Virginia Polytechnic Institute and State University

NSF Awards · FY 2025 · 2025-08

This project helps young students learn about robots and artificial intelligence (AI) ethics through a fun and creative afterschool program. While new technologies make our lives easier and more comfortable, they also bring up new problems. e.g., "How can people protect their privacy when using AI?", or "What happens if people depend too much on robots and AI?" Schools have begun teaching about robots, AI, and coding, but it is often done by sitting in a classroom and listening. Ethical topics may not always be discussed in such a setting. This project tries to let students learn robot and AI ethics by acting, moving their bodies, and using arts like music, drawing, and dance. This will help them better understand what is right or wrong when working with robots and AI in their everyday lives. The team hopes students can learn how robots and AI work and start thinking about how these tools affect people and the world around them. To make this happen, the team of researchers will first talk with students and teachers to find out what works and what is challenging. Second, they will design a hands-on curriculum and build tools for learning. Finally, they will run and test programs in schools and museums. The project will result in lesson plans, learning tools, data from the research, and useful tips for teachers and researchers. This research will also support anyone interested in teaching children about robot and AI ethics using creative methods. As demand for robots and AI literacy is rapidly increasing, schools introduce more education programs about robots and AI. However, formal education settings for this topic are still unfamiliar and intimidating. In this project, the research team will design and implement an embodied, informal STEAM (STEM + arts) education program. There will be a focus on robots and AI ethics for young learners (4th-5th graders) so that they can experience how to live with these technologies. This interactive learning program will consist of diverse modules (e.g., acting, dance, music and sound, and drawing). There are three primary research thrusts to the project. First, the team will conduct literature reviews, focus groups with stakeholders. They will identify experiences and challenges for learning AI and robotic ethics using surveys and interviews. Second, the project team will develop a creative afterschool program using participatory design workshops. The research team will also create technologies and tools to support the AI and robotic learning objectives. The final thrust will implement and evaluate the creative afterschool program in schools and museums. The proposed creative afterschool program will draw upon interdisciplinary expertise and experiences in psychology, computer science, engineering education, interactive arts, and human-robot interaction. This research contributes to the creation of a modularized curriculum, accessible interfaces, and tools for children. The outcomes will produce data and knowledge about children’s understanding and perception of robots and AI ethics. This project will produce guidelines for future iterations of this type of creative afterschool 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

A strong U.S. workforce is vital to maintain a standard of excellence and global leadership in science, technology, engineering and mathematics (STEM). However, the pace in which Americans are entering the STEM workforce is too slow to meet the pressing needs of industries like healthcare, military, manufacturing, and construction. New training methods are needed to improve how learners acquire STEM knowledge and skills to enter the U.S. STEM workforce. Immersive virtual reality (VR) as a training platform is a solution that can provide individual training and education for all Americans. VR experiences with high engagement are particularly effective as mechanisms for learning new and complex ideas. However, without real-time instructor feedback, many learners struggle to stay engaged. This project explores how eye tracking and artificial intelligence (AI) can be used to measure and guide learner engagement in VR training. By automatically detecting when learners are focused or distracted, the system can provide visual cues to support attention and understanding. The outcomes of this work will help more Americans gain the knowledge and skills needed for high-demand STEM careers. Also, novel opportunities will be opened for improving training approaches for people with attention or cognitive challenges. Overall, the knowledge generated from this project will enhance STEM training outcomes when using VR. This work will empower more Americans to advance our national welfare, prosperity and security. This project investigates how gaze-based models can be used for estimating engagement and reactively guiding user attention within immersive VR training experiences. Specifically, VR training will take place within construction safety training, as this domain has a rapidly growing workforce similar to other STEM disciplines. Currently, the speed at which construction workers and professionals gain knowledge and skills needed to complete job tasks safely remains inadequate. To perform construction work safely, trainees are required to be highly engaged during short, demanding, complex training interventions to reduce fatalities and injuries in the workplace. The project is structured around three thrusts in the following areas: (1) data collection and modeling approaches, (2) reactive designs to support engagement; and (3) validation with construction professionals. First, the project will develop real-time and post annotation methods to label engagement states. This process will use gaze, engagement, and training performance data from VR training sessions. These data will be used to reduce labeling bias and improve model accuracy. Then, machine learning techniques (e.g., deep-learning, spike, liquid networks) will be used to identify key fixations, saccades, and pupillary features as they relate to engagement and performance for building real-time engagement models. Various time scales, features, and models will be explored to produce optimal real-time estimations of learner engagement. Second, the research team will investigate how different activation functions can be used to trigger visual attention cues in response to user engagement levels (low, medium, high). Human-centered interventions will assess how these functions influence learner ability to initiate, sustain, or regain engagement. Third, the project will study the produced models using VR construction safety training sessions with industry professionals. The effectiveness and transferability of real-time attention guidance will be evaluated to improve VR trainings. Ultimately, these thrusts will advance our scientific understanding of gaze-based engagement modeling to learn construction safety materials, use AI to guide visual attention, and improve VR tools for workforce training outcomes. 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 supports the United States (US) portion of a binational US-United Arab Emirates (UAE) workshop, held in the UAE in 2026, to define a shared research vision and roadmap for the future of spectrum science and innovation. As usage of the radio spectrum has grown rapidly, driven by new systems and demands for wireless communications, remote sensing, and other capabilities, the limited available spectrum has become increasingly congested. Spectrum management, access, and sharing have become critical challenges with global implications, demanding new approaches that are efficient and secure. Addressing these challenges requires combining expertise across engineering, computing, economics, and policy. This workshop brings together diverse experts, identifies important spectrum research breakthroughs with broad societal benefit, fosters long-term US-UAE partnerships, and positions both nations at the forefront of spectrum innovation. The workshop focuses on three convergent themes. Theme 1: Spectrum Innovation Fundamentals examines advances in dynamic spectrum sharing, efficient coordination, and real-time optimization, with emphasis on algorithmic and theoretical foundations. Integration of artificial intelligence to support autonomous spectrum decision-making is a key component. Theme 2: Applications of Spectrum Innovations explores how emerging spectrum technologies can transform domains such as next-generation wireless networks, satellite communications, radar, and radio astronomy. The theme emphasizes enabling increasingly complex and data-intensive applications. Theme 3: Spectrum Security, Policy, and Economics addresses the socio-technical and governance aspects of spectrum access, including privacy, resilience, and regulations. It investigates how economic and policy frameworks can incentivize innovation while ensuring secure and fair spectrum use. The anticipated outcome of the workshop is a research roadmap that informs US-UAE spectrum research collaborations and helps pave the way towards future spectrum-dependent systems that are more adaptive, secure, and globally interoperable, which will enhance equitable digital access, resilient infrastructure, and scientific discovery. 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

Coding theory is a mathematical branch that dates back to the work of Claude Shannon and Richard Hamming in the late 1940s. They sought to ensure reliable communication and computation even in the presence of noisy channels and faulty machines. Over the years, the discipline, techniques, and challenges have evolved. Today, we are at an inflection point where we must control noise and errors along with several conditions for classical and quantum computers. In the classical setting, coding theory should satisfy other restrictions, like protecting data and ensuring tasks are performed quickly. In the quantum setting, coding theory is one of the needed pillars for the development of large-scale quantum computers that will revolutionize and benefit society. Specifically, quantum error-correcting codes are fundamental for the reliability of quantum computers and for applying and implementing quantum algorithms, like Shor’s, among others. In this proposal, we aim to develop more agile erasure recovery and repair for use in distributed storage, faster and secure frameworks for distributed computing, and fault-tolerant quantum error correction. Additional broader impacts include running online seminars in cyber-security, training of students and contributing to publicly available software. To address the proposed coding theory problems and challenges, the investigators harness their expertise in commutative algebra and algebraic geometry and build on the results of their long-standing collaboration as well as new developments in the field. Evaluation codes are generalizations of the ubiquitous Reed-Solomon codes, which are employed in a stunning array of applications to support data transmission and storage. Evaluation codes are a more nimble family based on evaluating rational functions over curves or higher-dimensional varieties or evaluating multivariate polynomials over points in affine space over a finite field, offering enhanced performance in a variety of settings. This project builds on recent advances, suggesting that the full capabilities of evaluation codes have not yet been realized. Algebraic function fields support reduced complexity distributed computing. Folded evaluation codes may provide additional noise control over smaller alphabets than their Reed-Solomon counterparts. Powers of codes play a role in distributed storage and, along with their duals, in fault-tolerant quantum computation. 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

Plastics represent a form of novel carbon, solid waste, and debris. Most plastics travel through freshwater environments to the ocean, making ponds an important place to study the impact of this novel carbon source. Ponds are widespread across various landscapes and provide many important services. Most notably, ponds provide resilience by capturing and removing storm water, carbon materials, solid waste, and debris. Depending on size and age, ponds can be sites of where carbon is emitted or stored, which is important regarding emissions of gasses to the atmosphere. Therefore, pond ecosystem services may be altered by captured plastic debris. The prolonged storage of plastic debris in ponds allows for the breakdown of plastic litter into smaller particles recognized as microplastics (particles <5mm). There are no current estimates of how long plastic and microplastic debris are retained in pond ecosystems. Thus, the unknown storage and breakdown of plastics can also release dissolved organic carbon, stimulating or inhibiting microbial activity that governs the ecosystem services ponds render to society. This award investigates the influence common plastic items have on microbial community structure in ponds, as these novel carbon sources provide a substrate for microorganisms (known as the plastisphere). The project will characterize biofilm succession on plastic and natural items and assess whether plastic-derived organic matter stimulates or inhibits microbial activity that governs ecosystem processes. Lastly, a mass-balance model will be developed to understand pond plastic and microplastic capture and removal. The integrated research and teaching program will provide environmental science to undergraduate students through field research opportunities, training in visual arts, and outreach to high school students. This award will provide foundational knowledge regarding how pond ecosystems respond to plastic pollution and understand the magnitude of response from various levels of ecological organization. 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 GPT-4, also known as foundation models, represent a groundbreaking advancement in artificial intelligence, powering diverse applications such as chatbots, information retrieval engines, and scientific discovery tools. The success of LLMs is primarily attributed to their vast internal knowledge learned from massive unstructured texts. However, LLMs' internal knowledge is inherently constrained by static snapshots of closed-world texts used for training. This limitation presents two key challenges when deploying such a closed-world LLM in an open-world environment where knowledge (including unstructured texts and structured data) is rapidly evolving. First, closed-world LLMs have a limited and static view of open-world knowledge, often leading to unfaithful yet overconfident hallucinations. Second, since they primarily process unstructured text, they struggle with tasks that require reasoning over structured data, such as databases or scientific records. This project addresses key challenges in artificial intelligence by developing new algorithms, theorems, and systems to ensure the reliability of advanced foundation models. At its core, the research focuses on an open-world foundation model (OWFM), a powerful AI model designed to interact with ever-changing real-world information. This model is built on an open-world knowledge network (OKN), a flexible and expandable data structure that organizes semi-structured information from diverse sources. Moving beyond unstructured knowledge and closed-world LLMs, this project establishes a new paradigm of knowledge organization and foundation models in an open-world environment through three key thrusts. The first thrust creates a highly expandable OKN across domains and builds an OWFM with plug-and-play modular components. The second thrust develops adaptation methods that enable OWFMs to accurately answer questions on rapidly evolving topics. The third thrust deploys the OWFM in two knowledge-intensive, high-stakes applications: medical diagnostic reasoning and financial portfolio generation. Beyond advancing scientific research, this project also integrates its findings into educational activities, ensuring broad dissemination and real-world impact. 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 · 2025-07

This project aims to serve the national interest by enhancing molecular bioscience education through implementing evidence-based teaching practices, and developing personalized, adaptable educational resources that improve student engagement, workforce readiness, and faculty teaching capacity in data science. Harnessing the magnitude of existing and potential data, and appropriately contextualizing it are crucial for advancing research and innovation within molecular biosciences. Students in this field require comprehensive training in both technical and durable data science skills, especially in understanding the cascading impacts of decisions made when interpreting data in complex ways, partially due to the prevalence of "black box" approaches that require greater scrutiny. Addressing this gap in content requires targeted educational initiatives that focus on both technical proficiency and contextual application of data science skills while accounting for external factors impacting student success. This initiative introduces the use of learner personas to develop multimodal, best-practice-informed educational materials tailored to student and faculty needs. Ultimately, these resources foster improved understanding, engagement, and application of data science in molecular biosciences. The primary goal of this project is to improve molecular bioscience education by developing learner-centered, data science–infused curricula, supporting faculty development, and strengthening students' preparation for a data-driven workforce. The project implements learner personas to tailor instruction, enhance student engagement, and support student centered teaching practices across diverse institutional contexts. A central component, the Molecular Data Education Hub (MDE-Hub), hosts open-access modular curricular resources, instructional guides, and case studies grounded in real-world molecular bioscience research. Faculty professional development workshops provide training in technical data science competencies and strategies for integrating data science into existing courses and curricula, while fostering a sustainable network of educators. A comprehensive evaluation plan examines the effectiveness of these curricular materials and professional development activities in improving student learning outcomes, faculty practices, and broader instructional impact. Through collaborative partnerships with Research-1 and predominantly undergraduate institutions, the project ensures that outcomes are scalable, providing long-term benefits for both molecular biosciences educators and students in teaching and learning data science. Overall, this project addresses prevalent issues of training students and faculty in technical and durable data science methods and prepares a more competent and workforce-ready cohort of molecular bioscientists. The NSF IUSE: EDU Program supports research and development projects to improve the effectiveness of STEM education for all students. Through the Engaged Student Learning track, the program supports the creation, exploration, and implementation of promising practices and tools. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2025 · 2025-07

This award supports a research program to study the biomechanical origins of bone fragility in aging and type-2 diabetic populations. The most basic building blocks of bone are collagen and mineral. Aging and diabetes change the microstructure of bone through increasing the number of interconnections (crosslinks) of bone’s collagen network. This increase in crosslinks is hypothesized to reduce bone’s resistance to fracture. The mechanisms that are responsible for the observed increase in bone fragility will be investigated during the project. The findings are likely to have significant implications for public health, particularly as diabetes prevalence rises. The project supports NSF's mission by promoting scientific progress, advancing national health, and potentially leading to targeted therapeutic treatments that could improve quality of life for all Americans. This work will reveal the role of advanced glycation end product cross-links on fracture behavior at quasi-static and dynamic loading rates representative of physiological and fall-event strain rates. Fracture experiments will be supported by elasto-plastic fracture theory, anisotropic stiffness tensors derived from non-destructive acoustic elastography, and high resolution in-situ imaging to provide new insights of active crack growth mechanisms. Because this study uses human tibia and fibula specimens obtained from healthy and diabetic individuals, we will for the first time be able to assess fracture behavior against bone specific quantities of 15 different advanced glycation end product cross-link types. Statistical analysis will aid in pinpointing the specific mechanisms and advanced glycation end products responsible for bone embrittlement and therefore support targeted therapeutic treatments to recover bone’s fracture resistance. 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 · 2025-07

This project supports seven PIs, one postdoctoral fellow, five graduate students, and two undergraduate students from the five U.S. universities to study how the availability of marine nutrients such as nitrate and phosphate may have fueled the expansion of eukaryotes (organisms with nuclei in their cells), transformed their ecological roles, and eventually revolutionized the marine ecosystem during the Tonian Period (1000–720 million years ago). This research will help scientists to better understand the ecological resilience of the marine ecosystem in the present and future. The project takes advantage of unique and complementary geologic records from two continents, leverages available collections and resources, and brings together an array of research expertise. It offers opportunities for the training of a globally engaged STEM workforce, as well as public outreach activities engaging national (geo)parks. This project will test the hypothesis that increasing nutrient availability in Tonian oceans drove the diversification and ecological rise of eukaryotes, which in turn transformed the scope of biodiversity from a prokaryote-dominated world to one teeming with eukaryotes. The researchers will systematically collect and integrate paleontological, geochemical, sedimentological, and stratigraphic data from early Tonian strata in North China and late Tonian strata in the Grand Canyon of Arizona. The data will be integrated with global compilations and an Earth system model to reconstruct nutrient availability, eukaryote taxonomic and functional biodiversity, and marine geochemical cycles to test the hypothesis stated above. The intellectual merit of the project lies in its potential to illuminate the complex feedbacks among nutrient availability, functional biodiversity, and biodiversity dynamics in a major transition in Earth history. The broader impacts of the project will catalyze multidisciplinary research, create synergies between the National Park System and research institutions, foster informal geoscience education, and prepare the next-generation of STEM workforce. This project is funded by the BIO/DEB Biodiversity of a Changing Planet (BoCP) Program and the GEO/EAR Life and Environments through Time (LET) 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 · 2025-07

Transient execution attacks represent a large class of serious computer security threats, where the attacker alters the intended execution of the code in for short periods of time, rather than permanently. The attackers may transiently execute arbitrary code, including instructions that are otherwise illegal. Such attacks have led to severe security breaches, including data leakage and breaking existing security mechanisms in hardware. There is an urgent need for efficient mitigation across different CPU platforms today and CPUs in the future. This project aims to develop an automatic evaluation framework and a set of efficient protection schemes for transient execution attacks across different CPU platforms. The project will first develop a novel reinforcement learning (RL)-assisted framework for agile attack surface analysis and mitigation evaluation across CPU platforms. With attack building blocks in the framework, the RL-assisted approach enables practical evaluation of attacks on black box processors without significant manual efforts, offering an effective tool to comprehensively evaluate existing and future mitigation strategies. Second, the project will explore commercial hardware security features to protect the integrity of transient execution efficiently. Existing research focuses on Intel hardware features, leaving many other hardware security features unexplored. This project defends the attack efficiently from a new angle by protecting pointer access during the transient execution. Third, the project will co-design block cipher and microarchitecture to offer a secure pointer protection mechanism against both transient execution attacks and memory safety attacks. The project will also integrate research with education and outreach by creating course modules to help bridge the gap between concepts and practice, engaging students at all levels with hands-on 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 · 2025-07

Examining the role of fellowships funding on engineering students and their professional development seeks to redefine graduate fellowships as transformative tools that promote all American engineering students’ success. By investigating how fellowships function beyond funding, we aim to highlight their potential as way to pursuing graduate education, mechanisms of financial support, and tools for fostering degree completion and workforce development. This approach will enhance their ability to navigate academic and professional challenges. Our work will guide engineering programs and faculty by equipping them with insights into fellowship design and strategies to provide support. This comprehensive approach will improve retention, accelerate time-to-degree completion, and better prepare students for fulfilling engineering careers. In the long term, the outcomes of this research will transform fellowship infrastructure at federal agencies, private organizations, and universities, aligning with national efforts on the engineering field and workforce. Our findings will influence strategies and policies at the national level, promoting organizational and conceptual changes in fellowship initiatives to recruit, support, and retain more americans graduate students. The proposed project will strengthen Engineering and Innovation in the US by advancing understanding of how fellowship variables impact engineering graduate students, addressing the issues and unintended drawbacks often overlooked in fellowship programs. While fellowships are widely seen as funding mechanisms, this research will uncover their implications, including their effects on student recruitment, academic experiences, graduation, and early career outcomes. The study will examine how fellowships shape engineering students’ professional development. Through a national-level assessment, the research will provide insights for institutions, programs, and faculty to design fellowships that better support all American students. The anticipated outcomes include conceptual models of fellowships as ways to expanding all American students participation, improving retention, and advancing professional identity. Students will benefit from an enhanced understanding of fellowships and their career implications, while institutions will gain tools to create fellowships that promote academic and professional success. Additionally, the project will produce a transferable set of best practices for funding agencies, academic programs, and faculty to ensure adequate support for fellowship holders, enabling their successful transition to the workforce. These findings will strengthen fellowship programs and advance the engineering 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 2025 · 2025-07

With support from the Chemical Structure, Dynamics & Mechanisms B Program of the Chemistry Division, Shabnam Hematian of the Department of Chemistry and Biochemistry at the University of North Carolina at Greensboro aims to develop a new class of photocatalysts to transform cheap and unreactive raw materials into value-added chemicals. These potential catalysts exploit the oxidizing power of oxygen in the air, as a green terminal oxidant, and the energy of light as a sustainable reagent for forming new and difficult-to-access chemical bonds. The goal of this research is to control the photochemical outcome and reaction selectivity through modulating the energy of the light source. Results from this project have the potential to enable faster and tunable reactions which are relevant in the synthesis of pharmaceuticals and other fine chemicals. This work provides a solid interdisciplinary training platform in the fields of photochemistry, synthesis, spectroscopy, redox, and kinetics for scientists at all levels. This group is also well-positioned to provide the highest level of education and training to engage students in science early in their careers (i.e., local high school and community college students as well as first-year undergraduate and graduate students), promoting recruitment and retention in the chemistry field. The discovery of effective homogeneous photocatalysts for the selective oxidation of substrates under mild conditions using dioxygen as the terminal oxidant remains an important objective in synthetic chemistry. The proposed work seeks to design and develop a new class of photocatalysts in which metal-dioxygen chemistry leads to the cooperative activation of dioxygen and formation of oxo-bridged heterobinuclear systems. In the developed systems, two different reactive high-valent metal-oxo intermediates are accessible through the photoactivation of two unique metal-oxo bonds which are driven from charge transfer excited states. In this project, the structure-function relationships of the developed oxo-bridged constructs will be investigated to understand how and to what extent electronic exchange coupling between the individual catalytic sites through an oxo bridging ligand in the ground state impacts the charge transfer transitions and how this coupling manifests itself in overall photoactivity. 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 · 2025-07

Predicting the flow of air or water over a vehicle or other device is key to making engineering improvements. When a rough surface is exposed to the flow, it is assumed that the only effect of the roughness is to increase the friction without otherwise changing details about the flow. However, this assumption is unproven in all but the simplest situations. The purpose of this project is to definitively resolve this issue, using the novel approach of comparing flows over different surfaces that generate the same friction. This work will enable more reliable analysis and design of many systems and vehicles. The project will engage and train researchers at postdoctoral, doctorate, undergraduate, and high-school levels, enable interdisciplinary international exchanges, and provide unique research experiences for hundreds of undergraduate engineers. The approach will combine state-of-the-art experimental capabilities and experience at Virginia Tech with leading edge computational expertise at the University of Cambridge. The goal is to reveal the limits and mechanisms of rough wall similarity, by comparing the properties and mechanics of non-equilibrium boundary layers generated over rough walls with different geometry but with the same effective sand-grain roughness. This comparison will be conducted for a systematic set of two- and three-dimensional flow geometries that include many of the non-equilibrium complexities found in practical applications. At Virginia Tech, experiments performed in the Stability Tunnel will include detailed mean flow and turbulence measurements at friction Reynolds numbers up to 20,000. Coordinated and complementary scale-resolving simulations at Cambridge will provide intricately detailed views of the turbulence dynamics that mediate the critical relationship between the roughness layer and outer region. The project will form the focus of post-doctoral training at the University of Cambridge and doctoral-student training at Virginia Tech. In addition, the project includes a series of sub-projects to be conducted by groups of undergraduate and high school students drawn from the Southwest Virginia Governor’s School. Furthermore, wind tunnel tests will be integrated with required undergraduate laboratory courses in Aerospace and Ocean Engineering and Mechanical Engineering, providing substantive research experiences to large numbers of undergraduate students annually. This project is jointly funded by the U.S. National Science Foundation Fluid Dynamics program and the UK Research and Innovation (UKRI)/ Engineering and Physical Sciences Research Council (EPSRC) under the NSF Directorate for Engineering – UKRI Engineering and Physical Science Research Council Lead Agency Opportunity. 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 · 2025-07

Per- and polyfluoroalkyl substances (PFAS) are fluorinated organic chemical contaminants that are commonly referred to as forever chemicals due to their persistence, stability, and resistance to natural environmental degradation processes. During the last two decades, PFAS have been increasingly detected in surface water systems (lakes and rivers) and groundwater aquifers which serve as sources of drinking water for many communities throughout the United States. In addition, many PFAS can be transformed through abiotic and biotic pathways in natural and engineered water systems to produce toxic perfluoroalkyl acids (PFAAs), including perfluorooctanoic acid (PFOA). There is a critical need for new data and knowledge to advance the fundamental understanding of the fate, transport, and reactivity of PFAS in drinking water systems. The overarching goal of this CAREER project is to investigate and evaluate the abiotic and biotic transformations of PFAS in drinking water distribution systems (DWDS) as the treated water transits through the distribution systems to customers’ taps. To advance this goal, the Principal Investigator proposes to integrate field studies of PFAS transformations in selected DWDS with controlled laboratory and pilot scale experiments. The successful completion of this project will benefit society through the generation of new data and fundamental knowledge to advance the design of engineering solutions and policy recommendations to address and mitigate PFAS drinking water contamination. Additional benefits to society will be achieved through student education and training including the mentoring of two graduate students at George Mason University. Many drinking water distribution systems (DWDS) exhibit environmental conditions comparable to those of natural aquatic systems in which the transformations of PFAS compounds and precursors to toxic perfluoroalkyl acids (PFAAs), including perfluorooctanoic acid (PFOA), have been observed. In addition, the components of DWDS (e.g., pipes, tanks, and water towers) can serve as substrates for the accumulation of scales/sediments and the formation of biofilms that cause/catalyze the abiotic/biotic transformations of dissolved contaminants in these systems. This CAREER project will test the hypothesis that PFAS transformations in DWDS are primarily mediated by the biotic transformations of PFAS compounds and precursors that accumulate in their scales and sediment biofilms. To test this hypothesis, the Principal Investigator (PI) proposes to evaluate and characterize the chemical and microbial processes controlling PFAS transformations in DWDS with a focus on storage facilities in the Mid-Atlantic region of the United States. The specific objectives of the research are to 1) investigate PFAS partitioning and transformations in drinking water storage facilities and develop methods and infrastructure for the design of controlled experiments to generate fundamental insights; 2) evaluate the effects of storage tank/sediment materials and environmental parameters on PFAS partitioning and transformations in drinking water storage facilities; and 3) evaluate the effect of treatment residuals on PFAS transformations in the biofilms of drinking water storage facilities. The successful completion of this project has the potential for transformative impact through the generation of new fundamental knowledge to advance the design and implementation of engineering solutions to minimize and mitigate PFAS contamination in drinking water storage facilities and distribution systems. To implement the educational and outreach activities of this CAREER project, the PI proposes to leverage existing programs and resources at George Mason University (GMU) to carry out curricular research on chemistry education to support learning and success for community college transfer, non-traditional and traditional students in environmental engineering (EE). More specifically, the PI proposes to 1) investigate the impact of student chemistry preparation, attitudes, and demographics on performance and interest in EE); 2) evaluate correlations between student performance in chemistry and mastery of concepts in introductory EE courses; and 3) use the knowledge gained from this research to design lessons to remediate student chemistry preparation gaps and prepare students for the successful completion of EE undergraduate education at GMU. 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 · 2025-07

This award funds the acquisition of high-temperature pyrolysis elemental analyzer (TC/EA) and coupled gas source isotope ratio mass spectrometer (GS-IRMS) for the Biogeochemistry Laboratory in the Geosciences Department at Virginia Tech (VT). This pair of instruments can measure the stable isotopes of hydrogen and oxygen in a variety of materials. Stable isotopes are stable atoms of elements with different numbers of neutrons in their nuclei; their small differences in mass lead to sorting during chemical reactions, such that stable isotopes act as natural tracers during a host of natural processes. The measurement of the stable isotopes of hydrogen and oxygen provide insight into the behaviors of these elements in different chemical reactions in the environment. This includes tracking the movement of water, the migration of animals, and the transfer of energy in food networks. Stable hydrogen and oxygen isotope analyses can also aid in reconstructing past changes in temperature and precipitation and understanding the formation of mineral deposits. Beyond research activities, the new instruments will also be used in teaching and outreach activities that will engage high school and college students and researchers both regionally and nationally. These instruments will significantly enhance the capabilities of the VT Biogeochemistry Lab and expand its research, education, and outreach activities. It will be critical to on-going, currently funded and future research in the laboratory. Ongoing projects include measuring oxygen isotopes in sulfate to track aspects of the sulfur cycle and measuring hydrogen and oxygen isotopes in bat guano and fossil tooth enamel to evaluate changes in past atmospheric precipitation. Potential future projects with researchers at VT include employing hydrogen and oxygen isotopes to estimate trophic transfer efficiency in aquatic food networks, track bat migration, and reconstruct past temperatures using phosphate minerals. The Biogeochemistry Lab at VT historically has an excellent track record of supporting student research and has enabled the research activity of 20 undergraduate, 52 graduate, and 8 visiting students, as well as 10 postdoctoral researchers and scientists from VT and 17 other institutions located in United States and internationally. The new instrumentation will also be used in hands-on teaching and outreach activities including: 1) a science summer camp for high school students 2) an established course for undergraduate and graduate students on stable isotope biogeochemistry; and 3) a regional stable isotope workshop for institutions in southern Appalachia. 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 · 2025-07

This project will address a critical need in engineering education by improving how students learn to collaborate effectively on teams—a skill essential for success in today’s complex, multidisciplinary professional contexts. Employers consistently rank teamwork as one of the most critical skills for engineering graduates; however, many new engineers feel unprepared to navigate interpersonal challenges in real-world projects. Despite widespread adoption of project-based learning (PBL) involving teamwork, instructional methods frequently emphasize evaluating final products rather than guiding the teamwork process itself, leaving students to learn vital teamwork skills through trial and error. Such limited guidance results in common challenges, including unequal participation, unresolved conflicts, and inadequate psychological safety—the belief that team members can safely take interpersonal risks without fear of negative consequences. These issues are particularly pronounced for first-year students, who often enter college with limited teamwork experience and find themselves poorly equipped to manage conflicts effectively. By investigating how students and faculty collaboratively shape psychological safety, manage conflicts, and adapt teamwork behaviors, this research aims to provide critical insights into fostering healthier, more productive team environments. The findings will directly support faculty in implementing effective instructional strategies, better preparing engineering graduates for collaborative workplaces. This work aligns closely with the National Science Foundation’s Research in the Formation of Engineers (RFE) program, advancing innovative teaching practices and developing essential professional competencies for engineers. This project will utilize a multiple-case study design involving two distinct first-year engineering courses, one each at Virginia Tech and Rochester Institute of Technology. The research will address three primary questions: (1) How do students foster psychological safety, manage conflict, and regulate team performance in first-year engineering teams? (2) How do students' perceptions of psychological safety influence their conflict management strategies and teamwork regulation? (3) How do faculty instructional and assessment practices influence students' teamwork behaviors, psychological safety, and conflict management? Employing an adapted version of Rousseau et al.’s (2006) integrative framework of teamwork behaviors, the project will collect comprehensive data in the form of student interviews, focus groups, team communication artifacts, and instructional materials. Analysis will involve inductive thematic methods and deductive framework application to identify the connections between faculty practices and student teamwork behaviors. The intellectual merit of this research lies in advancing the understanding of teamwork processes and faculty roles in supporting the adaptation of teamwork. Specifically, it contributes new knowledge on the intersection of instructional practices and student teamwork regulation, with a particular emphasis on psychological safety—an area that has been extensively studied in organizational behavior but remains under-explored in engineering education contexts. Broader impacts include enhancing engineering instructional practices, specifically improving faculty readiness to teach and manage teamwork. Beyond publications, the findings of this work will also be disseminated through workshops, which will equip faculty with actionable strategies for supporting student teamwork across engineering curricula, from introductory courses to capstone projects, ultimately contributing to more supportive and professionally effective teamwork environments. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2025 · 2025-07

The Spectrum and Wireless Innovation enabled by Future Technologies (SWIFT) and Next Era of Wireless and Spectrum (NewSpectrum) programs are multi-year, cross-directorate initiatives of the National Science Foundation (NSF). SWIFT focuses on advancing effective spectrum utilization and coexistence techniques, while NewSpectrum explores novel approaches to using and managing the wireless spectrum beyond the current paradigm of long-term exclusive-use license auctions. The NSF SWIFT/NewSpectrum Principal Investigator (PI) Meeting is an annual event that brings together NSF-funded investigators and researchers to share research findings, discuss challenges and opportunities, engage with NSF staff, and explore future research directions. This project supports the organization of the 2025 PI Meeting, to be held at Virginia Tech’s Innovation Campus in Alexandria, Virginia. The purpose of the NSF SWIFT/NewSpectrum PI Meeting is to provide a forum for technical exchange and information sharing among NSF-funded researchers and NSF staff. The two-day event will feature a tightly focused program including presentations by NSF staff, a keynote address by an invited speaker, short oral presentations by PIs, poster sessions, panel discussions, breakout sessions, and summary reports. The annual PI meeting provides substantial benefits to the spectrum research community. First, it enables PIs of funded projects to showcase their work, disseminate research findings to NSF staff and peers, receive constructive feedback to enhance project outcomes, and increase the visibility and impact of their research. Second, the meeting offers a valuable forum for PIs to actively engage with NSF staff—initiating dialogue, asking questions, and exchanging perspectives—while gaining insights into evolving programmatic priorities and strategic directions. Third, the event fosters networking among researchers, helping to identify shared interests and explore potential collaborations. Finally, the meeting supports career development by offering access to keynote talks, panel discussions, breakout sessions, and poster presentations, as well as opportunities to connect with fellow PIs. 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 · 2025-06

The recent wildfires in Los Angeles County in January 2025, which affected thousands of lives and caused substantial property damage, have underlined the urgent need for new, advanced, data-informed strategies for efficient and proactive management of such devastating events. However, the development of such data-informed solutions is still largely hampered by limited access to multisource, multi-resolution remote sensing imagery, leaving many in the machine learning and computational sciences communities unable to contribute robustly. This project uses large language models (LLMs) to extract properties and relationships from relevant LA fire data sources, storing them in a comprehensive knowledge database. By integrating complementary wildfire-related information, the framework facilitates monitoring of key physical parameters, such as real-time evacuation orders, meteorological variables, and air quality indicators. The project aims to develop an LA Fire Knowledge Graph-Agent (LAFireKG-Agent) platform--an autonomous and end-to-end LLM-based framework designed to meet the diverse data needs of end users, and enhance situational awareness for both safety and timeliness in wildfire risk management. The LAFireKG-Agent framework focuses on three key objectives: rapid decision-making, predictive modeling, and complex reasoning. Beyond these core capabilities, end-users, including computational scientists, environmental scientists, and risk managers, will be able to explore wildfire-specific questions, generate tailored insights, and receive data-driven recommendations. By integrating advanced machine learning and knowledge graph methodologies, this project will not only lead to more effective disaster preparedness and response strategies, but also promote open science and reproducible research in AI-driven environmental studies. The resulting tools and best practices will be shared through publicly accessible platforms, expanding research synergy among scientists, practitioners, and community organizations. 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 · 2025-06

The objective of this project is to support research that looks to examine building design and operation during disasters, focusing on people who have navigational challenges that may adversely affect their evacuation. It provides new insights into building features from the perspective of those who rely on mobility aids. The outcomes potentially lead to responsible, resilient buildings and contribute to knowledge at the interaction of disasters, built environment, and populations. Additionally, this project promotes interdisciplinary education in engineering, humanities, and design through new course modules and outreach initiatives. Building designers and operators often overlook unique obstacles posed during disasters due to a lack of evaluation methods that can integrate architectural design principles, the needs of people with mobility support requirements, and disaster resilience goals. This gap is further rooted in the absence of voices from people who use mobility aids and efficient mechanisms to incorporate their needs into digital platforms. This project, by leveraging the power of foundation models, which are large deep learning models trained on extensive datasets of images and text, looks to automate extraction and analysis of key building features. Furthermore, value of the developed models will be evaluated by assessing how well such features align with need hierarchies derived from a curated database of lived experiences during past flood events. The findings are expected to inform design decisions across disaster types, built environment scales, and population 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 · 2025-06

Understanding how the outer shell of the Earth moves and deforms is essential for explaining natural phenomena like earthquakes and volcanic activity. This project seeks to improve knowledge of tectonics by studying the deep structure beneath Africa, a continent that holds key clues about how the interior of the planet influences surface movements. Unusually hot mantle rocks are observed underneath Africa, as well as the East African Rift System, the most prominent and active continental rift on the Earth. Taking advantage of these features, the project will develop a detailed model of the deep structure beneath this continent. It will also revise how its tectonic plates move, and explore interactions between the Earth's outer shell with the deeper convecting mantle beneath Africa. The results of this research will help increase understanding of fundamental processes shaping our planet. For example, the findings may enhance earthquake hazard assessments, improve models of global plate motions, and provide relevant information to other aligned fields, such as geothermal exploration. This project uses advanced seismic and geodetic data to provide a more complete picture of Earth's dynamic interior. This project will try to advance the understanding of plate tectonics by investigating how the African continent interacts with the underlying convecting mantle. Specifically, it will test a long-standing hypothesis that lithosphere-asthenosphere coupling modulates the influence of mantle convection on Africa's surface motions, with broader implications for global tectonic processes. The research will integrate seismic and geodetic data from AfricaArray, Global Navigation Satellite System (GNSS) stations, and other sources to develop open-access, high-resolution continental-scale models of the kinematics of Africa, crustal and lithospheric structure, upper mantle seismic properties—including azimuthal anisotropy—and key mantle discontinuities. To test this hypothesis, the project will enhance and leverage the capabilities of the open-source software ASPECT (Advanced Solver for Planetary Evolution, Convection, and Tectonics) to enable improved modeling of lithosphere-asthenosphere viscous coupling at a continental scale. A key objective is to determine whether lithospheric motion in Africa and its surroundings is only weakly coupled to the underlying convecting mantle due to an anomalously hot and low-viscosity upper asthenosphere. Beyond scientific contributions, the project has the following major broader impacts. It will support annual AfricaArray meetings in South Africa and enhance capacity-building efforts through workshops on data science, seismic and GNSS data processing, and access to streaming datasets. These workshops will follow well-established Carpentries lesson structures, with a focus on training new instructors.. An independent professional evaluation will make sure its impacts are maximized. Undergraduate researchers will contribute to the development of seismic tomography models increasing their technical skills. 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 · 2025-06

Nature grows structures (e.g., bone, wood, and tissue) with remarkable functionality and efficiency by simultaneously tailoring both the structural shape and the spatial orientation of a (multi-)material system that has direction-dependent properties. Unfortunately, humans struggle to create such structures due to limitations in today’s manufacturing technologies, which cannot align composite materials in 3D space. The overall aim of this Future Manufacturing Research Grant (FMRG) award is to realize a future cybermanufacturing platform that is capable of precisely controlling the 3D orientation of multi-material systems. This award supports creation of the “Anisotropic Multi-Axis Layerless Additive Manufacturing” (AniMAL AM) system, which looks to leverage robotic arms outfitted with material extrusion tools that are driven by multi-axis printing toolpaths to enable volumetric, spatial control in composite materials. Controlling the organization and orientation of functional composite materials within a part could enable an estimated 10x structural and 70x thermal improvement over conventional additive manufacturing technologies. This looks to transform industries requiring tool-less fabrication of lightweight, multifunctional structures—such as aerospace and automotive—by reducing costs for complex parts while significantly enhancing performance. The award will also introduce, prepare, and grow a future workforce with the convergent skills required for future manufacturing careers. AniMAL AM represents a fusion of advanced technologies: a layerless, multi-axis 3D printing process that orients materials in different directions throughout a part volume, machine learning-enhanced design optimization, and a pair of material and process digital twins to simulate and control the manufacturing process at both mesoscopic voxel-level inclusion orientation and macroscopic part topology. AniMAL AM looks to enable a future manufacturing process to design, fabricate, and validate multifunctional composite structures with voxel-level inclusion orientation with improved performance and robustness. This seeks to yield new (1) understanding of the shear-induced orientation of dynamic inclusions during extrusion, (2) methods for planning robotic poses for multi-axis toolpaths, (3) approaches for quantifying uncertainty from multiple sources, and (4) topology optimization that accounts for multiaxis manufacturing constraints and treats voxel orientation and inclusion morphology as design variables. Integrating this research with strategic outreach and experiential learning activities looks to prepare a next-generation workforce to leverage these breakthroughs to revolutionize the manufacturing of advanced composite systems and improve performance across varied 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 · 2025-06

The broader impact/commercial potential of this Partnerships for Innovation – Mid Career Advancement (PFI-MCA) project will be the development of a new biosensor that has the capability to detect known and emerging environmental toxins and threats. A major advantage of this biosensor is that it will have the potential to detect toxins that have never before been characterized. Examples include emerging biowarfare agents, nanotechnology pollutants, novel toxins produced by fungi and yeast, and chemical poisons. The new biosensor will work by housing living microbial reporter cells, and monitoring their reactions to their environment in real-time. The types of responses observed in the reporter cells will help identity the toxin or threat within minutes. This new technology can be a first line of detection for guarding the public health, including food and water supplies. The project will help develop the new biosensor by monitoring the responses of specialized microbial reporter cells to their environment using surface-enhanced Raman scattering (SERS). The reporter cell responses will be measured when exposed several chemical, nanotechnology, and biological toxins in this project. This will be done in the presence of several biochemical additives that will guide the cells to respond in different ways to different toxins. Cell responses will be monitored by SERS using a portable field-deployable instrument. Artificial intelligence and machine learning (AI/ML) models will be constructed to map reporter cell responses to the type of toxin exposure. In addition to the toxins mentioned above, the biosensor will be tested on a library of banked drinking and river water samples to determine if it can detect previously measured contaminants. Following its development, efforts will be made to commercialize the new biosensor. 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 · 2025-06

Earthquakes generate seismic waves that propagate through the interior of the Earth, with their amplitude gradually decaying as they travel. This reduction in amplitude, known as attenuation or anelasticity, provides crucial insights into the properties of the materials they travel through. By analyzing mantle anelasticity, we can determine key characteristics of mantle materials, including their temperature, composition, and mechanical behavior. These properties are essential for understanding the formation, evolution, and dynamics of the planet. This project aims to improve our understanding of mantle anelasticity by analyzing the amplitudes of seismic waves from a wide range of seismic phases. This research will establish an improved reference model for mantle anelasticity, which is important for understanding the material properties of the mantle and improving earthquake magnitude estimates. In addition to advancing fundamental seismological research, this project will enhance geoscience education and outreach. The new dataset and corresponding Q model will be made publicly available, and the educational components will include undergraduate research opportunities, hands-on computer lab modules for geophysics students, as well as science communication through YouTube videos and exhibits at the Geosciences Museum. In contrast to the well constrained 1-D seismic velocity structure, the 1-D attenuation structure of the mantle remains poorly understood. In this proposed work, we will use S-wave amplitudes to determine the radial attenuation structure of the mantle. The new amplitude dataset will include minor-arc S, SS, SSS, SSSS, ScS, Sdiff waves as well as major-arc SSS and SSSS waves and ScS multiples. We will address major challenges in determining the radial structure of attenuation using body-wave amplitudes. In particular, the research activities will explore measures that can be used to assess geographic sampling to determine global average Q values, which will allow us to determine the number of independent observables and analyze the depth resolution of the radial attenuation model. We will employ linear inversion and nonlinear forward modeling to obtain an optimal 1-D model using all available S wave measurements, including those affected by mantle triplications. We will investigate the frequency dependence of attenuation in the frequency range between 5 and 40 seconds. The new global Q model will provide an important reference for calculations of synthetic seismograms and expected energy loss due to wave propagation. The improved model will enhance the resolution of the layered structure of mantle attenuation and help address key scientific questions related to asthenosphere thickness, transition zone rheology, and the thermal structure at the core-mantle boundary. 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 · 2025-05

Stream animals, in particular, require well-oxygenated environments to survive and reproduce, as they are accustomed to fast moving water. Environmental changes, such as increased temperature, flooding, and sediment pollution, can interact with bacterial activity to decrease dissolved oxygen in streams and thus, threaten stream biodiversity. The steam systems of the Appalachian U.S., appear to be suffering from these effects. This project will deploy high-frequency sensors across a forest cover gradient along Appalachian stream systems to test the overarching hypothesis that accelerating climate and land use change create low oxygen ‘hotspots’ on stream bottoms that cause local extinctions. Eastern hellbenders are a giant salamander species native to Appalachia and are highly sensitive to low oxygen. Using hellbenders as a model, the study will test whether low oxygen in stream habitats causes fathers to eat their young (filial cannibalism) at frequencies leading to population declines. Coupling sensors with new underwater video technology in innovative artificial nesting habitats, the study will link bacterial activity and oxygen to individual hellbender behaviors, cannibalism, and nesting success. Moreover, these findings will guide conservation actions, including releasing thousands of hatchlings (“head-starting”) to circumvent the population declines caused by filial cannibalism, thus preventing local extinctions, preserving genetic diversity of the species, and informing future actions to conserve declining stream biodiversity, including fishes, macroinvertebrates, and amphibians. The project will also build on a strong tradition of reaching underserved Appalachian communities through educational events, strategic engagement with community members, and recruitment of undergrads from Appalachia (often first-generation students) to serve on the integrated research and conservation action team. Deoxygenation of aquatic habitats is a recognized threat of climate change, but past work has largely focused on coastal ecosystems and lakes/reservoirs, leaving its effect on stream ecosystems as a significant knowledge gap. Recent advances in high-frequency sensor technology enable real-time quantification of dissolved oxygen (DO) dynamics in surface waters. However, DO measurements are rarely made in benthic stream microhabitats utilized by sensitive taxa that likely have distinct chemical environments from surface waters. Linking DO and biogeochemistry in benthic microhabitats with hellbender behavior and reproductive outcomes will transform scientific understanding of often siloed research themes – organismal, population, and ecosystem ecology – and reveal a heretofore unrecognized impact of climate change on freshwater biodiversity. The study will also be the first in any species to mechanistically connect anthropogenic change, microhabitat DO, and parental behaviors that ultimately affect population dynamics. In doing so, the work will solve a 50 yr conservation mystery. Unlike past efforts such as captive breeding and head-starting of 2–4-year-old hellbenders, data will be used to inform evidence-based actions by a Conservation Agency to rear and release hatchlings to circumvent the bottleneck at the nest caused by filial cannibalism. This action is relatively low-cost and low-risk and its efficacy will be assessed using manual surveys, infrared video surveillance, and new genomics tools. In addition to informing hellbender and other stream taxa conservation, this research will train first generation undergraduate researchers, graduate students, and postdoctoral fellows in collaborative team science, conservation biology, biogeochemistry, and science communication with the general public. 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 · 2025-05

This project aims to advance the science of optimization to enhance the reliability and efficiency of critical national infrastructure by developing methods that can adaptively formulate and solve problems in open-ended environments characterized by uncertainty and change. The project will bring transformative change to how optimization problems are formulated and solved in dynamic real-world settings by introducing a paradigm called Embodied Optimization (EO), which treats optimization as an interactive process that continually adjusts to its environment. This will be achieved through theoretical foundations, algorithmic advances, and practical validation in power systems applications. The intellectual merits of the project include developing novel theories for approximation and statistical complexity of solution functions, scalable adaptation methods for sparse networks, and techniques for multimodal context awareness in optimization. The broader impacts of the project include enhancing the resilience of critical infrastructure systems, training over 200 undergraduates in solving real-world problems through a redesigned machine learning course, and reaching approximately 250 K-12 students in central Appalachia through educational outreach activities. The project addresses fundamental challenges in optimization for open-ended environments through six integrated research thrusts: (1) investigating the theoretical limits of solution function approximation under realistic constraints, (2) developing scalable adaptation methods leveraging sparse network structures, (3) exploring offline learning and counterfactual reasoning to enhance robustness, (4) investigating multimodal context awareness for optimization problem formulation, (5) developing game-theoretic approaches for dynamic goal prioritization and balancing competing goals, and (6) studying the co-evolution of problem formulation and computation for rapid response to disruptions. The research is validated through case studies in critical load restoration and electric vehicle charging coordination, demonstrating EO's potential to improve the reliability and efficiency of power distribution systems while managing uncertainties and constraints. The project advances both the fundamental understanding of optimization in dynamic environments and the development of practical tools for societal-scale infrastructure 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.