Old Dominion University Research Foundation

NSF Awards · FY 2026 · 2026-07

The amount of dissolved organic carbon in the ocean is similar to the total amount of carbon dioxide (CO2) in the atmosphere. Some of this dissolved organic carbon includes small, rapidly cycling organic molecules that are not well-understood. The production, loss, and seawater concentrations of these molecules are influenced by sunlight-driven reactions, the activity of marine life, and exchange with the atmosphere. Rapid cycling keeps the concentration of these compounds low, making them difficult to measure, while allowing them to support carbon transfer through the surface ocean. This project will use an improved analytical approach to examine how sunlight and marine microorganisms control the production and loss of formaldehyde and acetaldehyde, two small organic molecules that contribute to surface-ocean carbon cycling. This work will provide new insight into ocean biogeochemistry and ocean-atmosphere connections, which are fundamental to environmental and economic well-being. The project will also support student training and public outreach activities that connect ocean carbon-cycle research with broader science education. Marine labile dissolved organic carbon (DOC), which encompasses hundreds to thousands of low-molecular-weight (LMW) organic compounds, mediates a major fraction of carbon flux through the DOC pool. These LMW organic compounds are present at low concentrations, often in the low- to sub-nanomolar range, and are rapidly produced and consumed on timescales of hours to days. This research project focuses on the cycling of two biologically labile compounds in the surface ocean: formaldehyde and acetaldehyde. These LMW aldehydes play important roles in the photochemical transformation of marine DOC, the microbial cycling of LMW organic compounds, and air-sea exchange. Despite the importance of formaldehyde and acetaldehyde in labile DOC cycling, the biogeochemical controls on their production and loss in seawater are poorly constrained. This gap is partly due to analytical challenges, including the potential for atmospheric contamination. To address this limitation, this project uses a closed system for formaldehyde and acetaldehyde sampling and analysis that minimizes atmospheric contamination. This three-year study aims to address three questions: (Q1) Do diel variations in formaldehyde and acetaldehyde concentrations in surface ocean waters reflect a balance among photochemical production, microbial cycling, air-sea exchange, and mixing? (Q2) How important are biological processes to the cycling of formaldehyde and acetaldehyde in the surface mixed layer relative to photochemical production? (Q3) How does seasonality influence the photochemical production and microbial cycling of formaldehyde and acetaldehyde at the Bermuda Atlantic Time-series Study station? Data obtained from underway measurements, hydrographic station sampling, and incubation experiments will allow the research team to investigate changes in formaldehyde and acetaldehyde concentrations in relation to their production and removal in seawater. As an interdisciplinary effort, this research project combines tools from chemical oceanography and atmospheric measurement. The closed-system sampling and analysis method developed and applied in this study can be broadly adapted to a wide range of LMW compounds for which atmospheric contamination is an issue. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2026 · 2026-07

This project provides scholarships for undergraduate and graduate students enrolled in Artificial Intelligence (AI) and Cybersecurity (CyberAI) programs at Old Dominion University (ODU) in exchange for their service supporting CyberAI mission of government organizations. The project supports an educational interdisciplinary program that relies on a number of high-quality courses in cybersecurity and AI, hands-on experience both at ODU and during summer internships at government organizations, participation in research projects supervised by CyberAI faculty, participation in extracurricular activities including competitions and outreach activities to prepare students for a variety of professions in CyberAI field. In addition to fulfilling required coursework, this project places a strong emphasis on experiential learning opportunities, research projects, and leadership development through teamwork depending on the student's preparation and aspiration. With a strong institutional track record in their Scholarship for Service (SFS) program for preparing students for public service careers in CyberAI, ODU and its School of Cybersecurity are uniquely positioned to successfully run this project. ODU is the first university in the nation to have its CyberAI programs validated by the National Security Agency (NSA). In addition, ODU is designated by the NSA as a National Center of Academic Excellence in Cyber Defense Research (CAE-R) a National Center of Academic Excellence in Cyber Operations (CAE-CO), and National Center of Academic Excellence in Cyber Defense (CAE-CD). Students selected for the program will receive scholarships while completing the required academic programs. The graduates will contribute to national and global STEM workforce competitiveness and national security as government experts in the use of AI in cybersecurity operations and the security and resilience of AI systems themselves. This project is supported by the CyberAICorps Scholarship for Service (CyberAI SFS) program which funds proposals to increase national capacity to educate and train professionals in Artificial Intelligence (AI) or Cybersecurity, and to support their placement and retention in the CyberAI mission of government organizations. The Scholarship Track funds academic institutions to award scholarships to students in exchange for their government service. The Innovation Track seeks transformative education proposals in the areas of AI, cybersecurity, or the integration of AI and cybersecurity. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2025 · 2025-12

The West Antarctic Peninsula is one of the fastest warming regions in Antarctica, impacting carbon fluxes from the atmosphere to the ocean through effects on phytoplankton blooms, the microscopic marine plants that form the base of the food web. This project investigates changes in the seasonal timing, amount, and type of phytoplankton as the West Antarctic Peninsula warms and experiences sea ice loss. This study of a critically important ecosystem’s carbon cycle and shifting seasonal timing of events is relevant to all polar systems in the face of global climate warming. The proposed research will broaden our understanding of the impact of climate change on the seasonal timing of primary production and carbon uptake in a region experiencing the most rapid rate of sea ice loss in the Antarctic. The Southern Ocean is a massive regulator of the global carbon budget, but many unknowns remain about the role of phytoplankton. This work will look at two features of how phytoplankton influence carbon uptake that are often overlooked: phytoplankton community composition and phenology (i.e., the timing of seasonal events). The goal of the proposed work is to assess how physical and biological factors affect the seasonal timing, magnitude, and long-term trends of carbon flux from the atmosphere to the ocean. Researchers will incorporate data collected in West Antarctica with numerical modeling to test possible reasons why things are changing, and to test specific variables at the base of the food web. Observations will be used to evaluate long-term change, and models will be used to investigate likely causes for observed shifts, such as changes in sea ice, wind, ocean physics, cloudiness, and phytoplankton community composition. The objectives of the proposed work are to (1) analyze phenology shifts, (2) investigate phytoplankton community composition using pigment and flow-through imaging data from the Palmer Long-Term Ecological Research program, (3) evaluate possible reasons for shifts in phenology using modeling case studies, and (4) compare the relative impacts of biological factors versus physical factors on carbon flux and phenology shifts using modeling experiments. Outreach and education activities include blog and video development for the Palmer Long-Term Ecological Research (LTER) program, mentoring of undergraduate college students, and K-12 lesson plan development in alignment with Polar Literacy Principles. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2025 · 2025-10

This project establishes a new Research Experiences for Undergraduates (REU) Site hosted by the Department of Computer Science at UNC Greensboro. Ten students will receive research training each summer in the foundations of graph machine learning and network analysis and their concrete applications in real-life networks. Graphs and networks have become ubiquitous in various scientific disciplines ranging from the Internet of Things, online social networks, brain networks, and molecules to protein-protein interaction networks. Analysis of large-scale networks can bring significant advances to our understanding of complex systems. Existing methods are purely empirical or lack in-depth foundational exploration, thus limited in processing complex graph and network data. This project aims to provide students the opportunity to undertake cutting-edge research in graphs and networks at a major research institute. The research training on Graph Learning and Network Analysis (GraLNA ) will contribute to developing a competitive next-generation network and AI workforce. Through various activities such as orientation workshops, invited lectures, hands-on projects, presentations, demos, and other professional development opportunities, undergraduate students will also enhance their professional skills. The first objective of this GraLNA project is to provide an experience of doing solid research for a diverse group of students especially those from Primarily Undergraduate Institutions. Students will gain an increased proficiency in research skills as well as oral and written communication skills. The second objective is to advance the theoretical understanding of graph learning and optimization, and to also develop new approaches to handling diverse types of complexities in graph and network data. Notable types of complexities include the distributed nature of many real-world graph data, privacy concerns arising from sensitive relationships and interactions encoded in graphs and networks, and specialized network data that involves rich domain knowledge and regulatory constraints. Student participants will engage with research projects centered around distributed graph analysis, federated learning, optimization, private graph analysis, network security, and structural and functional brain network analysis. The third objective of this project is to provide for both student participants and faculty mentors professional training and growth through a series of professional development activities and also provide junior faculty and Ph.D. students mentoring and co-advising experience respectively. 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

X-rays enable scientists to see the internal structure of materials on all length scales from the macroscopic down to the positions of individual atoms. In medicine, x-ray dose could be reduced by orders of magnitude with dramatic improvements in imaging modalities and detectability of soft tissue structure including tumors. New cancer treatment modalities would be possible. In spite of the huge importance of x-rays, the technology for producing them lags far behind the methods for producing ordinary visible light. First, there are no coherent sources of hard x-rays. Second, the brightest incoherent sources of x-rays are available in only a few large (on a billion-dollar scale) facilities. And third, all other x-ray work is done with very poor sources based on technology that is over 100 years old. Inverse Compton sources present a promising way of producing hard x-rays in an economical and widely accessible manner. Optimizing the performance of the existing and the design of the future inverse Compton sources crucially depends on high-fidelity computer simulations. Such simulations enable numerical tests of new experimental techniques, or even serve as diagnostic tools, replacing expensive hardware. Currently, such simulations are carried out by ad hoc, individually developed, disjointed pieces of software often written by scientists who are domain experts but not necessarily computing savvy. There is a definite need to develop fast and easily usable computer simulation codes that can be used by all research groups working on designing and developing better inverse Compton sources. The proposed comprehensive paradigm for calculation of radiation spectra in inverse Compton sources is a pivotal tool in ushering a new era in hard x-ray source technology. It will accurately quantify and optimize the performance of inverse Compton sources, resulting in substantial cost savings. This award by the Office of Advanced Cyberinfrastructure is jointly supported by the Division of Physics in the Mathematics and Physical Sciences Directorate. 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 Association for the Advancement of Artificial Intelligence (AAAI) Fall Symposium Series is an annual event, traditionally held on the East Coast of the United States in late October or early November. Designed to foster deeper engagement, the series provides a smaller, more intimate forum for researchers and practitioners to exchange ideas and explore advances in artificial intelligence. Each year, the symposium topics vary to reflect emerging trends and challenges in AI. The setting is intentionally relaxed and workshop-like, encouraging interactive discussions and collaboration. Typically, the series includes around seven parallel symposia, each attracting 40 to 75 participants, with total attendance ranging from approximately 300 to 500 individuals annually. This project provides funding for students from US colleges and universities to participate in the meeting. The AAAI Fall 2025 Symposium will feature eight symposia on forward-looking topics in artificial intelligence. These events serve as incubators for interdisciplinary research by bringing together participants from computer science, cyber security, health, ethics, and social sciences. Students will benefit from close interactions with senior researchers through presentations, workshops, and collaborative working sessions. The curated nature of each symposium ensures high-quality engagement and mentoring opportunities. The program will include rigorous selection and accountability mechanisms, ensuring that travel support recipients are actively involved in symposium activities. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2025 · 2025-06

This I-Corps project focuses on the development of a risk assessment modeling tool designed to deliver highly localized, seasonal evaluations of environmental hazards. The solution addresses a pressing national need to improve community resilience and economic preparedness in the face of increasing weather-related threats. Currently available tools often offer limited precision, presenting data at broader geographic scales and failing to account for the seasonal variability of hazards. This limitation makes it difficult for decision-makers in real estate, infrastructure planning, and insurance to accurately assess risk and plan mitigation strategies. The technology enables users to input a specific location and receive detailed hazard indices that reflect current vulnerabilities and also change over time and across seasons. Outputs are presented through intuitive visualizations, such as maps and charts, to facilitate use by both experts and non-experts. By making environmental risk data more accessible and tailored to specific parcels of land, this project enables better decision-making, supports safer urban development, and reduces long-term costs. The technology advances scientific understanding, strengthens economic resilience, and helps communities prepare for and respond to weather-related disasters. This I-Corps project utilizes experiential learning coupled with a first-hand investigation of the industry ecosystem to assess the translation potential of the technology. This solution is based on the development of a geospatial modeling platform that applies advanced algorithms to assess compound and seasonal natural hazards at high spatial resolution. The platform processes a range of public environmental datasets to evaluate hazard exposure, vulnerability, and preparedness metrics, integrating them into a composite hazard index. Unlike traditional tools that use static or low-resolution data, this technology supports dynamic, time-sensitive risk assessments tailored to the location and season. The platform incorporates advanced statistical normalization techniques, machine learning-based pattern recognition, and customizable timeframes for historical analysis. Risk factors are quantified and converted into user-friendly outputs including heatmaps, graphs, and summary tables. Early-stage validation studies have shown that the system can accurately predict risk-related outcomes when compared against observed hazard data. These results are evaluated using statistical metrics such as percent bias and correlation coefficients to ensure scientific rigor. The technology holds promise for transforming how professionals in planning, engineering, and environmental management access and apply risk information to make timely, location-specific decisions. 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

T-cells are an important type of white blood cell that play a vital role in the adaptive immune response. Cell-based therapies, especially genetically modified T-cells from the patients themselves, can be effective for a range of diseases including cancers. These modified T-cells are called Chimeric Antigen Receptor T-cells (CAR T-cells) and are engineered in a safe manufacturing facility to express CAR molecules on T-cell surfaces. Once CAR T-cells are infused into patients, the selective conjugation of tumor cell protein and CAR T-cell protein triggers T-cell mediated cytotoxicity that kills the tumor cells. However, there are significant barriers to their wider adoption as therapies. These include: (1) safety concerns related to CAR T-cells engineered by viral gene transfer, which is the current method for CAR gene transfer into T-cells. (2) the high expense of CAR T-cell therapy. A large portion of their cost is associated with producing the CAR T-cells in centralized manufacturing facilities. To address these critical issues, this Future Manufacturing Seed Grant (FMSG) research will investigate novel, low-cost mRNA based (viral-free), distributed CAR T-cell manufacturing strategies. The objective of this research is to develop good manufacturing practice (GMP) enabled low-cost mRNA-based CAR T-cell manufacturing technologies. This project will investigate how bolus injected external cellular mRNA molecules are translated into protein molecules in a time dependent manner. Additionally, this project will study how the initial cellular protein level from bolus injected external mRNA modulates the half-life of cellular protein expression. This will be accomplished via the following aims: (1) Theoretically and experimentally study the protein expression and half-life of electroporated T-cells with mRNA molecules, and (2) Investigate the feasibility of utilizing Machine Learning algorithms to build good manufacturing practice (GMP) protocols, including biomanufacturing platforms with built-in quality control and automation. The broader impact activities will focus on the development of a workforce to meet the needs of the biomanufacturing industry. Instructional modules on theory, current practices, and trends in biomanufacturing will be incorporated into the undergraduate and graduate engineering curricula at Old Dominion University and North Dakota 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 2025 · 2025-04

The turnover of organic carbon in the ocean plays an important role in regulating the ocean carbon cycle. The oceanic cycles of iron and carbon are tightly coupled. The supply of dissolved iron regulates ocean biology while organic carbon impacts the solubility and biological availability of iron in seawater. We strive to better understand the mechanisms and linkages between pools of iron and organic carbon in the oceans and to predict their sensitivity to future environmental and climatic changes. In this collaborative project, jointly funded with the U.K. Natural Environment Research Council, scientists from the U.S. and U.K. will combine field data from the Bermuda Atlantic Time-series Study (BATS) region and from the Eastern North Atlantic with targeted experimental studies and a state-of-the-art ocean biogeochemical model to better characterize organic carbon - iron linkages and their roles in past, present, and future changes in ocean biology and chemistry. The project will support the education and training of undergraduate, graduate, and postdoctoral researchers, and will connect rural K-12 and undergraduate students with the research through outreach activities. Field observations from the BATS and Cape Verde Ocean Observatory regions will be integrated with experimental studies targeting iron-organic carbon interactions across seasonal and spatial gradients. An ocean biogeochemical model will be used to constrain the processes that modulate interactions of iron with dissolved and particulate organic matter. Specifically, this project will examine the a ‘colloidal shunt’ mechanism, whereby a portion of the dissolved iron pool in the colloidal size range is not stabilized by complexation with organic ligands. This iron instead forms aggregates with organic carbon to form particulate matter that sinks out of the upper water column. The research will focus on the role of dissolved organic carbon and iron-binding organic ligands in mediating the colloidal shunt, the association of organic matter with thus-formed authigenic particulate iron phases, and the dissolution of these phases in the ocean interior as a function of oxygen. Potentially transformative implications of this research are that the colloidal shunt might vary in response to climate driven changes in ocean oxygenation, and that this process may provide a conduit for the vertical export of both particulate iron and organic carbon that augments the biological carbon pump in the subtropical and tropical oceans. 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

Sulfur is a major element in the ocean and is found primarily as dissolved inorganic sulfate. However, trace amounts of sulfur exist as dissolved organic sulfur (DOS). Research on DOS in the ocean began in the 2000’s. This is because it is difficult to measure DOS concentration in a solution having almost one million times more inorganic sulfate. While relatively small in quantity compared to inorganic sulfur, DOS plays critical roles in oceanic and atmospheric processes. For example, sunlight degrades DOS into carbonyl sulfide gas, which moves into the upper atmosphere, forming sulfate particles that block sunlight reaching the Earth. Many DOS compounds are essential for microbial growth in the ocean. Additionally, some DOS compounds react with essential (e.g., zinc) and toxic (e.g., mercury) trace metals to affect their solubility and biological availability. To uncover the mysteries of DOS, this study will utilize archived samples collected from over 100 locations in the Pacific Ocean from Alaska to Antarctica and collect fresh samples in the North Atlantic Ocean near Bermuda. The scientists will measure the total concentrations of DOS in these samples and determine the compounds that make up DOS. Graduate and undergraduate education is a central part of this project. The project will support one graduate student at Old Dominion University and graduate and undergraduate students at Texas A&M University-Corpus Christi. The scientists will share their research with the public through already scheduled lectures and forums. They will also develop and use a virtual reality (VR) experience to simulate what it is like to go to sea and collect samples for DOS. This research project will uncover the processes governing the marine DOS cycle in the Pacific Ocean, with three primary objectives: 1) Accurately quantify the DOS inventory across the entire Pacific Ocean to clarify its role in the global sulfur cycle. 2) Identify the abiotic and biotic processes responsible for DOS production and removal along two meridional Pacific transects, encompassing different biogeochemical regimes, hydrothermal plumes, and oxygen minimum zones. 3) Conduct a year-long, monthly collection of depth profiles at the Bermuda-Atlantic Time Series (BATS) to investigate the reactivity of DOS and its components. The research will leverage archived samples from three Pacific meridional transects, spanning from Alaska to Antarctica and Antarctica to Mexico, along with newly collected samples from the monthly BATS cruises. The project will improve understanding of the inventory and cycling of DOS in the ocean and provide research training opportunities for graduate and undergraduate students. Results from this study will be communicated to the public through lectures and a virtual reality experience. 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

Global sea levels are rising at unprecedented rates and will continue to reshape the coastline of densely populated regions both in the US and globally with implications for housing, transportation, agriculture, wildlife habitability, and tourism. Over the next 50 years, mass loss from the Antarctic Ice Sheet will be a dominant contribution to global sea level, but it is also associated with the greatest uncertainty in sea level rise estimates. Much of this uncertainty results from incomplete understanding of processes that occur near the Antarctic coast where there are close interactions between the open ocean, near-coastal waters whose properties are influenced by interactions with sea-ice, and ocean water that is carrying glacier meltwater originating from the Antarctic ice sheet itself. These regions also happen to be among the most biologically productive of all waters in the Southern Ocean, and the impact of climate-related biogeochemical changes here remain a blind spot in our understanding of a changing global carbon cycle. Current understanding of changes occurring around Antarctica are largely derived from decades of work in the Amundsen Sea. Yet, the melting of ice shelves in the neighboring Bellingshausen Sea are comparably high and pre-condition the physical and biogeochemical properties of the water that enter the Amundsen. Thus, the role of the “upstream” Bellingshausen Sea in ice sheet mass loss and ocean carbon uptake remains unconstrained, although models suggest this region can broadly influence these processes throughout West Antarctica. The Bellingshausen Sea: A Carbon and Overturning Nexus (BEACON) project will collect a broad suite of physical and biogeochemical observations needed to assess the Bellingshausen Sea’s role in the large-scale distributions of heat, meltwater, dissolved iron and other nutrients, and biological productivity. The research team will combine standard and trace-metal shipboard measurements, towed underway observations, and a small fleet of remote autonomous underwater vehicles aimed at capturing key transport pathways associated with narrow boundary currents located along the coast. These observations will capture dynamical processes related to mixing of water properties by ocean turbulence from centimeter to kilometer scales. This information about mixing will then be applied to an inverse-modeling framework to assess how changes in near-coastal processes in the Bellingshausen Sea impact larger-scale ice-shelf melt rates, nutrient supply to the upper ocean, the timing and intensity of seasonal primary production, and the oceanic uptake of carbon dioxide throughout West Antarctica. 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 Major Research Instrumentation (MRI) grant will support the acquisition of a combined wave generation and current circulation system for interdisciplinary research and for training graduate and undergraduate students. The system will contribute new knowledge in coastal and ocean science and engineering, improving the resilience of coastal communities, enhancing the health of coastal ecosystems, and advancing novel technologies in ocean engineering. The instrument will include a wave maker, a current circulation system, and a flume to contain the water and guide the flow. This wave and current flume system will enable the study of interactions between flow and natural elements, such as aquatic vegetation, coral reefs, and sediments. The instrument will also simulate fluid-structure interactions, where structures may include near-coast buildings, coastal protection measures like breakwaters and seawalls, nature-based solutions, and ocean engineering technologies such as wave energy converters or autonomous underwater vehicles. This new facility will foster regional collaborations in research and education that cross traditional disciplinary boundaries, encompassing engineering as well as physical and biological sciences, and will involve four universities. This grant will contribute to U.S. society by supporting fundamental research in critical areas for the U.S. economy, namely coastal resiliency and ocean renewable energy, while also training a STEM workforce through a cross-disciplinary approach. The wave and current flume (WCF) system will allow for the simulation of scaled-down waves and currents to conduct fundamental research on interactions between flow and natural and built features. The wave maker can generate monochromatic and random waves, while the current circulation system can produce co- or counter-propagating currents relative to the wave direction, enabling realistic modeling of wave-current interactions. Fundamental knowledge gaps in coastal and ocean engineering and science will be addressed using the WCF system. The instrument will be applied in several projects, including: (1) studying how aquatic vegetation in saltmarshes, as a natural barrier, reduces loads on near-coast structures, establishing a relationship between flood loads and vegetation properties; (2) examining how aquatic vegetation in sand dunes affects flow-sediment interaction and erosion, and parametrizing bed shear stress as a function of flow and vegetation properties; and (3) quantifying the performance of novel wave energy converter concepts. The instrument will also enable training for graduate and undergraduate students and will support coastal resilience and the blue economy in Virginia and nationwide. 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

Methane (CH4) is a gas that, although it has a much lower concentration in the atmosphere compared to carbon dioxide (CO2), possesses a much more potent greenhouse effect, possibly accounting for 20-25% of global warming since the Industrial Revolution. Methane has a short residence in the air, so that regulating the emission of this gas can have rapid and profound results for mitigating climate change. The amount of methane in the atmosphere has increased despite reductions in anthropogenic sources; accordingly, the processes of methane production in natural environments must be accurately assessed. Coastal wetlands account for ~40% of global methane emissions and these regions are in constant flux owing to sea-level rise, sediment accumulation, ecological shifts, and landscape dynamics. This project will investigate the present-day controls on methane emissions in coastal wetlands, assess their variability due to sea-level rise, and use field observations and experiments to develop models that integrate the numerous factors that control methane emissions from these environments. The study site will be in coastal Louisiana, which has ~40% of all coastal, tidally influenced fresh and saltwater wetlands in the U.S., and these wetlands experience some of the highest relative sea-level rise rates in the world. Nearly 1 billion people around the globe live in proximity to similar coastal wetlands, so that the results of this research will have broad applicability to solving large-scale problems. The project’s educational and outreach activities will leverage ongoing programs at the participating universities and further include the development of new resources that will be available to students and the public. The proposed spatiotemporal framework will combine field, experimental, and model-based approaches to determine methane emissions from a range of settings (e.g., elevation, salinity, distance from waterways, hydroperiod, temperature, vegetation, soil organic carbon) and time scales (decadal–centennial– millennial) in the Terrebonne-Timbalier Estuary of coastal Louisiana. Some of these vegetated wetland soils emit more methane annually than the soil carbon that they sequester. The research objectives will: i) assess spatiotemporal variability of methane inventories and emissions, ii) quantify soil and organic carbon age and sedimentation history, iii) determine the microbial and functional diversity from soils at different spatiotemporal scales and across geochemical gradients, iv) experimentally assess methane flux due to flooding (e.g., duration, frequency, depth) regime changes, v) integrate landscape change into hydrodynamic and biogeochemical models that account for changes in wetland configuration and sea-level, and iv) evaluate the best numerical parameters to simulate methane dynamics across microbe-to-landscape scales. This project is jointly funded by the Frontier Research In Earth Sciences (FRES) program, the Established Program to Stimulate Competitive Research (EPSCoR), and the Ecosystem Sciences program in the Division of Environmental Biology (DEB). 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

Marine animal tracking, at both individual and group levels, is crucial for wildlife conservation. It provides essential information and invaluable insights into population dynamics, health, risks, and vulnerability, all of which help shape conservation policies, management decisions and strategies. Traditional tracking methods face significant challenges in balancing cost and precision. They either require attaching transmitters to animals that communicate with radio receivers or satellites (high accuracy but expensive and invasive) or rely on manually produced sketches from photos of distinctive features such as scars (low accuracy and labor-intensive). The overarching goal of this project is to optimize this cost-precision trade-off by designing and delivering an artificial intelligence (AI)-driven system for individual photo-identification and tracking in conservation studies of Florida manatees, a threatened species. The system aims to streamline the creation, maintenance, query, and behavior analysis of manatees using photo-identification. This project will train several graduate students, and will advance collaboration between AI researchers and conservation scientists. In order to bring transformative advancements to current conservation capabilities, emphasizing cost-effective, evidence-based conservation planning, the project will 1) develop new algorithms grounded in explainable AI to identify and track individual manatees by their distinctive features, such as scars and markers, which serve as interpretable evidence for tracking; 2) support long-range spatio-temporal tracking by representing each animal as a series of sketch images throughout their lifespan, annotated with timestamps, geographic information, and metadata on life encounters; and 3) craft a framework for region-based conservation resource planning and management that models evolving patterns in local regions, including both natural and human-caused disturbances, to assess how local animal populations react to these regional changes. The collaborative research team will also extend approaches to additional threatened or endangered marine species (sea turtles, whales, rays). This project will have a lasting impact on the research community and education sectors by highlighting critical needs and showcasing viable design ideas in both conservation and computer science, and in their nexus. This project is jointly funded by the Division of Environmental Biology and Integrative Organismal Systems through the Partnership to Advance Conservation Science and Practice 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 2024 · 2024-10

The homeless population faces challenges in accessing consistent insurance and healthcare, leading to frequent visits to emergency rooms for health issues that could be managed at walk-in clinics. This reliance on emergency services creates a significant financial strain on hospital systems and cities. Moreover, a lack of cell phones hinders homeless persons' ability to access telehealth services. Limited clinic hours exacerbate health issues among the homeless community. This planning project is addressing the unique healthcare needs of homeless individuals through an innovative telehealth approach that will advance health equity and promote the well-being of vulnerable populations in Hampton Roads, Virginia, and beyond. The project investigates the effectiveness of implementing telehealth kiosk services to address healthcare disparities among homeless populations in Virginia and the United States. Through collaboration with healthcare providers, community organizations, and technology experts, the project team is planning to develop and deploy telehealth kiosks tailored to the unique needs and challenges faced by homeless individuals. By leveraging emerging technologies, such as generative artificial intelligence, Large Language Models, and interactive text conversation, patients access kiosks to obtain information regarding what type of care they need. By answering questions the technology will guide individuals to self-management, make an appointment at a nearby clinic, or seek emergency care. The project improves access to healthcare services, provides educational content for chronic disease, enhances health outcomes, and alleviates financial burdens on cities and communities. The project is embedded in the partner facilities that the homeless already go for their services and have trust in their service providers and offered services. Through rigorous evaluation and assessment, investigators measure the impact of telehealth kiosk services on healthcare utilization patterns, health outcomes, and overall well-being among homeless populations. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2024 · 2024-10

Once a novel coronavirus or a new variant is detected, how likely would the novel coronavirus or new variant transmit from person to person, and how sick could patients become? What kind of new coronaviruses could cause future pandemics? Knowing the answers to these questions can help nations make proper strategic decisions. The dilemma is how to predict the behavior and pathogenic severity of new viruses as early as possible. A team of researchers thinks they have found ways to answer these questions by developing new artificial intelligence software tools to predict the virus’s behaviors based on its genome sequence. This team of researchers recognizes the potential bias in machine learning applications and the need to increase diversity in the future artificial intelligence workforce. Leveraging their expertise in genomics, data science, artificial intelligence, genetics, infectious disease, chemical engineering, public health, and communication, this team of researchers will organize training workshops and activities providing culturally responsive teaching of artificial intelligence, data science training to teachers, and context-relevant coding experiences to high school students. The team will promote public trust in science and discernment of misinformation through community outreach. This research team will prototype a deep learning model based on biological knowledge and hypotheses that can predict viral pathogenic fitness from genomic sequences to test the potential rules for viral pathogenicity. The team will explore several methods to correct the sampling bias in viral genomic surveillance in order to accurately estimate the fitness of a viral strain. The team will investigate the mutation and recombination profiles in all available bat coronavirus genomes from the Southeastern Asia and build a prototype geospatial model to predict the recombination probability for all available bat coronaviruses. Leveraging their expertise in genetics and macromolecular structure modeling, the team will test a few candidate genes in SARS-CoV-2 for potential pathogenic rules. Based on the outcomes of these pilot projects, the team will be able to estimate the pathogenic fitness of an emerging SARS-CoV-2 variant or another novel coronavirus. This award is supported by the cross-directorate Predictive Intelligence for Pandemic Prevention Phase I (PIPP) program, which is jointly funded by the Directorates for Biological Sciences (BIO), Computer Information Science and Engineering (CISE), Engineering (ENG) and Social, Behavioral and Economic Sciences (SBE). 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

Throughout the United States, juvenile justice systems use juvenile risk and need-assessment (JRNA) scores to identify the likelihood a youth will commit another offense in the future. This risk assessment score is then used by juvenile justice practitioners to inform how to intervene with a youth to prevent reoffending (e.g., referring youth to a community-based program vs. placing a youth in a juvenile correctional center). Unfortunately, most risk assessment systems lack transparency and often the reasons why a youth received a particular score are unclear. Moreover, how these scores are used in the decision-making process is sometimes not well understood by families and youth affected by such decisions. This possibility is problematic because it can hinder individuals' buy-in to the intervention recommended by the risk assessment as well as mask potential error in those scores (e.g., if youth have risk scores driven by a particular item on the assessment). To address this issue, project researchers will develop automated, computer-generated explanations for these risk scores aimed at explaining how these scores were produced. Investigators will then test whether these better-explained risk scores help youth and juvenile justice decision makers understand the risk score a youth is given. In addition, the team of researchers will investigate whether these risk scores are working equally well for different groups of youth (for example, equally well for boys and for girls) and identify any potential errors in how they are being used in an effort to understand how equitable the decision making process is for the range of youth involved in juvenile justice. The project is embedded within the juvenile justice system and aims to evaluate how real stakeholders understand how the risk scores are generated and used within that system based on actual juvenile justice system data. More specifically, this project aims to understand how risk assessment scores are currently being used in the juvenile justice system and how interpretable machine learning methods can be used to make black-box risk assessment algorithms more transparent (without reverse engineering them given that most assessments are proprietary). The team of researchers endeavor to understand the way that juvenile justice risk scores are being used through the analysis of quantitative data from the juvenile justice system (which details the risk scores and justice system decisions) and through qualitative data collected via key informant interviews. In the second phase of the work, the team of researchers will train various interpretable machine learning algorithms to predict youth's risk scores (which are currently generated by a proprietary, black-box algorithm). The team will also predict the sentencing dispositions for youth based on these risk scores and other pertinent data collected by the juvenile justice system. The project team will then test and measure how understandable a series of the automated explanations derived from these machine learning methods are to youth, families, judges and probation officers. The goal of this step will be to identify algorithms that are highly predictive of the risk score and dispositions, respectively and then to identify methods that provide clear, human-interpretable explanations of the risk and dispositions to key stakeholders throughout the process. This step will also allow researchers to optimize methods for explaining outcomes by possibly identifying one method that is more understandable for explaining risk scores to youth compared to another method that is more understandable for their families or probation officers, for example. Finally, the project team will also explore the potential for error throughout the process (from risk scoring to the use of the scores) and ways in which these interpretable algorithms can be used to help identify, quantify and mitigate challenges. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2024 · 2024-09

Salt marshes have potential to store large amounts of atmospheric carbon by accumulating sediment and exporting dissolved carbon to the adjacent ocean. Lateral carbon export, or carbon outwelling, is impacted by tides and seaward groundwater flow. To date, studies of salt marsh carbon outwelling have focused on periods when biological activity is relatively high (i.e., spring through fall). As a result, prior studies have often neglected export during cold seasons, assuming that marshes are either impermeable due to frozen conditions or biologically inactive. This oversight limits annual water and carbon budget estimates to only a fraction of the year, potentially underestimating salt marshes’ carbon storage potential and influence on the coastal ocean carbon budget. This project will address this knowledge gap by investigating the magnitude and drivers of cold season water and carbon export in North Atlantic salt marshes. This understanding is critical for refining estimates of carbon outwelling and improving understanding of the relative role of temperate salt marshes as a carbon sink. The project will develop a Science Exploration and Education from a Kayak (SEEK) program to teach middle-school students how to sea kayak while also teaching them coastal science through hand-on learning in the field. This project will also train a postdoctoral researcher, graduate student, and multiple undergraduate students through summer programs. Cold-season hydrological dynamics and biogeochemical processes have seldom been evaluated in salt marshes, preventing accurate estimates of local and global carbon outwelling. This project’s goal is to better understand cold-season freeze-thaw processes, groundwater flow dynamics, and lateral carbon export in North Atlantic salt marshes. The project will (1) monitor soil temperatures and groundwater and surface water levels, salinity, and temperature in four salt marshes between Massachusetts, USA, and Nova Scotia, Canada, to gain better understanding of the extent and duration of marsh freezing; (2) quantify groundwater discharge in summer through spring, as well as during storm events, to compare discharge magnitude and sources across all seasons; and (3) evaluate seasonal variability in salt marsh porewater carbon concentrations and quantify carbon outwelling from the marsh platform to the tidal channel across seasons and following winter events. This project will result in increased understanding of coastal hydrological processes, refined carbon outwelling estimates, and improved projections of carbon outwelling dynamics in mid- to high-latitude salt marshes. 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 · 2024-08

Old Dominion University (ODU), partnered with Dominion Energy, Southeastern Wind Coalition, Hampton Roads Workforce Council (HRWC), and Centura College, proposes to collaborate with other regional industry and academia to host a one-day Offshore Wind Workforce Conference at ODU in Norfolk, Virginia in 2024. Keynote speakers from industry and academia will present and discuss current and forecasted offshore wind workforce and training needs and issues. Multiple subject matter expert panel discussions from industry and academia will discuss these and other important topics that are relevant to this industry, including: needed offshore wind workforce skillsets, STEM curriculum requirements, emerging trends in related technology, and research and development needs. This conference will be open to all traditional and non-traditional students at ODU as well as other regional training institutions and community colleges. A workforce development conference specifically focused on offshore wind will have significant positive impacts on regional companies, ODU students and faculty, and the community at large and will increase industry, academia, and government partnerships. It will also provide an opportunity for attendees to learn about the latest industry trends, technologies, best practices, and career opportunities. It will contribute to a workforce pipeline that promotes the participation of women, veterans, persons with disabilities, and underrepresented minorities. Attendees will be able to connect with potential employers, mentors, and peers, opening doors for collaboration, job opportunities, and partnerships. 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 · 2024-08

The IEEE International Conference on Data Mining (ICDM) has established itself as the world’s premier research conference in data mining. It provides an international forum for the presentation of original research results and the exchange and dissemination of innovative and practical development experiences. The conference covers all aspects of data mining, including algorithms, software, systems, and applications. Student travel awards permit full participation by those who are primary authors on accepted papers. A PhD forum and a Women in Science Research Forum are part of the agenda, and will help early career researchers to learn and exchange cutting-edge research ideas and help them communicate on different aspects of career development. Data mining and machine learning are now being broadly applied to nearly all disciplines, transforming science, engineering, medicine, healthcare, finance, business, and ultimately society itself. The award will be used to provide travel support for students and early career researchers, with a special focus on women and minorities, for the following activities: 1) To help fund the travel of Ph.D. students who are primary authors of full papers that have been accepted to the technical program; 2) To help fund the travel of Ph.D. students who are participating in the Ph.D. Student Forum; and 3) To help cover the travel expenses of women researchers to participate in the Women in Science Research Forum. This proposal aims to provide the crucial funding needed to support the participation of graduate students and early career researchers who will become future leaders in the science and engineering field. As an effort to engage young researchers, the IEEE ICDM 2024 will involve them in the meeting organization and include mentoring activities in the conference 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 2024 · 2024-08

Caregivers play an essential role in STEM identity development, interest, and persistence; however, more research is needed to deepen our understanding of multigenerational informal STEM learning and informal STEM learning alongside caregivers. This Partnership Development and Planning project brings together STEM educational researchers, STEM professionals, community organizers, and caregivers. Together they will work toward research that explores rightful presence for Black girls and their caregivers in the context of multigenerational informal STEM learning, particularly around emerging technologies. Educational researchers, from Old Dominion University and Indiana University-Indianapolis will deepen and extend an existing research-practice partnership to include Community-Based Organizations (CBOs), STEM professionals, and caregivers. Butterfly Village (Hampton Road, VA) and Girls STEM Institute (Indianapolis, IN) are CBOs that offer holistic programming intentionally designed to support Black girls' intersectional identities. These CBOs engage Black girls in the relevancy of STEM knowledge and skills and emphasize the importance of real-world applications via Connected Learning Theory. Both CBOs provide counterspaces—spaces that foster community and are created by and for people who experience oppression. In these counterspaces girls voluntarily work on collaborative projects, join in group discussions, and engage in STEM learning through social interaction. As community organizers, a future goal for these CBOs is to support the utilization of emerging technologies (e.g., AI, data science, cybersecurity) to connect learning activities to real issues. Based on this goal, STEM professionals from local organizations—Drone Force Indiana and Virginia Modeling, Analysis, and Simulation Center—with expertise in digital transformation technologies join this partnership to ensure their needs and perspectives are included in the framing of the future research project. Both CBOs were intentionally designed for the girl participants and are redesigning after noting the interest in STEM learning experiences among caregivers (via observations and direct feedback). By establishing a research-practice collaborative all partners will share in contributing to the framing of their future work together, including research questions, learning design, methodology design, intended goals, and outcomes of future multigenerational informal STEM counterspaces. Partnership development processes are grounded in two theoretical frameworks: Rightful Presence and Community Cultural Wealth. The tenets of rightful presence will support partners in developing trust where each recognizes differences in cultural backgrounds, race, and power while also acknowledging possible tensions that may arise due to these differences. Using the Community Cultural Wealth framework, partners will explore the various forms of capital brought to the research practice collaborative, such aspirational capital where the collaborative envisions what this partnership can accomplish together for the future of Black girls and their caregivers. Key activities grounded in these frameworks include sharing personal perspectives and cultural artifacts while developing shared norms for working together; mind mapping and visualizing to identify goals, areas for collaboration, and milestones; meeting co-design and consensus building; roundtable discussions, feedback, and reflections; and closing commitment circles to commit to next steps, roles, and collaboration activities. Through these activities, the team will answer preliminary questions such as: How can a partnership between caregivers, community spaces, STEM experts, and researchers be effectively established? What are the barriers and facilitators to collaboration between partners? How can a research practice collaborative in partnership with researchers, caregivers, community members, and STEM professionals design a study of informal STEM learning spaces that facilitate positive change for Black girls and their caregivers? Culturally Responsive Evaluation will provide formative feedback on processes, strategy, and honoring of each partners’ community, capital, and perspectives. Summative evaluation will emerge though co-facilitated visioning sessions to establish anticipated and unanticipated outcomes of the partnership processes that will be shared with the broader informal STEM learning field. This Partnership Development and Planning project is funded by the Advancing Informal STEM Learning program, which seeks to advance new approaches to, and evidence-based understanding of, the design and development of STEM learning in informal environments. This includes providing multiple pathways for broadening access to and engagement in STEM learning experiences. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2024 · 2024-07

Over a century ago, the Research Vessel Albatross collected fishes from the Philippines, now stored at the Smithsonian Institution. The archive provides the potential for rare insights into how fish have evolved in response to fishing, habitat loss, and other challenges. The research will compare historical and modern fish and will focus on blue sprat, a small coastal species important for food. The research findings can help understand adaptation across many species facing similar challenges. The project will also support paid research internships for students with limited access to careers in science. The project will host workshops to build international exchange with the Philippines. Finally, this research can inform fisheries by identifying fishing zones and where seafood was caught. This project will help to understand the architecture and genomic origins of rapid adaptation, in part by testing the hypothesis that local adaptation provides the raw material for rapid evolution through time. Species objectives include to 1) assemble and annotate high-quality genomes to understand genetic architecture in blue sprat (Spratelloides delicatulus); 2) resequence the genomes of ~1000 individuals across at least five sites in historical and modern eras to identify loci targeted by spatially divergent or temporal selection, and 3) measure morphology and growth to test for the functional importance of genomic variation. The project will focus on historical (1907-1909) samples held by the Smithsonian Institution and modern samples collected in collaboration with Silliman University. The ethanol preservation by the R/V Albatross is a unique scientific accident that provides excellent DNA preservation over the last century. 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 · 2024-07

The broader impact of this I-Corps project is the development of point-of-care device for routine screening applications to identify early-stage pancreatic cancer (PC). Currently, patients with early-stage pancreatic tumors have a five-year survival rate of about 90%, compared with a 6% survival rate for cancers detected at the late-stage. However, PC is difficult to detect early as it has no early symptoms; instead, symptoms appear when the cancer has advanced to late stages. Further, current screening methods used in clinical care (e.g., proteomics and imaging) can only detect late-stage PCs, providing little help to the patient. This device is designed to analyze blood samples and identify early-stage PCs. This solution may allow for early treatment to improve patient outcomes and reduce the financial burden on healthcare systems. This I-Corps project utilizes experiential learning coupled with a first-hand investigation of the industry ecosystem to assess the translation potential of a highly sensitive microRNA (miRNA) detection device for early detection of pancreatic cancer (PC). The miRNA detection is based on hybridizing the target miRNA molecules with their complementary fluorophore-labeled DNA molecules. An alternating current (AC) is used to selectively isolate the labeled miRNA-DNA hybrid molecules on the electrodes and enhance the fluorescence intensity. The fluorescence intensity is then analyzed to calculate the molarity of miRNA target. AC electric fields have been shown to produce less damage to miRNA molecules and generate significantly less heat than direct current (DC) fields. In addition, AC fields may stretch molecules and hybridize the miRNA-DNA molecules with fewer false positive results as compared with DC fields. This technology is under evaluation to be used to detect early-stage pancreatic cancer and the device will be designed to be used in routine medical applications in point-of-care settings. The goal is to simplify diagnostic workflow and create a platform technology for cancer biomarker detection. 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.