North Carolina State University

NSF Awards · FY 2027 · 2027-01

Bacteria adapt rapidly to their environments through the accumulation of mutations and the action of natural selection. These processes have major consequences for human health, agriculture, and industry, as harmful bacteria can quickly evolve resistance to treatments and colonize new environments. For this reason, identifying the genes that enable bacterial adaptation is of great importance. Current methods for identifying selection in bacterial genomes make overly simplistic assumptions about which mutations are harmful, beneficial, or neutral within a gene. The proposed work aims to develop improved methods for determining which mutations are truly neutral and which are most beneficial or deleterious. In addition, new datasets will be used to obtain more accurate estimates of the effects of different types of mutations on gene function. These advances will then be integrated into a new tool designed to more precisely estimate the strength and nature of selective pressures acting on individual genes. Overall, this project will improve the sensitivity of tests for selection and provide valuable new insights into bacterial evolution. The broader impacts of this project include delivering a new computational tool to the research community and that may facilitate advances in biotechnology, and providing training opportunities for students through research projects in microbiology, sequencing technologies, and high-performance computing. Selection is a core process shaping the evolution of all forms of life. Although many methods exist to detect and characterize selection on genes, most are not well suited for bacteria. Common approaches focus on protein-coding genes and compare rates of synonymous versus non-synonymous substitutions, assuming that synonymous mutations are neutral while non-synonymous ones are not. In practice, this binary view is overly simplistic: synonymous sites can still be subject to selective constraints, and different amino acid changes can vary widely in their effects on gene fitness. To address these limitations, this project proposes a new framework to better characterize neutral evolution in bacteria. By analyzing short intergenic regions, specific codons, and pseudogenes, it aims to establish more accurate baseline estimates of neutral evolution. It will also incorporate data from deep mutational scanning experiments to quantify how individual mutations influence fitness more accurately. These new measures will be integrated to a novel approach that uses continuous amino acid exchangeability matrices within a maximum likelihood framework to improve the inference of selection. Beyond providing a quantitative measure of selection, this framework can also distinguish qualitatively different substitution patterns, enabling us to separate the molecular signatures of relaxed versus positive selection. Ultimately, this approach will support the development of improved null models of evolution and enhance our ability to distinguish among different selective regimes. The broader impacts of this project include delivering a new computational tool to the community and that may facilitate advances in biotechnology, and providing training opportunities for students through research projects in microbiology, sequencing technologies, and high-performance computing. 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 · 2026-09

Humans are directly dependent on plants for the majority of caloric intake and indirectly through the ecosystem services they provide. Fungi are among the most economically important plant pathogens in both agricultural and non-agricultural populations, influencing the structure and productivity of plant populations. Mitigating disease caused by these fungal pathogens requires an accurate understanding of species richness, their distribution, their relationships to other pathogenic species, the plant species they infect, and the genetic features that allow them to be pathogens. Cercospora species are fungi that are known to cause disease on nearly every major crop plant in the world, including important staples (e.g. rice). However, these fundamental questions - How many species are there? Where are they found? What host species do they impact? What genetic features allow them to cause disease on some hosts and not others? - need to be addressed in a modern context to enable research into mitigating their impacts. The goals of the project are to answer these questions and develop an accessible data interface that will allow other researchers to leverage this information to ultimately mitigate disease. While fungi have long been overlooked, training will be provided to high school, undergraduate, and graduate students to be the next generation of fungal biologists helping to solve global issues caused by fungi. Understanding how fungi impact plant populations requires a natural classification scheme that can be cross-referenced with ecological, morphological, geographical, or any trait-based information to better understand the life history of individual species. This information has enormous ecological and economic impacts because fungi are the most economically important plant pathogens. The genus Cercospora is among the most speciose genera of fungi, but our understanding of species limits and evolutionary relationships is constrained by a lack of accessible data. This project will address this gap by (i) producing a phylogenomic-based revision of the genus Cercospora and assessing global species richness of one of the most ubiquitous and economically important foliar fungi on the planet, (ii) characterize genome and transcriptome evolution across the genus, and (iii) produce an integrative database collating metadata that facilitates hypothesis testing in a phylogenetic framework. Data will be collected from type specimens, field work conducted in North America, and modern collections for genomic/transcriptomic studies developed. New species will be described, and barcode loci for the cost-effective integration of collections from around the world developed. A framework for integrating multiple data layers through relational databases that are linked to a hierarchical phylogenetic structure will be made available through a dedicated web interface to researchers interested in exploring hypotheses in Cercospora evolution, identifying new Cercospora isolates and species, expanding on existing data or adding new data layers. This interface will allow a broad group of users, from molecular biologists to applied plant pathologists, to explore hypotheses and develop solutions to fundamental and applied problems. The next generation of taxonomists in mycology, comparative genomics, and bioinformatics will be trained to help mitigate the impacts of fungal plant pathogens. 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 · 2026-09

Modern technologies like lithium-ion batteries, electric vehicles, and advanced manufacturing depend on critical minerals. These minerals are often found in seawater, brines, and wastewater. The minerals of interest mix with other minerals of similar size and chemical behavior. Separating nearly identical ions is very difficult. Membrane-based separation methods are energy-efficient, but designing membranes that can tell similar ions apart is a challenge. This project will improve membrane design by analyzing how the membrane’s chemistry and internal structure control the motion of ions through membranes. The project will use quantum calculations and other simulations to produce models that predict the motion of ions. By identifying chemical features that help certain ions pass more easily than others, the project will support better membrane design. The results will improve U.S. critical mineral recovery, water treatment, and energy technologies, while also training students through coursework and K–12 outreach activities. This project focuses on water-stable metal–organic framework (MOF) membranes as a model platform to investigate how membrane chemistry, structural heterogeneities, and ion–ion interactions control selective transport among chemically and physically similar ions. The study targets technologically important separations relevant to critical mineral recovery, including lithium and sodium ions, as well as selected heavy metal ions. An integrated multiscale modeling framework is employed, combining ab initio quantum calculations, molecular dynamics simulations, and transport models grounded in statistical thermodynamics. Atomistic simulations are used to characterize interactions among ions, membrane atoms, and the local chemical environment that govern ion selectivity. Insights from these simulations inform physically based transport models that link molecular-scale mechanisms to membrane-level selectivity and permeability. In addition, machine-learning models are incorporated in a mechanism-aware manner as surrogate representations of transport behavior learned from physics-based simulations, enabling efficient exploration of chemically and structurally defined parameter spaces. Model accuracy and predictive trends are validated through close collaboration with experimental partners. The expected outcomes include mechanistic insight into selective ion transport in chemically heterogeneous nanoporous membranes, predictive modeling frameworks that bridge atomistic and membrane scales, and broadly applicable computational tools for separation, energy, and water technologies. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2026 · 2026-07

The design of engineering systems – from chemical plants to molecular design – must be as effective as possible within limits imposed by costs, resources, physical laws, and safety rules. To find the best solution, engineers use computer programs called optimization solvers. Solvers rely on complex algorithms that need careful tuning by experts to perform well. This project will create a framework to better understand how these solvers work, focusing on how they rule out poor solutions and move toward the best one. By analyzing large amounts of data generated by the solvers and applying machine learning methods, the project will identify patterns to explain solver behavior. The results will help engineers interpret solver decisions and enable solvers to automatically improve their own performance, becoming faster and more accurate without manual adjustment. This data-driven approach can strengthen industrial automation systems, support materials and drug design, and speed up the development of new processes and products. In addition, the project will support education by building data science and computational skills in chemical engineering courses and by encouraging younger students to explore careers in STEM. This project adopts a novel dynamical-systems perspective to represent, learn, and optimize global optimization algorithms. In this framework, optimization algorithms are modeled as dynamical systems whose states evolve in function spaces defined over the feasible domain, with their evolution governed by linear operators acting on these spaces. This representation enables a unified and principled analysis of diverse global optimization methods. Specifically, the dynamics of widely used global optimization algorithms in process systems engineering and machine learning—such as stochastic gradient Langevin dynamics, branch-and-bound, outer approximation, and black-box Bayesian optimization—will be learned directly from solver snapshot data or historical execution traces. By identifying the underlying linear operators that characterize algorithm evolution, the associated dynamic modes - functions that evolve linearly under the learned dynamics - can be extracted. These modes provide mechanistic insights into solver behavior, including pruning, fathoming, and optimality gap reduction, while also enabling accelerated convergence through informed extrapolation of solver trajectories. Algorithm tuning parameters are interpreted as control inputs that influence computational performance metrics such as accuracy and efficiency. Automatic solver tuning is then formulated as an optimal control problem defined over the learned algorithm dynamics, allowing systematic and data-driven optimization of solver performance without manual heuristics. The resulting framework is fully end-to-end, encompassing data collection, operator learning, mode extraction, and solver tuning, and is broadly applicable across classes of global optimization algorithms. To demonstrate its practical advantages and scientific impact, the enhanced solvers will be applied to molecular design tasks - benchmarking against existing generative modeling approaches - as well as transition pathway optimization in molecular simulations to uncover chemical reaction mechanisms. 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 grant provides support for the 2026 INFORMS Simulation Society (I-Sim) Research Workshop, to be held at North Carolina State University, Raleigh, North Carolina, 31 July to 3 August 2026, centered on the theme “Simulation in the Age of Digital Twins and AI.” The workshop will convene leading researchers in stochastic simulation, artificial intelligence, optimization, applied probability, and statistics to address emerging theoretical challenges arising from the integration of simulation with learning-based and data-driven systems. The goal of the workshop is to identify uniquely emerging areas of AI and Digital Twins integration. As simulation is increasingly embedded within adaptive, real-time decision environments—such as digital twin architectures—classical theoretical guarantees no longer directly apply, creating a need for new foundational frameworks. The workshop will focus on this specific gap of knowledge. The workshop will feature plenary lectures, invited presentations, panel discussions, and a Summer School aimed at graduate students and early-career researchers. Particular emphasis will be placed on expanding participation through targeted support for researchers from EPSCoR jurisdictions. All materials, including recorded talks and a workshop report, will be made publicly available to maximize dissemination. By fostering cross-disciplinary collaboration and identifying key research directions, the workshop will contribute to advancing the theoretical foundations of simulation in next-generation engineering 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

The IEEE Quantum Week, which is also known as the IEEE International Conference on Quantum Computing and Engineering (QCE), is the premier global platform for advancing quantum computing and engineering, bridging the gap between quantum science and industry, and fostering collaboration across research, development, and commercialization. The 2025 QCE attracted more than 1750 attendees from academia, industry, and government. The 2026 QCE, to be held in Toronto, Ontario, Canada on September 13-18, 2026, is expected to have a similar number of attendees. To advance next-generation advanced signal processing in the quantum era, this Workshop on Quantum Signal Processing will bring together researchers in quantum computing and researchers in classical signal processing and information theory to develop a shared language, identify common principles, and chart emerging research frontiers at their intersection. To support the training of next-generation scientists and engineers working on quantum signal processing, this NSF grant will provide travel support for 25 students and 10 postdoctoral researchers to attend the workshop and conference. The awardees will be recruited and selected by the workshop organizing committee. The travel support will benefit their career developments and contribute to the U.S. quantum engineering workforce development. The 2026 IEEE QCE is financially co-sponsored by IEEE Quantum Technical Community, IEEE Computer Society, IEEE Technical Community on Software Engineering, IEEE Photonics Society, and IEEE Council on Superconductivity, with technical co-sponsorships from IEEE Technology and Engineering Management Society, IEEE Electronics Packaging Society, IEEE Signal Processing Society, IEEE Electron Device Society, IEEE Consumer Technology Society, IEEE Power & Energy Society, and IEEE Entrepreneurship. The support by many IEEE organizations illustrates strong multidisciplinary interests on this topic. The Workshop on Quantum Signal Processing will have three sessions featuring nine invited talks, followed by a session of discussion and brainstorming. There will also be a concurrent poster session during the workshop to further stimulate interactions among participants. Students and postdoctoral researchers attending the workshop can also attend other meetings and events during the IEEE Quantum Week, broadening their networking with a large number of attendees and their learning of the latest developments in quantum information science and engineering. 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

It is well understood that genetic information encoded in DNA is passed on to future generations. However, genetic information can be modified without altering the DNA sequence, and these modifications can be passed on to future generations. Epigenetics is the study of this processes which shapes the three-dimensional organization of DNA and thus controls gene activity. Dysregulation of epigenetic processes has been implicated in numerous human diseases. Despite decades of research, the precise "epigenetic grammar" -- the rules by which specific combinations of epigenetic modifications collectively shape chromatin structure -- remains elusive. Understanding these molecular mechanisms is critical for advancing fundamental biology and improving the diagnosis and treatment of human diseases. This project will integrate artificial intelligence (AI) with physics-based computational simulations to uncover how epigenetic regulation modulates chromatin structure and function, including its role in the dynamic compartmentalization of DNA within the cell. This project aims to establish detailed molecular links between specific epigenetic modifications and genome function, guiding the rational design of therapeutic strategies targeting epigenetic dysregulation. The tools and methods developed through this project will enable predictive, mechanistic studies of biomolecular assemblies beyond chromatin. The educational activities will launch an AI-visualization suite to engage students from K-12 to graduate levels, both regionally and nationally, in data science, computational modeling, and biomolecular visualization. This project employs a predictive, sequence- and epigenetic-specific simulation model to investigate how epigenetic modifications and regulatory proteins modulate chromatin organization. A central computational challenge in studying epigenetic regulation is the need to simultaneously model large-scale chromatin organization and fine-grained chemical interactions at residue resolution. This project will address this gap by integrating physical modeling with data-driven approaches to build a predictive, residue-level simulation model that quantitatively captures sequence- and epigenetic-specific molecular interactions across hundreds of nucleosomes. Using this model, this project will: (1) Examine key epigenetic modifications -- including acetylation, ubiquitylation, and methylation -- and their interactions with regulatory proteins, focusing on their effects on higher-order chromatin structure and gene regulation; and (2) Elucidate how epigenetic regulation drives chromatin phase separation and governs the biophysical properties of chromatin condensates in the crowded in vivo nuclear environment, as well as how this environment, in turn, modulates biomolecular interactions. By linking chromatin phase separation, epigenetic profiles, and regulatory proteins, this project aims to reconcile different observations of epigenetic effects, decipher the epigenetic grammar underlying chromatin organization, and identify critical chromatin interactions that drive genome compartmentalization. Ultimately, our research will deepen our understanding of genome organization and enhance our ability to evaluate, forecast, and design preventive strategies to mitigate the adverse impacts of epigenetic dysregulation on the genome and epigenome. 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

Trusted Execution Environment (TEE) is an important component of secure cloud computing, providing code to run in an enclave that is isolated from the cloud provider's infrastructure security vulnerabilities. As TEE gains more popularity, the inflexibility of its enclave becomes an apparent hindrance for its deployment with increasingly complex applications. In particular, current enclaves have a good compute abstraction, but lack data abstraction. This project will investigate a new data abstraction to allow data to exist and managed separately from an enclave. A good data abstraction for enclaves can address essential shortcomings of current enclaves: (i) lack of efficient support of inter-enclave communications and data sharing; (ii) lack of support for intra-enclave isolation; (iii) insufficient scalability. Addressing the lack of data abstraction will also help improve TEE's capabilities for CPU-GPU collaboration, multi-sockets, and memory disaggregation and pooling. The success of this project will enable secure enclaves to host a broader range of applications. Given that more and more computation is performed in the cloud, enabling more cloud applications to be protected improves the cloud security and privacy, and hence computing in general. The project has plans to encourage participation in the research by K-12 and undergraduate students in all demographic groups. 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

Tensor contraction sequences are foundational computations in quantum chemistry, quantum physics, and materials science. As these workloads grow in scale and complexity, scientists increasingly rely on optimization techniques — mixed precision, reordering, and fusion — to reduce runtime and memory costs. While effective, these optimizations can introduce subtle numerical errors that are difficult to detect and that erode confidence in scientific results. The project's novelties are a scalable formal verification framework for optimized tensor contraction sequences and a principled method for connecting correctness guarantees directly with performance optimization. The project's impacts are more reliable and reproducible scientific computing, open-source tooling for the broader high-performance computing (HPC) and formal methods communities, and interdisciplinary training opportunities that bridge formal verification, numerical analysis, and high-performance computing. This project develops a scalable, sound, and precision-aware formal verification framework for reasoning about optimized tensor contraction sequences under interacting optimization strategies. The project creates automated modeling and property-checking methods for mixed-precision tensor contraction sequences using domain-specific satisfiability modulo theories (SMT) encodings in quantifier-free floating point (QF_FP), automated formula generation, and counterexample-guided abstraction refinement. It extends verification to contraction-order optimization and fusion through incremental SMT solving with solver-state reuse, portfolio-based solving, and equality-saturation methods that preserve tight numerical bounds. The project further integrates verification into multi-objective design space exploration, using interaction-aware analysis and graph-based performance–error optimization to identify Pareto-optimal implementations across FLOP count, memory usage, and verified numerical error. The expected result is a practical methodology for discovering tensor contraction implementations that are both high-performance and provably correct for mission-critical scientific workloads. 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

As engineering systems become more complex, it becomes progressively harder to obtain accurate physical models that describe their behavior. This had led to a shift from classical model-based control to data-driven and learning-based techniques, including reinforcement learning (RL). Despite the widespread use of RL algorithms across several domains, their design hinges crucially on the idealistic assumption of perfect feedback data for learning decision-making policies. However, in practice, data streams can be noisy with significant uncertainty ("heavy tailed probability distributions"), can contain out-of-distribution samples, and can be corrupted by sophisticated adversaries. Failing to account for such non-ideal feedback can lead to dire consequences in safety-critical autonomous systems. As it stands, there is limited theoretical understanding of the interplay between model uncertainty and adversarial data in the context of data-driven control. This NSF CAREER project aims to address this critical research gap by developing a foundational framework of robust RL that bridges the gap between theory and practice, allowing for the reliable deployment of RL algorithms in applications with little tolerance for error, such as self-driving cars, robotics, and healthcare analytics. To achieve this, the intellectual merit of this project will involve building formal connections between the rich area of algorithmic robust statistics and ideas from RL, optimization, and control. The broader impacts of the project include a tight integration of the technical results with new data science courses, engagement with local high school teachers via the Science House outreach unit at the PI's institution, and industry outreach. This project comprises three interlinked thrusts that collectively address the technical challenge of providing non-asymptotic performance guarantees for decision-making algorithms subject to time-correlated, streaming data corrupted by heavy-tailed noise processes and outliers. Focusing on Markov Decision Processes (MDPs) with finite state-action spaces, the first thrust will identify information-theoretic fundamental limits on performance imposed by contaminated reward feedback. By leveraging tools from robust statistics, the next step will focus on design and analysis of novel RL algorithms with function approximation that achieve such limits. The second thrust will investigate the benefits of collaboration in multi-agent and federated RL under spatial corruption, subject to communication constraints. Finally, the third thrust will develop robust model-based and model-free data-driven control algorithms for unknown dynamical systems with continuous state-action spaces, and delineate the effects of corrupted data on system stability. The developed technical tools will advance the field of robust statistics by enabling robust mean and covariance estimation under correlated data, and that of RL, by building a finite-time theory for stochastic approximation with inexact updates. 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

Chronic wounds represent a significant global health burden characterized by high healthcare expenditures, elevated amputation risks, and increased mortality. Current diagnostic methods rely on subjective visual assessments, frequently resulting in delayed or inaccurate clinical interventions. While smart bandages integrated with battery-powered sensors that monitor wounds have been developed, clinical translation is hindered due to their bulkiness and reliance on potentially toxic materials. This CAREER project seeks to address these challenges with a multi-pronged, integrated approach that involves innovative sensing technologies, batteries, and wound fluid sampling. The project will develop a new class of wearable miniaturized biochemical sensing devices powered by thin-film batteries that are fully composed of non-toxic, wound-friendly materials. These devices will enable user-friendly, real-time, at-home wound assessment and timely intervention without interfering with the delicate healing process. Furthermore, the project involves education and workforce development through industry partnerships, K-12 workshops, and training programs for citizen scientists. There is a critical need for advanced wound monitors that are user-friendly, biocompatible, sustainable, and cost-effective. This project addresses this requirement through three integrated research thrusts: Thrust 1 focuses on the development of non-toxic, biofuel cell-based sensor arrays for the precise detection of molecular biomarkers relevant to healing in wound exudate; Thrust 2 involves the engineering of high-performance, non-toxic thin-film batteries; and Thrust 3 integrates these components into a smart bandage featuring nature-inspired microfluidics and wireless electronics for autonomous, real-time assessment. The system's efficacy will be validated using a murine wound model, with sensor data analyzed via classical statistical and artificial intelligence/machine learning (AI/ML) models to identify early indicators of impaired healing. The project embodies significant novelty across advanced sensors, battery technology, microfluidics, and low-power electronics which will find immediate applications beyond wound care as well, for example, in areas such as personalized diagnostics and defense. 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 · 2026-03

The award will support the five-day ICM satellite conference “Algebraic and Geometric Representation Theory,” which will take place at the University of Virginia in Charlottesville, VA from July 17-21, 2026. The conference will include fifteen plenary lectures covering topics in algebraic and geometric representation theory. These talks by leading researchers will provide potential directions for participating graduate students and junior researchers to enhance their research programs. Another key component of the workshop will be contributed talks that will provide opportunities for conference participants to present their own research. Ample time for informal discussion among participants will be allocated between talks and at the end of each conference day. The main lectures will be videotaped and made accessible via YouTube to the general mathematics community. Representation theory of Lie algebras, algebraic groups, Lie superalgebras and related algebraic structures has become a central research area in mathematics, with numerous applications in many areas of mathematics and theoretical physics. This workshop will showcase some of the recent algebraic and geometric advances in Lie theory. Several lectures will entail the deep work of the theory of canonical bases for quantum groups developed by Lusztig and Kashiwara, with connections to a wide variety of applications using categorification in representation theory. New methods in derived categories involving perverse sheaves will be introduced as a way to understand modular representations of reductive algebraic groups. Other connections to geometric methods involve the use of monoidal triangular geometry to understand the representation theory of finite tensor categories. Categorical invariants that include Hochschild cohomology and support theories via cohomology rings will be a prevalent tool throughout the workshop. More information can be found on the conference website: https://math.virginia.edu/ims/selie_icm2026/. 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

Coastal ecosystems are among the most valuable and vulnerable places on earth. These ecosystems support numerous keystone organisms, including habitat-forming species, like seagrass, that serve as the foundation for community structure and function, while also providing critical ecosystem services to people. Unfortunately, seagrasses are increasingly impacted by human activities and have experienced significant degradation and loss over the last century. Further, changes in habitat suitability is causing seagrass species to shift their geographic distributions, resulting in losses of some species and gains of other species in many coastal regions. Along the United States Atlantic Coast, the temperate seagrass, Zostera marina (eelgrass), has declined considerably in recent decades at the southern edge of its range, while the subtropical seagrass, Halodule wrightii (shoal grass), has increased in abundance at the northern edge of its range (North Carolina), where the two seagrass species overlap. This project aims to assess where and how H. wrightii could expand into higher latitudes of the western Atlantic coast, potentially replacing Z. marina as the dominant habitat-forming seagrass. Further, the results of this research will provide critical information and approaches for determining how seagrasses and their associated ecosystem services may shift as habitats become more or less suitable. The outcomes of this project will be used and incorporated into coastal ecosystem management plans, and seagrass monitoring, mapping, and restoration efforts in the United States. Predicting responses of habitat-forming, foundation species is key for ensuring the long-term maintenance of ecosystem structure, functions, and services. Distinguishing eco-evolutionary characteristics of edge-of-range populations that could facilitate or inhibit range expansion can improve predictions for how species may adapt or migrate as habitats become more or less suitable. The overarching objective of this proposal is to determine how shifts in habitat suitability (i.e. temperature, light availability, and biotic interactions) will affect productivity, eco-physiology, gene expression, genetic structure, and colonization potential of a subtropical seagrass at its leading northern range. This project aims to identify ecological, physiological, and genetic mechanisms that may facilitate the expansion of an understudied foundation seagrass species, H. wrightii, into higher latitudes of the western Atlantic coast. The Project Team will examine and evaluate population structure, phenotypic plasticity, and genetic differentiation among H. wrightii populations at range edges versus interiors (stress vs. comfort zones). Manipulative mesocosm and field experiments will be used to evaluate H. wrightii acclimation potential to biotic and abiotic stressors, as well as the effects of a temperate seagrass species, Z. marina, on H. wrightii productivity and gene expression. This project will expand understanding of the abiotic and biotic mechanisms that underlie organismal resilience by examining variation in colonization potential and genetic differentiation of a habitat-forming species at its northern range limit. 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

This project will investigate the impacts of rapid shifts between extreme wet to extreme dry weather on fuel loading and consequent wildfire risk. Throughout the project, stakeholders look to co-develop and test fire-adaptive management policies against established forest management, with the ultimate goal of reducing wildfire risk and improving safety in rural and WUI communities. Additional broader impacts of the project include the training of two graduate students and one postdoctoral fellow to address these complex challenges and to continue advancing the science of wildfires in the Southeast United States and other humid forest regions. Wildfire dynamics in humid forests, including the Southern Appalachian Mountains are complex and not well understood. Fuel flammability and loading are likely the key drivers of fire ignition and spread, which are dependent on factors including forest disturbances and ecosystem-atmospheric moisture dynamics. This project focuses on the fire risk posed by rapid oscillations between moisture extremes occurring in a short period of time (weeks to months). The central hypothesis is that hydroclimatic rapid oscillations increase fire risk by first causing forest disturbances (such as downed trees and debris accumulation) during extreme wet and stormy events that will rapidly become fuel in subsequent drought conditions. Thus, the objectives include a characterization of these hydroclimatic events and their large-scale drivers, as well as their relationship with observed fires. Field experiments will simulate fuel drying following disturbances, which will be complemented with fuel moisture mapping and modeling using remote Earth observation data and geocomputational approaches. Lastly, the LANDIS-II forest dynamics model will be used to produce scenarios of fire spread and severity under different hydroclimatic conditions determined in collaboration with stakeholders. 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

This project addresses the critical gap in K–12 educator preparation for integrating rapidly evolving generative AI (GenAI) tools into classroom practice. While GenAI promises to transform learning environments, most secondary teachers lack both the technical proficiency and pedagogical frameworks to leverage these technologies effectively and safely. This project will develop and deliver a four-week experiential learning in AI and MerryQuery (ExLAIM) program by bringing together in-service K-12 teachers, undergraduate computing majors, and industry mentors to co-design AI-integrated curricula, assessments, and implementation plans tailored to classroom contexts. Building on evidence-based strategies in project-based and active learning, participants will engage in a learn-apply-reflect cycle, using MerryQuery, a retrieval-augmented generation chatbot, to scaffold lesson planning and assessment design. Participants will work in groups to iteratively prototype AI-enhanced instructional modules that integrate safety considerations, outlier detection, and reliability checks for classroom implementation. Structured mentorship with faculty, graduate students, and industry mentors, along with peer collaboration, will reinforce skill acquisition and facilitate integration into emerging technology careers. The project’s intellectual merit lies in uniting cutting-edge AI tool development with constructivist teacher learning, generating novel insights into how GenAI can be embedded within K–12 pedagogies to enhance instructional effectiveness. Broader impacts include expanding access to AI-focused professional development for educators in under-resourced and geographically dispersed school districts in North Carolina, developing graduate and undergraduate students who can work across disciplines, strengthening cross-sector partnerships, and creating open-access curricular resources shared nationally to other teachers through the Computer Science Teachers Association. By empowering educators and undergraduate students as AI leaders, this initiative will foster a sustainable pipeline of AI-literate students and prepare them for future STEM careers at the intersection of human and artificial intelligence. This project is co-funded by the NSF IUSE: EDU Program which supports research and development projects to improve the effectiveness of undergraduate STEM education for all students. 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 2026 · 2026-01

This Research Experience for Teachers (RET) program equips teachers with the knowledge, tools, and resources they need to teach nanotechnology to their students including the equipment and techniques that are used to fabricate or manufacture at the nanoscale, ways to “see” and identify atoms in a manufactured structure, and the ability to relate research to real-world applications. Nanotechnology is impacting sectors as diverse as medicine, textiles, and electronics including quantum. However, concepts in nanotechnology are often complex and teachers are not readily exposed to tools and techniques because of their expense or availability. This RET program immerses teachers in state-of-the-art research laboratories to learn nanotechnology concepts and contribute to a larger research project. Educators develop unique curricula that they share with hundreds of students in their classrooms, schools, and districts, engaging students in innovative engineering research and advanced technologies. The research focus is timely as the demands for a skilled U.S. technical workforce increase. This program provides educators the experiences, knowledge, and tools needed to communicate the underlying science effectively, allowing them to train a generation of students who better understand nanotechnology and its role in addressing societal grand challenges. By developing curricula that cross multiple grade levels, teachers create a foundation of nanoscience knowledge that is reinforced as students progress academically. They inspire and prepare their students for engineering careers founded in nanotechnology, a field where demand for skilled workers is expected to grow substantially in the coming decade. Teachers' professional development is enabled through participation in university research projects as well as tours and usage of nanotechnology core facilities at North Carolina State University, the University of North Carolina at Chapel Hill, Duke University, and the Joint School of Nanoscience and Nanoengineering (University of North Carolina at Greensboro and North Carolina A&T State University). Participants are recruited from North Carolina school districts and community colleges. Teams of 2-3 teachers are assembled to enable nano-focused curriculum development integrated across multiple grades. Each team is advised by a faculty research mentor at one of the academic institutions. Participants create and characterize nanoscale materials or devices, connect their work to real-world applications, and gain experience with state-of-the-art research tools and techniques. Currently, most K-12 and community college students have limited access to this field and the resources necessary to support nanotechnology fabrication and development. The first week is dedicated to orientation activities; the following weeks weave project work with curricular development. To cap off the program, educator teams finalize curricular materials and share their research experiences with fellow RET participants in a symposium. Upon return to their home institutions, educators implement their curricula and continue to work with open-access user facilities at the participating universities. Teachers also strengthen their relationship with the universities and reinforce students’ understanding of size and scale through a cohort-wide citizen scientist project that connects their classrooms to ongoing research at the Science and Technologies for Phosphorus Sustainability (STEPS) Center, an NSF-funded Science and Technology Center. By training the trainers (i.e., teachers), this program has the potential to impact thousands of students and to encourage their pursuit of STEM 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 2026 · 2026-01

As technology advances and an increasing number of devices enter our homes and workplace, humans have become an integral component of cyber-physical systems (CPS). One of the grand challenges of cyber-physical human systems (CPHS) is how to design autonomous systems where human-system collaboration is optimized through improved understanding of human behavior. A new frontier within this landscape is afforded by the advent of low-cost, low-power millimeter-wave radio frequency (RF) transceivers, which can be exploited almost anywhere as part of the Internet-of-Things, smart environments, and personal devices. RF sensors provide a unique, information rich dataset of high-resolution measurements of distance, direction-of-arrival, and micro-Doppler signature in a non-contact, non-intrusive fashion in most weather conditions and in the dark. This CAREER project aims to pave the way for new and innovative RF-enabled CPHS applications in service of society and a better quality-of-life by transforming current fixed-transmission RF sensors into intelligent devices that can autonomously respond to human and environmental dynamics to optimize CPHS performance. Due to the burgeoning commercial sector utilizing radar across a variety of fields, such as transportation, health and human-computer interaction, this project features integrated academic preparation for multi-disciplinary, convergence research at both undergraduate and graduate levels to educate a new generation of engineers with experience in RF sensing, machine learning, signal processing and CPHS applications. Through K-12 outreach activities and recruiting at local historically black colleges and universities (HBCUs), this project will enrich and motivate students to study STEM fields, laying the foundations for a diverse and globally competitive STEM workforce for the future. This CAREER project simultaneously addresses critical challenges currently limiting effective exploitation of RF sensors in CPHS, such as the problem of RF data scarcity for training deep models, the wide range and continuity of possible human movements, the presence of other people and obstacles, and the dynamic nature of real-world scenes. Specific contributions include the development of 1) physics-aware ML techniques that leverage the domain knowledge embodied in models with data-driven deep learning; 2) spatio-temporal parsing techniques to extract and recognize human signal components from RF data streams to improve robustness of RF-CPHS under real-world conditions; and 3) a new task-cognizant, fully-adaptive RF sensing framework to improve performance and robustness of RF-CPHS for varying tasks in dynamic real-world environments. The proposed fully-adaptive RF framework also paves the way for collaborative, multi-modal RF-CPHS by exploiting information learned from RF and other sensor modalities in its decision process. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2025 · 2025-11

Viruses are the most abundant biological entities on Earth, infecting all major categories of life. To study viruses, the viral genomics community has generated enormous amounts of scientific data through advanced gene sequencing and other techniques. Exploring and extracting scientific insights from data of this size is challenging and requires accessible training that can teach the research community how to use the advanced computational tools and infrastructure needed for research. At the same time, the rapid development of novel algorithms and Artificial Intelligence-driven methods for analyzing this data outpaces current training opportunities, both for scientists who use computational infrastructure and for the research computing professionals who design and maintain these systems. A critical gap exists in equipping both groups with practical skills to leverage shared, high-performance computing systems and integrate reproducible scientific processes into the nation's advanced research computing frameworks. By helping scientists use cutting-edge, AI-driven tools to understand viruses better, this project promotes the progress of scientific understanding of viruses, their interactions with their hosts, and their effects on biological systems. The broader impacts of this research support public health, environmental understanding, and national preparedness. This work also builds a stronger research community by making training accessible to a broad range of scientists and students. The iVirus Cyberinfrastructure (CI) Training Initiative will develop six modular, self-paced, online training resources to enable effective use and development of scalable pipelines in NSF-supported CI ecosystems. The training modules will be designed around the principles of Findable, Accessible, Interoperable, and Reusable (FAIR) software, emphasizing interactive learning, reproducibility, evolvability, and sustainable design. The modules will be open-source, portable across CI platforms, and designed to meet the diverse needs of researchers and developers working in viral ecology. Training content will be developed through a unified pipeline that includes (1) interactive instructional design, (2) modular training components, (3) expert input and curated test datasets, and (4) community engagement and dissemination. A key feature of this project is the integration of hands-on viral genomics (viromics) CI training into the annual Ohio State University Viromics Workshop, where materials will be piloted and refined through participant feedback. Together, these resources will help close the skills and knowledge gaps in viral ecology CI training, enabling a broad range of researchers and developers to accelerate discovery and innovation using NSF-supported computational resources. 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 North Carolina State University. A total of 30 scholars pursuing interdisciplinary master's degrees in biochemistry will receive scholarships averaging $20,000 per year for up to five years. Scholars will receive faculty and peer mentoring, and the project will build strong scholar cohorts through a focused two-week onboarding experience, shared office spaces for scholars, and regular social and professional networking opportunities. The overall goal of this Track 2 Scholarships in STEM project is to increase STEM degree completion of academically talented, low-income graduate students in biochemistry 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 biotechnology and other key areas of need. The project will be assessed by an experienced evaluator that will explore scholars' experiences in the program, career preparation, the effectiveness of mentoring activities, and the development of scholar networks. The data generated 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

Modern artificial intelligence (AI) and data-intensive applications, from language models to scientific simulations, demand ever-growing computational capability. Yet, conventional electronic-based computing systems face a critical bottleneck: the energy and time required to move data between processors and memory now exceed the costs of performing the calculations themselves. This imbalance has slowed progress in AI and deep learning, while contributing to rising energy demands. This project explores a transformative computing paradigm grounded in photonic-based, in contrast to the conventional electronic-based paradigm, enabling information processing through light. Using diffractive optical neural networks (DONNs), our team will develop specialized, ultrathin computing systems composed of engineered nanostructures that manipulate light waves. These DONN-based systems are capable of performing inference tasks at the speed of light while consuming significantly less energy than conventional electronic platforms. These breakthroughs will support future embedded and edge devices, from smart sensors to autonomous systems, enabling AI to scale sustainably. The project will expand the American workforce in AI and optical computing by integrating education and outreach. The PIs will connect students from local community colleges to research opportunities and build upon successful undergraduate and K-12 outreach programs at both NC State and the University of Minnesota. The team will introduce new AI- and photonics-focused courses into the undergraduate curriculum and engage students through REU programs, design projects, and public-facing events such as youth AI labs, science competitions, and museum activities. Together, these efforts will prepare students with expertise in optics, nanofabrication, and machine learning, ensuring they are equipped to lead future innovations in photonic computing. This project seeks to design, fabricate, and experimentally validate a new generation of metasurface-based diffractive optical neural networks (DONNs) that overcome key limitations of existing optical accelerators. The research targets three major advances: (1) Multi-modality: developing a unified DONN framework capable of processing diverse data types, including images, text, and graphs, and integrating programmable metasurface layers to significantly broaden its application scope. (2) Scalability: enabling deep, large-scale network inference using iterative train-prune-retrain, weight clustering, and tile-wise sparsification to minimize diffraction errors and optical crosstalk, combined with nonlinear activation reduction techniques such as low-degree polynomial network approximations for efficient, stable inference; and (3) Robustness to physical non-idealities: integrating fabrication-aware optimization and phase smoothing to mitigate meta-atom geometry variations, inter-element crosstalk, and environmental instabilities. The DONNs will employ multi-channel metasurface layers that leverage wavelength and polarization multiplexing for parallel, multi-task processing, and will incorporate architectural strategies such as shared diffractive layers, spatially distinct task routing, and optical skip connections to extend functionality to transformer and graph-style architectures. Fabrication will follow a staged cleanroom-to-foundry pipeline, with TiO2 nanofin metasurfaces prototyped at NC State and scaled through commercial foundry tape-outs to achieve device areas exceeding 250 μm2. The resulting systems will be experimentally benchmarked for accuracy, energy efficiency, and throughput across different workloads. By tightly integrating algorithmic innovation, photonic device engineering, and experimental validation, this work will establish the foundations for compact, energy-efficient, and adaptive optical AI processors, offering a pathway toward practical deployment in embedded and edge-computing applications. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2025 · 2025-10

Non-Technical Description: The development of solution-processable semiconductor materials has the potential to revolutionize emerging technologies in electronics and quantum engineering technologies by enabling scalable, cost-effective manufacturing methods. Among these materials, metal halide perovskite nanocrystals exhibit exceptional properties suited for advanced technological applications. However, their widespread adoption faces significant limitations due to presence of heavy metals, such as lead. This project aims to accelerate the discovery of high-performance, lead-free perovskite nanocrystals through the integration of high-throughput experimentation, artificial intelligence (AI), and advanced data-sharing strategies across multiple institutions. By establishing networked "self-driving laboratories" (SDLs) capable of autonomously exploring extensive materials synthesis parameter spaces, this research is expected to drastically shorten discovery timelines from years to weeks or months. The project's broader impacts include the development of new educational programs designed to train a skilled workforce proficient in AI-driven and autonomous scientific research methodologies, thereby promoting broad participation in innovative STEM careers. Technical Description: This research addresses the critical challenge of discovering lead-free metal halide perovskite nanocrystals by establishing distributed SDLs that integrate automated flow chemistry systems, colloidal nanoscience, and machine learning algorithms. The project aligns directly with NSF’s Designing Materials to Revolutionize and Engineer our Future (DMREF) program and supports the objectives of the Materials Genome Initiative (MGI), aiming to create a robust, scalable framework for accelerated semiconductor materials discovery. A key technical innovation involves modular flow reactors with independently tunable reaction conditions, significantly expanding the accessible synthesis parameter space for semiconductor nanocrystals. The project will employ federated learning approaches to analyze and integrate experimental data from cloud-connected SDLs situated across multiple institutions, facilitating predictive modeling of synthesis parameters and resulting material properties. Outcomes of this project will include the establishment of a publicly accessible, AI-ready experimental database, serving as a valuable resource for the broader materials research community. Additionally, educational efforts will focus on developing innovative curricula and workshops to disseminate knowledge in autonomous experimentation and materials discovery, thus strengthening national expertise and capacity in AI-driven research and 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 2025 · 2025-10

In K-12 computer science (CS) education, collaboration is a key practice for all grade levels in CS standards, but there are still significant gaps in understanding how students can be supported in collaborating. In addition to being a central workforce skill, collaboration also supports students' sense of agency, sustained engagement, and conceptual understanding of CS. This project advances the field's understanding of student collaboration in CS education, focusing on upper elementary students. The project explores how different models of collaborative programming can impact quality of collaboration, student engagement, learning outcomes, and attitudes toward CS. By investigating these models, the project is determining how varying the types of CS activities, number of computers, student roles, and pairing strategies can influence students' collaborative practices; and through these their conceptual understanding of CS, their interest in the subject, and their teachers' confidence in leading CS instruction. The findings will help educators better support students' sustained engagement in programming activities, conscious control over their learning, and understanding of CS concepts. Dissemination of these findings is through both reports of research results and through models of instruction that can be employed with different CS curricula. This study of fourth and fifth grade classroom work in computer science compares and examines student learning across different conditions of dyad collaboration on computers. One condition has two students work at one computer; another condition has them work in sync on two computers in the same online space; and a third condition has them work in sync on two computers while guided to play roles of proposer and reviewer. The idea is to mimic problem solving through collaboration as it is important in CS industry, while also leveraging the collaboration as learning support. Prior research has found that assigning roles helps students be more productive, and this study examines this in more detail both in terms of the outcomes, quantitatively documented, and qualitatively in terms of the processes of learning. North Carolina State University and SRI partner in this work, which is conducted in elementary schools in North Carolina and California. A pilot study of 24 students and focus groups refines instruments and procedures. A larger study is conducted with 300 4th and 5th graders. This project is co-funded by NSF's EDU Core Research (ECR) program. The ECR program emphasizes fundamental STEM education research that generates foundational knowledge in the field. Investments are made in critical areas that are essential, broad and enduring: STEM learning and STEM learning environments, broadening participation in STEM, and STEM workforce development. This project also is co-funded by the Innovative Technology Experiences for Students and Teachers (ITEST) program, which 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 · 2025-10

Opioid use disorder (OUD) and addiction affects approximately 3.7% of U.S. adults (9.37 million) and caused more than 70,000 deaths from fentanyl overdoses in 2023. However, we have a limited understanding of where, when, and how opioids modulate the diverse behavioral outputs of the brain. This is partly due to the limited technology available for in vivo opioid monitoring in the brain. This project aims to develop new technology to monitor fentanyl in the brain. The developed technology has a significant impact in several settings. It offers urgently needed technologies to understand with high spatiotemporal resolution how opioids modulate diverse behavioral outputs of the brain. Moreover, the underlying bioelectronic design principles and knowledge generated will be applicable to other fields, including biosensors for therapeutic drug monitoring, immune response tracking, and chronic disease management. The project will involve high school and undergraduate students. Students will receive training in experimental techniques, data analysis, and scientific writing. New course modules leveraging the results of the work will be incorporated into existing undergraduate and graduate courses at North Carolina State University and the University of Connecticut. The goal of the project is to develop and characterize a wireless bioelectronic system for high-performance fentanyl monitoring in the brain of freely moving small animal models. To achieve this goal, we will: 1) isolate, characterize, and engineer aptamers targeting fentanyl, a primary opioid associated with OUD, 2) develop an implantable nanoporous electrochemical aptamer-based (nanoEAB) fentanyl sensor, and study the structure–property relationship of a new surface coating to improve its in vivo longevity, and 3) establish and validate a wireless bioelectronic system for fentanyl monitoring in the brain of freely moving animals. The project will significantly advance the design and development of wireless bioelectronic systems for high-performance fentanyl monitoring in the brain. Additionally, the developed fentanyl sensors could serve as a technology platform for developing wearable emergency response systems for real-time opioid monitoring and closed-loop delivery of therapeutic drugs such as naloxone. Finally, due to the generalizability of the aptamer selection and nanoEAB platform, this technology will serve as a template for designing sensors for monitoring other molecules of biomedical interest by simply replacing the aptamers functionalized on the sensor surface. 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

Chronic venous insufficiency is a vascular disease in which damage to leg veins due to high blood pressure reduces efficient blood flow back to the heart. Currently, understanding of the mechanical and biological processes responsible for venous valve damage is limited. This BRITE Relaunch project aims to provide the first data on how the cells of the venous valves respond to mechanical forces associated with high blood pressure and tissue damage. In addition, the research seeks to develop computer models to simulate these processes at the tissue and cellular levels. This work will add to fundamental knowledge and looks to broadly impact the design of replacement valves, on heart surgery using veins, and more. It intends to provide data on cell signaling to help improve therapies for chronic venous insufficiency and other vascular diseases, such as deep vein thrombosis. The Principal Investigator will engage students through seminars, workshops, and online resources. The project will also support education, outreach, workforce development, and entrepreneurship initiatives, including activities for K–12 students and those at Primarily Undergraduate Institutions. In this BRITE Relaunch project, objective 1 looks to generate and share the first mechanobiological characterization of venous valve leaflet endothelial and interstitial cells, including baseline in-situ and in-vitro data and responses to physiologic and pathologic biophysical stimuli (e.g., cyclic stretch, fluid shear stress). Datasets will cover morphological analysis, growth kinetics, immunofluorescent staining (focused on mechanotransduction proteins), PCR-based mRNA quantification, Western blot protein levels, and atomic force microscopy measurements of cell stiffness. Objective 2 seeks to provide the first data on early mechanobiological effects of hypertension on ex-vivo cultured venous valves. These datasets look to build on Objective 1 by adding biaxial mechanical testing and confocal scans to map protein expression changes to stresses and strains under normal and elevated pressures and flows. Objective 3 intends to develop and openly share multiscale FSI models to explore venous valve degeneration. These new tools and insights intend to create the first cellular-level basis for understanding venous valve degeneration and its role in chronic venous insufficiency, benefiting both science and society. Together, these efforts intend to significantly advance the biomechanics and mechanobiology of venous valves and set the stage for future breakthroughs in venous disease prevention and treatment. 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

Creating economic development in under-resourced areas using technological innovation requires converging various groups at multiple scales, such as local communities, colleges, local and state government agencies, and support organizations for entrepreneurs, investors, and businesses. No broadly applicable framework for rural community engagement in emergent innovation exists to guide the development of rapid outcomes for all Americans while ensuring opportunities for medium- and long-term workforce development. The overarching goal of this project is to develop a framework that merges social, psychological, and economic considerations to guide community selection poised for economic transformation. The overarching objective of this project is to establish an extended Lab to Market framework for harmonizing indicators across multiple social and economic dimensions. The North Carolina Ecosystem Technology (NC EcoTech) Developmental ENGINE, located in coastal North Carolina, will serve as a test case to design and deploy exploratory mechanisms that create scalable and reproducible systems of innovation-based economic growth and workforce development. This project hypothesizes that identifying and prioritizing community assets within a given ENGINE Region of Service (ROS) will maximize the impact of technology and innovation, enabling workforce development and local livelihood considerations over the long term. This project will explore this objective in twenty counties in Eastern North Carolina. It will use a mixed-method approach, combining economic impact analysis, focus groups, questionnaires, and scenario planning activities. This project will (1) integrate social, psychological, and economic variables in L2M Frameworks, (2) develop the substructure embedded within an L2M to clarify and intentionally integrate beneficiaries throughout ENGINE lifecycles, and (3) identify drivers and barriers that may limit the scaling up of ENGINES through the ENGINE timeline. This project will produce a shareable L2M framework for other NSF ENGINES operating in under-resourced rural regions where there is limited research on successful innovation-driven approaches. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.