an antibiotic), ibuprofen (an analgesic) and 2,4-dichlorophenoxyacetic acid (an herbicide). These hypotheses will be tested with the following specific objectives 1) elucidate the nature of the PS interactions during their self-assembly; 2) understand the role of composition and surface functionality on PS assemblies swelling and supramolecular structure, 3) examine different routes for self-assembly of PS in 3D structures and 4) unveil the effects of supramolecular structure, composition and surface on the overall adsorption capacity of the PS assemblies. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2025 · 2025-10

Virtual Personal Assistants (VPAs) such as Amazon Alexa, Google Assistant, and Apple’s Siri are essential in modern smart homes due to their ease of use and ability to perform a variety of tasks. Some challenges of VPA use include 1) difficulty for deaf or hard-of-hearing users who cannot rely on auditory commands; 2) difficulty in understanding speech patterns that VPAs do not recognize; and 3) safety risks associated with children potentially accessing inappropriate content. This project will develop novel technologies and mechanisms to enhance the usability and safety of human-VPA interaction in smart home environments. This research also supports broader social goals such as engaging STEM students in the development of advanced algorithms, improving computer science curricula at participating institutions, and strengthening awareness of accessibility and safety issues in technologies. This research project proposes a suite of innovations to improve usability and safety in human-VPA interactions. First, this project develops an end-to-end system that enables users to interact with VPAs using sign language as input, effectively bypassing voice-based interaction barriers. Second, this project proposes a novel acoustic testing tool to identify usability issues caused by VPA’s that are not trained on certain speech patterns. Finally, this project develops a situational multi-user access control mechanism that allows primary users (e.g., parents or homeowners) to specify access control policies for non-primary users (e.g., children or guests) for safe human-to-VPA interactions in multi-user smart homes. The work will provide insights on design principles for usable and safe human-VPA interactions. The research is integrated with education and outreach efforts including the development of curriculum modules, research experiences for undergraduate and graduate students, and public engagement activities to disseminate findings and raise awareness about usable technology design. 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 Battery Manufacturing Project addresses a national priority by responding to the urgent need for a skilled workforce in lithium battery manufacturing and recycling, a rapidly expanding sector essential to energy growth and economic security. As U.S. demand for lithium batteries is projected to increase nearly sixfold by 2030, the country faces an estimated shortfall of more than 120,000 trained workers. Despite this growth, production and recycling capabilities remain constrained by midstream workforce gaps and limited access to specialized training. This project, led by Clemson University and the Massachusetts Institute of Technology in partnership with community colleges, career and technical education (CTE) centers, industry, and workforce agencies, seeks to build a strong talent pipeline by equipping CTE learners, two-year college students, and incumbent technicians with the knowledge and skills needed for career entry and advancement. By investing in targeted recruitment, technical education, and hands-on experiential learning, the project promotes the progress of science, advances economic prosperity, and supports U.S. leadership in battery technology. It also supports the development of a workforce capable of managing end-of-life battery recycling and resource recovery, which are critical to circular economy strategies and global competitiveness. The Battery Manufacturing project will implement a coordinated, cohort-based training model to address skill gaps in lithium battery manufacturing and recycling. The project will begin by refining a preliminary list of knowledge, skills, and abilities (KSAs) through structured collaboration with industry partners to ensure alignment with current and future workforce needs. These validated KSAs will inform the design of an interdisciplinary curriculum that includes classroom instruction, virtual simulations, ADA-compliant virtual laboratories, and experiential learning modules. Instructional content will be delivered alongside mentoring, career exploration, and faculty development, supported by the creation of stackable digital badges and a certification framework. Over 240 participants in South Carolina and Michigan will take part in pilot cohorts, which will be used to evaluate curriculum effectiveness, learner outcomes, and the scalability of the model. Target populations include career and technical education students, two-year college learners, and incumbent technicians. A mixed-methods evaluation strategy will assess learning gains, technical proficiency, and credential attainment. Data will inform iterative improvements and help determine the transferability of the program to other regions. The project’s integrated approach will advance workforce readiness, strengthen educational infrastructure, and establish a replicable model for lithium battery workforce development nationwide. 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

Whether entering a password, using FaceID, or confirming a push notification, people authenticate (prove their identity) to access accounts and information. While authentication tools like passwords, multi-factor authentication, and biometrics help keep data secure, their design and frequency can interrupt important tasks and cause people time, effort, and frustration. These burdens can lead users to take shortcuts that make authenticating easier but threaten security, such as reusing passwords across accounts. This project addresses the human side of authentication by examining how often, when, why, and how people authenticate and the challenges they face while doing so. By identifying common patterns and barriers across contexts, including contexts that are key to the future success of AI, this research informs the design of authentication systems that are more usable, efficient, and aligned with users' daily lives. The research insights will ultimately support US national interests by strengthening cybersecurity and enhancing access to information and services by making systems more secure and easier to use. This project takes an interdisciplinary approach to improve authentication by focusing on human factors. Little systematic research has been carried to understand how often, when, and how people authenticate, and the associated personal and professional costs of authentication. By evaluating authentication across domains and contexts, this project produces novel, authentication-tracking tools to obtain detailed metrics about users’ daily authentication patterns and assesses cross-cutting themes from two domains: traditional large organizations and microtask crowdsourcing platforms (which underpin progress in AI). An annotated database of observed authentication events, combined with surveys, time diaries, and interviews, helps identify usability challenges, interruptions, and associated risks. Findings are applied to propose future directions for forward-thinking authentication systems that minimize costs, enhance usable security, and consider how to preemptively address threats posed by advancements in AI. These insights promote a more trustworthy cyberspace and support national cybersecurity. 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 U.S.–Swiss project tackles a major barrier in the development of silicon photonic chips — a key technology for faster, energy-efficient data movement in computing, communications, and sensing. Current design methods are slow, expensive, and hard to scale. This project introduces Fusion Network (FusNet), a novel approach that combines simulation and design optimization into a single, fast process. By using advanced computing hardware and AI-guided techniques, FusNet drastically reduces the time and effort required to create complex photonic circuits. Beyond technical innovation, the project builds a global training pipeline in semiconductor design, featuring U.S.–Swiss student exchanges, curriculum integration, and industry-aligned programs with Cadence Design Systems. The results — including software tools and design methods — will be shared openly to benefit the broader research and engineering communities. FusNet is a hardware-accelerated framework that unifies electromagnetic simulation and optimization in a real-time loop, enabling one-shot photonic device design. It leverages structural similarities between forward and adjoint methods to merge the simulation and optimization processes, improving efficiency while maintaining accuracy. FusNet’s streaming architecture is implemented on high-throughput dataflow hardware, such as CGRAs and FPGAs, overcoming traditional bottlenecks in memory access and parallelization. The project includes algorithmic innovation, scalable hardware design, and experimental validation through chip fabrication. The work addresses fundamental challenges in computational photonics, optimization theory, and hardware-software co-design, with strong applications in AI systems, HPC interconnects, and optical sensing. This collaborative US-Swiss project is supported by the US National Science Foundation (NSF) and the Swiss National Science Foundation (SNSF), where NSF funds the US investigator and SNSF funds the partners in Switzerland. 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

Understanding what drives the evolution of new species is a central question in biology. Groups of species that have recently evolved provide a good system for trying to understand the genetic changes that led to establishment of new species. This research combines the fields of genomics, developmental biology, ecology, and physiology to examine a new lineage of flowering plants in Hawaiʻi in the genus Bidens (family Asteraceae). The project will generate new genome assemblies and experimentally identify the genetic and developmental changes responsible for leaf, fruit/seed, and flower evolution in this group of species. This project will also provide training in inter-disciplinary evolutionary concepts and approaches for undergraduates, graduate students, and postdoctoral researchers, including those from underrepresented groups; improving the scientific workforce in the United States by preparing them to strongly contribute to scientific research, education, and/or technological advancements. This project will use newly developed genome sequencing methods to infer the broader evolutionary history of Polynesian Island Bidens, along with continental relatives. The updated understanding of how Bidens reached remote Pacific islands and diversified will provide the backbone for comparative evolutionary genomics of our six target species (three Hawaiian endemics and three continental). Comparing these genome sequences and differences in gene expression will allow us to identify the genetic changes that contribute to the unique ecological and morphological diversity of the Bidens adaptive radiation. Concurrent with the other objectives of the project, undergraduate students at UH Mānoa (a Native Hawaiian serving institution) will receive year-long internships in Hawaiʻi and short-term exchanges at Auburn (AU) and Wisconsin (UWM) via AHi-WiRE; Auburn-Hawaiʻi-Wisconsin-Research Exchange to receive training in plant evolutionary genomics. 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 growing reliance on next generation wireless systems such as 5G and 6G demands highly secure and resilient communication frameworks that support latency-sensitive and high-throughput applications. A key enabler of these systems is the use of deep learning models for critical tasks including signal classification and modulation recognition. However, these models are vulnerable to wireless adversarial attacks, in which small, intentionally crafted perturbations added to normal communication cause model malfunction and further degrade network performance. To address these vulnerabilities, the project develops a framework that enhances the robustness of automatic modulation recognition under adversarial attacks in the next-generation wireless systems. The project's novelty is a bottom-up design from a communication pair to the whole network on addressing the fundamental limitations of deep learning models in adversarial wireless environments. The project's broader significance and importance are to improve the security of communication infrastructure that supports critical applications like autonomous transportation, industrial automation, and public safety. Additional contributions include the release of a large-scale wireless dataset for academic use, the integration of research outcomes into undergraduate and graduate curricula, and the engagement of students at all levels, including K-12, through interdisciplinary training and outreach activities. The research agenda comprises three integrated research thrusts. Thrust 1 develops a Transformer-based architecture that extracts stable features from both time and frequency domains to improve the reliability of modulation recognition in the presence of adversarial perturbations. Thrust 2 designs a noise-adaptive adversarial training scheme that adjusts perturbation intensity based on real-world environmental noise, thereby enhancing model resilience. Thrust 3 extends the defense to network-wide scenarios by proposing a reinforcement learning-based strategy for adaptive transmission power control that mitigates adversarial interference while maintaining energy efficiency. The proposed methods will be evaluated using software-defined radio platforms and wireless datasets collected from real-world 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

Sunflowers, daisies, and their relatives belong to a family of plants that make up ca. 10% of flowering plant biodiversity and include numerous species of horticultural, medicinal, and industrial value. This group of flowering plants also contains economically important food crops including artichoke, lettuce, safflower, and sunflower. It is considered one of the most successful plant families due to its large size and global distribution. Key to the success of the family is its inflorescence (a capitulum or flower head) which resembles a single, large flower but is actually an aggregate of many small flowers. This unique floral structure plays an important role in pollinator attraction and is a major determinant of yield in many of the family’s crop species. Despite the importance of the capitulum, little is known about the genes involved in its development. Understanding how inflorescences develop has the potential to improve food security through optimization of floral structures for yield in crops, and by accelerating progress toward new crop development. This project will increase available genomic resources for the family and result in the development of novel tools for gene editing in the family. This work will shed light on the genes involved in the development of the capitulum inflorescence in an economically important family and provide valuable information that will facilitate efforts for optimizing inflorescence architecture in related crops. This project will provide educational opportunities for diverse students and researchers at multiple training levels, through directed efforts to recruit individuals from traditionally underrepresented groups. This project integrates comparative genomics, inflorescence developmental transcriptomics, molecular evolutionary analyses, and functional approaches to decipher the genomic basis of a key floral trait – the capitulum – in the sunflower family (Asteraceae) and related flowering plant lineages. This project will enable the testing of hypotheses related to the role of gene duplication and genome evolution in driving evolutionary novelty, the evolutionary forces involved in the origin of the capitulum, and the repeatability of the evolutionary process across plant lineages. The integrated approach will enable the testing of predictive hypotheses about inflorescence development in Asteraceae and related flowering plant lineages. The primary scientific goals are to: (1) decipher the molecular basis of the Asteraceae capitulum using comparative transcriptomic approaches; (2) determine whether the independent origins of capitula arose via common evolutionary processes and genomic mechanisms; and (3) analyze the functional role of key capitulum genes, targeting established stem cell regulatory genes and candidates identified through comparative/evolutionary genomic analyses. This project will generate high-quality genomes and curated inflorescence transcriptomes for multiple species complemented by comparative genomic and evolutionary analyses. These resources and the resulting data will be disseminated via peer-reviewed publications and public presentations and will be made freely available via deposition in public repositories and databases including the National Center for Biotechnology Information Sequence Read Archive (NCBI-SRA; https://www.ncbi.nlm.nih.gov/sra), Phytozome (https://phytozome-next.jgi.doe.gov/), the Gene Expression Omnibus (GEO; http://ncbi.nlm.nih.gov/geo), FigShare (https://figshare.com/), and Dryad (https://dryad.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 2025 · 2025-10

Graphics Processing Units (GPUs) have become indispensable for accelerating compute-intensive applications across various domains. To meet the surging demand for GPU computing power, many cloud service providers now offer GPU-as-a-Service (GPUaaS) through virtualization technologies that allow multiple users to efficiently share physical GPU resources. However, the security implications of GPU sharing, particularly the risks of sensitive information leakage between co-resident tenants, remain largely unexplored. This project undertakes a pioneering effort to understand and mitigate such risks. The project's novelties are the first investigation of previously unknown side-channel threats in GPUaaS, a comprehensive measurement study of GPU resource sharing policies in real-world deployments, and the development of practical countermeasures easily adoptable in today's cloud infrastructure. The project’s broader significance and importance are stronger data-privacy guarantees for cloud users and actionable security guidance for the fast-growing GPUaaS market. The research in this project proceeds through three interconnected thrusts. The first thrust seeks to break new ground by uncovering the first practical side-channel attacks in GPUaaS, which exploit GPU microarchitectural components, specifically caches and translation lookaside buffers (TLBs), to expose sensitive information from virtual desktop users and extract the proprietary design of neural networks during their inference process. The second thrust conducts systematic measurement studies to understand GPU resource management and co-residency patterns in public clouds, for which it develops novel virtualized GPU fingerprinting techniques and analyzes factors that influence the likelihood of tenants sharing the same physical GPU. The third thrust designs effective and practical countermeasures against the identified attacks, including a detection-based approach that repurposes CUDA unified virtual memory features to monitor GPU TLB activities and identify anomalies, and a moving target defense that leverages the non-linear address mapping properties of GPU caches to significantly increase the difficulty of constructing cache eviction sets. Alongside the research, the project also incorporates extensive educational activities and outreach efforts to enhance cybersecurity education and 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.