Virginia Polytechnic Institute and State University

NSF Awards · FY 2026 · 2026-08

Safeguarding the resilience of America’s vast natural resources depends on scientific knowledge to help streamline and direct monitoring efforts. Forecast models are critical for prioritizing resources in a fast-paced, changing world, but knowing what data is needed to accurately parameterize these models remains a challenge. For example, wild animal populations are made up of both males and females, and it is generally assumed that both are equally sensitive to environmental changes. However, new research is revealing that this may be an oversimplification. Knowing when and where such vulnerabilities are most likely to arise will be key to increasing the efficiency and efficacy of monitoring operations. To validate findings, the project will leverage machine learning to analyze model outputs, while also applying cutting-edge biotechnology to track the small-bodied amphibians in their natural environment. These efforts will contribute to the generation of large, publicly available ecological monitoring datasets, ideal for AI model training. The project will also advance the education and training of the nation’s future STEM workforce with a new, hands-on research course at Virginia Tech, with datasets also implemented as modules in classes and summer data camps for undergraduates and high schoolers. In each case, students will be trained in the use of Artificial Intelligence to automate, debug, and translate code, enhancing data literacy and teaching foundational concepts in computer programming through engagement in relevant, real-world challenges. The goal of this project is to advance knowledge of the ecological conditions in which differences in thermal plasticity are most likely to arise and when they will be important for population adaptation. Plethodontid salamanders are well suited to these investigations as they exhibit natural variation in trait-specific life histories associated with abiotic conditions, and thermal acclimation capacity has been shown to vary by trait and reproductive condition. This work will provide robust empirical tests of the ecological factors underlying trait-specific thermal plasticity by characterizing and comparing variation in thermal acclimation capacity (Aim 1) and thermoregulatory behavior (Aim 2) across natural elevational gradients. Empirically derived estimates, together with machine learning algorithms, will then be used to construct and analyze mathematical models exploring how variation in individual energetics and population recruitment emerge from realistic patterns of trait-specific plasticity (Aim 3). Finally, empirical patterns and model predictions will be validated in marked, wild populations of salamanders using biotechnology-enabled remote monitoring, which will generate large, publicly-available datasets spanning multiple dimensions of ecological data (Aim 4). Both mechanisms will serve to train the next generation of scientists in use of AI to enhance programming capabilities, data literacy, and quantitative reasoning. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2026 · 2026-08

Video is rapidly becoming the dominant medium for communication, education, and entertainment, yet creating high-quality video content remains costly and technically demanding. Current artificial intelligence models can generate impressive videos from text descriptions, but they operate as black-boxes, offering users little control over the content, motion or camera perspective. This project aims to develop a new paradigm for controllable video generation by unlocking the rich but hidden capabilities embedded within powerful pre-trained video generation models without requiring any additional training or curated data. The resulting framework will give users director-like control over visual content, motion, and camera dynamics and will make powerful creative technologies broadly accessible. Integrated education and outreach activities will broaden understanding of generative artificial intelligence across audiences from elementary school students to creative industry professionals. The research is organized into three interconnected thrusts. Thrust 1 establishes a foundational understanding of text-to-video models by investigating how their internal mechanisms, including noise, attention, and positional embeddings influence generation. Thrust 2 builds on these insights to develop training-free methods for controlling content, motion, and camera viewpoints. Thrust 3 integrates these capabilities into a multi-agent collaboration framework that autonomously reasons, plans, and iteratively refines video synthesis, with built-in self-repair capabilities. The project will produce open-source tools, datasets, and interactive demonstrations, contributing both theoretical understanding of latent representations in video diffusion models and practical advances toward generative cinematography as a universally accessible medium. 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

Generative Artificial Intelligence (AI) is rapidly transforming software engineering, with a substantial portion of new code being generated by AI. As generating code becomes easier, the bottleneck shifts to maintaining it through testing, debugging, and repairs. Effective software maintenance fundamentally depends on a deep, accurate understanding of software behavior, yet it remains unclear how well current AI models truly understand software. This project systematically studies and improves AI's understanding of code in realistic maintenance settings. The project's novelties are a principled, evidence-driven methodology for characterizing and strengthening AI model's code comprehension beyond ad hoc benchmarks. The project's broader significance and importance are improved reliability and trustworthiness of automated coding assistants, which benefits software developers as well as scientists and engineers who increasingly rely on AI-generated software. The project establishes a foundation for studying, assisting, and advancing Large Language Models' ability to understand code for effective software maintenance. The research first assesses how well contemporary models understand software and develops a framework that automatically generates controlled, unseen, and dynamic proxy tasks as targeted assessments of code understanding. Based on the cataloged weaknesses, the research investigates software maintenance task redesign strategies that accentuate cognitively demanding code and task patterns to enhance model comprehension. To address the challenge of increasing reliance on self-generated training data, which can cause performance plateaus, this work introduces a new usage paradigm that leverages human expertise and traditional program analysis signals to create high-quality learning examples. This allows models to improve code understanding while reducing cognitive effort by reusing prior reasoning. The expected outcomes include robust evaluation methodologies, improved alignment between large language models and software semantics, and open-source tooling. This research strengthens the reliability of AI-assisted software development and benefits the broader scientific community, which increasingly depends on AI-generated software. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2026 · 2026-07

This project will examine how undergraduate students who are also military veterans develop engineering identity, professional confidence, and a sense of belonging over time as they move through capstone design experiences in engineering and engineering technology programs. The project also explores how engineering identity interacts with an identity as a veteran within a capstone environment. Students who are also military veterans ("Student Veterans") bring valuable prior experiences to engineering education, including leadership, teamwork, technical training, mission-focused problem solving, and experience working under real-world constraints. However, little is known about how these prior experiences shape their transition into engineering roles, especially during capstone design, where students are expected to integrate technical knowledge, communicate with teammates and stakeholders, make design decisions, and begin seeing themselves as members of the engineering profession. This project will study how Student Veterans make meaning of their prior military identity while developing an emerging engineering identity, given that both are strong role identities. This work will contribute to our understanding of the professional formation of engineers which includes investigating how and why people become engineers, how identity develops through formal and informal educational experiences, and how engineering programs can better support students who enter engineering through nontraditional pathways. The project also expands the engineering education research community, as required by the funding program. The work here will serve the national interest by strengthening understanding of how military-affiliated students, adult learners, career changers, and other non-traditional students can be supported in engineering education and prepared for engineering careers. This is important for the nation’s technical workforce given that critical sectors such as advanced manufacturing, semiconductors and microelectronics, artificial intelligence, quantum information science, biotechnology, infrastructure, defense, and energy depend on graduates who are technically capable, adaptable, collaborative, and able to solve complex problems. The project will advance knowledge in engineering education by focusing on understudied undergraduate experiences, and produce recommendations that engineering and engineering technology faculty, advisors, veteran-support offices, and program leaders can use to improve capstone instruction, mentoring, advising, and student-support practices, improving the efficiency and effectiveness of undergraduate STEM education. This project will use an exploratory qualitative case study design focused on Student Veterans enrolled in engineering and engineering technology capstone design experiences at Austin Peay State University. The study will use an engineering identity framework that examines interest, competence, and recognition as key dimensions of how students come to see themselves as engineers. The project will address three research questions: (1) how Student Veterans experience and describe their developing engineering identity during senior capstone design projects; (2) what aspects of the capstone environment, such as teamwork, mentorship, technical challenges, sponsor interaction, and project expectations, support or inhibit identity formation; and (3) how Student Veterans reconcile their prior military identity with their emerging identity as engineers. The research team will collect data through a longitudinal sequence of semi-structured interviews conducted across the capstone experience, supplemented by relevant capstone artifacts when participants provide consent. The research team will analyze the data through first- and second-cycle qualitative coding, within-case analysis, and cross-case analysis to identify patterns in how identity develops over time. Data analysis will expand researchers' understanding of Godwin's identity theory, which hinges on the concepts of recognition (by peers and instructors), competence (in technical work), and interest (in engineering as a discipline). By studying veterans, the team will increase understanding of the importance of those concepts on identity formation more generally. The project will be a collaboration between Austin Peay State University and Virginia Tech, with engineering education research mentoring supporting the principal investigator’s development as a scholar in professional formation of engineers. The project will also include mentoring for a graduate and undergraduate research assistant, who will gain experience with human-subjects research ethics, qualitative data practices, research documentation, and scholarly dissemination. Expected outcomes include peer-reviewed publications, conference presentations, a refined understanding of how capstone experiences shape Student Veteran engineering identity, and evidence-based recommendations for supporting Student Veterans and other nontraditional students in engineering capstone environments. The findings will lay the groundwork for future engineering education research on identity formation, transition to practice, and inclusive capstone design education across institutional contexts, as well as practical insights for institutions of higher education with large military populations, or seeking to improve its support of military populations. 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

Better teaching and more capable artificial intelligence (AI) both depend on understanding how learning can become faster, more efficient, and more flexible. Structured learning, in which information is presented in a meaningful order so that learners first understand simpler concepts and then build toward more complex ones, is known to improve learning. However, it is still not well understood how this kind of scaffolding helps the brain turn experience into flexible knowledge that can be used to intelligently solve new problems. This question is especially important because humans are far better than current AI systems at applying what they have learned to new situations, whereas many of the current AI systems require substantial retraining even when only small parts of a task or environment change. This project investigates how different parts of the brain work together during structured learning to build knowledge about how a task is organized. By revealing how structured learning helps the brain build flexible and accurate knowledge from fewer experiences, this research will advance understanding of efficient human learning and intelligence, inform better teaching, and provide insights relevant to AI research on how systems might learn from less data and perform better in new situations. The project examines how the structure of learning experiences shapes the brain mechanisms that support abstraction, generalization, and flexible decision-making. It tests the idea that well-structured curricula help the brain organize knowledge compositionally, so that smaller pieces of knowledge act like building blocks that can be combined in new ways to solve problems that were not directly taught. Across three related studies, the research asks when structured and unstructured learning begin to diverge, how structured experience changes how the brain represents tasks in the hippocampus and prefrontal cortex, and how prior structured learning shapes the brain representations that support more efficient learning in new, more complex tasks. To address these questions, the project combines behavioral experiments with magnetoencephalography (MEG) and functional magnetic resonance imaging (fMRI) to track when brain representations of task structure emerge, how they are organized, and how they transfer across tasks under different learning conditions. There are plans to use computational models and analyses of brain activity patterns to track how learners come to understand hidden task structure and how the brain’s representation of that knowledge develops over the course of learning. Together, these studies aim to establish how structured learning shapes brain representations that allow reusable knowledge to be flexibly combined across tasks, supporting efficient learning and problem solving in new situations. In doing so, the project aims to advance understanding of the brain mechanisms that support intelligence and flexible decision-making, while providing insights relevant to education and AI research on how learning can become more efficient and generalize more effectively. 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

Modern science and engineering increasingly rely on numerical simulations of systems with large numbers of interacting variables: new quantum materials with intricate atomic geometries, hot plasmas inside fusion reactors, time-resolved medical image, and many more applications. Storage and manipulation of such high-dimensional information are often impossible using conventional methods, because the required memory and runtime grow exponentially with the number of variables. This project develops a new set of mathematical tools that represent this information in compact, structured form and use carefully designed random projections to extract answers far more efficiently than was previously possible, while rigorously ensuring a near-zero probability of failure. These techniques directly address scalability bottlenecks in modern AI and machine learning pipelines, where high-dimensional tensor operations are increasingly central to large-scale model training and inference. The resulting methods accelerate discovery in areas central to national priorities, including quantum science, where they enable improved quantum chemistry calculations, and the design of next-generation quantum moire materials for electronics; fusion energy, where they support fast digital twins of plasma turbulence in tokamak reactors; and medical imaging, where they speed up reconstruction of dynamic scans from limited data. The project also trains undergraduate and graduate students at Virginia Tech in modern computational mathematics, develops new courses that integrate tensor-based algorithms with high-performance scientific computing, and supports K-12 outreach through the Blacksburg Math Circle using visually engaging topics such as the geometry of moire patterns. All algorithms and software are released openly so that researchers across many disciplines can adopt, extend, and benefit from the advances. This project develops a new class of randomized sketching operators, the block-structured TTStack sketches, that combine the storage efficiency of tensor-train representations with rigorous embedding guarantees of Johnson-Lindenstrauss type. These sketches are designed to overcome two well-documented limitations of existing tensor-network sketching: the exponential scaling in tensor dimension that afflicts Khatri-Rao embeddings, and the absence of provable subspace embedding guarantees for current Gaussian tensor-train sketches. The research pursues four coordinated objectives. First, it establishes oblivious subspace embedding and distortion bounds for TTStack sketches and characterizes the trade-offs among sketch size, accuracy, and block structure. Second, it develops scalable randomized algorithms that operate directly in tensor formats, including sketch-and-compress routines for rank-increasing operations, randomized Krylov solvers such as TT-GMRES and randomized Lanczos for linear and eigenvalue problems, and streaming tensor-train approximation methods that overcome current dimensional limitations. Third, it builds theoretical foundations for non-Hermitian iterative eigensolvers in tensor format, enabling reliable ground-state calculations for transcorrelated quantum chemistry Hamiltonians where standard variational methods fail. Fourth, it validates the methods on demanding benchmarks drawn from quantum many-body physics, gyrokinetic plasma turbulence compression, dynamic medical imaging reconstruction, and atomic-scale modeling of multilayer moire quantum materials. High-performance open-source Julia implementations, designed for algorithmic experimentation and reproducibility, are released to the community to support broad adoption and to seed further interdisciplinary advances in computational science. 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

ALPS, which stands for Applications and Libraries for Physics Simulations, is a freely available package of open-source software. This software enables important scientific research and education in various areas of advanced physics research such as quantum computing. It is used by researchers in over 50 countries and has resulted in thousands of publications. This project will transition ALPS software development and maintenance from a project largely led by a single research group into a collaborative open-source ecosystem with many participating contributors, putting it on sustainable long-term footing. Steps to transition ALPS into an open-source ecosystem includes development of recruitment protocols, trainings, and clear management protocols. ALPS will become a broadly supported, distributed community effort with a clearer leadership structure. ALPS comprises a wide array of physics modelling software that enables studies of condensed matter and materials physics, quantum computing, atomic and molecular optics, quantum chemistry, and many other areas of physics and engineering. The project establishes infrastructure to sustain maintainability and the quality of the code base, establishes clear recruitment protocols and strategies, improves accessibility and documentation, and develops user training materials. 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

Access to reliable broadband Internet shapes who can participate fully in modern life, from schoolwork to healthcare to job opportunities. Yet understanding where reliable Internet is actually available turns out to be surprisingly hard: directly measuring every community is too expensive and slow to do at national scale. This project develops techniques for learning the patterns behind where and how Internet service providers build their networks, making it possible to estimate where reliable Internet is available, identify gaps, and support efforts to ensure public money intended to connect communities reaches those that need it most. Beginning from the observation that Internet networks are planned and built according to geographic, economic, and regulatory constraints, this project develops a modeling framework for broadband infrastructure that learns spatial and temporal patterns in how Internet service providers deploy their networks. Drawing on techniques from Internet measurement and machine learning, the project is organized around three predictive tasks: forecasting how providers change claimed coverage over time, inferring likely service availability from observable spatial and demographic features, and assessing the validity of claimed service performance. By reframing broadband measurement as a prediction problem, this work expands the methodological toolkit available for reasoning about Internet infrastructure at scale under uncertainty. The project strengthens public oversight of broadband deployment by developing open tools that help researchers, communities, and regulators to evaluate whether Internet service is available where providers claim it is. The models, datasets, and software produced in this project will be integrated into public dashboards for use by local, state, Tribal, and federal policymakers; community advocates; individuals; and researchers. The project also establishes a Broadband Data Clinic, an experiential learning program in which undergraduate students work with community partners on real-world broadband data challenges. This clinic will provide hands-on training at the intersection of networking, data science, and public-interest technology, while delivering free technical assistance to communities facing broadband data challenges. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2026 · 2026-06

The Collaborative Research in Computational Neuroscience (CRCNS) program supports a broad spectrum of investigators advancing computational understanding of nervous system structure and function, mechanisms underlying nervous system disorders, and computational strategies used by the nervous system. The goal of this meeting of CRCNS Principal Investigators is to foster interaction and collaboration across this vibrant community, highlighting the intellectual advances and broader impacts of CRCNS awardees. The meeting, scheduled for November in Alexandria, Virginia, is hosted by the Virginia Polytechnic Institute and State University and the planned schedule includes poster presentations, talks, and plenary lectures, covering all areas of computational neuroscience represented by funded projects in the program. The meeting is planned to include projects involving the United States, France, Germany, Israel, Japan, and Spain, sponsored by NSF and eight other partner agencies. This international meeting should have a significant impact on the participants and the future of computational neuroscience, including applications to artificial intelligence, biotechnology, and translational research. The meeting results are planned to be publicized to the research community through publications and the meeting website. The broader impacts of the meeting are to facilitate progress in the field and stimulate conversations, connections, and collaborations that will lead toward a better informed and effective CRCNS research community and resulting technologies that will be beneficial to all Americans. The conference is also planned to occur in the DC area at the same time as the Society for Neuroscience conference, providing an opportunity to foster new collaborations and showcasing the current projects supported by CRCNS. 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

This project aims to develop the cryptographic foundations for secure communication in the post-quantum era. Most current public-key cryptographic systems, which underpin the security of modern digital infrastructure, rely on the hardness of problems such as integer factorization and the discrete logarithm problem. These problems can be solved efficiently by quantum algorithms (using Shor’s algorithm), making today’s cryptographic systems vulnerable to future large-scale quantum computers. Post-quantum cryptography seeks to replace these vulnerable systems with new cryptographic primitives based on mathematical problems believed to remain hard even against quantum adversaries. While recent standardization efforts have primarily focused on basic primitives such as encryption and digital signatures, many emerging applications, including decentralized systems and privacy-preserving protocols, require more advanced cryptographic functionalities. This project addresses this gap by designing and analyzing post-quantum cryptographic protocols with advanced features. The research focuses on algebraic structures arising from lattices, error-correcting codes, and group actions, including isogeny-based cryptography. Key objectives include the construction of protocols supporting advanced functionalities such as blind signatures, threshold signatures, multisignatures, and verifiable random functions, together with rigorous security proofs based on well-defined computational assumptions. The central methodological tools include zero-knowledge proofs and group action frameworks. The project will also investigate improvements in efficiency, security, and scalability of the protocols. Expected outcomes include new provably secure cryptographic schemes, conceptual advances in the use of algebraic methods for post-quantum cryptography, and contributions to the training of students through research mentoring and outreach activities. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2026 · 2026-05

This Faculty Early Career Development Program (CAREER) grant will advance national energy security and economic welfare by developing improved tools for optimizing complex energy and industrial systems. Critical infrastructures, such as electric power grids and chemical refineries, depend on solving large optimization problems to determine safe and efficient operating conditions. Current optimization tools are inadequate for large-scale planning and operational needs, limiting the ability to operate these energy systems efficiently, modernize them, and maintain resilient operations. This project will create a new generation of optimization algorithms that use machine learning to leverage shared structure in real-world applications, significantly accelerating solution times while preserving mathematical guarantees. It will develop new machine learning techniques to guide key algorithmic decisions in optimization algorithms while ensuring scalability, generalizability, and data efficiency. These advances have the potential to transform how energy systems are designed and operated, enabling more efficient operations, improved reliability, and lower operational costs and environmental impact. The educational plan will introduce optimization and machine learning concepts into high-school classrooms through an interactive web-based tool, teacher workshops, and partnerships with regional schools. Undergraduate research, new graduate modules, and interdisciplinary workshops will prepare the next-generation workforce at the interface of artificial intelligence, optimization, and engineering. This research will build a unified, theory-driven framework that leverages machine learning to enhance branch-and-bound algorithms for the guaranteed global optimization of mixed-integer nonlinear programs. It will (1) formulate new expert branching policies and develop supervised graph-based machine learning methods to imitate them; (2) create semi-supervised learning methods to generate high-quality feasible solutions and warm-starts; (3) use machine learning to accelerate decomposition algorithms; and (4) design generative machine learning models that construct realistic and varied families of mixed-integer nonlinear programs. These contributions will advance the understanding of effective branching strategies, reduce reliance on expensive labeled data, enable generalization to large-scale problems, and aid in benchmarking and sustained innovation. The project will produce open-source algorithms and benchmark libraries for power systems and refinery optimization, enabling rigorous evaluation of machine learning-guided global optimization methods. These contributions will significantly improve the efficiency, robustness, and scalability of global solvers, advancing the scientific foundations of global optimization and supporting high-impact applications across energy and large-scale industrial systems. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2026 · 2026-05

This project seeks to understand how marine animal body size, and thus biodiversity, have been shaped by environmental change over Earth’s history. By assembling the most comprehensive database of fossil body size measurements spanning the last 575 million years, the project will test whether the appearance of new marine animals follows predictable patterns linked to environmental factors. While most paleontological work has examined why animal taxa disappear, this study focuses on how new taxa originate, filling a fundamental gap in understanding of biodiversity generation. The findings will improve forecasts of how today’s rapidly shifting environments may affect species emergence and ecosystem resilience, issues that intersect national interests in food security, coastal economies, and biodiversity. The project will (1) build on a standardized database of marine animal body size measurements, incorporating previously unpublished Ediacaran body-size data; (2) test whether size bias of origination differs among major taxonomic groups, varies through geologic time, and changes consistently under distinct environmental regimes; and (3) evaluate the influence of sampling completeness on observed selectivity patterns. Body size is the chosen metric because it is easy to measure and correlates with key animal traits such as metabolic rate and generation time. Statistical models will be applied to assess correlations between body size trends and proxies for marine anoxia, temperature, and other environmental variables. The resulting analyses will quantify origination selectivity, a dimension that has been largely undocumented, and will generate open-access data for the broader scientific community. The anticipated outcomes include improved predictive tools for assessing how future environmental change may shape biodiversity, and training the STEM workforce. 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

Many important artificial intelligence computing systems rely on algorithms to make decisions about scheduling, routing, and the use of limited resources. Traditional algorithms are dependable because their performance can be guaranteed in all cases, but they often cannot take advantage of recurring patterns in data that could make them faster or more effective. By contrast, machine learning methods can detect useful patterns, but their guidance may become unreliable when conditions change or when the learned estimates are inaccurate. This project develops new algorithmic methods that combine the reliability of traditional algorithms with the adaptability of machine learning. The resulting advances can improve the efficiency and robustness of computing systems that support modern infrastructure, while also helping train students in an emerging area at the intersection of algorithms and data-driven decision-making. This project studies learning-augmented algorithms, also known as algorithms with predictions, which use machine-learned forecasts to improve performance while retaining provable worst-case guarantees. The research will investigate several directions: determining how to achieve strong improvements using fewer or weaker predictions; designing prediction frameworks that apply across broad classes of algorithmic problems; and extending the learning-augmented framework to problems that have previously been studied through other beyond-worst-case approaches. These efforts will establish new performance guarantees, clarify when prediction-guided methods are effective, and deepen understanding of the benefits and limitations of combining machine learning with rigorous algorithm design. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2026 · 2026-05

Support will be provided for 12 or more US-based early-career scientists and graduate students to attend the 2026 International Linear Algebra Society Conference, May 18 - 22, 2026 at Virginia Tech. Linear algebra serves as the foundation for many exciting innovations in data science, machine learning and artificial intelligence, quantum information science, computer science, and engineering. This international conference brings together experts from across the world. Hence, it provides an excellent networking opportunity for young US scientists, introduces them to the frontier of linear algebra and its applications, and helps them develop important collaborations. Preparing the next generation of US-based linear algebra experts to make important contributions in linear algebra theory and computational methods, and their real-world applications in artificial intelligence, cyber-security, and quantum computing, is essential to US scientific preeminence and will have a lasting impact on US economic competitiveness, welfare, and national security. Efficient and reliable computation in high-dimensional linear and multilinear algebra lies at the core of modern science and engineering, underpinning advances in modeling, simulation, optimization, and control, and sustaining a wide range of scientific and technological advances, ranging from engineering, data science, and artificial intelligence, to cryptography, and quantum computing. For the 2026 ILAS conference, the organizers aim to maintain a strong balance between theoretical developments, algorithmic innovation, computational applications, and linear algebra education. The participation of a substantial contingent of highly talented early-career linear algebra researchers will strengthen the breadth and vitality of the linear algebra community and reinforce the continued leadership of the United States in this foundational field, which is vital to US economic competitiveness and national security. The conference features several mechanisms that support the participation and research careers of young US-based linear algebra scientists, such as low registration fees, a mentoring panel, excellent networking opportunities, and additional funding support for US-based early-career scientists and graduate students. The conference (i) advances workforce development by engaging Ph.D. students and early-career scientists in discussions and presentations of state-of-the-art research in linear algebra and its applications and by providing mentorship and increased visibility within the community, (ii) strengthens undergraduate education by providing educators with opportunities to interact with leading linear algebra researchers, (iii) stimulates new ideas for NSF and industry supported research collaborations, and encourages partnerships between academia, industry, and government, and (iv) increases US economic competitiveness by strengthening continued US leadership in this foundational field. 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

Immersive virtual and augmented reality technology is increasingly used to support entertainment and socialization applications. However, manipulative or deception techniques in virtual and augmented environments are rapidly developing and present a risk to user autonomy, privacy, and livelihood. This project explores emerging manipulative designs, develops models of their effects on user decision-making, and builds tools for detecting deceptive patterns in these environments. This project investigates risks from the perspective of visual perception, modeling how user gaze behavior responds to manipulative visual stimuli. The project establishes gaze guidance techniques that make use of perceptual response to motion in the user’s periphery to guide attention and evaluate the resulting risk of deception. The project team is developing methods that support the detection of deceptive designs by leveraging sensor data across virtual and augmented applications. Project findings will inform future developers of these 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 2026 · 2026-05

Non-technical Description: The project consists of the acquisition of a versatile instrument for experimental characterization of solid electronic materials and electronic devices over a range of temperatures, from above room temperature to deep cryogenic temperatures. The instrument enables studies by a community of scientists and engineers aiming to extend the limits of a wide range of electronic materials and devices. Important impacts exist in both science and technology regarding microelectronics, sensors, and quantum information science. Research projects enabled by the instrument use a combination of variable and low temperatures and high magnetic fields for the characterization and understanding of electron states in new materials. The instrument is specifically designed for ease of use and for high measurement throughput. These attributes also enable educational activities using the instrument, to produce a trained workforce in microelectronics, semiconductors, and quantum sciences via educational partnerships and an experiential open inquiry course meshing with quantum technologies degrees. The instrument also sees use by a nearby technology company for research and development projects. Technical Description: The project pursues the acquisition of a variable temperature instrument towards electronic and quantum materials and electronic device measurements. The instrument consists of a cryogen-free cryostat with a variable temperature insert space in which top loading sample probes are inserted for measurements. A helium-3 probe allows sample temperatures from 0.3 kelvin to about 60 kelvin. A standard probe allows sample temperatures from 1.3 kelvin to 325 kelvin. The instrument is equipped with a superconducting magnet for controlled bipolar magnetic fields up to 8 tesla. The system is capable of fully automated cooldown and control, and it features fast sample cooldown for higher measurement throughput. The instrument is cryogen free, so that its operation does not use costly, scarce and strategic liquid helium. The research enabled by the instrument lies in low temperature and variable temperature magnetotransport measurements, meaning electronic characterization under a magnetic field of solid-state materials and electronic devices, using excitation frequencies from low to ultra-high. Major research projects include the characterization of semiconductor structures with applications in electronic devices, high-power electronics and photovoltaics, development of piezoelectric and magnetostrictive materials for quantum technologies, advances in the use of nuclei as spin bearing entities with long lifetimes for novel data storage platforms, studies of quantum spin transfer in spintronics, characterization and development of quantum materials and of magnetic sensors based on quantum interference, and studies of nanofilament memristors. 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

In the coming decades, quantum computing is expected to expand the frontiers of scientific computing by enabling computational tasks that are otherwise difficult to perform. A fundamental task in this area is learning the properties of quantum states, with applications to verifying quantum devices, developing quantum algorithms, ensuring the security of quantum communication, and probing the foundations of quantum mechanics. However, quantum systems are highly sensitive to noise, making it difficult to build reliable quantum computers in practice. This project develops new methods for learning quantum states that remain reliable even in the presence of noise. Understanding how to mitigate noise is essential for designing scalable, fault-tolerant quantum hardware and enabling reliable implementation of advanced quantum algorithms. This project will also support the education and training of students and contribute to educational activities, including summer camps and lecture materials, that help prepare the future quantum information workforce. By advancing fundamental research and education in quantum information science, this work will contribute to the continued development of quantum technologies. This project develops new theoretical frameworks and algorithms for learning quantum states in practical settings. The research will design and analyze algorithms that are robust to noise and data fluctuations and characterize their performance and limitations. It will develop methods for classifying quantum states, analyzing their behavior in noisy environments, and exploring their applications. The project will also develop methods for learning quantum states from their preparation devices, with an emphasis on identifying when such tasks can be performed optimally. These approaches combine techniques from quantum information theory and optimization to produce algorithms with provable performance guarantees and practical applicability. These results will deepen understanding of quantum state learning and inform the design of efficient and reliable quantum algorithms suitable for use on real quantum hardware. 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

With the support of the Macromolecular, Supramolecular and Nanochemistry Program in the Division of Chemistry, Yuanbing Mao of the Illinois Institute of Technology (IIT) and Tao Li of Northern Illinois University (NIU) will investigate the molten salt synthesis of metal oxide nanoparticles. Nanoparticles are attracting wide interest due to their exceptional properties enabling a wide range of applications in clean energy, photonics, pharmaceutical industry and more. Molten salt synthesis has emerged as a reliable and scalable method to synthesize different types of nanoparticles. In these studies, the Mao-Li collaborative team will use light scattering techniques to follow the nanoparticle growth processes by the molten salt synthesis at Illinois Institute of Technology and Northern Illinois University through a partnership with Argonne National Laboratory. The team seeks to help elucidate the synthesis mechanisms of nanoparticles in molten salts to guide the synthetic process. The collaborative team plans to introduce specific educational activities on nanochemistry and advanced characterization into the graduate curriculum. The IIT and NIU teams also aim to broaden participation via diverse recruiting of underrepresented students by providing research experiences for undergraduate and graduate students. The team will work to recruit prospective female and minority students and to perform outreach presentations to K-12 students. This collaborative project focuses on the elucidation of the complex nucleation and growth phenomena in the molten salt synthesis of metal oxide nanoparticles. The NIU/IIT team will combine X-ray absorption spectroscopy and small-angle X-ray scattering to achieve in situ monitoring of nanoparticle growth with high temporal and spatial resolution. The team seeks to unravel the nucleation and growth mechanisms of molten salt synthesis for metal oxide nanoparticles. This is to be achieved by determining particle size, size distribution, and chemical composition over a wide range of time and length scales, from early precursor reactions and metastable intermediates and aggregated states to the final growth states. This study is designed to explore the mechanism of nanoparticle growth under molten salt conditions and compare the characteristic empirical nucleation and growth phases reported in the recent literature with that observed in the present study. This investigation should answer establish whether nonclassical nucleation and growth processes are generally observed for the molten salt synthesis process and how these complex systems can be described in terms of their nucleation and growth kinetics. If successful, such insights and increased chemical understanding would provide guidance to the scientific community for molten salt synthesis of other classes of materials. The scientific implications of this work could be quite substantial; the ability to rationally synthesize a given size and polymorph or phase of nanoparticles is highly desirable. These studies have the potential to provide guidance how to control these features of nanoparticles, features that dictate their mechanical, optical, catalytic, and electronic properties. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2026 · 2026-02

The United States faces a narrowing window to reestablish leadership in future global networking technology standards - the foundational rules that determine how next-generation communications systems (5G/6G, cloud/edge, AI-driven network management, and critical Internet infrastructure) are built, secured, and deployed. While U.S. innovation remains unmatched, U.S. influence in international networking standards bodies such as International Telecommunications Union (ITU) and Third Generation Partnership Project (3GPP) has weakened. Peer competitors are actively conducting coordination and collaboration to shape standards to favor their industrial policies and intellectual-property portfolios. Without a cohesive national strategy, the United States risks diminished influence over the technical and security foundations of future global internet/communications ecosystem undermining economic competitiveness, technology innovation, and supply chain resilience. The proposed Workshop is aimed as a forum for achieving strategic alignment via an all-hands approach among academia, government and the private sector to potentially: 1. Strengthen U.S. presence and influence in standards to compete globally by reducing barriers to market entry; 2. Spur domestic innovation in key supporting industries (cloud, semiconductors, AI infrastructure, telecommunications) through stable, widely adopted standards and encourage greater private-sector R&D investment by providing clearer pathways for commercialization; 3. Incentivize broad, sustained U.S. participation from startups, subject matter experts (SMEs), academia, and open-source communities who currently lack resources but drive much of the innovation shaping next-generation networks; 4. Establish national standards coordination framework that synchronizes R&D priorities, spectrum policy, and procurement with standards development timelines; and 5. Protect and advance U.S. security and economic interests to support secure-by-design architectures, supply chain transparency, interoperability, and resilience. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2026 · 2026-02

Reproductive organs such as the cervix and vagina change during pregnancy to aid fetal growth and delivery. The tissue structures in these organs also reorganize. Defects in this reorganization can lead to complications such as spontaneous preterm birth. The connections between tissue structure abnormalities and pregnancy complications are not well understood. This project will address this knowledge gap by using diffusion magnetic resonance imaging (MRI) to observe how tissue structures change during pregnancy. MRI imaging will be combined with computer simulations and machine learning to create three-dimensional models that can track tissue changes. The results will provide a tool for early diagnosis of abnormalities during pregnancy and labor, which can improve healthcare outcomes. The project will help train undergraduates, graduate students and pre-college students in engineering, high-performance computing, and machine learning. Abnormalities in microstructural reorganization of cervical and vaginal tissue have serious effects during pregnancy, but correlations between microstructural alterations and adverse outcomes are not well understood. This project will integrate advanced modeling and medical imaging to enable non-invasive, in vivo mapping of reproductive tissue microstructure throughout pregnancy. Microscopy data of murine reproductive tissue will be used to generate realistic 3D tissue domains that will be combined with computational models to enable voxel-scale simulations of diffusion MRI physics in tissue. Diffusion MRI protocols will be optimized to increase sensitivity to tissue microstructure and to train data-driven models that estimate tissue microstructural organization. Models will be experimentally validated using preclinical MRI scanners. This project will provide unprecedented insight into how reproductive tissues remodel during and after pregnancy. The project will create a validated modeling platform of diffusion MRI physics, which applies to all soft tissues where microstructural organization plays an important role in physiological function, such as muscle, renal, and cancer tissues. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2026 · 2026-02

NON-TECHNICAL SUMMARY This research project is addressing a major challenge in designing advanced metal alloys that resist corrosion in harsh environments. High entropy alloys, which contain several elements in nearly equal amounts, show strong potential for corrosion resistance, but identifying the best compositions is difficult because the design space is extremely large. This project is developing a machine learning framework that integrates experimental measurements and physics-based descriptors to predict corrosion rates and identify why alloys fail. By learning from both laboratory data and computer simulations, this data-driven approach accelerates materials discovery far beyond traditional, slow, and expensive trial-and-error testing. The framework is also revealing the mechanisms that govern the formation and breakdown of protective surface films on alloy surfaces. This research is promoting the progress of science by generating new understanding of corrosion processes in complex alloys and by enabling the design of next-generation materials with improved durability. This project is advancing the national health, prosperity, and welfare by supporting the development of longer-lasting materials for critical sectors such as energy, transportation, marine, aerospace, and biomedical engineering, where corrosion causes costly failures. The project is also strengthening workforce development by training students in materials science, electrochemistry, machine learning and data science. Through outreach events for K–12 students, the project is inspiring future scientists and engineers and expanding participation in STEM fields. All data, models, and software created through this award are being shared openly, ensuring broad public benefit. TECHNICAL SUMMARY This research project is developing an integrated experimental, computational, and machine learning framework to predict and understand corrosion behavior in high entropy alloys. The work focuses on quaternary alloy systems and is advancing a physics-informed machine learning model that combines electrochemical measurements, density functional theory calculations, and mechanistic descriptors. The project is identifying the material features that govern corrosion rates, passivation behavior, selective dissolution, and surface film stability in high entropy alloys. The technical objectives include: (1) developing a predictive model that provides corrosion rate estimates and uncertainty bounds for a broad set of alloy compositions; (2) refining model performance through active learning, where new experiments are guided by regions of high prediction uncertainty; (3) identifying controlling mechanisms by analyzing the descriptors selected by the machine learning model, then validating them through surface characterization and atomistic simulations; and (4) generalizing percolation-based passivation theory, originally developed for binary alloys, to multi-principal element systems. This generalized framework is revealing how the connectivity of elements that promote passivation influences the formation and repair of protective surface films across a wide compositional space. This work is generating new knowledge on the multiscale relationships among composition, electronic structure, surface reactivity, and corrosion resistance in concentrated alloys. The approach is producing a mechanism-guided pathway for alloy design that reduces reliance on empirical screening and lowers the cost and time required to discover corrosion-resistant materials. The research aligns with the mission of NSF by advancing fundamental scientific understanding and providing tools for rapid materials discovery. Broader impacts include an open-source machine learning toolkit, publicly available data resources, and training for students in interdisciplinary materials design. Project outcomes support national interests in infrastructure durability, energy efficiency, and materials reliability. 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

The transformation of cheap and abundant light alkanes (from natural gas) could have far-reaching implications on the chemical and energy sectors yet remains a formidable challenge due to the lack of efficient catalysts/catalytic systems. While various processes for alkane transformation to a host of petrochemical products have been extensively studied during the past decades, only steam methane reforming, propane dehydrogenation, and steam ethane noncatalytic cracking are produced at large-scale. This project explores an alternative to current catalytic systems known as ammonia-assisted reforming (ammoreforming) of light alkanes. Ammoreforming involves reacting ammonia (NH3) with a light alkane (such as ethane, C3H8) to produce hydrogen cyanide (HCN – an important industrial chemical) and hydrogen (H2). Significantly, the reaction avoids production of either carbon monoxide or carbon dioxide, thus setting the stage for circular usage of carbon with minimal generation of greenhouse gas. The project will strengthen academic research and educational programs in chemistry/chemical engineering at both Mississippi State University and Northern Illinois University. The project leverages several programs at both institutions aimed at stimulating K-12 students’ interest in STEM careers and providing underrepresented undergraduates opportunities to develop research skills. The project focuses on the design and synthesis of efficient non-noble metal - NixGay intermetallic compound (IMC) based catalysts - by the understanding of the structure/performance relationships and catalytic mechanisms of the ammoreforming of light alkanes. The project focuses on several fundamental aspects of ammoreforming over the IMC catalysts, including (1) understanding the effect of oxalate precursor’s composition on the structure, surface properties, and particle size of the NixGay IMC catalysts and their relationships to the catalytic performance of ethane ammoreforming; (2) understanding the influence of oxalate thermo-decomposition and annealing on the structure and surface properties of the NixGay catalysts and the catalytic performance; (3) elucidating the reaction-induced surface/bulk reconstruction and identifying the surface reaction intermediates and catalytic mechanism. The research will be carried out through the combination of in-situ/operando X-ray characterizations (including XRD, SAXS, and XAS) at the Advanced Photon Source of Argonne National Laboratory, operando-DRIFT-MS, and relaxation type transient experiments. 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

In this CAREER project, funded by the Chemical Structure, Dynamics & Mechanisms B Program of the Chemistry Division, Wei Liu of the Department of Chemistry at University of Cincinnati is studying the fundamental reactivity of copper complexes for synthesizing important organic molecules. Compared to the precious metal analogues, copper catalysts are less expensive, less toxic, and have higher abundancy in the earth’s crust. However, the catalytic performance of copper complexes lags largely behind that of their precious metal counterparts, largely owing to limited understanding of the fundamental reactivity of copper complexes. The goal of this research is to study the reactivity of copper complexes that are plausible intermediates in catalytic Cu-mediated bond construction reactions. Investigations of the structure and reactivity of these complexes is directed at providing a deeper mechanistic understanding of known copper-catalyzed bond-forming reactions, enabling the development of improved copper catalysts, and inspiring the development of new copper-catalyzed transformations. Dr. Liu’s education plan focuses on increasing public awareness of the importance of organic chemistry in daily life and on the retention of underrepresented minorities in science through outreach programs at the Cincinnati Museum Center, research-based educational experiences, and online resources. High-valent organo-Cu(III) compounds have long been proposed as key intermediates in many Cu-catalyzed transformations. However, most reported examples of isolated Cu(III) species are stabilized by rigid macrocyclic chelating or perfluorinated ligands, and few of these examples provide experimental evidence for elemental reactions in Cu catalysis. In this project, the Liu research team will investigate the synthetic accessibility of reactive organo-Cu(III) compounds, study the elementary reactions of well-defined Cu(III) species, explore the structure of catalytically relevant Cu(III) complexes, and work to exploit their reactivity to discover new Cu-catalyzed transformations. An improved fundamental understanding of the reactivity of Cu(III) compounds, including homolytic dissociation, reductive elimination, and oxidative addition, would not only provide insights into existing reactions that involve Cu(III) intermediates but also inspire the development of novel Cu-catalyzed transformations. 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 · 2025-12

This project organizes a Workshop on Spectrum Data Science in 2026 held at the Virginia Tech Innovation Campus at Potomac Yards in Alexandria, Virginia. The workshop brings experts from academia, industry, and government together to discuss new research, needs, techniques, and directions for data collection and sharing for use for developing novel techniques to optimize the use of the electromagnetic spectrum for communications, active sensing, passive sensing, positioning, navigation, timing, and other scientific and commercial uses. The workshop aims to inform planning for and early investments towards the federated spectrum data ecosystem that was discussed in the 2024 National Spectrum R&D Plan. Such an ecosystem and the data it produces can give the United States a strategic advantage in its own spectrum use, protect and enhance scientific uses of the electromagnetic spectrum, and create new businesses, jobs, and opportunities in the STEM fields through the collection, processing, and dissemination of spectrum data. 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

The overall goal of this project is to elucidate the changes in cell wall composition and cell wall mechanics that allow maize primary roots to maintain growth under water limited conditions. Limited water availability is a major environmental factor constraining plant development, in turn adversely affecting plant performance and crop yields. One of the prominent responses of plants to water limitation is the maintenance of root growth, enabling access to water from deeper soil profiles. Substantial changes in cell wall composition are implicated in root growth maintenance under water limitation. However, due to technical challenges, the molecular and physiological mechanisms involved in root growth maintenance under water limitation remain unknown. Such understanding is critical for improving crop productivity in normal and stressful environments and for sustainable bioenergy production. This research integrates biochemical and biomechanical information from sub-cellular to organ-level responses of root tissues to decipher the functional role of cell wall components in regulating root growth. Successful completion of these state-of-the-art studies will provide proof-of-concept for quantitative analyses of plant cell walls exhibiting different compositional and functional characteristics. This multi-disciplinary approach will enable the identification of design rules for the interactions of different components within the cell wall matrix and their impacts on plant growth and morphogenesis under normal and stressful environments. The fundamental knowledge and the technological advances developed through this project will ultimately enhance agricultural productivity under normal and stressful environments by allowing predictions about how plants, especially crop plants like maize, will respond to climate change. The project will provide interdisciplinary training and mentoring for a graduate student at the University of Central Florida, an Hispanic-Serving Institution, and at least two undergraduate researchers, contributing to workforce development. Maize primary and nodal roots preferentially maintain growth under water stress conditions, compared to shoot tissues that show growth inhibition. Within the primary root growth zone, the apical region completely maintains cell elongation and growth even under severe water stress, whereas the basal region shows reduced cell elongation and growth deceleration. These spatially differential responses are associated with changes in cell wall yielding properties and potentially changes in cell wall composition. The overall goal of this project is to elucidate the changes in cell wall composition and wall mechanics that enable primary roots to maintain growth under water stress conditions. The specific objectives are to first reveal the differential cell wall compositional changes occurring within the growth zone of maize primary roots grown under water limitation compared to well-watered primary roots, and subsequently to assess the mechano-chemical changes occurring in the cell walls of the growth zone of primary roots under water stress to correlate them with cell wall extensibility and root growth. Integration of cell wall compositional analyses with biochemical and biomechanical studies from sub-cellular to organ-level scales will enable deeper understanding of plant growth under normal and water limited conditions. This multi-scale approach will unveil how components interact within the cell wall matrix and how they impact cell expansion and plant growth under water stress conditions. In the long-term, knowledge from these studies will pave the way to selectively alter cell wall components to promote stress-responsive growth in plants and optimize them for sustainable food and energy production. 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.