North Carolina State University

NSF Awards · FY 2025 · 2025-09

Multiple perspectives and collaborative input are required to envision transformative solutions to the complex interplay of cascading weather and environmental hazards. Yet civic institutions, scientists, and practitioners are often disconnected from decision-making processes and rarely have opportunities for mutual learning. This project aims to bridge the gap between geoscientists' understanding of natural hazard impacts and practitioners' ability to translate this knowledge into actionable solutions. Partnering with leaders from government, industry, and non-profits across North Carolina, the project will address local and regional environmental challenges through participatory workshops. The workshops will combine learning sessions on key topics with innovative community engagement activities grounded in real-world data and predictive scientific models. By convening individuals with lived experiences of hazard events and those in at-risk areas, the workshops will foster peer learning, scenario-based dialogue, and the co-creation of potential solutions. The project also seeks to catalyze broader public conversations about long-term futures by encouraging deliberation on trade-offs among development, conservation, and population trends, while acknowledging the difficult choices necessary to build system resilience. Findings and tools will be incorporated into student training and professional development for educators who work with the public, including museum staff and librarians. A persistent challenge facing resilience-building efforts stems from the lack of frameworks that integrate scientific expertise with on-the-ground operational and decision-making experience. This project’s integrative framework will advance the translation of Earth system science into actionable insights for local and regional practitioners while shaping a community-driven scientific agenda. This project proposes that a community-driven co-creation process can increase the perceived efficacy of collective solutions, build trust in science-based tools for guiding interventions, and facilitate cross-jurisdictional decision-making to address regional environmental challenges. Phase 1 will investigate how novel engagement strategies affect participants' understanding of complex, interconnected challenges and their perceived self-efficacy to implement co-developed solutions. Scientific modeling and analysis will inform the development of the workshops focused on North Carolina’s mountain and coastal regions, addressing challenges such as constrained infrastructure corridors, varied geologic and topographic conditions, dynamic land-use and land-cover changes, shifting population patterns, and varied landscape literacy. The resulting portfolio of solutions is expected to inform on-the-ground decision-making and contribute to regional resilience strategies. In Phase 2, the project aims to co-develop advanced analytics that respond in real-time to different intervention types and locations, enabling dynamic forecasting of potential outcomes, and supporting adaptive planning across scales. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2025 · 2025-09

As artificial intelligence (AI) continues to advance and transform society, it is essential that researchers work in direct partnership with teachers to prepare students to understand the world in which they are growing up. Advancing this goal across K-12 education requires a clear understanding of how to introduce AI concepts to elementary school students and how to effectively support teachers in doing so. The PrimaryAI scale-up project advances foundational knowledge in K-12 AI education that leverages immersive problem-based learning pedagogies for upper elementary learners in grades 3 to 5. The project will reach over 5,000 upper elementary students and more than 60 teachers while expanding the research and implementations across multiple states. The project team will partner with teachers from rural communities in Alabama, Indiana, and North Carolina to engage their students in authentic AI-infused problem solving. This approach aims to foster students' interest in science, technology, engineering, and mathematics (STEM) and equip them with fundamental AI knowledge they will need to thrive in the future. The project will investigate key factors that influence successful scaling of an AI education curriculum across multiple state contexts. It will examine the interplay among teacher professional development, localized classroom adaptation, collaborative design methods, and student learning and interest. These elements are central to understanding the conditions for implementation and mechanisms that sustain and expand the use of AI curricula on a large scale in rural upper elementary classrooms. The project will address three primary research questions: (1) What AI concepts serve as entry points for rural teachers to integrate AI into instruction, considering local contexts and individual pathways? (2) What are the impacts on student outcomes for learning, engagement, and STEM interest across rural contexts? and (3) How do local factors in each state's rural context influence the reception, implementation, and outcomes of PrimaryAI? Research questions will be addressed using multiple data sources as part of Design-Based Implementation Research (DBIR) (Fishman & Penuel, 2018). Pre-and post-tests will be used to assess impacts on student learning and interest. The research team has developed assessments for AI concepts, AI planning, computer vision, and machine learning (Chakraburty et al.,2023). To address the first question, the team will collaborate with teachers from rural communities in Alabama, Indiana, and North Carolina. The team will document ongoing collaborative discussions, professional learning processes, teacher designs, and plans for implementation. For the second question, the project will conduct comprehensive analyses of student outcomes using pre-post assessments of AI knowledge and skills, student engagement, STEM interests, observations of student interactions, and student interviews. Additionally, a cross-case analyses to explore commonalities and differences across various rural contexts and implementations will be conducted. To address the third question, a detailed case studies within each rural community to understand local factors such as pedagogical goals, student interests, community priorities, and educational policies is planned. Outcomes will include locally-contextualized versions of the PrimaryAI curriculum, comprehensive teacher professional development guides, case studies that detail successful strategies and challenges, and recommendations for scalability. Ultimately, the project will advance understanding of effective practices and approaches for integrating AI education into rural elementary classrooms. This project is 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 · 2025-09

This award supports research in commutative algebra and algebraic geometry, with strong connections to combinatorics and singularity theory. Commutative algebra provides the local algebraic framework for algebraic geometry—a central area of modern mathematics concerned with understanding the geometric structure of solution sets to systems of polynomial equations. These solution sets, known as algebraic varieties, appear throughout mathematics and are often challenging to study due to their intricate geometric and algebraic features. To address these challenges, the principal investigator will investigate the rich multigraded structures that naturally arise in many such varieties. This research will generate new insights at the intersection of algebra, geometry, and combinatorics while contributing to the training and development of the next generation of mathematical scientists. The project also includes mentoring and training of graduate students, as well as fostering a learning community at the principal investigator’s home institution for those interested in related research topics. The research will center on four main directions, each guided by the unifying presence of graded or multigraded structures. The first project aims to develop new formulas in elimination theory, compute the defining equations of certain blowup algebras, and establish novel criteria for detecting integral dependence. These formulas and computations will have important tangible applications in singularity theory and applied areas like geometric modeling. The second project aims to study the multidegree and K-polynomials of multiprojective varieties—for example, by investigating the saturated Newton polytope and Lorentzian properties. This work will advance the understanding of combinatorially-defined polynomials, such as skew Schur, Schubert, and Grothendieck polynomials. The third project aims to investigate key structural properties of the Hilbert scheme of a multiprojective space. The guiding theme is to understand the closed subschemes of a multiprojective space and their possible deformations. The fourth project aims to describe nonreduced scheme structures using Noetherian differential operators. This approach will provide innovative applications of differential operators in commutative algebra and algebraic geometry. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2025 · 2025-09

The research studies the impacts of coastal and marine resource shifts on local fishing communities. The investigators focus on tracking the multi-directional relationships that emerge in ocean and marine spaces when new resource management policies and laws are implemented. They also measure the changes and adaptations of communities to these changes in resource and marine management in these regions over time. Research findings contribute to generalizable social scientific theory on resource management and coastal compliance norms and policies. Broader impacts of the research inform collaborative exchanges and scientific sharing between stakeholders. Training of a postdoctoral trainee, and both graduate and undergraduate student participation in data collection, analysis and publications support the development of early career scientists in cross-disciplinary social and coastal science. The research contributes to our understanding of human interactions with the biological environment and expands on our NSF commitments to investment in scientific discovery in the area of biotechnology. Research results produce generalizable results for our understanding of resource management in coastal regions across the globe. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2025 · 2025-09

With the support of the Chemistry of Life Processes program in the Division of Chemistry, Professor Makris from North Carolina State University is studying the structures and functions of a newly discovered class of enzymes from bacteria termed the heme oxygenase-like dimetal oxidases/oxygenases (HDOs). The HDOs that are the focus of this proposal are involved in the generation of compounds that serve as drugs or enable pathogens to proliferate. The goal of the research is to provide a molecular rationale for how structurally similar proteins can mediate different transformations with amino-acid substrates. Bioinformatics, structural, and mechanistic enzymology will be leveraged by students to create a publicly available online database (HDoBase) that will facilitate HDO discovery. The research project seeks to determine how structurally similar dimetal proteins can perform radically different transformations on substrates. A combined approach that uses time-resolved structural, kinetic, and spectroscopic methods will evaluate the key principles underlying reaction diversity in four representative HDO subtypes and will streamline future enzyme discovery and annotation. Studies are aimed at the structural elucidation of diferric-peroxide intermediates that are common to the reaction trajectories of many HDOs. The program will investigate how these species are differentially tuned to catalyze transformations directly, or generate downstream oxidants for substrate C-H and N-H bond activation, or radical hole-hopping reactions. The combined roles of the secondary coordination sphere, substrate-positioning, and substrate-type in this process will be systematically evaluated. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2025 · 2025-09

The 2025 IEEE Global Communications Conference (IEEE GLOBECOM 2025), held in Taipei, Taiwan on December 8 - 12, 2025. IEEE GLOBECOM is one of the two principal conferences organized by the IEEE Communications Society (ComSoc), held annually to showcase advancements in communications and networking research. Drawing over 2,900 international participants, including academics, researchers, and industry professionals, GLOBECOM facilitates significant knowledge exchange through its technical symposia, industry forums, workshops, and exhibitions. IEEE GLOBECOM 2025 will feature an extensive technical program and industry-focused events. Participation in IEEE GLOBECOM offers students an invaluable opportunity to engage in cutting-edge research, expand their professional network, and gain career-enhancing exposure to academic and industrial leaders in the field of communications and networking. This project supports students from US universities to attend IEEE GLOBECOM 2025 in person. Students will have the opportunity to present their work and be exposed to state-of-the-art developments in the field. They will also have the opportunity to interact with peers from institutions worldwide, meet with senior researchers, and participate in discussions that are likely to shape the future of the field. This grant will target students who will substantially benefit from attending this conference but have limited travel funds. Priority will be given to first-time attendees. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2025 · 2025-09

Genetically engineered cell therapies are transforming how doctors treat challenging diseases like cancer, sickle cell disease, HIV, and autoimmune conditions. These treatments work by giving a patient’s own cells a new ability to find and fight disease more effectively. A crucial step in making these cell therapies accessible is by swiftly delivering new genetic material into cells using viral vectors in a liquid medium. Porous materials that guide the flow of cells and viruses dramatically improve delivery, but we do not yet fully understand how they work. Understanding how porous materials enhance cell therapy production will help make cell therapies more reliable and affordable for patients in dire need of treatment. This project will use artificial intelligence, computer modeling and experiments to determine how flow inside porous materials efficiently produces genetically modified cells. In answering this important scientific question, this project will engage students from the high school to graduate levels and help train future scientists and engineers. By integrating research outcomes in classrooms and through partnerships with industries and foundations, this work will also help spread awareness of advanced engineered cell therapies and how they can improve health outcomes. This research will develop a mechanistic understanding of how liquid flow in microfluidic devices and porous scaffolds enhances the efficiency of viral transduction in engineered cell therapies. The project will integrate experimental studies with detailed mathematical modeling to quantify cell-virus collision frequencies and the biological pathways that lead to successful gene delivery. A combination of computational fluid dynamics, discrete particle simulations, and electrostatic interaction models will be used to capture the complex flow physics at the microscale level. Novel neural network approaches, including generative adversarial models and neural net optimization algorithms, will design porous biomaterials and predict the interactions involved in viral binding, internalization, and gene integration in cells. By rigorously varying flow conditions, the research will uncover how convective and diffusive transport mechanisms impact cell-virus encounters and transduction success rates. The resulting models will provide design principles for improving gene delivery platforms and could also shed light on related biological processes, such as immune cell infiltration in tumors. The project outcomes will advance both the theory and application of cellular transduction for next-generation gene and cell therapies. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2025 · 2025-09

This award supports research that seeks to advance both fundamental and practical design methods and principles in human-system interactive design. The revolution in multi-source energy generation has driven rapid development of energy storage systems (ESSs) across industries from automotive to power plants, designed to accommodate various applications and use cases. Yet, ESSs are not standalone systems in real-world scenarios; they are dynamically managed by users who can make spontaneous decisions. The overarching research goal of this project is to create a human-driven synergistic framework between human and ESS to optimize system-level performance and increase the affordability of system operational costs. It seeks to bridge the gap between in-situ user behaviors and the engineering system design process and further promote the prevalence of ESS-powered applications to the general public, which can be transferred and adapted to different ESSs affected by active, dynamic user behaviors. This research looks to contribute to the design science of human-ESS interaction by unifying multiple domains, including behavioral and computational science, firmly grounded in theoretical foundations. Specifically, the research aims to: 1) Characterize dynamic human behaviors based on goals and intents using data collected from the longitudinal naturalistic study. 2) Develop new theoretical models to assess ESS performance influenced by dynamic human behaviors. 3) Create a novel unified optimization design framework to devise human-ESS interaction strategies to optimize overall system performance. 4) Build a new hybrid testing platform to validate and evaluate the design framework. Ultimately, the new design methods and principles look to be introduced as a new modeling paradigm that comprehensively accounts for human-induced dynamics in broader human-system applications, which will lead to unprecedented performance optimization of the overall system design to benefit all Americans. The project will also support education and outreach efforts to provide a research platform and opportunities in human-system design for all 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 2025 · 2025-09

Wave-based imaging techniques are commonly used in geophysical and medical industries to obtain information about an unknown medium by measuring the travel time of reflected waves. For instance, in seismic exploration, seismic energy is used to probe beneath the surface of the Earth and is integral for exploration of economic deposits of oil, gas, or minerals, but also for engineering, archeological, and scientific studies. This is done either passively (using a naturally occurring earthquake) or actively (using a source of seismic energy, such as an explosive charge or seismic vibration) where energy is directed into the Earth. The echoes of seismic waves as they are reflected off of discontinuities in the subsurface are then measured across a measurement area. Similarly, medical ultrasonography is an imaging technique used to create an image of internal body structures such as tendons, muscles, joints, blood vessels, and internal organs. Ultrasound images, also known as sonograms, are created by sending pulses of ultrasonic waves into tissue using a probe. The ultrasound pulses echo off of tissues with different reflection properties and are returned to the probe, which records and displays them as an image. The mathematical foundation behind both of these applications is based on the classical Fermat's principle in physics: a wave takes a path between two locations that can be traveled in the least time. Travel time of a wave defines a mathematical model in which the distance between two locations is measured using a clock instead of a ruler. This type of physically-motivated mathematical framework is commonly studied in the field of differential geometry. This research project develops a stronger understanding of the mathematical theory of seismology and ultrasonography, having a particular emphasis on models with time-dependent material parameters and models that describe anisotropic mediums such as the human body or the subsurface of the Earth. This project focuses on the mathematical theory of indirect measurements arising from seismic exploration and ultrasonography in medical imaging, with particular emphasis on formulating new as well as solving longstanding and challenging geometric inverse problems in these contexts. These problems are formulated in the language of hyperbolic Partial Differential Equations (PDE), with the goal of finding the unknown coefficients of the PDE from a boundary measurement. Since many physical quantities are coordinate invariant, it is conventional to model a terrestrial planet or a human body by a compact, connected Riemannian manifold with boundary. Under these assumptions a fundamental hyperbolic inverse boundary value problem is to recover the unknown geometric structure from the hyperbolic Dirichlet-to-Neumann map (DN-map). This can be accomplished by reducing the PDE-based problem to a geometric problem which carries information about the unknown coefficients of the respective Partial Differential Operator. The project introduces many different reduction methods and solutions to the respective geometric problems, containing three main lines of research: 1) Inverse Problems in Linear Elasticity, introduces elastic inverse problems which can be solved by a reduction to the boundary rigidity and its linearization. These problems go beyond the conventional while insufficient Riemannian formalism. 2) Hyperbolic Inverse Problems with Time-Independent coefficients introduces uniqueness and stability problems for hyperbolic operators on compact and non-compact manifolds. 3) Hyperbolic Inverse Problems with Time-Dependent Coefficients focuses uniqueness problems for general time-dependent hyperbolic inverse problems and invertibility of the light ray transform with partial data. The powerful mathematical methods developed to attack these geometric inverse problems will expand beyond the scope of this project and can be applied for instance in the control theory of PDEs and integral geometry. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2025 · 2025-08

This conference will support travel and participation costs for U.S. university students and early career scientists to attend the upcoming US-Africa conference on nanotechnology and engineered human environments. The conference will take place in Nairobi, Kenya from April 13-17th, 2026. The conference is organized in partnership with the Africa Materials Research Society (A-MRS). The core objective is to advance knowledge and enhance understanding in nanotechnology and engineering, to promote collaboration and progress in the fields, and foster integration of cognitively broad perspectives in these areas. This US-Africa conference will play a crucial role in unifying perspectives among global practitioners. The goal is to create a platform for leading U.S. scientists to interact and initiate lasting collaborations with emergent international engineers. The US-Africa conference on nanotechnology and engineered human environments will take place in Nairobi, Kenya from April 13-17th, 2026. This conference will play a crucial role of injecting new perspectives into the U.S. engineering research community and will also enable deeper understanding of how engineering affects and/or relates to the social and physical environment. The conference thematic talks will bridge ideologies by bringing together international engineers and scientists to discuss a common topic, enabling emergent convergence of otherwise isolated ideologies that advance fundamental engineering and science. As a result of this event, participants will be able to transcend basic and applied research, fostering knowledge sharing, collaboration, and exchange activities with leading experts presenting new findings and setting future directions. Highlighting a commitment to education and building research capacity, the conference provides a unique platform for international students to interact with each other and leaders. This year's conference will encompass a wide range of topics including nanotechnology, nanotechnology in energy, frugal science, and the intersection of nanotechnology, human interaction and the environment. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2025 · 2025-08

Materials made of grains - like sand, agricultural products, and pharmaceutical powders - are not only some of the most common materials in our daily lives but also cover the surfaces of other planets. Predicting how they will flow on Earth has been a long-standing challenge for engineers. These difficulties will only grow as missions are planned at lower gravity to explore asteroids, the Moon, and Mars. In this project, the team will create powder-flow experiments small enough to fly on the International Space Station. These experiments, performed by astronauts at both high and low gravity, will be compared to results from experiments performed by students here on Earth. Using this data, reliable digital twins will be created and tested, which are computer models that mimic the observed flows. Through these efforts, cutting-edge training will also be provided to students. This project will explore granular flow behavior by conducting and modeling experiments under different gravity conditions, both on Earth and aboard the International Space Station (ISS) using the Multi-use Variable-gravity Platform (MVP). This specialized facility employs a centrifuge to simulate a wide spectrum of gravitational forces - from near-weightlessness to conditions exceeding Earth gravity - offering a rare opportunity to examine how granular systems respond outside typical terrestrial environments. This project will investigate two central hypotheses. The first posits that granular flows are strongly influenced by the magnitude of gravity. To test this, rotating drum flows composed of materials ranging from uniform beads to regolith simulants will be analyzed. By examining features like interface geometry and internal velocity fields, it will be determined whether observed behaviors follow theoretical predictions for gravity-dependent scaling laws, or deviate in measurable ways. The second hypothesis suggests that existing continuum modeling frameworks, grounded in fundamental mechanics, can be extended to accurately capture granular dynamics in low-gravity regimes. These conditions may amplify secondary effects such as cohesion or particle softness, which will be incorporated by the team into model refinements. Using data from both ground and ISS experiments, the team will iteratively calibrate and validate the models. Success will be defined by identifying dominant constitutive ingredients across different gravity levels. This will provide crucial evidence that well-constructed continuum models can serve as predictive digital twins for granular processes relevant to planetary exploration, where direct experimentation is limited or impossible. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2025 · 2025-08

Participatory science, ranging from citizen to community science, refers to ways that everyday people collaborate with scientists in meaningful ways. There are thousands of participatory projects, each of which can engage thousands, tens of thousands, or even millions of people. Projects occur across varied disciplines such as astronomy, biochemistry, conservation, engineering, and environmental sciences. Research suggests that people engaged in participatory sciences can develop skills, adopt new behaviors and attitudes, increase civic engagement, and more. Some learning outcomes may lead young people into STEM careers, and for adults, lead to everyday science learning. Studies of youth engagement in participatory sciences focus on out of school time experiences. Yet, much remains abstract and aspirational with participatory sciences for adults, which occurs during outside-of-work or leisure time. This Literature Review and Synthesis project is the next step needed to move towards understanding the participatory sciences as sites of adult lifelong learning and leisure. The disciplines, number of projects, and amount of people engaged creates high variation in project structures and outcomes and requires strengthening theoretical foundations of adult science learning in leisure contexts in order to gain new insights. Ultimately, the investigators will move the field forward, in research and practice, with stronger understanding of adult engagement in participatory sciences as a form of lifelong learning and leisure. The study also serves the national interest by improving the participatory sciences, which help advance scientific discovery in numerous disciplines. The goal of the award is to assess and bolster the theoretical foundations for understanding adult learning in the participatory sciences. The project addresses two research questions: (1) What theories have shaped understanding of the scope and nature of adult learning experiences in the contributory-style participatory sciences? (2) How can the elements and premises of leisure theories and learning theories be synthesized to improve understanding of the broad scope and nature of learning experiences in participatory sciences? The team of researchers will answer these questions through the completion of two phases. One phase is a Systematic Literature Review (SLR) to characterize the theories used in existing published research about multiple types of learning in participatory sciences. The primary product will be an exhaustive list of theories that are currently used, their alignment with learning strands, and estimate of the extent of atheoretical studies. The second phase is a Theoretical Integrative Review (TIR) to inform an iteration of theory that can guide future research and practice on learning in participatory sciences. The team will engage practitioners in this work through surveys, focus groups, virtual workshops, and in developing a resource such as a toolkit. By integrating theories and knowledge from leisure studies with learning theories, this synthesis research will bring new insights, and new research questions, to inquiry about lifelong, purposeful leisure and learning in participatory sciences. This Synthesis award is funded by the Advancing Informal STEM Learning (AISL) program, which seeks to advance new approaches to, and evidence-based understanding of, the design and development of STEM learning in informal environments. This includes providing everyone with multiple pathways for accessing and engaging in STEM learning experiences. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2025 · 2025-08

Nontechnical Summary Chirality is a geometric property of a material that lacks mirror image symmetry. For example, the left hand cannot be transformed into its mirror image, the right hand, by any combination of rotation and translation. This project investigates how structural chirality at the molecular level can be harnessed to control the quantum property of electrons known as spin, without relying on the movement of electrical charge. Spin-based electronics, or spintronics, offers compelling advantages by reducing power consumption and heat generation in devices used for data storage, sensing, and computing. This project investigates structural chirality in semiconductors made from organic and inorganic components, called hybrid semiconductors. By tailoring molecular and crystal structures to manipulate spin transport, investigators will enable new mechanisms for spin transport. These findings will offer a path toward compact, reconfigurable spintronic devices that function without containing magnetic elements. The project integrates research with education and outreach efforts. New course modules and research opportunities for undergraduate and graduate students that will provide students with accessible resources in emerging technologies and contribute to the development of the next-generation semiconductor workforce. Technical Summary The research investigates how molecular chirality—specifically, the handedness and orientation of organic cations—modulates spin transport in low-dimensional chiral hybrid organic-inorganic semiconductors composed of alternating molecular cations and metal halide octahedra. The central scientific hypothesis is that anisotropic spin absorption, where spin current is preferentially absorbed in one direction over another, is determined more by the orientation and strength of the molecular chiral axis than by the overall symmetry of the crystal. To test this, the research team will synthesize a library of chiral hybrid organic-inorganic semiconductors with tailored molecular chirality and tunable alignment between the chiral axis and the crystal screw axis. Using spin-pumping and ultrafast magneto-optical Kerr effect techniques, pure spin current will be injected into the hybrid semiconductors, and the anisotropic spin absorption will be characterized as a function of the angle between spin polarization and chiral axis. By systematically varying molecular structure and chirality, the researchers aim to reveal the correlation between the degree and orientation of chirality and spin absorption anisotropy. The project will also develop new chirality descriptors that can be used to correlate structural features with spintronic behavior. This project will not only advance the fundamental understanding of spin transport in non-magnetic materials, but also establish new design principles for functional spintronic components that are tunable, scalable, and energy-efficient. By integrating material synthesis, structural characterization, and spin transport measurements, this work pushes the frontiers of low-dimensional and hybrid materials, enabling tailored quantum functionalities through the control of structural chirality. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2025 · 2025-08

Autonomous driving, aided by machine learning (ML), has the potential to improve safety and efficiency while reducing road congestion. However, today's autonomous vehicles (AVs) struggle under arbitrary conditions due to the complexity and variety of scenarios encountered in practice. Meanwhile, increasingly advanced driver-assist systems with partial AV capabilities have been deployed without standardized testing and certification requirements, in particular with regard to computational aspects. This work aims to fill this gap. The objective of this work is twofold: (1) Create an environment supporting seamless transitions from in-lab digital twin testing over a hybrid co-simulation environment, part lab part physical, to fully deployed AV road testing. (2) Systematically identify minimum requirements, verification opportunities and limitations from which testing scenarios can be derived for lab, hybrid, and entirely physically deployed Avs, supporting the AV development cycle and certification. The project will bala the capabilities and limitations of verification within automotive control and its synergy to derive test scenarios for certification across development stages (digital twin, co-simulation, actual AV). The hypothesis is that vehicle verification and certification become synergistic in complementing each other and ideally need to be conducted only once (for verification) or at most twice (for testing) across the three stages. This project focuses on a subset of driver-assist systems with highly interacting subsystems. The work will investigate three levels of increasing complexity, ultimately requiring ML inference for object tracking, to test our hypothesis of synergy between verification and minimal testing for certification. Specifically, the project addresses research challenges of (1) real-time scheduling under hard and soft deadlines, including mixed-criticality scheduling and bounds on execution time for multicores and accelerators, (2) soundness of certification via verification at each development level and (3) both capabilities and limitations of validation with and without re-certification at each level. Expected results will advance Computer Science, Engineering and Cyber-Physical Systems (CPS) for low-level control systems while, within Civil Engineering, deriving test scenarios for certification from high-level requirements. It is this focus on the intersection between areas that facilitates the development of new methods for transitioning from digital controls to testing scenarios supported by our co-simulation approach with gradually increasing physical interaction. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2025 · 2025-08

This award will enable the development of advanced cyberinfrastructure to digitize and integrate over one million dragonfly and damselfly (Odonata) specimens from major natural history collections across the United States. The project, called Di-ODE (Digital Integration of Odonata), will create a unified, publicly accessible digital platform through Odonata Central, linking high-resolution specimen images with critical data such as collection localities and species identifications. This initiative will expand access to these important biological resources for scientists, educators, students, and the public. Di-ODE includes robust training programs to build skills in biodiversity data science and collections digitization. The project will enhance STEM education, promote data literacy, and engage community scientists, contributing to environmental awareness and scientific literacy. Through outreach and digital accessibility, Di-ODE will strengthen efforts to monitor environmental change and inform freshwater conservation across the globe. The project will transform how Odonata biodiversity data are accessed and analyzed by the research community. Dragonflies and damselflies are ecologically sensitive indicators of freshwater health and have been the focus of major studies in evolutionary biology, systematics, and biogeography. However, much of the valuable specimen data remains locked in poorly accessible physical collections. Di-ODE addresses this gap by creating efficient, scalable digitization workflows, using customized optical character recognition (OCR), advanced georeferencing, and data management tools. The resulting infrastructure will enable novel research in global change biology, comparative ecology, and phylogenetics. By improving data quality and access, Di-ODE will foster cross-disciplinary collaboration and provide a model for digitizing and mobilizing data from other invertebrate 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 2025 · 2025-08

This project aims to assist people with spinal cord injury (SCI) in walking and maintaining balance. It uses a technique called Functional Electrical Stimulation (FES), where small electrical currents, when sent to the muscles, help them move. However, muscles using FES rapidly fatigue, making it hard to stay balanced or walk safely. Current FES technology requires transformative advancements that reduce or delay the fatigue onset using approaches that mimic electrical signaling like natural muscle signals sent by the brain. In this project, the research team will develop a new approach to send signals to the muscles through specially designed electrical wires that wrap around the nerves in the leg. This will be done by creating innovative computer programs that control the new stimulation approach, similar to the approach used by the brain, which will help the muscles maintain force for a longer duration. The team will also develop and test new computer programs to maintain balance in people who have trouble standing after SCI. In the future, this could help them avoid falls, walk more safely, and assist critical limb or organ functions like picking things up or breathing. Functional Electrical Stimulation (FES) is a promising technique for assisting gait and improving impaired postural balance in individuals with spinal cord injury (SCI). Unlike voluntary muscle contractions, FES-induced contractions quickly lose their ability to generate force. Current FES technology is ineffective in delaying muscle fatigue, causing force declines and insufficient limb forces that make it difficult to recover from significant balance or gait disturbances. This project aims to develop a new stimulation approach using a neural cuff electrode (NCE) that wraps directly around a peripheral nerve in the lower limb. The central objective is to create computationally efficient, data-driven methods for controlling the NCE implant and a hybrid exoskeleton to support balance function. In the first objective, the research team will use a low dimensional data-driven model created using stimulation inputs and sensitivity of various electrode configurations. The derived control strategy will be optimized to mimic a physiologically similar muscle recruitment order. Both the data-driven model and NCE control algorithms will be validated in a porcine model. In the second objective, the team will evaluate algorithms with theoretically proven safety guarantees that will allow postural and reactive balance using transcutaneous FES and a wearable exoskeleton. This project is expected to result in next-generation balance control technologies that can retrain impaired balance in people with SCI, reduce fall risk, and restore functional walking. Once successful, these algorithms may also be applied to support other critical motor functions after SCI, such as reaching, grasping, breathing, and bladder control. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2025 · 2025-08

Large Language Models (LLMs) such as ChatGPT, Llama, Claude, and Gemini and their empowered applications (such as retrieval-augmented generation systems and autonomous agents) have been widely integrated into advanced cyberinfrastructure (CI) systems to enhance data management, collaboration, and scientific discovery by assisting with tasks such as large-scale text analysis, automated data classification, knowledge extraction, and domain-specific question answering. However, many research studies have shown that LLMs and their applications are vulnerable to various attacks, such as jailbreak, prompt injection, knowledge corruption, data poisoning, and privacy attacks. These attacks pose significant concerns for integrating LLMs into CI systems, as well as broad applications in security- and privacy-critical domains such as healthcare, finance, and law. Despite various research studies that have identified the cybersecurity risks associated with LLMs, there remains a huge training gap among many stakeholders. This gap stems from two factors: emphasis on utility and efficiency over security, and lack of expertise in LLM security. This training gap is particularly concerning as CI systems increasingly rely on LLMs for critical decision-making, code generation, and sensitive data analysis, which potentially exposes them to sophisticated cyber threats, especially for security-critical CI systems. This project aims to bridge this training gap. This project will develop a CyberTraining program to train undergraduate and graduate students across the nation to identify, analyze, and mitigate different attack vectors targeting LLM-empowered advanced CI systems. The program is centered on eight core training modules, which serve as its foundational framework. Based on these modules, a series of sustainable training activities are developed to prepare, nurture, and grow the workforce for supporting the development of LLM-empowered advanced CI systems. The training activities include 1) Hands-on exercises through an interactive learning platform that helps students gain practical experience in LLM security; 2) A two week onsite summer bootcamp designed to foster deeper engagement with faculty and industry mentors during the training modules while promoting professional development; and (3) Degree and curriculum development that selectively incorporates training modules into courses related to AI and Cybersecurity. The training modules and materials developed in this project will also be made publicly available for broad adoption. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2025 · 2025-08

Matroids are fundamental combinatorial objects that simultaneously capture the notion of independence in topics as diverse as graph theory, linear algebra, and field extensions. Recently, geometry has entered the scene and has provided new perspectives for tackling long-standing problems in matroid theory. Behind the resolution of each of these conjectures is an algebraic invariant that exists for any matroid, but whose properties are predicted by geometry. The broad aim of this project is to study these algebraic invariants with a focus on uncovering their impact and applications throughout combinatorics. This project includes mentoring graduate students, creating a learning community at NC State for graduate students interested in combinatorics, and initiating a middle-school outreach activity in Raleigh, NC. The first project aims to give a simpler and more general definition of the intersection cohomology module of a matroid, a module that played a key role in the proof of Dowling–Wilson's top heavy conjecture from 1975. This new definition will work over positive characteristic fields and thus will lead to positive characteristic analogues of Kazhdan–Lusztig polynomials of matroids. The project will also introduce a matroidal analogue of Stanley's local h-polynomials with the aim of shedding light on classical problems in graph colorability and matroid realizability. The second project aims to understand to what extent there might be a "Chow theory" for arbitrary partially ordered sets. In the settings of matroids, polytopes, and Coxeter groups, the Chow polynomials of posets have interpretations in terms of well-studied notions in the three respective areas. The guiding theme is to prove results for arbitrary graded bounded posets by discovering commonalities among these three settings; and, on the other hand, to interpret results that hold for arbitrary posets in these three specific settings. The third project aims to understand log-concavity phenomena in algebraic combinatorics and commutative algebra. On the one hand, new techniques from commutative algebra will be introduced into the study of log-concavity of polynomials with the goal of investigating log-concavity conjectures for Schur-like polynomials; and, on the other hand, the theory of Lorentzian polynomials will be used to investigate log-concavity questions for natural polynomials arising in commutative algebra. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2025 · 2025-08

This Faculty Early Career Development (CAREER) award will support research focused on understanding the prevailing conditions for propagating instabilities and creating methods for regulating their behavior in deployable shell structures. Highly deformable thin shells can be elastically stowed and self-deployed while exhibiting superior stiffness to mass ratios when compared to other shape changing mechanisms. However, excessively tight packaging causes prolonged and disordered motion during deployment. This is driven by propagating instabilities consisting of the buckled formation and travel of localized folds. The underlying factors that cause multiple fold propagations, including interaction behaviors between folds, remain fundamentally unexplained. If left unregulated, the resulting motion could cause incomplete deployment and damage due to collision or entanglement. This award will support research focused on elucidating the mechanics of propagating instabilities that inform the robust design and packaging of deployable shell structures. The outcomes are anticipated to include new design principles for future deployable and adaptive structures that will advance the frontiers of space exploration, robotics, and morphing vehicles. Furthermore, this award will spark early interest in STEM and motivate students to pursue STEM careers through lab internships for high school students, research opportunities for undergraduate students, and exhibitions at local STEM events and museums. The main objective of this research award is to characterize the structural, dynamic, and packaging parameters that govern multiple fold propagation behaviors in deployable shell structures. A computational framework will produce response maps that visualize the motion and interaction of these deformations while strain energy stability landscapes will explain the underlying mechanisms. Novel fold interaction behaviors will be fundamentally understood including convergence, merging, reflections, divergence, and bifurcation. Experimental validation of the deployment paths will be provided by discrete, full-field, and embedded measurement techniques. The second objective is to investigate packaging and design methods that modulate multiple propagating instabilities in composite shells by (1) increasing the stability level of packaged folds that interact with and dissipate the energetic propagation of highly unstable folds, and (2) altering the local shape and layup in the traveling path of folds to manipulate their movement. The generated knowledge will be of the fold arrangements and localized changes to the shell that effectively suppress excessive deployment motion. This will be extended to more complex shell profiles and 3D shell formations, which will help explain how propagating instabilities behave and are modulated in kinematically coupled and variable curvature shell structures. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2025 · 2025-08

An increasing number of engineering innovations rely on functional surfaces that transmit mechanical forces, thermal fluxes, electromagnetic fields, sensing signals, and other physical information. Although soft polymers are excellent candidates for synthetic adhesives and coatings on functional surfaces, it is unclear why surface roughness, often due to manufacturing processes, damage, or design topography, sometimes compromises the polymer’s ability to fully bond with the surface, thereby disrupting its function. This BRITE Relaunch award supports fundamental research that seeks to elucidate the multiscale mechanisms of adhesion, friction, and delamination on non-smooth polymer surfaces. Insights from this research will promote technological advances in areas critical to the strategic interests of the United States in aeronautics, biotechnology, microelectronics, and smart materials. Synergistic partnerships with research and educational institutions across the US will cultivate STEM student training and professional development, increase participation in STEM education, research and innovation, and foster collaborations with local high schools and predominately undergraduate institutions to reach a broader population. Non-smooth surfaces have long been thought to improve adhesion with soft polymers by increasing surface energy and delaying delamination failure. Recent studies, however, have shown that surface roughness leads to adhesion hysteresis wherein parts of the surface come in and out of contact. Hysteresis reduces the effective contact area and can cause stress concentrations triggering local damage progression. This project seeks to characterize adhesion, friction, and delamination on non-smooth interfaces with soft polymers using multiscale computational models. The methodology looks to leverage atomistic-level simulations to inform a continuum-level interface formulation for bonding with soft polymers that will account for adhesion hysteresis, stress concentrations, and delamination patterns on surfaces with random topologies. Experimental tests on 3D-printed samples will be conducted to validate the numerical models. The methods intend to will facilitate understanding of interface mechanics and functional surface design for a wide range of engineering 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 · 2025-08

CRISPR is a new gene-editing technology that is revolutionizing medicine, agriculture, and biotechnology. CRISPR offers hope for treating genetic diseases and creating improved crops and foods. These applications require manufacturing the protein complexes needed for CRISPR at high purity and large scale. This project will develop an innovative technology to purify these enzyme complexes. The research team will create a purification toolkit that can efficiently separate and collect only the most active forms of CRISPR complexes, making the purification process faster, more reliable, and cost-effective. This project will speed up medical discoveries and support the growing bioeconomy. In addition, it will provide hands-on training in advanced biomanufacturing and provide educational opportunities in STEM fields. This project supports the national interest by strengthening U.S. leadership in biotechnology and preparing a skilled workforce for the future. This project aims to develop an affinity-based purification platform for CRISPR ribonucleoproteins (RNPs). RNPs are complexes of Cas nucleases and guide RNAs (gRNAs) that are essential for CRISPR. The core challenge addressed by this effort is the scalable, high-purity isolation of active CRISPR RNPs from cell lysates. The project will develop "Sterically-Modulated Affinity by RNA Trigger" (SMART) peptide ligands. These ligands will overcome current manufacturing limitations by removing both process- and product-related impurities, particularly Cas proteins mis-complexed with host RNAs. SMART peptides tethered to a solid support will selectively capture apoCas (inactive Cas protein) from lysates. The pure, active RNPs will then be released by adding gRNA. The project will (1) elucidate the molecular and kinetic mechanisms governing Cas9 capture and gRNA-regulated release. This will be done through a combination of experimental and computational methods; (2) extend the SMART ligand discovery platform to other high-value CRISPR nucleases, including Cas12a and Cas13b; and (3) integrate these findings into a robust and optimized chromatographic purification process. This project will deliver the first stimuli-responsive affinity purification technology for CRISPR-Cas systems. This will accelerate the widespread adoption of CRISPR tools. Additional benefits will come from training a modern STEM workforce through targeted educational programs, and fostering technology transfer through partnerships with industry and regulatory agencies. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2025 · 2025-08

The ability of plants to regrow an entire organism from just a few cells is a truly remarkable feat of nature, widely used in agriculture and biotechnology for centuries. For example, methods like taking cuttings from a plant to grow new ones, or grafting different plant parts together, rely on this natural capacity. Modern applications, such as developing improved crop varieties through genetic engineering, also depend heavily on understanding and harnessing this unusual plant characteristic. Despite its importance, the precise molecular mechanisms that allow a differentiated plant cell to essentially "reset" and begin forming a new plant are not fully understood. This project aims to unravel these fundamental processes, which are crucial for overcoming current limitations in developing new and improved plant varieties that can better withstand environmental challenges or produce more food and resources. By understanding how plants regenerate, this research will provide foundational knowledge to improve crops and contribute to a more sustainable future for agriculture. Broader Impact activities will include training of students and outreach to the local community via the Plants4Kids program in North Carolina and Science Bound Saturday events at Iowa State University. This project focuses on elucidating the molecular mechanisms by which somatic plant cells reprogram to regenerate organs and whole plants. While plant hormones, particularly auxin, cytokinin, and ethylene, are known to be critical regulators of this process, the specific roles of local auxin biosynthesis and its interplay with other hormonal and regulatory networks remain largely unexplored. The research aims to: 1) identify specific auxin biosynthetic genes involved in the initial stages of cellular reprogramming using a comprehensive collection of whole-gene translational reporters; 2) determine the functional significance of localized auxin biosynthesis in promoting plant regeneration; and 3) delineate the regulatory networks downstream of these key auxin biosynthetic genes involved in callus formation and subsequent plant regeneration. This will be achieved through single-nuclei RNA-seq analysis at precisely chosen time points, guided by a novel triple hormone response sensor that provides cellular resolution of auxin, cytokinin, and ethylene activities, linking transcriptional changes to morphological events. This award is co-funded by the IOS-Developmental Systems Cluster and the IOS-Physiological Mechanisms and Biomechanics Program. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2025 · 2025-08

Throughout much of the US climate change will be felt largely through its effects on warm season extreme events like flooding rains, heat waves, fires, and droughts. Basic thermodynamics suggests that the severity and frequency of these events should increase, for instance the hottest heat waves are likely to get hotter in a warming climate and storm intensity is likely to increase because warmer air holds more moisture. The thermodynamic arguments help but the full suite of processes that affect extreme events is extensive and involves a broad range of spatial scales, from the multi-kilometer scale of thunderstorms to the hemispheric scale of the jet streams that drive weather systems. The broad scale range complicates efforts to study extreme event change using weather and climate models, as a brute force effort to simulate all the relevant processes at all the relevant spatial scales, occurring over the decades-long progression of global warming, is not practical even on the largest computers. Climate models can simulate the full global climate system for decades and even centuries but at resolutions too coarse (perhaps 100km grid spacing) to represent the scales of intense storms. In particular they do not capture the mesoscale convective systems (MCSs) which account for much of the severe weather over the continental US. An alternative approach called pseudo-global warming (PGW) uses a high-resolution model to simulate an observed extreme event, and the simulation is repeated with modifications to the ambient conditions to represent the warmer climate. PGW simulations are quite valuable but they only allow consideration of how climate change affects the severity of specific events, thus they do not enable research on changes in the frequency of occurrence of extreme events. Also, PGW simulations are typically conducted using regional models and thus do not properly represent the effects of changes in the hemispheric-scale atmospheric circulation. This project develops a methodology for looking at extreme event change in a warming climate which addresses the multi-scale issue and enables examination of extreme event frequency and other aggregate statistics. First, a high-resolution global model, the Model for Prediction Across Scales (MPAS) is used to simulate the weather and climate of the past 30 years (1990 to 2019). With a grid spacing of 15km the model is capable of representing MCSs. Second, extreme events are identified in this "nature run" and resimulated with modifications to sea surface temperatures and other surface conditions to represent future warming. The modifications are generated using climate model simulations from the Coupled Model Intercomparison Project (CMIP). The resimulations are a form of PGW only with a global domain, so that changes in intensity can be examined accounting for the full range of spatial scales. Third, a set of 30 warm season (May to November) MPAS simulations using CMIP model output is generated to represent future climate change. The warm season simulations follow the PGW approach but the full season duration means that the simulations do not follow particular events but instead show how a typical season of extreme events changes due to warmer conditions. One issue to be addressed with these simulations is the effect of changes in the jet streams over North America on floods and heat waves, as climate models typically show a reduction in jet-level wind speed over the continental US with increases in speed to the north and south. The work is of societal as well as scientific interest given the damaging effects of extreme events and the value of better information on extreme event change to guide decision making. The project also provides support and training to five graduate students and an undergraduate research assistant. Simulations generated in the project are made available to the research community, and reduced versions of the output are hosted on a JupyterHub to provide access to researchers at the universities participating in the project through Jupyter Notebooks. Outreach is conducted through the Junior Curator program North Carolina Museum of Natural Sciences (NCMNS), a program for high school students interested in field biology and conservation. The students collect field mesaurements of local weather events and their impacts, including insect outbreaks, mold, flooding, and other after-effects of heavy rain. Activity guides are created based on these activities and disseminated through the National Association of Geoscience Teachers. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2025 · 2025-08

How can we measure the cancellation that occurs when complex wave behavior is broken down into simpler, more manageable components? Is there a way to quantify the constructive and destructive interference produced as waves traveling in different directions interact? One way to answer this question is using Fourier decoupling, a mathematical theory that has recently led to spectacular advances on various longstanding conjectures across diverse areas of mathematics. The PI aims to broaden the scope of Fourier decoupling by extending it to new settings such as alternative ways of measuring cancellation and more general geometric surfaces currently not covered by the known theory. Another goal is to build a unifying framework between Fourier decoupling and the seemingly unrelated mathematical theory of efficient congruencing which will bring new ideas to the areas of Fourier analysis and number theory. Furthermore, the PI plans to apply techniques from Fourier decoupling to the analysis of Boolean functions in computer science, with the goal of deepening our theoretical understanding of areas such as algorithmic learning theory. Finally, the project will involve undergraduate and graduate students in research, the organization of international summer schools, and develop an early-career analysis community across the US advancing both scientific discovery and educational development. In more detail, the project focuses on advancing Fourier decoupling theory and its applications across harmonic analysis, number theory, and theoretical computer science. Key goals include: (1) interpreting the 2012 Parsell-Prendiville-Wooley argument on translation-dilation invariant systems in decoupling language; (2) developing new mixed norm decoupling inequalities relevant to dispersive partial differential equations and extending known Strichartz estimates; (3) adapting the high-low method to the moment curve setting to derive logarithmic power bounds for decoupling constants, giving potential number theoretic applications; (4) proving new refined decoupling estimates and applying them to problems like the Mizohata-Takeuchi conjecture; and (5) exploring connections between Euclidean harmonic analysis and Boolean function analysis, especially the Bohnenblust-Hille inequality and the Friedgut-Kalai-Naor Theorem, with implications for learning theory. Expected contributions include sharper bounds in decoupling theory, new decoupling theorems for highly general surfaces, improved maximal function bounds for the Schrödinger equation on the torus, and new analytic tools for use in algorithmic contexts. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2025 · 2025-08

This project aims to serve the national interest by organizing coordinated multi-site replication studies on open computing education research questions. Research relies on the ability to replicate studies to understand the limits and applicability of research findings and to build confidence in results. Replication studies are especially important in computing education research where results are drawn from human subjects studying computing. The goal of this IUSE:EDU level two Institutional and Community Transformation project is to create cohorts of researchers to design replication packages for rigorous research that will support cross-study meta-analysis, build fundamental knowledge on key topics, and provide the foundation for computing education research theory building. The project will involve approximately ten core replication designers and forty replication participants impacting thousands of students across the United States. A replication design team will be convened to create replication packages that address research goals and questions of interest to the computing education research community. A project team will then be formed to recruit a series of replication participants who will run the study in their context using the replication packages produced by the replication design team. Members of the computing education research community will be invited to run the replications in their classrooms, contributing to the overall investigation into the topic. Results of these studies will address common research questions and lay the foundation for computing-specific educational theory. The results of the coordinated, multi-site replication studies will provide deeper insights into important computing education research topics and will provide a better understanding of the impacts of various contextual factors on improving student learning and success in computing. The project evaluation will consist of formative and summative assessments of the replication process and objective measures such as publications and citations. The NSF IUSE: EDU Program supports research and development projects to improve the effectiveness of STEM education for all students. Through the Institutional and Community Transformation track, the program supports efforts to transform and improve STEM education across institutions of higher education and disciplinary communities.  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.