Mississippi State University

NSF Awards · FY 2026 · 2026-10

Computing and cybersecurity skills are increasingly essential across nearly every sector of the modern economy, yet many students in Mississippi and similar rural states have limited exposure to these fields, particularly in schools with fewer resources. Teachers play a pivotal role in shaping student interest in science and technology careers, but many lack the research experience needed to bring authentic computing content into their classrooms. This project addresses that gap by immersing high school and community college educators in hands-on cybersecurity research at Mississippi State University, enabling them to return to their classrooms with deeper knowledge, stronger confidence, and ready-to-use instructional materials. By strengthening teacher expertise in computing and cybersecurity, the project generates a multiplier effect that reaches hundreds of students annually, expanding access to computing pathways in communities. The project also supports workforce development by preparing a pipeline of students who are better equipped for careers in computing, national security, and related fields that are critical to the nation's economic strength and security. This award supports a Research Experiences for Teachers (RET) site in which thirty educators participate over three years in a six-week summer research program at Mississippi State University. Participants are embedded in faculty-led research groups focused on areas such as machine learning for radio frequency interference detection, algorithmic resilience in wireless networks, software reverse engineering, adversarial attacks on robot vision systems, and cybersecurity of electric vehicle charging infrastructure. The first week provides foundational training in computing concepts, research ethics, and laboratory practices, after which participants engage directly in ongoing research projects under faculty mentorship. Beginning in the fourth week, teachers develop standards-aligned curricular modules that connect their research experiences to mathematics and computing courses at the secondary and community college level. Academic-year follow-up activities include faculty classroom visits, virtual mentoring, peer collaboration through a shared digital platform, and presentations at state and regional teacher conferences. Program effectiveness is assessed through a mixed-methods evaluation framework examining teacher self-efficacy, curricular integration, and long-term changes in classroom practice. The project is led by faculty in the Department of Computer Science and Engineering at Mississippi State University, in partnership with the Starkville Oktibbeha Consolidated School District, Hinds Community College, and others. 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 · 2026-09

The United States is home to nearly 4,000 species of native bees, which are important for the ecosystem. Unfortunately, declines in many economically important species have been documented in past years. Recent studies found regenerating forests that are managed for timber in the United States can be refuges for wild and native pollinators, including rare and economically important species of bees. However, despite this knowledge, there remains a lack of sustainable management practices for conservation of wild bees in managed forests. Moreover, monitoring bee pollinators in forests is currently very difficult and unfortunately destructive in nature, as it requires lethal trapping of individuals, which are then identified in a laboratory. Lethal trapping methods can have negative impacts on pollinator populations and are labor-intensive and inefficient. Furthermore, pollinators may be shifting their activity based on changes in average temperature, and we currently do not have effective ways to track these changes. The primary goal of this project is to develop, test, and implement non-lethal methods for monitoring pollinators in forests using acoustics and camera-based artificial intelligence (AI). Native bee species in the Unites States contribute as much as 3.5 billion dollars annually to agricultural pollination, but bees are on the decline. This project will develop AI technology that can be deployed in a field setting to automatically identify pollinator species in real time, thereby tracking patterns of activity. By combining different cutting-edge AI techniques, the system will learn and adapt over time, making it more accurate and user-friendly. The goal is to create easy-to-use software that can help track pollinators in the wild, giving scientists and conservationists valuable insights into how structural changes affect these important species. The new technology will enable assessment of the status of pollinators across forests in the southeastern and northeastern United States in real-time, tracking of changes in pollinator activity, and determination of how changes in the forest landscape may affect pollinator abundance and diversity. This information will then be integrated into a harvest scheduling program that forest companies can use to help them in conservation planning, which is part of sustainable forest certification programs. This research will inform conservation strategies, helping protect pollinators and the ecosystems that depend on them. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2026 · 2026-08

Invasive species pose increasingly daunting threats to biodiversity, agriculture, and more. Moreover, they serve as compelling natural laboratories for addressing fundamental questions such as how species adapt rapidly to new environments. Nearly 70 species of terrestrial snails and slugs have been introduced into the contiguous US, but they are critically understudied. This project focuses on two genera of invasive slugs that are widespread in North America—Arion and Deroceras. Although they are known crop pests, very little is known about their invasive histories or the traits that make them successful invaders. The proposed work will develop new genomic resources for these groups, infer the history of their invasions into North America, and identify factors that have promoted their rapid spread. To accomplish these goals, the proposed work will take advantage of the flexibility offered by Artificial Intelligence. Furthermore, the project will develop an undergraduate course on invasive species and engage in outreach to local gardeners. By providing students with opportunities to analyze biological datasets and contribute to the proposed outreach, the course will emphasize the hard and soft skills that are critical in today’s workforce. Slugs of the genera Arion and Deroceras have been introduced and become established in North America and are recognized as crop pests both within the US and internationally. Furthermore, both genera are represented by multiple, independent invasions in North America and possess variation in traits likely to impact invasion trajectories (e.g., the propensity to reproduce via selfing), making them ideal natural laboratories for studying the ecological and evolutionary drivers of invasions. Despite this, the identities, distributions, and sources of invasive populations remain unresolved. The overall objectives of the proposed work are to characterize the invasive histories of Arion and Deroceras in North America and to identify factors that determine the invasion trajectories and range limits of introduced populations. The proposed work will attain these overall objectives by pursuing three specific research objectives. First, this project will characterize the invasive history of Arion and Deroceras in North America using phylogenetic approaches. The proposed work will collect genomic data from museum specimens to identify the species affinities, distributions, and putative sources of introduced populations. Second, it will infer the population invasion histories of select widespread and narrowly distributed introduced species in Arion and Deroceras. Using population-level sampling, the proposed work will characterize the direction and rate of spread and the role of introgression between newly introduced and established or native populations. Third, it will identify ecological and life history traits that predict invasion trajectories and ranges in Arion and Deroceras. The proposed work will use species distribution models, trait data, and methods from landscape genetics to identify factors predictive of invasion trajectories. The expected outcomes are basic knowledge of the invasive histories of these taxa and insights into the factors that impact invasion trajectories. This work will also set up these two systems as model taxa for future investigations into the ecological and evolutionary causes and consequences of invasions. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2026 · 2026-08

Across the tree of life, populations diverge upon isolation by geographic barriers, exchange migrants upon secondary contact, and adapt to environmental pressures. These processes leave signatures in species’ genomes, which can be used to understand the factors shaping biodiversity. However, popular methods for disentangling these signatures are limited both in terms of efficiency and accuracy, and Artificial Intelligence (specifically, machine learning) offers a powerful alternative. Despite recent advances, machine learning approaches have yet to reach their potential in this field and remain limited in the processes they can consider, their applicability across organisms, and their accessibility to researchers with varying levels of technical expertise. The proposed work will develop robust, user-friendly machine learning tools for investigators studying the drivers of diversification. Furthermore, the proposed work will use these tools to illuminate the evolutionary histories of several empirical systems, including fruit flies, mosquitoes, plants, snails, and slugs. By creating well-documented, user-friendly tools, this work will provide a valuable resource to the broader community of evolutionary biologists. Furthermore, the work will support NSF’s desired societal outcome of the development of a globally competitive workforce by hosting workshops (both virtual and in-person), and training a postdoctoral researcher, a graduate student, several undergraduates, and high school students in machine learning and software development. The overall objectives of the proposed work are to develop robust, user-friendly machine learning tools for population genetic inference and to apply these tools to uncover the drivers of diversification in several empirical systems. The rationale for the proposed work is that it will facilitate more accurate assessment of the processes driving diversification across the tree of life based on genomic data. The proposed work will accomplish these overall objectives by pursuing three specific aims. (1) Expand the current implementation of popai, a Python package for demographic inference using machine learning, and compare it to state-of-the-art methods. This aim will expand the diversity of data formats, network architectures, evolutionary models, and inferential tasks available in popai. (2) Develop machine learning tools to detect model violations and make robust inferences in their presence. The proposed work will explore the impacts of several model violations likely to be prevalent in empirical systems and develop new tools for detecting violations. Further, the proposed work will use domain adaptation, a machine learning approach, to arrive at robust conclusions in the presence of model violations. (3) Apply these tools to uncover the drivers of diversification in several empirical systems, including a co-distributed group of terrestrial invertebrates and plants from the Pacific Northwest of North America. The proposed work is novel and important because it will allow researchers to specifically identify those model violations likely to mislead inference and to arrive at accurate inferences in the presence of such model violations without directly modeling them. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2026 · 2026-04

Stars end their lives in large explosions called supernovae. During these explosions new elements are created and dispersed into the galaxies that host the dying stars. The newly created elements provide the seeds for the formation of the next-generation of stars and planets, such as our Solar System. Understanding these explosions, their role in element formation, and their connection to our Solar System is a long-standing goal of nuclear astrophysics. The supernovae events also leave behind compact objects such as neutron stars—the densest matter in the Universe. The gravitational pull of these dense neutron stars can suck in material from nearby companion stars. This material undergoes nuclear reactions on the surface of the neutron stars releasing energy as powerful bursts of X-rays. Studying these bursts provides insight into the neutron star interiors. Computer models of nuclear reactions in supernovae show that just a few key elements, if observed, are sufficient to probe these extreme stellar environments, whereas models for nuclear burning on the surface of neutron star show that few reactions are key to understanding the shape of bursts observed using space-based telescopes. This project will measure some of the critical nuclear reactions in the laboratory to shed light on the production and destruction of elements, and energy generation under extreme stellar conditions, helping validate the models and reducing their uncertainty. This research will use radioactive beams at the Facility for Rare Isotope Beams, as well as stable beams at the University of Notre Dame, together with advanced detection systems, to provide first ever measurements of several important stellar reactions. Beyond advancing and driving fundamental science, the project will also train graduate and undergraduate students on these experiments producing the next generation of nuclear scientists and strengthening the nation’s scientific workforce. A Physics Olympics program at Mississippi State University will engage high school students—especially from rural districts—with limited access to physics courses, offering hands-on exposure in an out-of-classroom setting. Current physics students preparing to become teachers will help run the program, fostering innovation and collaboration in science and math education. This project will measure reaction cross sections for key astrophysical reactions at accelerator-based nuclear physics laboratories to elucidate the production and destruction of ⁴³K and ¹⁰Be in core-collapse supernovae (CCSNe) and to constrain energy generation from nuclear burning in type-I X-ray bursts on accreting neutron stars. Observation of ⁴³K in CCSNe can probe explosion energies, while ¹⁰Be is critical to understanding conditions at the birth of the Solar System; its excess in meteorites is established, but its origin remains debated. Reactions near the ⁶⁴Ge waiting point—a defining feature of the rapid proton-capture process powering type-I X-ray bursts—govern energy generation and hence shape of X-ray burst light curves. Measurements will be conducted at the Facility for Rare Isotope Beams (FRIB) using short-lived radioactive beams and the Active Target Time Projection Chamber (AT-TPC), and at the Nuclear Science Laboratory at the University of Notre Dame employing direct and indirect techniques with isotopically enriched targets. Experimentally, this work will deliver the first direct (p,α) measurement with the AT-TPC at FRIB, establishing a foundation for future studies of similar reactions. Overall, the results from this research will provide critical nuclear physics input for CCSNe and XRB models. Graduate and undergraduate students will play central roles in the experiments, and, together with the Physics Olympics program for high school students, the project will advance discovery while strengthening nuclear physics and STEM workforce development. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2026 · 2026-01

This project will investigate the impacts of rapid shifts between extreme wet to extreme dry weather on fuel loading and consequent wildfire risk. Throughout the project, stakeholders will co-develop and test fire-adaptive management policies against established forest management, with the ultimate goal of reducing wildfire risk and improving safety in rural and WUI communities. Additional broader impacts of the project include the training of two graduate students and one postdoctoral fellow to address these complex challenges and to continue advancing the science of wildfires in the Southeast United States and other humid forest regions. Wildfire dynamics in humid forests, including the Southern Appalachian Mountains are complex and not well understood. Fuel flammability and loading are likely the key drivers of fire ignition and spread, which are dependent on factors including forest disturbances and ecosystem-atmospheric moisture dynamics. This project focuses on the fire risk posed by rapid oscillations between moisture extremes occurring in a short period of time (weeks to months). The central hypothesis is that hydroclimatic rapid oscillations increase fire risk by first causing forest disturbances (such as downed trees and debris accumulation) during extreme wet and stormy events that will rapidly become fuel in subsequent drought conditions. Thus, the objectives include a characterization of these hydroclimatic events and their large-scale drivers, as well as their relationship with observed fires. Field experiments will simulate fuel drying following disturbances, which will be complemented with fuel moisture mapping and modeling using remote Earth observation data and geocomputational approaches. Lastly, the LANDIS-II forest dynamics model will be used to produce scenarios of fire spread and severity under different hydroclimatic conditions determined in collaboration with stakeholders. 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 Major Research Instrumentation Program (MRI) award supports the development of a groundbreaking machine learning-assisted multi-material 3D printer with a novel hybrid deposition process, enabling transformative research in polymer composites and smart additive manufacturing. As the first system of its kind, the instrument seeks to pioneer the integration of continuous fiber reinforcement and liquid-like polymer inks, allowing unprecedented precision in manufacturing high-performance composite structures and functional devices. The platform’s unique capability to combine multiple curing and anchoring functions with machine learning-driven process optimization seek to advance research in process-structure-property relationships and in-situ monitoring, filling a critical gap in existing multi-material printing technologies. This innovation is expected to catalyze nationwide efforts in smart manufacturing, improve utilization of natural bio-polymer resources, and foster new opportunities in AI-powered engineering and design. The 3D printing system will serve as a regional and national research hub, supporting a wide range of science and engineering disciplines through collaborative access across academic institutions and industry. Housed in a shared facility, it will offer training and research opportunities to students, postdoctoral researchers, and faculty, while its remote access and shared governance model will actively engage primarily undergraduate institutions in the region. By equipping researchers with advanced tools to design and prototype smart composite devices, the project intends to elevate regional competitiveness and contribute to workforce development in key areas such as machine learning, materials science, and advanced manufacturing. The hybrid additive manufacturing platform looks to also enable high-fidelity production of next-generation composites, supporting diverse applications from aerospace structures to bio-integrated devices, and reinforcing the U.S. leadership in smart, sustainable manufacturing technologies. 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

Multi-agent systems (MAS) are pervasive with applications in various areas such as computer networks, robotics, and power grids. For example, multi-robot systems play a critical role in our society, including industrial robots in car assembly lines, hundreds of drones in a light show, and many vehicles in future autonomous ride-sharing services. Sequential decision-making is crucial to construct functional, intelligent MAS that can meet our needs. Multi-agent reinforcement learning is an approach that facilitates machine learning through feedback that reinforces the desired behavior. However, this approach requires a quantitative reward function that is oftentimes unavailable or hard to design. Formal methods (FM), by nature, can accurately specify and verify software and hardware systems. This project aims to combine the two approaches in order to construct multi-agent systems that are more scalable, easier to understand, and safer. The investigators will also disseminate research findings, integrate them in teaching, and train graduate and undergraduate students. Specifically, this project has the following three research objectives. Objective A develops novel specification-guided Multi-agent Reinforcement Learning (MARL) approaches that optimally decompose the specification and assign the resulting parts to agents. The principled and interpretable methods can learn a “correct by construction” scheduler to decompose signal temporal logic specifications optimally, corresponding to the credit assignment in specification-guided MARL. Objective B aims to analyze and explain the emergent coordinated behaviors of MARL by mining specifications from the trajectory rollouts of the policies. An interaction graph in the MAS is first learned by graph neural networks, followed by a novel template-free, generative approach for specification mining in a hierarchical and scalable manner. Objective C aims to enable the transfer of MARL policies across a distribution of tasks by simultaneous specification inference and policy learning. A specification-guided meta MARL approach is developed based on approximate Bayesian inference. This work can lead to more scalable and robust autonomy in factories, warehouses, hospitals, and homes. Concrete examples include large-scale self-driving vehicles, off-road autonomy in the wild, and advanced air mobility. 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

Artificial Intelligence (AI) and Machine Learning (ML) are rapidly transforming industries and are expected to drive more technological progress over the next decade than witnessed in the past century. Future careers in sectors such as healthcare, cybersecurity, logistics, and advanced analytics will require a strong foundational knowledge of AI/ML, with job growth in these fields projected to outpace that of many other occupations significantly. This shift in workforce demand underscores the need for structured retraining and upskilling programs, particularly at the early stages of higher education. To meet these workforce needs, our project will offer a structured continuum of experiential learning activities that combine academic instruction with practical, hands-on training in real-world AI/ML environments. These activities are designed to prepare learners at the beginning of their academic or technical training journey, ensuring alignment between educational content and workforce expectations. The project’s evaluation will generate a roadmap of experiential learning models that can inform scalable, high-impact approaches to AI/ML workforce development. The goal of this project is to prepare cohorts in early-career stages with foundational and applied skills in AI/ML, enabling them to pursue technology-focused careers across various sectors. The program follows a structured academic-industry model consisting of three modules: a semester-long AI/ML course focused on core principles and hands-on projects using tools like Jupyter notebooks; a semester-long internship with industry partners- Potentia Analytics and Securin, to apply learned skills to real-world AI/ML problems in healthcare and cybersecurity domains; and a professional development component offering mentorship, guest lectures, career workshops, and exposure to industry environments. Students will be recruited from community colleges and undergraduate institutions, particularly those at the beginning stages of their academic and technical education. Through this integrated experience, participants will develop both technical competencies and professional readiness, bridging the gap between academic learning and workforce expectations. The ExLENT Program, supported by the NSF TIP and EDU Directorates, seeks to support experiential learning opportunities for individuals to increase their interest in and access to career pathways in emerging technology fields. 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

Abstract Title: Developing next-generation transparent and broadband light-emitting diodes from earth-abundant and nontoxic materials for lighting and display applications. Abstract: Light-emitting diodes (LEDs) are increasingly used in households, industries, and automobiles for both lighting and display purposes. Globally, the current LED market is valued at over $70 billion, accounting for more than 50% of the market share. In the United States, household and industrial lighting accounted for ~16% of the total electricity generated in 2017, and LEDs saved around 185 terawatt-hours (TWh) of energy. Therefore, the use of LEDs is projected to increase dramatically in the coming decades. To meet this global demand, there is a need for low-cost, earth-abundant, and non-toxic new materials for LED applications. This CAREER proposal targets the development of efficient LEDs that are cost-effective, stable, non-toxic, earth-abundant, and solution-processable, made from ternary copper halides. The ternary copper halides proposed here exhibit broadband emission with tunable colors across the spectrum, making them more suitable for lighting applications. The proposal aims to understand the underlying mechanism of emission to tune the emission colors for targeted applications rationally. Most importantly, unlike many existing materials, the ternary copper halides exhibit nearly 100% transmittance in the visible region, showcasing great potential for developing transparent LEDs used in emerging technologies such as virtual reality screens, automobile head-up displays, TV screens, and transparent window lighting. The proposed educational and outreach activities train the 4-H youth leaders and K-12 students across the state in fields that intersect robotics and semiconductors. The educational plan is specifically designed to provide students in rural areas of Mississippi with exposure and access to emerging fields. Low-dimensional ternary copper metal halides are advancing rapidly in LEDs due to their exceptional optical properties, including near-unity PLQYs and tunable broadband emission with strong exciton binding energies. Additionally, copper is earth-abundant and non-toxic, and the copper halides are solution-processable and stable to ambient conditions. This proposal aims to develop efficient, broadband, and transparent LEDs that emit various colors using ternary copper halides as emissive layers. High efficiency and tunable emission will be achieved by rationally controlling the carrier concentration in the emissive layer. Concurrently, this proposal seeks to tackle the mechanism of broadband emission in copper halides using state-of-the-art pump-probe X-ray absorption spectroscopy (TR-XAS). Further, the role of electrons/holes in the overall emission will be uncovered by selectively injecting holes into the emissive layer. Finally, this proposal aims to build highly transparent, broadband copper halide LEDs (T-LEDs) due to the high transmittance of copper halides in the visible-NIR region. In addition to the research plan, the proposal outlines a comprehensive educational and outreach plan that targets training students from a younger age in semiconductor electronics and robotics. Several tasks with hands-on experiments are designed to tackle the challenges of current enrollment in materials chemistry and the future need for a skilled workforce in electronics. 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

The Noyce Track 2 project aims to serve the national need of preparing highly qualified STEM teachers. This project plans to support 24 fellows specializing in computer sciences, engineering, biological sciences, mathematics and statistics, and physical sciences by offering comprehensive teaching fellowships, including scholarships, teaching licensure exams fees, textbooks, structured mentoring and salary supplements. Project components are designed to enable high-achieving prospective teachers to become elementary and secondary STEM educators proficient in active learning, reflective practices, and instructional excellence. This project at Mississippi State University includes partnerships with multiple high-need local education agencies (LEAs) in proximity to Starkville and Meridian, Mississippi, as well as the East Mississippi Center for Educational Development (EMCED), Mississippi Children's Museum-Meridian, and Mississippi Arts and Entertainment Experience (The MAX). Project goals include recruiting and preparing 24 new STEM teachers over two cohorts, increasing STEM enrollments and completion rates within the Mississippi Alternate Routing Teaching (MAT) programs, and ensuring fellows' sustained retention in the STEM teaching positions. This project is poised to implement an iterative evaluation strategy. Evaluation of the project will be guided by the following evaluation questions: (a) What is the effectiveness of the project's instructional preparation and mentoring model in enhancing STEM teacher performance? (b) To what extent do project strategies influence STEM teacher retention in high-need school districts? The results of this project will be disseminated to help enhance the field. This Track 2: Teaching Fellowships project is supported through the Robert Noyce Teacher Scholarship Program (Noyce). The Noyce program supports talented STEM undergraduate majors and professionals to become effective K-12 STEM teachers and experienced, exemplary K-12 teachers to become STEM master teachers in high-need school districts. It also supports research on the effectiveness and retention of K-12 STEM teachers in high-need school districts. 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

Many rural US residents live in aging, substandard housing with limited access to insurance, infrastructure, and services, leaving them at risk of displacement and financial loss from extreme weather events. This Smart & Connected Communities Integrative Research Grant (SCC-IRG) project addresses a critical national challenge: how to help rural communities better forecast and plan for housing resilience in the face of natural hazards. The project is developing a new risk assessment and housing planning tool that will first be piloted in rural communities across the Mississippi Delta and Gulf Coast regions, where growing risks from flooding, hurricanes, and other natural hazards pose threats to lives, homes, and livelihoods. The assessment and planning tool aims to help local decision-makers and public workers assess housing vulnerabilities, explore adaptation strategies, and prioritize investments that protect lives and improve affordability. This work advances scientific understanding and community engagement, and delivers practical solutions to improve safety and sustainability in rural regions. The project also contributes to STEM education through creative outreach, including hands-on experience, exhibits, and design workshops for rural K-6 students. This project is using geo-sensing, artificial intelligence, participatory research, systems engineering, and educational outreach to advance understanding of risk and is developing an integrated, multi-scale decision-support platform. The platform combines AI-driven building risk detection, agent-based modeling, and community-defined priorities to guide rural housing resilience strategies. The research has the core objectives of assessing structural and social vulnerabilities by integrating satellite imagery, demographic data, and qualitative inputs from residents, developing and validating a housing evaluation index through a hybrid agent-based model enhanced with deep reinforcement learning, and co-designing adaptation strategies with local stakeholders, supported by scenario testing and participatory evaluation. The platform aims to deliver real-time simulations and visualizations of hazard impacts, housing outcomes, and intervention trade-offs. Data collection includes focus groups, interviews, and participatory mapping in six rural counties. Outputs are designed to inform zoning, mitigation planning, housing investments, and public health efforts. Through hands-on training and informal STEM education, the project ensures both capacity building and the ethical, community-driven application of smart technologies. 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.

NIH Research Projects · FY 2025 · 2025-09

Overall Project Summary The Mississippi State Chemical Laboratory (MSCL) is ISO/IEC 17025:2017 accredited (No. 90603) and operates as both a state regulatory agency and a fee-for-service laboratory. MSCL ensures the quality and safety of human foods, animal feeds, fertilizers, pesticide formulations, agricultural limes, and petroleum products sold in Mississippi. Building on achievements from initial FDA Laboratory Flexible Funding Model (LFFM) support, MSCL seeks to further expand its analytical scope and strengthen partnerships with federal and state entities, thereby enhancing the state’s capacity to identify and mitigate chemical contaminants in food and feed. This proposal, “Strengthening Mississippi’s Integrated Food Safety System by Enhancing the Chemical Testing Capabilities,” submitted in response to FOA RFA-FD-25-007, aims to further expand MSCL’s capacity. The long-term objectives of this proposal include bolstering surveillance for emerging food and feed contaminants, reinforcing laboratory infrastructure to maintain high-quality analytical services, and promoting collaborative partnerships among state and federal agencies. In line with these objectives, MSCL has identified three specific aims: 1. Expand both compounds of interest and matrices in currently accredited methods to detect a broader range of emerging contaminants and toxins in food and feed. By enhancing instrument sensitivity and optimizing workflows, MSCL will improve sample throughput and efficiency, ultimately supporting proactive surveillance and quicker response times. 2. Strengthen laboratory infrastructure by investing in advanced equipment, staff training, and quality assurance measures to maintain and elevate MSCL’s capacity for high-quality analyses. These efforts will provide robust data to inform regulatory actions and protect agricultural interests within the state. 3. Enhance collaborative partnerships by participating in multi-laboratory proficiency testing, engage with peer laboratories to exchange best practices, and maintain close coordination with state and federal entities for joint investigations, leading to rapid identification and mitigation of contamination incidents to foster a safer, more integrated food safety system. To achieve these goals, MSCL will implement a research plan that includes participating in proficiency testing and performing analysis of emerging contaminants in samples in preparation for food defense. Staff will receive ongoing training with peer laboratories to ensure consistent application of best laboratory practices. Through these strategies, MSCL will strengthen its role as a partner in the integrated food safety framework, providing faster, more reliable chemical testing results that protect both human and animal health against evolving food and feed safety threats.

NIH Research Projects · FY 2025 · 2025-09

Project Summary The Mississippi Veterinary Research and Diagnostic Laboratory (MVRDL) is a full service, AAVLD accredited, all species, central reference laboratory. The molecular section personnel consist of a section supervisor with a B.S., M.S, and Ph.D. in veterinary medical sciences with more than twelve years of experience in molecular biology and virology in the veterinary diagnostic laboratory, one Sr. Research Associate with seventeen years working experience in the molecular diagnostic laboratory at MVRDL and eight years in related medical research experience. Three laboratory technologists; one technician with one year working experience in molecular diagnostics and over ten years of research lab working experience, and two recently hired technicians that started last month, one with two years of working experience in forensic laboratory and the other with one year teaching experience in biology for 9th grade. During 2024, the section conducted over 15,000 PCR procedures (conventional PCR, real time PCR, sequencing). The MVRDL is a member of the VET-LIRN and is one of the veterinary diagnostic labs that participated in Avian Influenza testing for commercial poultry, backyard birds, wildlife surveillance across eight states of Mississippi river, and milk, which helps address the concerns of people of milk. We also participated in an interlaboratory comparison of whole genome sequencing and Avian Influenza on milk. Due to the outbreak of Avian Influenza in Mississippi poultry industry, the need for the testing in surveillance and control increased significantly. Also, Mississippi State is one of the states that initiated milk testing weekly. Currently, all the liquid handling procedures are being done manually, considering our increasing number of tests, this increases the turnaround times and increases the possibility of human error. Our laboratory will benefit from obtaining an Integra ASSIST PLUS pipette workstation, that can prevent the risk of cross contamination and increase the efficiency of workflow. To maintain the short turn-around times, and keep efficiency of diagnostic tests, we are requesting funding to purchase a pipette workstation. The MVRDL has infrastructure, expertise and capabilities and is willing to collaborate to increase the capacity of the Vet-LIRN Program Office VPO in handling increased sample workflow during emergencies. MVRDL is committed to performing these services in an efficient, accurate and timely fashion and to reporting results to the required agency.

NIH Research Projects · FY 2025 · 2025-09

Abstract Natural products continue to provide inspiration for the development of small molecules (new chemical entities) for use in medicine. Nitrogen heterocycles are found in many FDA-approved drugs and therefore are considered privileged structures. Thus, the investigation of natural products containing nitrogen heterocycles appears to provide high synergy and a high likelihood of discovering new and useful bioactive molecules. This proposal is divided into three aims, each of which involves the construction and elaboration of nitrogen containing heterocycles. Aim 1 is directed towards the total synthesis of ceratinadin B, a member of the bromotyrosine- derived natural product family. Isolated through bioactivity-guided fractionation, this molecules possess activity as an inhibitor of mycothiol S-conjugate amidase a part of the biosynthetic pathway for the assembly of mycothiol. Inhibition of mycothiol biosynthetic enzymes may be useful in anti-tubercular therapy. Ceratinadin B contains two distinct structural domains, a relatively common chiral spiro isoxazoline and an unusual imidazolyl-quinolone fragment for which no synthetic studies have been reported. A general solution for the asymmetric construction of the spirocylic fragment is proposed based on a Wacker inspired Pd/Cu-co- catalyzed dearomatizing spirocyclization reaction to assemble the spiro isoxazoline. It is proposed to use de novo construction of the bis heterocycle through a novel variant of the Gould-Jacobs reaction to assemble the 3-hydroxyquinolone and a classical Hantzsch-like synthesis of the aminoimidazole. Preliminary studies suggest that this sequence of events is feasible. Condensation of the two heterocyclic building blocks will afford ceratinadin B. The synthetic natural product and advanced precursors will be evaluated as anti-tuberucular agents in cellular assays (M. smegmatis/M. marinum) through collaborators. Aim 2 seeks to investigate a recently discovered tandem reaction sequence that generates quite complex polycyclic azacycles from readily available building blocks. The optimization, scope, and limitations of this sequence as well of the evaluation of post-cyclization transformation. Potential application to the total synthesis of “-izidine natura; products are outlined. Aim 3 focuses on the development of an asymmetric total synthesis of terrazoanthines A-B which were recently isolated from an Ecuadorian invertebrate. It is planned to employ a desymmetrizing α-fuctionalization of a 4- substituted cyclohexanone to permit the asymmetric annulation of the guanidine moiety through a Hantzsch azole synthesis.

NSF Awards · FY 2025 · 2025-09

Nontechnical Description: This Major Research Instrumentation (MRI) award supports the acquisition of an advanced scanning electron microscope (SEM) at Mississippi State University, significantly enhancing the capacity for advanced materials research and training across Mississippi. This instrument allows researchers across a broad range of disciplines, including engineering, chemistry, and biology, to examine a wide range of materials essential for discovery and advancements in health, energy, and manufacturing sectors. The microscope enables non-destructive evaluation of materials over multiple length scales. Housed in an open-access imaging facility, the instrument facilitates and broadens access from researchers in academia and industry from Mississippi and surrounding states, offering both on-site and remote access. This award enables the expansion of workforce training and economic development, boosting innovation and economic growth in Mississippi and beyond. Technical Description: A fully integrated SEM equipped with energy dispersive X-ray spectroscopy (EDS), electron backscatter diffraction (EBSD), and a scanning transmission electron microscopy (STEM) detector, all controlled through a unified software platform, enables nanoscale, multimodal chemical and structural analysis. The SEM is designed to operate under low vacuum, low acceleration voltage, and beam deceleration conditions, allowing for the detailed examination of beam-sensitive and nonconductive materials without the need for conductive coatings. EDS enables precise elemental mapping and composition analysis, while EBSD offers spatially resolved crystallographic information, including grain orientation and phase identification. The STEM detector enables high-contrast imaging of internal nanostructures, including core-shell particles, voids, and inclusions in metals. These combined capabilities are critical for understanding complex material systems. The instrument supports a wide range of research activities, including the development of additively manufactured metals, optoelectronic nanomaterials, soft materials, nanocomposites, energy-efficient membranes, biomedical scaffolds, and structural components for aerospace applications. Remote access and data sharing tools extend the system's utility to institutions across the state and neighboring areas, expanding interdisciplinary collaboration and enabling broader access from researchers in both research-intensive and emerging research institutions. This project is jointly funded by the NSF's Division of Materials Research (DMR) and the Established Program to Stimulate Competitive Research (EPSCoR). This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2025 · 2025-09

This Research Experiences for Undergraduates (REU) site award to Mississippi State University, located in Starkville, MS, supports the training of 10 students for 10 weeks during the summers of 2025–2027. Funded by the Division of Chemistry and the Established Program to Stimulate Competitive Research (EPSCoR), the program immerses participants in collaborative research projects at the intersection of chemistry, engineering, and environmental science. Participants investigate the interconnections between water quality, fertilizer use for enhanced food production, and renewable energy, using interdisciplinary approaches to tackle these complex challenges. The program’s goal is to develop environmentally engaged scientists, and a workforce prepared to address critical global issues. In addition, seminars and workshops promote entrepreneurial thinking, providing participants with tools and insights into pathways for starting small businesses. Research projects focus on energy security, water quality, food security, and related challenges. Participants explore the conversion of biomass and energy crops into biofuels, the use of biochar (a byproduct of biofuel production) for water purification, and the enhancement of food security through biochar soil amendments. These projects involve growing biomass, converting it into biofuels, and repurposing byproducts to clean water and improve soil quality. Through these hands-on experiences, participants gain exposure to research instrumentation, fostering enthusiasm for science and equipping them with practical skills for careers in the chemical sciences or graduate school. Alongside the research experience, seminars and workshops cultivate an environmentally conscious and entrepreneurially minded cohort capable of addressing societal challenges and advancing solutions in the chemical sciences. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2025 · 2025-09

Internet search offers incredible learning opportunities by making information more accessible than ever before. This project examines whether internet search leads to shifts in thinking style and if it affects self-directed learning. The project also aims to investigate whether interventions which encourage people to use their memory as they conduct internet searches could influence search processes. This work seeks to grow understanding of how people can effectively use their mental abilities alongside digital tools. The project also includes STEM research opportunities for trainees and broad dissemination of project outcomes through online public forums. There are two major goals of the proposed research. First, this work aims to examine the extent to which using internet search might affect people’s ability to direct their own learning by changing users’ cognitive style. Prior work suggests that habitually using internet search can impart a low-effort approach to cognitive tasks, so the proposed work plans to investigate the extent to which such a shift in cognitive style influences learning on an unrelated task. Second, this work aims to investigate short-term and longer-term interventions to counteract potential effects of internet use on learning and cognitive style by encouraging users to practice using their cognitive abilities alongside internet search. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2025 · 2025-09

The aim of this project is to advance disaster decision making, risk assessment, and management science by incorporating access to shelters and the ability to seek shelter into the national disaster risk assessment. Management decisions on resource allocation and emergency preparedness planning are hindered when disaster risk is not well understood. The existing tools and resources available to emergency managers often lack measures of shelter accessibility and the ability to seek shelter, which can escalate natural disasters into human disasters. By advancing existing measures, this translational project equips emergency managers with the knowledge and tools needed to cope proactively with disasters such as wildfires, hurricanes, tornadoes, and coastal floods. The potential societal benefits in this project include engaging emergency directors, planners, and operators to minimize redundancy and tool fatigue and ensure that outcomes align with their needs, and improving the well-being and survival of populations by identifying gaps in shelter access and prioritizing the allocation of shelter and mobility resources. This effort is guided by a vision of improving disaster risk understanding within a framework that integrates community resources and capabilities into risk assessment, management, and decision making. The research team achieves this goal through three research activities, co-produced in close collaboration with emergency managers across the nation. First, the research team develops a comprehensive, risk-based national shelter accessibility model to advance the state of the art in shelter accessibility measurement by accounting for both the availability and accessibility of shelters, as not all shelters remain functional during disasters. Second, the research augments existing national measures by integrating shelter accessibility and evacuation capabilities to enhance both short-term and long-term emergency management decisions. Third, the researchers create a science-informed decision-making tool to test risk perception and decision making in emergency management, enabling emergency directors, planners, and operators to explore how short-term and long-term strategies can provide evacuees with a better chance to survive. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2025 · 2025-09

The relative abundances of atomic nuclei, and thus the elements found in nature, require production mechanisms in extreme environments, such as those present during the Big Bang in the early universe and inside stars. One cannot create such conditions in laboratories. However, using theoretical methods, one can extrapolate terrestrial measurements to astrophysically-relevant densities and temperatures. The list of atomic nuclei, many of them short-lived, is in the thousands and nuclear theory plays a crucial role in analyzing the properties of known nuclei from measurements. In this broader picture, this project develops and applies the effective field theory (EFT) formalism to calculate several reaction rates central to the understanding of the stars' evolution, including our Sun. Key reactions for understanding the abundances of life-giving oxygen and carbon in stellar synthesis will be studied. Solar reactions that help probe physics beyond the Standard Model of particle physics will also be calculated with high precision. In addition, lattice EFT method for exact numerical calculations will be developed. The broader impacts of this research include training graduate students in nuclear physics, as well as in numerical and analytical work, for an academic or industry career that benefits society. This project builds on past work by the PI and his collaborators on halo nuclei and lattice EFT. Halo nuclei have excess protons or neutrons that form a halo around a core. The small separation energy of the valence nucleons is used as a systematic expansion parameter in the calculation. Alpha burning on carbon-12 in massive stars determines the carbon-12 to oxygen-16 ratio. Currently there is an order-of-magnitude uncertainty in the reaction rate estimates and the EFT calculation will be used for a model-independent reaction rate estimate. Alpha capture on helium is crucial for boron-8 production through a subsequent proton capture in the Sun. The energetic subatomic neutrinos from the decay of boron-8 probes physics beyond the Standard Model. The PI and his collaborators will address the discrepancy between current measurements and theoretical calculations of the radiation angular distribution emitted in this reaction. Lattice EFT is the formulation of the theory on a space-time lattice, and it allows for exact numerical calculations without resorting to a perturbative expansion. A coupled-channel calculation of helium-3 and neutron converting into triton and proton is performed using lattice EFT. The reverse reaction controls helium-3 production in Big Bang Nucleosynthesis. The research in this project aligns with major U.S. investments in rare isotope beam experiments. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2025 · 2025-09

This National Science Foundation Research Traineeship (NRT) award to Mississippi State University will develop a smart agriculture energy innovation network (SAGEIN) certification program that integrates distributed renewable energy systems, smart agriculture technologies, artificial intelligence (AI), and entrepreneurship to build workforce capacity for rural communities. The integration of distributed renewable energy systems with smart agriculture represents a critical frontier for addressing interlinked national challenges of energy security, rural economic development, and long-term sustainability under environmental variability while creating new paradigms for sustainable food-energy systems. The SAGEIN program creates a comprehensive training ecosystem where research innovations can be effectively translated into practical solutions that drive rural economic development. The unique stakeholder-driven framework identifies authentic rural energy challenges while providing direct pathways for implementation and technology transfer. The project anticipates training three hundred and fifty (350) MS and PhD students, including fifteen (15) funded trainees, from mechanical engineering, agricultural sciences, economics, and human sciences, preparing them to become research entrepreneurs and technical experts in the rapidly evolving rural energy sector. The SAGEIN program implements a three-phase educational model designed to transform graduate students into research entrepreneurs through integrated technical and professional training pathways. Technical training consists of a 12-credit certification program covering four sequential courses: renewable energy assessment, smart agriculture integration, AI-driven solutions, and entrepreneurial ventures. The research agenda advances knowledge through three convergent themes that test specific hypotheses: (1) integrated local energy systems, including solar, wind, small hydro, and low-carbon thermal technologies, can be co-located with agricultural operations to enhance land-use efficiency and support animal agriculture through optimized physical-biological interactions; (2) mixture-of-expert AI frameworks can effectively balance competing objectives in complex rural energy-agriculture systems; and (3) multi-dimensional resilience metrics can quantify vulnerability across coupled rural distributed energy infrastructure and agricultural operations. The program will advance fundamental knowledge integrating agriculture and energy using intelligent resource management and assessing the resilience of rural infrastructure. Outcomes will include new frameworks for sustainable rural energy development, data-driven tools for system optimization, and practical strategies for bringing innovations to market while preparing a workforce ready to lead in an evolving energy economy. The NSF Research Traineeship (NRT) Program is designed to encourage the development and implementation of bold, new, and potentially transformative models for STEM graduate education training. The program is dedicated to effective training of STEM graduate students in high priority interdisciplinary or convergent research areas through comprehensive traineeship models that are innovative, evidence-based, and aligned with changing workforce and research needs. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2025 · 2025-08

This Pathways to Enable Open-Source Ecosystems (POSE) project enables the rapid advancement of computational modeling approaches and their transition to practical applications. Development of computational tools requires collaboration among applied mathematicians and physicists, who develop the models of physical systems; computational scientists, who understand the complexity of programming high performance computing systems; and application engineers, who understand the physics of the engineering system. One of the biggest challenges for computational tool development is the lack of collaboration between these experts. LOCI, a general-purpose framework for developing numerical solvers and computational simulations, encourages collaboration by providing a modular framework where independently developed physical, numerical, or computational models can be integrated seamlessly. The project improves a community-driven open-source ecosystem (OSE) that allows porting of advances from different disciplines into the application with ease. LOCI also enables innovation in the computational engineering discipline through rapid community feedback. In particular, the consortium will help advance Loci-CHEM, a high-speed flow solver widely used for space launch and hypersonic system applications. This POSE project focuses on the development of an open-source ecosystem for the LOCI framework, providing a data-driven paradigm for specifying solver algorithms. LOCI provides an auto-parallelizing capability which simplifies the development and deployment of complex, large-scale simulation software, enabling efficient execution on supercomputers. The LOCI consortium facilitates community-driven enhancements to the code base, promoting the exploration and development of new scientific use cases, and implementing a governance structure that aligns LOCI's development with community needs. The consortium will maintain and sustain LOCI’s existing capabilities and acts as a catalyst for future advancements. To achieve these objectives, the project will: 1) determine the best non-profit structure for the consortium; 2) develop the framework for governance including an advisory board and working groups to represent the community's interests; 3) document and streamline the onboarding process for new users and developers; and 4) develop strategies for the integration of external contributions to the LOCI source code. Wider adoption of the framework will transform the way computational models are developed, while its modular nature will make scientific software development more efficient. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2025 · 2025-08

This Pathways to Enable Open-Source Ecosystems (POSE) project builds a dynamic, collaborative community dedicated to advancing self-driving technologies through open-source innovation. By uniting stakeholders from industry, government, and academia around the Mississippi State University (MSU) Autonomous Vehicle Simulator (MAVS), a free, physics-based simulation platform, this project accelerates the development, knowledge sharing, and real-world deployment of autonomous systems. Although building and physically testing self-driving vehicles is slow, expensive, and potentially dangerous, simulation is fast, cheap, and safe. This project enables community access and governance of a high-quality simulator, allowing contributions to self-driving technologies from a variety of sources. This project develops an open-source ecosystem to support the growth and governance of MAVS through community contributions, allowing the capability and use of the software to grow with the number of users. The solution also allows the users to guide and contribute to the development of new simulator features that match the technology needs of the emerging field of self-driving cars, incorporating simulations of new types of sensors, environments, and vehicle behaviors. This POSE project establishes a sustainable open-source ecosystem to support the MAVS software, an open-source software for simulating self-driving vehicles. The project creates a formal governance structure for MAVS, a process for contributing new features and code, and enable input from the growing community of MAVS users. This solution includes setting priorities such as the development of new cameras and radar-based sensor models for self-driving vehicles. Additionally, this project creates a process for addressing feature requests for MAVS. While MAVS already includes interfaces to commonly used tools like the robotic operating system, future feature requests may include the development of other interfaces or new terrain simulation properties to support simulated experimentation in on-road and off-road terrains. Because MAVS has supported a broad domain of experiments – from large-scale teaming exercises to cybersecurity analysis, the team expects feature requests to cover a wide variety of applications. This project will define a process for ordering the priority of new features in MAVS, ensuring the long-term utility of the software. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2025 · 2025-08

Zeolites are special crystal-like materials used to speed up chemical reactions. They are useful because they are stable and can be adjusted to work in different ways. Scientists are interested in using them to help turn plant-based materials (called biomass) into fuels and useful chemicals. This project focuses on studying how chemical reactions happen inside the tiny pores of zeolites—pores that are less than 2 nanometers wide. These reactions take place in liquids and in constantly changing environments, which makes them hard to study using traditional methods based on quantum mechanics. To solve this, the project will create new machine learning models that can predict how atoms interact with each other. These models will still be accurate like quantum methods but much faster and easier to use. This will help scientists study bigger and more realistic systems and better understand how molecules move and react inside the small pores of zeolites. In the end, this project will give scientists a deeper understanding of how zeolites work and how to use them more effectively to turn biomass into valuable products. It will also train college and graduate students in advanced computer modeling, helping prepare them for careers in science and engineering. The goal of this project is to elucidate the role of solvent and confinement on diffusion and chemical transformations within the nanopores of zeolites. This goal will be accomplished by developing accurate machine-learned interatomic potentials based on the potential energy surface obtained from first principles molecular dynamics (FPMD) simulations to model the reaction-diffusion process in microporous zeolites with pores smaller than 2 nm. The multi-atomic cluster expansion framework will be used to develop machine-learned interatomic potentials (ML-IAPs) using FPMD simulation trajectories. Zeolites will be selected based on the topologies with increasing pore diameter, allowing a systematic investigation of the effect of pore size and confinement on diffusion, kinetics, and local solvation environment within the pore. Model chemical transformations considered include Carbon-Oxygen, Carbon-Carbon, and Carbon-Hydrogen bond activation, such as dehydration and isomerization of monosaccharides to initial platform chemicals. The central hypothesis of the project is that the development of computationally efficient machine-learned interatomic potentials with quantum mechanical accuracy will allow modeling systems that closely mimic the experimental conditions, thereby elucidating thermodynamic factors that underpin selectivity and reactivity in the liquid phase heterogeneous catalysis. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2025 · 2025-07

Past hybridization among closely related species can leave traces of genetic variation from endangered or even extinct species in the DNA of present-day animals. This phenomenon, known as ghost introgression, is often overlooked but is a reservoir of preserved genetic variation from endangered or extinct species found in present-day genomes of the related common species. These hidden reservoirs could be essential for conserving adaptive potential in the future. This project re-envisions the conservation value of ghost introgression and how it can be leveraged to support endangered species recovery. The project will characterize the ecology and population dynamics of Gulf Coast canids that carry varying amounts of red wolf ghost ancestry in their coyote genomes and inhabit a broad geographic range. First it will develop a non-invasive genetic tool to monitor and assess the ecological conditions that promote the persistence of red wolf ghost ancestry. Further, the tool will be used to identify individuals of high conservation value, as measured by their degree of unique red wolf ghost ancestry and thus have the greatest potential to resuscitate endangered red wolf ghost genetic variation. The conservation partner, the Endangered Wolf Center, will then implement a short-term breeding experiment to enhance ghost ancestry based on a careful pairing design in a captive breeding facility. The project integrates information and efforts across communities and organizations to pioneer new options for endangered species recovery programs in the future. The project will also involve public outreach and education, and engagement with managers with a focus on resolving human wildlife conflicts and conservation of key predators. Canids along the American Gulf Coast carry signatures of red wolf ghost introgression, yet little is known about the factors that support the persistence of such. The project will combine in- and ex-situ studies and develop a framework for evidence-based conservation in a natural landscape using population ecology and empirical genomics. First, canids will be captured, genetically sampled, and radio-monitored across a gradient of mortality risk and available resources to quantify the functional linkage between ghost introgression and ecology. Morphometrics and individual-level fitness correlates will also be considered to develop a landscape prioritization tool to identify areas for future conservation efforts. Second, a SNP panel will be developed to non-invasively monitor large landscapes for ghost introgression of red wolf DNA and behavioral ecology traits. The application of this technology will be for large-scale, cost effective, long-term, non-invasive monitoring and continued identification of conservation priority individuals. Third, an optimization framework will be developed to identify and rank individuals that maximize ghost genetic variation while prioritizing the genomic architecture of red wolf ancestry, noting that longer block lengths of endangered genetics are preferred for maintaining genome integrity. Finally, the project will attempt to revive ghost variation through an innovative short-term captive breeding experiment, challenging the existing endangered species conservation tenets to include ghost variation as a trailblazing method to protect imperiled species and diversify their genomes. This project will serve as a model, evaluating the potential of leveraging ghost introgression to preserve the genomes of endangered species that face the immediate threat of extinction. This project is jointly funded by the Divisions 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.