Virginia Polytechnic Institute and State University

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

NON-TECHNICAL SUMMARY: This research project investigates how disorder at the atomic level in materials can be deliberately controlled to create new and customizable properties for technological application. The focus is on high entropy oxides, a unique class of materials formed by combining multiple chemical elements in unconventional combinations. By examining how this disorder influences magnetic and electronic behaviors, the research uncovers strategies for designing advanced materials tailored for technologies like energy storage, electronics, and data storage. A central question in materials science is how much atomic-level disorder can be manipulated to achieve specific, desired properties. This project addresses that question, providing crucial insights into the relationship between disorder and material performance. It also develops an understanding of how different processing conditions can fine-tune these properties, paving the way for practical applications in cutting-edge technologies. This project fosters education in science and engineering through the development of online learning tools to support graduate students from various academic backgrounds, hands-on research opportunities for undergraduates, and community outreach programs that spark interest in science among middle and high school students. By advancing the ability to engineer materials through controlled disorder and promoting STEM education, this work drives technological innovation while inspiring and preparing the next generation of scientists and engineers. TECHNICAL SUMMARY: This project investigates the influence of compositional complexity on cation inversion in high entropy spinel oxides (HESOs) and its impact on their magnetic and electronic properties. High entropy spinel oxides are a class of materials stabilized by high configurational entropy, enabling unique combinations of cation site occupancy. The research aims to test the hypothesis that compositional complexity and processing conditions can be used to finely control cation inversion, enabling tailored functional properties such as tunable magnetism and electronic behavior. The research addresses three key questions: (1) how does increasing the number of components in high entropy compositions affect cation inversion; (2) how does variation in component ratios influence inversion trends; and (3) how do synthesis and processing conditions, such as temperature and pressure, impact inversion and functional properties? The scope of the work includes synthesizing bulk, single-phase HESO compositions with varying degrees of configurational complexity through solid-state processing methods. Combinations of laboratory-based and synchrotron characterization techniques, including but not limited to X-ray diffraction, X-ray absorption spectroscopy, magnetometry, and computational modeling, are employed to investigate the structure-process-property relationships. This approach provides insight into how entropic contributions can overcome conventional crystal field stabilization energies, influencing site occupancy and property tunability. The findings will contribute to the broader understanding of thermodynamic behavior and disorder in HESOs and the tunability of said disorder, while supporting data generation for machine learning applications in complex 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 2025 · 2025-04

Governmental and non-governmental institutions use various prioritization practices for allocating scarce resources. For example, homeless services may prioritize households based on risk assessments, while schools may accept students into "gifted and talented" programs by a combination of test scores and classroom observation. Communities also must decide how to allocate human resources across space and time, such as deploying police officers across different beats for crime prevention or outreach workers across neighborhoods for eviction prevention. This project aims to understand the ways algorithmic techniques for prioritization and resource allocation can best be used for societal benefit in such domains. Results will inform researchers broadly studying fairness, accountability, and trustworthiness of AI, algorithmic game theory and mechanism design, multiagent systems, and human-AI interaction. In addition, the work will impact policy through collaborations with community partners and support the transdisciplinary training of diverse students. At a technical level, the project will focus on several research problems important for developing fair and trustworthy AI. These include: (1) The design of algorithmic techniques for facilitating individualized deployment of scarce societal resources based on (potentially poorly calibrated and semantically ambiguous) risk scores, using rank information and/or learned transformations of cardinal risk scores. (2) Developing foundational models for fair and efficient deployment of human resources (e.g., police officers, caseworkers, schoolteachers, and specialists) across space and time, including definitions of fairness in such settings. (3) Using interpretable machine learning to characterize current human decision-making in public-facing positions and analyzing the efficiency and fairness of current approaches versus algorithmic ones. (4) Elicitation of truthful information to improve societal decision-making, using ideas from mechanism design and audit games. (5) The design of algorithmic decision support tools that can align the incentives of agents with the local agencies they represent while allowing continued use of discretion. Together, these research thrusts will advance the field of fair, accountable, and trustworthy AI, especially in the context of high-stakes societal decision-making. 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 · 2025-04

Nontechnical Description Hybrid perovskites are renowned for their exceptional ability to harness sunlight for applications in solar cells and optoelectronic technologies. In addition to their photovoltaic properties, certain hybrid perovskites exhibit molecular ferroelectricity—a characteristic that enables efficient charge storage and switching. This property holds significant potential to enhance the performance of solar cells and electronic devices. However, the soft and dynamic nature of their ionic lattice poses considerable challenges in understanding and precisely controlling ferroelectric behavior. In this research project, the research team aims to find new ways to control ferroelectricity in hybrid perovskites using light instead of electricity. By using light, the team will avoid problems caused by the materials’ soft structure and unwanted reactions during testing. The goal is to create materials that have stable ferroelectric properties even at room temperature. This could lead to the development of new, efficient electronic devices such as memory storage systems that use light to switch states, rather than relying on physical electrical contact. Furthermore, the PI aims to inspire and train the next generation of scientists by involving broad range of students in cutting-edge research. Through hands-on demonstrations and outreach to young learners, from preschoolers to high school students, the project will spark interest in science and technology. The project will also enable real-world applications, such as improving data storage or creating new ways to control electronic devices using light. Technical Description This project will investigate light-induced ferroelectric polarization in hybrid halide perovskites to address limitations in traditional approaches to ferroelectric characterization. Ferroelectricity in 3D lead-based hybrid perovskites (e.g., MAPbI3) is hindered by their soft ionic lattice, rapid structural dynamics, and unwanted ionic effects near interfaces during electric-field-based measurements. Instead, the PI will explore a hypothesis that an optically induced electric field can avoid these issues and allow precise control over crystal structure polarization, paving the way for enhanced dynamic lattice distortion critical for generating ferroelectric polarization. The approach combines novel materials design with advanced spectroscopy tools to uncover metastable polar phases in halide perovskites, stabilizing them at ambient conditions. These transient non-equilibrium polar states, typically absent from phase diagrams, offer opportunities for non-contact manipulation of ferroelectric domains using light. By bridging the gap between transient and long-lived polar phases, the research team will aim to unlock their potential for functional ferroelectric, ferromagnetic, and multiferroic applications. This five-year project will focus on probing light-matter interactions in soft dielectric halide perovskites. By stabilizing metastable polar phases and understanding their dynamics, the team will aim to advance the fundamental knowledge of ferroelectric and multiferroic phenomena in these materials system, addressing key challenges in achieving long-lived, stable polarization states under ambient conditions. Additionally, the project will emphasize broadening participation in STEM, offering mentorship and training opportunities to students, and engaging young learners through outreach initiatives to inspire the next generation of innovators. 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 · 2025-03

A Digital Twin is a virtual replica of real-world objects and processes intended to enable efficient and lower risk decision making by supporting testing in the virtual world prior to real world implementation. This is a planning proposal to create a Industry University Cooperative Research Center (IUCRC) for Digital Twin Research, Innovation, and Collaboration Hub (DT-RICH). Upon successful completion of the planning phase, Virginia Tech (VT) researchers will join with researchers from other participant universities to propose the full IUCRC. DT-RICH aims to enhance Digital Twin translational capability with better digital replication, prediction, and what-if testing for optimal solutions. These advancements will strengthen the ability to answer complex scientific questions, tackle engineering challenges, and improve human life. DT-RICH will systematically advance Digital Twin research and innovation by addressing cross-domain challenges, developing open standards for interoperability, and creating infrastructure for heterogeneous data fusion to better replicate real-world problems. The center will leverage spatiotemporal computing to integrate domain knowledge and AI/ML simulations, enhancing prediction capabilities, and enabling improved forecasting and decision-making. It will focus on quantifying uncertainty to build trust, examining digital twin’s full lifecycle to ensure reliability and sustainability, and adopting a socio-technical approach that incorporates ethics and community involvement. Additionally, DT-RICH will explore human-environment interactions, emphasizing well-being and sustainability. DT-RICH will engage with active members from industries to develop methodologies and technologies that translate research into commercialization. Collaborating with those members, the team will adopt research findings to enhance operations, products, and services, while leveraging partnerships to publicize achievements. Open-source software will be made available through GitHub under Apache 2.0 licenses, and fundamental research findings will be shared with members and via journals and conferences. The team also aims to create new courses focusing on digital twin technology that reaches all students, from high school students to postdoctoral scholars. 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 · 2025-01

Master’s-level engineers are critical for the technology workforce as the nation seeks to continue to advance national health, prosperity and welfare and to secure national defense. However, even though there are four times as many engineering master’s recipients as PhDs in the United States, we know almost nothing about their experiences, motivations, career planning, and skills required in industry. Most prior research on engineering graduate students has focused on doctoral students. This project will focus on this critical segment of the workforce, with an initial focus on mechanical engineering, so that we can systematically understand how to better prepare master’s students for their jobs so that they can make contributions in their careers from the outset. As workforce demands continue to increase for engineers who have master’s degrees, and as technology continues to change at a rapid pace, our research will use cutting-edge generative artificial intelligence (AI) techniques to illuminate the specific skills employers want from employees who have engineering master’s degrees, which can inform graduate curricular offerings. Our research will also help identify potential strategies for recruiting more students to engineering master’s programs, in particular domestic students, which is a critical need for the future workforce. The findings of this project will better inform students, employers, administrators, and those considering master’s degrees, about the skills desired and expected of mechanical engineering master’s recipients. This project will advance novel applications of natural language processing (NLP) coupled with interview research to understand the skills and benefits of terminal engineering master’s degrees, with a preliminary focus on the mechanical engineering discipline. The quantitative element of the project will involve analysis of a data set of over a decade of engineering job postings and apply an algorithm to extract skills from job advertisements to advance understanding of the engineering workforce, and of methodological development of NLP techniques. The qualitative element will involve collection and analysis of interviews with current master’s students about their reasons for pursuing a master’s degree, including desired skills. The project will mix these qualitative and quantitative analyses to identify mis(alignments) between what is communicated from the workforce about desired skills via job advertisements and current perceptions of the workforce from current master’s students. This research will fill an important gap in research on master’s-level engineering students, building knowledge about motivations for pursuing a master’s degree and employer expectations, including the most marketable skills. The NLP approaches developed in this project will apply to other employment sectors, disciplines, education research questions, and fields beyond engineering education research. 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 · 2025-01

Many animals, including many songbirds, thrive in altered environments such as cities because they modify their behavior. All changes in behavior have to be regulated by the brain, but we do not understand all the cellular and neural mechanisms that allow some species to cope with environmental changes such as urbanization. Two scientific advances can be applied to identifying changes in the brains of wild songbirds living in cities to understand how they cope with novel urban environments. First, ‘brain transcriptomics’ now allows us to measure the amounts of messenger RNA, the gene signals within cells that are converted into brain chemicals, and register the levels of every gene signal within the brain. Comparing these brain gene signatures between birds from urban and rural habitats will identify brain signaling pathways that differ with changed environments. Second, advances in gene manipulation tools make it possible to temporarily block specific gene signals and determine how behavior changes. Blocking signals and showing a change in behavior is critical to identifying the key gene signatures that allow wild birds to cope with altered habitats. Combining these tools from neuroscience with research on urbanization will resolve the gene, cellular, and neural mechanisms that allow some wild songbirds to cope with urbanization. Understanding these foundational biological responses will help us understand how these environmental changes impact other animals, including humans. Research findings will be disseminated through talks at Birder’s Clubs in three states, participation in the annual science festivals and Emory Brain Awareness week events. Human-induced rapid environmental change threatens biodiversity and especially impacts songbirds. While some species are in decline because of human impacts on the environment, other species have traits that allow them to cope with rapidly changing conditions. For example, wild animals living in urban habitats are reliably bolder and more aggressive than individuals living in less disturbed habitats. Despite concerns about the impacts of anthropogenic change on wildlife, we do not yet understand many of the neural and molecular mechanisms that underlie species' behavioral responses to environmental change. Recent advances in brain transcriptomics present an opportunity to describe the contribution of networks of genes, and thus interacting neural and endocrine processes, to the behavioral responses of free-living, wild animals to rapid anthropogenic change. However, resolving the causal role of specific genes to behavioral outcomes requires manipulating genes identified in such comparisons. Oligonucleotides are gene manipulation tools that could be used in freely behaving (non-model system) animals to reduce gene expression and determine how specific genes contribute to behavioral shifts. This work leverages differences in aggression between urban and rural male song sparrows to (1) characterize differences in brain gene networks associated with behavioral adjustments in urban male birds and (2) resolve the causal contribution of identified differences in gene expression on behavior using gene manipulation tools. Collectively this work will advance our understanding of the mechanisms that permit behavioral adjustments to urbanization and apply powerful tools from neuroscience to field studies of animal behavior. 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 · 2025-01

Inefficient methods for transformation and regeneration of recalcitrant plant species prevent widespread applications of genome editing technologies for both basic and applied research in established and emerging crop species. Overcoming these limitations is particularly relevant in monocotyledonous crops, such as maize, which alone provide most of the calories consumed by humans. In this project, maize lines expressing genes that promote regeneration, also known as morphogenic factors, will be used to provide a thorough understanding of the molecular events leading to the successful formation of new plants starting from differentiated tissue. This knowledge will be instrumental in developing new strategies for improving transformation of maize and other plant species, and will be integrated into course-based undergraduate research experiences (CUREs) as well as hands-on transformation workshops. The proposed research will exploit a morphogenic-based system called “GGB” to understand how certain morphogenic regulators reprogram somatic cells to develop into embryos and identify key regeneration genes that could be targeted to improve transformation efficiency in recalcitrant genetic backgrounds. This will be accomplished by the identification of direct targets of regulation of the GGB components via single-cell transcriptomic and DNA-binding approaches, and by the development of a diverse panel of maize inbred lines expressing the GGB morphogenic regulators. By exploiting the regenerative capacity of this system, protoplast regeneration, a challenging but advantageous system for the rapid generation of non-GMO edited plants, will also be revisited. This research will provide insights into the molecular basis of tissue- and genotype-dependent regeneration, helping to identify and eventually bypass roadblocks to regeneration, and will facilitate the development of high-throughput systems for genome-editing and transgenic line generation in diverse genetic backgrounds. This project is jointly funded by Genetic Mechanisms (BIO-MCB). Emerging Frontiers (BIO), and the Plant Genome Research Program (BIO-IOS). 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 · 2025-01

Large Igneous Province volcanism is associated with extraordinary mantle melting and voluminous eruptive episodes, which have been linked to major mass extinctions through the past half-billion years of Earth’s history. Significant research over the last three decades has brought the extreme nature of these events into focus. But controls on the nature and tempo of recovery after these catastrophic events remain unknown, despite implications for potential climate system tipping points. In particular, unexpectedly protracted periods of warm climate and delayed environmental and biological recovery following some Large Igneous Provinces underscore a fundamental lack of understanding of the gases released during the waning stages of these events and/or controls on global climate. This project will carry out a multi-disciplinary effort combining field observations; high-resolution records of volcanism, climate, weathering, and life; and numerical modeling to understand co-evolution of solid and surface Earth during perturbation and recovery. This is a project jointly funded by the National Science Foundation’s Directorate for Geosciences (NSF/GEO) and the National Environment Research Council (NERC) of the United Kingdom (UK) via the NSF/GEO-NERC Lead Agency Agreement. This Agreement allows a single joint US/UK proposal to be submitted and peer-reviewed by the Agency whose investigator has the largest proportion of the budget. Upon successful joint determination of an award recommendation, each Agency funds the proportion of the budget that supports scientists at institutions in their respective countries. This project is co-funded by the Directorate for Geosciences to support AI/ML advancement in the geosciences. This project addresses a fundamental unanswered question: what processes shape climate and biotic recovery from major Large Igneous Province-driven carbon cycle perturbations? The project aims to test the new overarching hypothesis that a large-scale transition in crustal rheology shuts down Large Igneous Province volcanism, but continued mantle melting drives cryptic Carbon Dioxide release and delays climate and biotic recovery. If correct, this hypothesis implies that cryptic degassing—Carbon Dioxide release through the crust decoupled from eruption rates—is a key, and previously unaccounted for, control on the climatic conditions and tempo characterizing recovery. To test this hypothesis, this project pursues four key scientific objectives: 1) development of high-resolution, multi-disciplinary records of volcanism and weathering, 2) coupling of models of mantle geodynamics, magma transport, and outgassing, 3) assimilation of records of past climate and weathering into climate-biogeochemical modeling to invert for outgassing fluxes and place top-down constraints on interior evolution, 4) integration of paleobiological databases with records and modeling of volcanism, climate and weathering to test factors shaping which types of organisms thrive beyond recovery. The project leverages three powerful natural laboratory Large Igneous Provinces and climate events, building from the youngest and best-resolved, the Columbia River Basalts and Mid-Miocene Climatic Optimum; to the more voluminous North Atlantic Igneous Province, Paleocene-Eocene Thermal Maximum, and Early Eocene Climatic Optimum; and finally to the Siberian Traps, catastrophic end-Permian mass extinction, and early Triassic hothouse. The project will carry out a sustained outreach/inreach effort in northeastern Oregon, the epicenter of Columbia River Basalt volcanism and site of project field work. Activities aim to humanize science and enhance education through engagement of rural communities. Project PIs and students will engage school-age children in Oregon and New Jersey, and global Large Igneous Province researchers through: a data portal and set of virtual field trips; Large Igneous Provinces for Kids programming in the form of visits to Wallowa county schools and ‘Write a Scientist’ correspondences with project scientists; and a field forum at the mid-point of the project that will welcome the Large Igneous Province community. 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 · 2025-01

Rotating detonation combustors (RDCs) coupled to highly diffusive mixers enable compact, green, and efficient energy production. RDCs operate through the injection of an air-fuel mixture which is detonated through a reactive shock wave rotating at supersonic speeds and fed through the mixer to cool and slow the flow before it reaches a turbine which ultimately harnesses the energy. RDC-mixers hold great promise to revolutionize power and propulsion systems, but they are difficult to model/optimize due to unsteady mixing, extreme temperatures, and high-speed diffusion. This collaborative project aims to develop models and methodologies that enable optimization of the RDC-mixer for maximal fuel efficiency. The investigators will leverage a three-pronged meta-modeling framework featuring an innovative digital twin, a novel statistical surrogate model, and a physical experiment involving a high speed wind tunnel in which the mixer will be assessed through high-frequency optical and probe-based measurement techniques. RDC-mixer-turbine systems are directly impactful to clean energy and heat production, but their potential impact is even broader. Diffusing elements and mixers are used in a variety of applications, ranging from aviation, aerospace, agriculture, refrigeration cycles and heat exchangers. The mathematical modeling foundations developed in this project will be widely applicable to computer simulation experiments and digital twins. This project is organized into three aims. First, motivated by the complexities of the digital twin, a gradient-enhanced Bayesian deep Gaussian process surrogate will be developed to provide non-stationary flexibility, uncertainty quantification, gradient-enhancement for improved accuracy, and gradient predictions to facilitate Bayesian optimization. Second, the digital twin of the RDC-mixer will be developed at reduced computational costs as existing simulations of RDC-mixers require weeks of compute time. Tailored unsteady boundary conditions are proposed to separate the computational fluid dynamic simulations for the combustor and mixer, which will enable faster computation. The digital twin will incorporate steady and unsteady flows, meshing, and adjoint solvers to provide gradient information at minimal cost. Third, a novel calibrated Bayesian optimization framework will be developed to first optimize calibration parameters of the digital twin, then use these with a bias-correction model to sequentially optimize the physical experiment. The physical model will be used in the calibration feedback loop to train the bias-correction model and to test and validate the best designs. Collectively, the surrogate model, digital twin, and physical experiment will enable effective optimization of the RDC-mixer design for optimal fuel efficiency. 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 · 2025-01

The Super Dual Auroral Radar Network (SuperDARN) Workshop 2025 will be held in Roanoke, Virginia, in June 2025. SuperDARN is an international network of radars operated by science and engineering institutions located in more than a dozen countries worldwide. The network consists of over 30 low-power radars extending across three latitudinal tiers from mid-latitudes, through the auroral zone, and into the polar cap, in both hemispheres. Four US universities, with Virginia Tech as lead institution, form a US SuperDARN collaboration that is funded by NSF. The SuperDARN array provides remotely sensed observations of various geophysical phenomena in the Earth’s upper atmosphere. These observations have contributed to significant advances in geospace research and plasma physics. The annual workshop of the SuperDARN collaboration is an opportunity for SuperDARN researchers to coordinate on scheduling and operations, new technical developments, software and analysis, and scientific collaborations. The SuperDARN Workshop serves as an excellent platform to exchange ideas about recent trends and developments in SuperDARN operations and research. The annual SuperDARN Workshop has a track record of fostering strong international collaborations with broad scientific and engineering impact, not only within the SuperDARN community, but across the entire space science discipline. The Workshop proceeds as a conference with a sizable but manageable number of attendees. The sessions are held over a one-week period. The scientific sessions take place in a single conference room and are ordered thematically, for example ‘large-scale plasma convection’, ‘ultra-low frequency waves’, ‘atmospheric gravity waves and traveling ionospheric disturbances’, etc. The SuperDARN collaboration is managed at the highest level by the SuperDARN Executive Council and working groups commissioned on an ongoing basis to manage tasks such as scheduling, software development, data distribution, and coordination with outside groups of researchers such as satellite mission teams. Attendance by graduate students and early career researchers will be supported. Graduate students get exposure to both the operational and scientific aspects of SuperDARN, as well as to the broad range of research activities carried out by the global SuperDARN community. The Workshop also serves as an excellent platform for both students and early-career researchers to develop collaborations, work on new research avenues, and explore new exciting career paths. 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 · 2025-01

Thermal management is a major bottleneck in electronics cooling. Traditional techniques can no longer provide the necessary cooling. State-of-the-art devices use phase change (condensation and boiling), but this is limited by the direction and magnitude of gravity. The proposed concept overcomes this limitation by amplifying gravity using acoustic waves for improving heat transfer. The outcomes of the proposed work will make important contributions to basic science and benefit society by sustaining progress in the semiconductor industry. The project includes integrated education and outreach programs to motivate, inspire, and enrich the educational experience of K-12 students. Using experiments and modeling, the goal of the research program is to develop a comprehensive framework to effectively manipulate droplets and bubbles during phase change for enhancing heat transfer rates for thermal management applications. By superposing gravity with an acoustic field, the research program aims to demonstrate unprecedented heat transfer rates in condensation and boiling. Using state-of-the-art thermal-fluidic experimental facility and theoretical and numerical modeling, the research program investigates the heat transfer rates in condensation and boiling with three principal objectives: 1) improving the heat transfer rate in dropwise condensation and the critical heat flux in pool boiling by superposing gravity with tunable radiation pressure of acoustic waves, 2) developing a theoretical framework and analytical model for acoustically enhanced condensation and boiling, and 3) implementing acoustic wave-assisted film-wise condensation. The proposed research is expected to advance basic science by producing new knowledge that enables beyond-gravity condensation and boiling. The work will benefit society by enabling forward progress in the semi-conductor industry by providing efficient cooling for the next-generation compact microelectronic devices. It also benefits space exploration studies in microgravity environments where condensation and boiling are practically impossible due to the absence of gravity. 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 · 2025-01

During this three-year IRES project,18 U.S. students, recruited nationally, will go for 8 weeks per year to Melbourne (Australia) to engage in research and related professional activities in electric power systems. The project builds on a long-established collaboration between the Virginia Tech PIs and experts at Monash University, Australia. The overarching research theme of this project is to enable integration of renewable energy components in the grid through the concept of grid-forming (GFM) inverter-based resources (IBR). The industry and government interest in GFM has recently significantly increased as a potential solution for large-scale adaptation of renewables at the power system transmission level. Simultaneously, this is an area of research in which Australia has significantly more experience than the rest of the world due to their natural geography as a large island with uneven population distribution. This program is mutually beneficial and strengthens our existing U.S.-Australia collaboration by providing a framework for conducting research projects of common interest in a global sense while also training U.S. students in this important area of technological advancement. Students will gain firsthand experience of different practices in distribution and transmission systems. Our alumni will be a cohort of U.S. individuals with highly desired skills for industry and graduate programs nationally and internationally. This program strengthens students' training by offering a balanced experience, including living and working in a foreign country, research, visiting global world-class companies, and professional development workshops. These efforts increase their marketability in today world's global workforce as they gain experience in working with global electric power systems. The intellectual challenges that need to be addressed in this project include operation across different timescales, control and coordination of a large set of small generation units, and lack of mechanical inertia. This work is expected to contribute to (i) algorithms to study feasibility of grid-forming operation of inverters, (ii) simulation methods and algorithms capable of handling systems with a large number of dynamical states, and (iii) power sharing of a massive number of inverters. The students will work on the design of algorithms and methods to enable operation of the power system with grid-forming inverters and to eventually ensure the same or higher level of reliability and security as the existing synchronous generator-dominated power system. 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 · 2025-01

This project aims to investigate relative contributions of different sources to the observed large-scale gravity waves (GWs) in the thermosphere and their impacts on tides, planetary waves (PWs), and circulations. Atmospheric waves are important drivers for coupling between different layers of the atmosphere. It has been also recognized that gravity waves can also propagate into the thermosphere and impact the ionosphere. These GWs can affect tides and planetary waves, and large-scale waves also affect the propagation or dissipation of GWs, resulting in the impacts on the ionospheric variability. However, quantitative understanding of importance of lower atmospheric GW sources in the thermosphere/ionosphere and their impacts on dynamics, including interaction with tides/PWs and changes in circulations, are still missing. Advancing knowledge of GW sources and their interaction with tides will improve our understanding of coupling processes and the lower atmospheric influences on space weather forecast, which are great interest to our society because of satellite communications and GPS accuracy. This work will improve our understanding of GWs in coupling processes, GW sources and their impacts on the ionospheric variability. and the lower atmospheric influences on space weather forecast. This work will support undergraduate students and two women scientists. The team will conduct controlled simulations to isolate GW sources from below (i.e. from the troposphere) and above the stratosphere. Then, relative contributions of these sources to the observed large-scale GWs in the thermosphere and their impacts on tides, PW, and circulations will be investigated. This project will use the Specified-Dynamics Whole Atmosphere Community Climate Model with Thermosphere and Ionosphere Extension (SD-WACCM-X) simulations and satellite observations (ICON-MIGHTI and TIMED-SABER) from 30 km to 250 km altitudes. Using these observations and simulations, this project aims to answer the following science questions: (1) What are the relative contributions of GW sources from the lower atmosphere and above the stratosphere/mesosphere to thermospheric GWs? (2) What are the impacts of large-scale GWs on wind structure and circulations? and (3) How do large-scale GWs impact atmospheric tides and PWs? 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 · 2025-01

This research examines the links between food security and the access and usage of natural resources, particularly wildlife. This study focuses especially on regions that are undergoing processes of urbanization, which has been cited as the cause of increasingly inequitable access to resources and, by extension, food insecurity. In this project, the researchers use survey methods in a broad sample of communities to examine the extent to which social and economic factors can help people to gain access to wildlife and mitigate food insecurity. Owing to the sensitivity of the research questions, the study also advances new survey methods for eliciting reliable reports of sensitive behaviors. The project also contributes to the training of undergraduate and undergraduate students. With a focus on access and use of natural resources in urban and peri-urban areas, this project complements previous geographical studies of natural resource use in rural areas. By sampling across a number of communities along the rural-urban gradient, the researchers account for the extent to which proximity to wildlife resources interacts with social and economic determinants of access and usage and the resulting impacts on food insecurity. This research contributes to the interdisciplinary field of political ecology. Analyses of collated secondary data provide complementary insights into the geography of wildlife consumption as a function of proximity to protected areas, urbanization, and economic development, among other spatial variables. Insights from this work are shared with conservation organizations and disseminated to other local partners. 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 2024 · 2024-12

Extreme heat disasters are increasingly common across the US, with many cities experiencing multiple consecutive days above 95°F (35°C). Extreme heat directly impacts drinking water quality by increasing the water temperature within distribution systems. High temperatures can compromise the efficacy of disinfection against microbial pathogens by increasing the decay rates of disinfectant residuals in distribution systems. Warmer temperatures can simultaneously stimulate growth of opportunistic pathogens which can cause acute illness or death. Thus, the failure of disinfection in drinking water distribution systems during an extreme heat disaster could cause a community outbreak of illness that would further stress hospital resources and lead to loss of life. This Disaster Resilience Research Grants (DRRG) project will contribute to understanding the risk of extreme heat to the microbial and chemical safety of drinking water and help identify engineering solutions to build water system resilience. The findings will inform water utility disaster response and preparedness plans, ensuring the ability to provide clean water during extreme heat events. Findings will be communicated to utilities through stakeholder organizations and targeted outreach to at-risk water systems, such as those serving low-income communities along the Southwestern border that experience frequent extreme heat events. This project provides an enriching experience for graduate and undergraduate trainees at two diverse public universities and will introduce underrepresented students to exciting, impactful STEM research. The transfer of knowledge between two early career investigators will prime both labs for future innovations in water quality and resilience engineering. This project will evaluate the effect of extreme heat on efficacy of disinfection in drinking water distribution systems and evaluate a novel engineering solution to increase resiliency. Most disinfection studies are limited to <30 °C, which is not informative for high water temperatures possible during extreme heat events. Simulated distribution system experiments will be conducted under extreme heat conditions (35-60 °C) to elucidate disinfectant decay kinetics of conventional chlorine and chlorocyanurates, an emerging chlorine alternative recently approved for drinking water treatment. We anticipate that chlorocyanurate disinfection will be more resilient to high temperatures and maintain higher microbial protection than conventional chlorine. The growth kinetics of legionellae and the required disinfection exposure to achieve inactivation will be determined with simulated distribution system experiments under extreme heat conditions, comparing conventional chlorine and chlorocyanurate disinfection. These experiments will produce chemical kinetics and microbial inactivation models that will be combined with a heat transfer model fit to real distribution system temperatures from a Southwestern US city to quantify the anticipated disinfection failure rate under a range of extreme heat scenarios. In each scenario, the failure rate with chlorine will be compared to the proposed chlorocyanurate intervention. This project takes an interdisciplinary approach to determine the extent to which extreme heat events compromise disinfection and microbial safety in drinking water distribution systems. The integration of aquatic chemistry, microbiology, and thermodynamics will produce holistic understanding of disinfection efficacy under extreme heat conditions. Bacterial inactivation results will provide critical insights into the persistence of this dangerous pathogen in drinking water distribution systems under extreme heat. This work will advance scientific understanding of how to mitigate health risk in US drinking water systems increasingly subjected to extreme heat. This award is co-funded by the NSF CMMI Disaster Resilience Research Grants and CBET Environmental Engineering Programs. 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 2024 · 2024-11

Responsible Engineering Across Cultures: Investigating the Effects of Culture and Education on Ethical Reasoning and Dispositions of Engineering Students Engineering is more cross-cultural and international than ever before, resulting in potential disagreements about (in)appropriate courses of action, which can impede engineering work. Despite high rates of international enrollment and an increased focus on global dimensions of engineering in US programs, ethical issues arising from global engineering have been insufficiently addressed. To address these issues, this project will assess the impact of culture and education on ethics among engineering students in North America, Europe, and Asia. Understanding if and how diverse cultural backgrounds and educational experiences affect professional decision-making and collaborations requires empirical investigation, to develop training that addresses the kinds of challenges engineering students, practitioners, programs, and organizations will increasingly encounter in the globalized world. This project will be beneficial for training the next generation of engineers who are competent in working professionally and ethically in the global context and are responsive to the value of diversity in quality and sustainable engineering work. The goal of this project is to identify educational interventions with the greatest effects on ethical reasoning and dispositions of engineering students, whether these effects differ among cultural and national groups, and if/how to modify these interventions to respond effectively to cultural and national differences. To do so, researchers from Colorado School of Mines, University of Pittsburgh, Delft University of Technology, and Shanghai Jiao Tong University will implement mixed-method, quasi-experimental, longitudinal, and cross-sectional research to: (1) determine the effects of culture and foreign language on the ethical perspectives of first-year engineering students; (2) assess the relative effects of culture and education on these perspectives over four years; (3) use engineering ethics assessment tools across cultures and countries to examine their cross-cultural validity. Findings from this project will be essential to develop educational interventions that effectively respond to the globalized environments of contemporary engineering practice. They will also contribute to the development of more inclusive engineering education, by identifying perspectives potentially marginalized in the reigning paradigms. Finally, this project has implications for the development of responsible research education at the graduate level. Despite the fact graduate student bodies in STEM fields have become increasingly international, limited work has focused on developing culturally responsive ethics curricula for graduate students from diverse backgrounds. 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 2024 · 2024-10

This project aims to serve the national interest by designing, implementing, and evaluating a new engineering education pedagogy called Scaffolded Ethics Autobiography (SEA), through which engineering students construct their own moral narratives, the stories of their moral lives. SEA is assumed to promote students’ professional engineering identity, which is an engineering identity with explicit ethics components. Engineering educators have explored how to help students shape engineering identity, as it is believed to be associated with persistence in the profession. At the same time, undergraduate engineering programs must include instruction in professional ethics to meet accreditation criteria. This project will bridge the separate efforts to facilitate engineering students’ engineering identity development and professional ethical development through SEA, which opens new possibilities to effectively achieve both goals. This project will help engineering students grow as ethical engineers with strong intentions to persist in engineering careers, which aligns with significant national interests. In Phase 1, this project will design the SEA module, including the necessary materials for the SEA module implementation, such as module description and the assessment rubric, after constructing a new model of professional engineering identity. In Phase 2, this project will evaluate the effectiveness of the SEA module in promoting engineering students' moral narratives and professional engineering identity development. Existing measures in engineering identity and newly developed assessment rubric will be used for the evaluation. In Phase 3, this project will connect the SEA module with engineering students' career decisions, by examining how engineering students' engineering identity, whose development will be facilitated by the SEA module, is related to their career choices. 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 2024 · 2024-10

Nontechnical description: Data centers consume ~2% of the global electricity. However, the efficiency of power delivery in data centers is only 70~80%. This efficiency is largely limited by the resistive loss on the lateral power delivery board of the 48-to-1/0.8 V point-of-load (PoL) converter. This issue is particularly serious for AI processors as they are more power hungry than conventional CPUs. Vertical power delivery, which places the PoL converter directly underneath the AI processor, can shorten the power path and reduce the resistive loss by 10 times. However, this approach requires aggressive miniaturization of the PoL converter with 5~10 times frequency upscaling, the realization of which is limited by current power semiconductors. The goal of this project is to address fundamental knowledge gaps for realizing the 48 V vertical power delivery by heterogeneously integrating the wide-bandgap (WBG) microelectronics consisting of gallium nitride (GaN) power device, CMOS, and sensor, together with the PCB-integrated magnetics, power electronic circuitry, and advanced packaging. The intellectual merits of the project include establishing the knowledge base regarding the semiconductor materials, devices, magnetics, circuitry, and packaging under a co-design framework to enable the envisioned ultra-high-frequency, miniaturized PoL converters. Four industrial collaborators will form an advisory board to guide the team in research, education, and IP development. The broader impacts of the project include (1) enable tremendous energy savings in data centers, with an annual reduction in carbon emission equal to ~6.5 million passenger vehicles, (2) enhance U.S. competitiveness in WBG semiconductor manufacturing. In addition, this project will train future students in the fields of WBG semiconductors, microelectronics, and power electronics. Technical description: To improve the device performance for frequency upscaling, the project deploys a novel multi-channel GaN material architecture, which comprises 5~15 vertically-stacked two-dimensional hole gas (2DHG) and/or two-dimensional electron gas (2DEG) channels. The objective of the project is to address the fundamental knowledge gaps in multi-channel materials, power and CMOS devices, magnetics, circuitry, and packaging to enable the ultra-high-frequency, miniaturized PoL converters. The studies are guided by a multi-scale co-design framework that comprises a machine learning-aided material-device co-design and an electro-thermo-mechanical component-package-board co-design. This project will focus on research activities in the following five aspects: (1) The optimal doping schemes and fundamental transport properties of the WBG multi-channel material will be probed. (2) Novel device architectures, such as high-k gate stack and superjunction structures, will be integrated into the multi-channel power transistor and multi-channel CMOS for achieving high performance and monolithic integration. (3) A deep learning model trained by the experimental data augmented by physical simulation will be explored for material-device co-optimization. (4) Novel integrated magnetics and 48/0.8 V single-stage circuit topology will be developed. (5) Optimal package architectures and cooling approaches will be identified using a component-package-board co-design framework to achieve low power delivery impedance, high heat dissipation, and good 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 2024 · 2024-10

Water reservoirs provide many societal services in the United States and worldwide including flood control and sources of hydroelectric power, drinking water, and irrigation for agriculture and farming. Despite their vital roles, reservoirs are gradually being filled with sediments as land use and climate change exacerbate soil erosion and sediment transport and deposition in natural and engineered surface water systems. Few suitable sites for new dam construction exist in the United States thereby emphasizing the need for cost-effective management of existing reservoirs. In 2024, the US Army Corps of Engineers (USACE) and the State of Kansas are scheduled to pilot test a novel water-injection dredging (WID) process (an untested but potentially transformative approach) in a federal reservoir in the State of Kansas with the aim of inducing turbidity currents and exporting trapped reservoir sediments to downstream waterways, which could prove crucial for addressing the global concern of reservoir sedimentation and its impact on water security. However, key questions remain regarding the ability of WID to restore reservoir sediment storage capacity, its environmental implications to in-lake water quality, and its downstream effects to channel morphology and aquatic ecosystems. To address these knowledge gaps, the Principal Investigators (PIs) of this project propose to leverage the USACE-Kansas WID field test to collect and analyze sediments, nutrients, and aquatic species count data with the goal of generating fundamental scientific and engineering knowledge on the transport efficacy, mechanisms, and environmental responses following the implementation of WID process in a water reservoir. If WID is shown to be viable, with minimal impact on downstream river ecosystems, the successful completion of this project will benefit society through the generation of new data and fundamental knowledge that could be used in reservoirs around the globe, transforming sediment management, and reducing costs associated with existing dredging techniques. Additional benefits to society will be achieved through student education and training including the mentoring of one undergraduate and one graduate student at the University of Kansas and two undergraduate students and one graduate student at Kansas State University. Existing reservoir sediment management techniques have limited effectiveness because they (1) do not restore natural downstream sediment continuity, (2) require transport, storage, and disposal of dredged materials, and (3) are costly to implement. The basic premise of the water-injection dredging (WID) process is to spray a jet of fluid into the bed of a reservoir, entrain sediments into the overlying water, and initiate a density current (akin to an underwater avalanche) to mobilize stored bed sediments toward the reservoir outlet. While WID has successfully been applied to ports and rivers, it has yet to be tested in a water reservoir thereby raising critical questions regarding its potential efficacy and environmental impact. This project will address these knowledge gaps. The specific objectives of the research are to 1) evaluate the physical mechanisms by which human-induced turbidity currents propagate in reservoirs, using high-frequency turbidity sensing data and computational fluid dynamics modeling; 2) evaluate shifts in reservoir water quality by monitoring thermal stratification and redox conditions using in-situ physicochemical sensors and laboratory experiments before, during, and after the WID field test; 3) assess channel and floodplain accretion rates before, during, and after the WID field test; and 4) continuously assess the response of fish and macroinvertebrate communities to sediment releases and the induced biological, chemical, and physical changes in water quality and habitats throughout the WID demonstration project. The successful completion of this research could transform how reservoirs are managed, potentially extending the usable lifetime of large water storage infrastructure across the globe. To implement the educational and training goals of this project, the Principal Investigators (PIs) will collaborate with the University of Kansas (KU) Self Engineering Leadership Fellows (SELF) program to develop and deliver a workshop for college students to conduct hands-on research with large environmental datasets and develop science communication skills, culminating in a presentation to the State of Kansas and Army Corps of Engineers. In addition, the PIs plan to integrate the findings from this research into relevant course modules and outreach activities at KU and Kansas State University. 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 2024 · 2024-10

This award focuses on understanding how complex, deployable structural systems can be utilized in geotechnical infrastructures to reduce their overall size and therefore transportation, material, and life-cycle costs. Deployable underground structures are ones that change size or shape after being installed under the ground. This allows for larger foundation capacities to be achieved with less installation effort and cost. Using deployable mechanisms to increase foundation capacity is a novel concept for geostructures since these mechanisms have only been utilized in aerospace and over-ground applications. More specifically, this research focuses on deployable underground structures with awns that increase their size underground once they are installed. The idea can be applied to foundation piles for offshore wave and wind energy converters, land-based wind turbines, and tall buildings. Future advances from this research will enable increased sustainability of geostructures to create systems that uses less material and have better packing for transportation while maintaining the required load carrying capacity. Furthermore, in this project advanced visualization techniques such as virtual reality will be used to develop outreach tools that introduce students to concepts of structural deployment and advanced soil-structure interaction. The specific goal of the research is to unlock this fundamental concept of underground awn deployment by gaining the fundamental physical insight required to mechanistically describe the soil-structure interactions occurring during the deployment process. The grand challenges addressed are to 1) understand soil-structure interaction of deployable, compliant structures and 2) demonstrate underground structural system deployment using an awn arrangement that enhances system foundation capacity. Development of form-finding methods with load-transfer informed soil-structure hybrid models in combination with statistical uncertainty analysis for health diagnostics will be used to advance the knowledge base on nonlinear soil-structure interaction. This work will also advance scientific knowledge on structural analysis for deployable structures and computational modeling for soil-structure interaction. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2024 · 2024-10

This Growing Convergence Research project brings together researchers from engineering, physics, geo- and materials sciences with the goal of establishing a convergence framework to address the feasibility of mineral detection (MD) of dark matter. There is overwhelming evidence from astrophysics and cosmology that there is about five times as much dark matter as there is ordinary matter, i.e. the stuff we are made of. In MD, one studies geological samples of gram- to kilogram-scale, which have been exposed to dark matter interactions for billions of years. This allows MD to have the potential to match or exceed the sensitivity of conventional experiments. MD may therefore provide a path to answering the question of what dark matter actually is. In MD, the interactions of crystals with dark matter results in permanent changes to the crystal lattice which can be measured much later than the original interaction. This long intervening time combined with the geological changes the samples have encountered is a challenge for the interpretation of dark matter signals in MD. The changes to the crystal lattice are happening at the nano-scale and thus methods which can record nano-scale features scattered over a large, cubic millimeter to cubic centimeter, volume are required. This also implies a challenge in terms of data volumes and subsequent analysis. In addition, a dedicated simulation effort, from particle transport to molecular dynamics, is required to gain a theoretical understanding of damage formation and permanence. This project will test the feasibility of the MD approach to detecting interactions between ordinary and dark matter. 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 2024 · 2024-10

This project brings together researchers, practitioners, and regulators around the intersection of human-computer interaction (HCI), networking, and network policy to address the shortcomings of current Internet technologies. The Internet was originally designed for a small set of uses in university research labs, but today it serves billions of people worldwide. This rapid expansion has left some communities, particularly rural and marginalized groups, underserved and sometimes harmed by current network technologies and policies.To address these issues, this project will conduct a workshop in Winter 2025 at Virginia Tech's DC campus. This workshop will bring together experts in HCI, networking, and policy to discuss and explore the overlap between these fields. The goal is to identify opportunities and challenges and to develop a research agenda that considers the human elements of emerging telecommunications technologies. The workshop will center on four major agendas: (1) Applying HCI Methods to Network Design; (2) Bringing Networking Questions to HCI; (3) Improving Network Access in Disadvantaged Communities; and (4) Measuring Human Elements in Networks. These agendas will help bridge the gap between technical network development and the social needs of diverse user communities. The workshop will result in the formation of working groups to share findings through research publications, the identification of interdisciplinary research opportunities and policy recommendations, and a final report outlining the workshop's findings and proposed future directions. Ultimately, by fostering collaboration between HCI and networking experts, this project seeks to create more inclusive and effective network technologies. The Internet and related technologies are now vital to education, economic opportunity, governance, and more. Ensuring that these technologies serve all communities fairly is critical. This project aims to lay the groundwork for future research and policy changes that address these important issues. 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 2024 · 2024-10

Artificial intelligence (AI), computer vision, and machine learning have seen advances in research, driven in part by the creation and sharing of large datasets. These types of datasets do not exist for the virtual reality (VR) research communities. This planning grant forms a framework for the creation of large VR-related datasets. Experts in the VR community will create, capture, and share these needed VR datasets. This data will include head, hand, and eye tracking data. They will collect information on user behaviors and interactions while using VR systems. These datasets will advance VR software development, techniques, and applications. This will allow software advancements like observed in the VR hardware consumer markets. A primary goal of this planning grant is to design and create a large VR dataset infrastructure. This project will address an important gap in virtual reality research. A team of experts in VR will help capture multi-modal VR data (e.g., head tracking, eye gaze) and set up sharing of this data. They will also track interactions and behaviors with VR systems. VR experts will administer standardized questionnaires within most VR applications including consumer applications. It is expected that this will result in ecological validity of the datasets. The project plans to capture and publish a large dataset to help form the framework for use by the VR community. The project will provide an open-source toolkit for researchers to capture their own VR datasets. It is a goal that they will then contribute their datasets back to the VR research community. A key part of this project is to form an advisory board of computing experts to guide VR research directions. The project will organize a full-day workshop to engage the VR research community. This will help to identify community needs and priorities to advance research. The participants of this workshop will support the development of this important infrastructure. 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 2024 · 2024-10

Mobile networks are a critical infrastructure of national interest, providing communication services essential for supporting our society’s economy, public health, safety, and security. However, resilience became a crucial requirement of the mobile network design only with recent 6G pre-standardization activities. This project aims to enable natively resilient 6G by leveraging the equivalence across different network resources, components, and functions to afford a new degree of freedom to withstand and recover from attacks and failures. One of the significant challenges addressed by this project is the creation of metrics and methods to quantify resilience in the context of mobile networks, informing the development of novel studies and algorithmic approaches to resource management in the presence of wireless security threats, e.g., jamming or eavesdropping, or unintentional failures related to equipment malfunction or congestion. The proposed research program strengthens the technological and research cooperation between the U.S., the Republic of Ireland, and Northern Ireland, while providing educational opportunities for graduate students and postdoctoral researchers from international collaboration and working on a geographically distributed team. The research findings and developed knowledge will stimulate joint dissemination and engagement with relevant international stakeholders that define the roadmap for developing and adopting 6G systems. This project is a collaborative effort part of the US-Ireland R&D Partnership, enabling natively resilient 6G networks by leveraging the equivalence between different network resources, components, or functions, referred to as fungibility. By adopting concepts from risk theory, complex networks, and computational biology, this project quantifies the capacity of wireless networks to withstand and recover from disruptions while maintaining service continuity. This project proposes new metrics to understand the fungibility of network resources for creating redundancies and proposes innovative resource management strategies and optimization algorithms. The technical work is divided into four research thrusts. Characterizing the resilience of mobile network deployments, exploring fungibility between spectral and spatial resources and deployment options under attack scenarios. Quantifying the impact of novel attacks against different Radio Access Technologies (RATs), and develop new methods that utilize equivalencies between different RATs to remain operational. Proposing new definitions of robustness and degeneracy from computational biology, describing functionally equivalent topologies, and investigating how both concepts benefit radio resource management and topology control in mobile networks. Finally, examining the fungibility between disaggregated Radio Access Network (RAN) functions and functional splits, and proposing orchestration strategies resilient to compromised mobile network infrastructure due to attacks or failures. 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.