University of Kentucky Research Foundation

NSF Awards · FY 2027 · 2027-01

This Research Infrastructure Improvement (RII) EPSCoR Research Fellows project provides a fellowship to an Assistant Professor and training for a graduate student at the University of Kentucky. This work is conducted in collaboration with Dr. Shripad Tuljapurkar at Stanford University. Through the fellowship, the PI will investigate how organisms allocate energy to growth, survival, and reproduction across their life cycles. The research will examine why species that differ greatly in body size, lifespan, and metabolic rate can nevertheless achieve similar long-term reproductive success. To address this question, the project will advance a new scientific framework called the Equal Fitness Paradigm, which links metabolism of organisms to life history, population dynamics, and energy flows in ecosystems. By combining approaches from ecology, physiology, and population biology, the project will improve understanding of the fundamental processes that shape the diversity of life on Earth. The work will strengthen collaboration between Kentucky and Stanford researchers, train students in quantitative ecological methods, and develop open-source data tools that support research, education, and applied natural resource management. This project will integrate numerical and metabolic theories of life history to advance the Equal Fitness Paradigm (EFP), a framework that links energetic constraints to life history evolution and population dynamics. The research will develop mathematical connections between metabolic scaling theory and matrix population models to quantify trade-offs among survival, growth, and reproduction across species. Using published databases for wild organisms, the project will establish computational workflows to standardize life history parameters and incorporate energetic variables for comparative analyses across taxa. These analyses will evaluate how energetic and numerical constraints produce common scaling relationships between energy use, generation time, and reproductive output resulting in approximately equally fit strategies. The fellowship will enhance research infrastructure by expanding faculty expertise in mathematical biology and strengthening collaboration between the University of Kentucky and Stanford University. Training activities will provide graduate students with advanced skills in population modeling, ecological theory, and data science. The resulting open-source datasets and analytical workflows will support future research and education in ecological theory, biodiversity science, and applied population studies. This project is supported by the EPSCoR Research Infrastructure Improvement Program: EPSCoR Research Fellows, which supports early- and mid-career investigators in eligible jurisdictions to develop collaborations at the nation’s private, government or academic research institutions. 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

Outdoor air pollution, especially particulate matter with a diameter of 2.5 microns or less, is a major threat to public health. Pollution levels in cities often vary among neighborhoods. Building design, street layout, traffic, and weather affect how pollution spreads and accumulates. However, current air pollution models often cannot fully explain why some places become pollution hot spots or how city design could reduce exposure. This project will study how the shape of cities, such as building heights and street layouts, affects air pollution levels. The research will combine air quality measurements, geospatial and traffic data, and artificial intelligence (AI) models to understand where and when pollution accumulates. The project will produce accessible tools to help city planners and engineers to identify risks and explore mitigation strategies. Students will help deploy air sensors and analyze the data while workshops will train planners and public health officials to use these tools to support healthier cities. This project will develop a mechanistically-informed modeling framework that integrates 3D urban morphology data, real-time environmental observations, and interpretable AI to quantify how the built environment influences PM2.5 exposure. It will include two interconnected objectives. First, annual air quality models will be developed for six major U.S. cities by combining standardized morphological indicators from the Local Climate Zone (LCZ) framework (e.g., sky view factor, building surface fraction) with regulatory monitoring and low-cost sensor data to train interpretable machine learning models. These models will link urban structural features to pollution concentrations and use Shapley Additive explanations (SHAP) to interpret the nonlinear relationships. Second, high-resolution hourly exposure models will be developed in Lexington, Kentucky using a dense sensor network, mobile monitoring, and time-series traffic and meteorological data to capture dynamic pollution patterns. Model performance will be validated through cross-validation and additional field campaigns with undergraduate and graduate students participating in sensor deployment and data analysis through hands-on STEM training. The resulting pollution maps and morphological risk indicators will be interpreted into an interactive geospatial dashboard for scenario analysis. Overall, this project will combine interpretable AI, advanced monitoring, and urban morphological analysis to advance scalable exposure modeling methods and provide transferable insights into how urban form modulates pollution dispersion. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2026 · 2026-06

Population forecasts are used to manage threatened and endangered wildlife populations. Natural resource managers use these predictions to anticipate future changes in species abundance, assess extinction risk, and prioritize management interventions. Inaccurate forecasts may lead to erroneous interventions or inefficient uses of limited resources including funding and personnel. Despite the widespread adoption of population forecasts over the last four decades, there have been few efforts to assess the historical performance of these predictions. This project will use the growing number of long-term monitoring datasets collected by research scientists and state and federal agencies to assess the forecast performance of population models. Findings will inform management strategies for threatened populations by identifying the types of data and models that generate accurate forecasts. Outcomes of this project include improved guidance for natural resource managers on effective monitoring strategies for threatened populations, and the development of a framework that can be applied to evaluate other historical ecological forecasts. The research will train the next generation of scientists with modeling and programing skills, handling and development of databases, and the development of AI-ready databases for the scientific community. Population ecologists have been making predictions on the risk of population decline and extinction for almost 40 years. While there has been some past work evaluating forecast ability in stable populations, most assessments of population viability forecasts have been through indirect methods, thus, there is little empirical evidence assessing the long-term accuracy of these forecasts. This project will apply a retrospective approach to assess the reliability of population predictions by developing a publicly available database of historical population viability forecasts linked to updated monitoring data of vertebrates, invertebrates, and plants. This database will be an asset for natural resource managers and scientists studying the properties of ecological forecasts, while the analysis will provide real-world measures of forecast accuracy and precision by comparing predicted trends to updated monitoring data. The project will determine how the life history of target species interacts with statistical survey methods and demographic model details to influence forecast skill. This project provides the first comprehensive evaluation of published population forecasts using monitoring data. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2026 · 2026-02

The atmosphere adjacent to the ground is called the atmospheric boundary layer. It plays an important role in interactions between Earth’s surface and the atmosphere. Air flow in the boundary layer can be turbulent, which is difficult to model. Nevertheless, the success of weather models depends critically on accurate representations of the boundary layer. Current models do not account for large, organized, rolling air currents that strongly affect flow inside the boundary layer. Most studies of these organized rolling motions, which are called coherent structures, are based on laboratory experiments or computer simulations. This project will use formations of fixed-wing drones to collect real-world data on air movement inside the boundary layer. The studies will be designed to identify key factors that govern the formation of organized rolling motions and their subsequent behavior. The project will provide opportunities for undergraduate students to participate in research. They will be key members of the team, serving as system operators, safety officers, and aircraft managers, while being trained on the scientific objectives of the research. The measurements will focus on evaluating several hypotheses about the formation and influence of a structure called horizontal convective rolls. The project will investigate (1) the critical stability threshold at which horizontal convective rolls develop; (2) whether their formation mechanism represents an evolution of superstructures observed under neutral stability conditions; (3) the contribution of roll structures to mass, momentum, and energy transport under conditions in which they form; and (4) their influence on the anisotropy of smaller-scale turbulence. Measurements will be conducted using a leader-follower approach, in which a lead aircraft is tasked with navigation and the rest are tasked with maintaining their relative spatial separation. This separation will be designed so that wind velocity measurements made by each aircraft can be used to determine the velocity gradient tensor, yielding unprecedented information about the vorticity and turbulence within the coherent structures. This information will support the broader objectives of the project by enabling advances in boundary layer parameterizations, ultimately enhancing the accuracy of meteorological models. 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

Computer-aided design (CAD) is a key component of engineering education and practice. Computer-aided design software is used to create digital 2D drawings and 3D models which are used across all phases of the design process for visualization, simulation, analysis, and production in various engineering disciplines, architecture, animation, and more. Since CAD is increasingly being incorporated into high school and undergraduate engineering education, improving CAD education has the potential to transform learning for hundreds of thousands of students. While computer-aided design technology is ever-growing in its importance, updates to CAD education are not keeping pace. Most research related to CAD education focuses on spatial skills, or the 3D thinking skills required to envision 3D designs. There are however problems with traditional assessments of spatial skills, causing many of these assessments to be inaccurate. Moreover, other factors besides spatial skills influence CAD learning, yet little research investigates these other aspects of students’ experience with CAD training. Because the spatial skills paradigm may not be as important to CAD learning as we once thought, studying alternate the pathways is important to ensure a well-prepared engineering workforce. This research will use a multi-case study and media analysis to develop a comprehensive picture of the CAD learning ecosystem, triangulating data from a broad range of sources including CAD classrooms, student clubs which use CAD, professional engineers who use CAD, and CAD media that is widely used for self-study in informal learning settings, such as YouTube tutorials. Through the multi-case analysis, the research team will construct a map of the ecosystem in which people gain CAD expertise and identify non-spatial factors which contribute to participation in the CAD workforce. The educational plan for this project will focus first and foremost on sharing the research findings with CAD educators and students through a guidebook that uses visual storytelling to bring the case studies to life. The educational plan will also strengthen the CAD courses offered in the PI’s institution by incorporating human-centered design for healthcare, connecting CAD with engineering work that has a direct impact on people and connecting the PI’s professional experience in medical device design to the PI’s teaching. This research answers the call to look for counter theories that challenge deficit models by placing a focus on structural issues within the educational ecosystem instead of deficiencies within students. Additionally, this research will enhance our understanding of how informal learning and online media contribute to engineering workforce preparation and participation. This project will contribute to NSF’s goal of improving engineering education. 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

With the support of the Chemistry of Life Processes program in the Division of Chemistry, Dr A.-F. Miller from the University of Kentucky is studying how electron transfer bifurcation is interfaced with other reactions in metabolic pathways of diverse organisms. Microbes can convert solar energy to a maximally useful, energy-dense form and store the energy for use after sunset. Thus, life can exploit cheap, abundant energy sources to generate high-energy fuel. The microbes achieve this two-prong performance by electron transfer bifurcation ('bifurcation'). Moreover, the microbial systems use earth-abundant metals and fully renewable materials such as proteins and vitamins that can be produced in unlimited quantities. The Miller group will apply a broad range of research tools to the examination of the properties of proteins involved in bifurcation and the conditions in which the process is efficient. Prof Miller will also offer hands-on workshops and public-friendly course modules in which chemical concepts are made accessible by drawing on the aesthetic appeal and interest of the students and the public in plant pigments and textile art. In bifurcation, the energies of two moderate-energy electrons are pooled to yield one high-energy electron. The other electron from the pair is recycled by metabolic pathways that vary from organism to organism, and in essence, balance the budget. The Miller group will survey an expanded range of bifurcating electron transfer flavoproteins (ETFs) to broaden our knowledge of what enzyme systems can conduct bifurcation, and to obtain a more complete picture of what metabolic contexts support bifurcation. The team will characterize the interactions between proteins that connect bifurcation to supporting recycling systems and investigate tuning of electron-transferring flavins' reactivities by the proteins involved. Similarly, the team will elucidate mechanism(s) used to prime the bifurcating flavin for its role. These aims will deploy the group's expertise in spectroscopy, quantum chemical calculations, protein engineering, and thermodynamic measurements. 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

Spatial skills are usually considered to be important for success in STEM (Science, Technology, Engineering, and Math). However, the way these skills are currently taught does not always connect with real-world engineering work. Currently, many spatial skills training materials use simple blocks or cubes which are much easier to work with than the complex objects that engineers will encounter on the job. A typical spatial problem might ask a student to rotate a cube shape in their mind. But a civil engineer might need to envision a complex building or a three-dimensional landscape. Research shows that students learn these spatial skills better when the training is connected directly to the specific type of engineering they are studying. However, we currently lack an understanding of the spatial problems that today’s engineers encounter. This project will study how spatial skills are used in two areas of engineering: civil and mechanical engineering. The findings will help us understand what kind of spatial skills are important to each area of engineering. The outcomes of the research will have a direct impact on recruiting and retaining students in engineering who have a range of spatial skills, advancing our understanding of the professional formation of engineers. Rather than continuing to promote the idea that general spatial skills are important for engineering degree attainment, the proposed work utilizes a discipline-specific approach to classify the ways in which engineers represent and communicate spatial information in different work contexts. To address this knowledge gap, we will focus on two disciplines, civil and mechanical engineering, and will 1) use ethnographic methods to identify spatial problems embedded in practice, 2) verify the ethnographic findings through interviews of practicing engineers nationwide, and 3) use data from 1) and 2) to identify which spatial skills are important in contemporary engineering practice in civil and mechanical engineering. This project will yield rich descriptions of the spatial problems that engineers encounter in practice and identify the resulting spatial skills needed to solve those spatial problems. The research approach is informed by prior research that indicates that real-world spatial skills are discipline-specific and answers the call for a deeper understanding of discipline-specific spatial problems and skills. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2025 · 2025-11

Ecosystem change can cause animals to become out of sync with other species that provide critical resources like food or nesting habitat. This loss of synchronization results from mismatches in cues that indicate time of day or year. For example, many animals use daylength and temperature to coordinate their behaviors with their environment. Sometimes, these two factors no longer line up; for example, temperatures are changing while daylength is staying the same. The goal of the current study, which is co-funded by the National Science Foundation and the Kavli Foundation, is to determine how daylength and temperature cues are integrated in the nervous system to impact behavior in the honey bee (Apis mellifera L.), a species responsible for ~$15 billion in crop pollination across the U.S.A. annually. Honey bee pollination success, critical to food security, requires foraging worker bees to synchronize their activity with floral resource blooms. This study implements innovative behavioral tracking, molecular and imaging methods, and functional studies of the nervous system to determine how current and projected daylength and temperature combinations impact honey bee behavioral rhythms. The honey bee thrives in diverse environments, making it an ideal species to identify features of the nervous system that confer resilience to ecosystem change. Project goals include providing high school students opportunities to learn about beekeeping, environmental science, and STEM careers. Cue mismatches resulting from dynamic environmental conditions can cause maladaptive behavioral timing. Temperature profiles are shifting while daylengths remain the same, causing a mismatch between the two main abiotic cues animals use in combination to synchronize their behaviors with their environment. The impact of such mismatches depends on how cues are prioritized and integrated in the nervous system. Honey bee foraging activity is entrained to, and acutely impacted by, both daylength and temperature conditions. However, no study has investigated how these cues are integrated in the nervous system. Investigators will assess how daily temperature cycles influence rhythmic neuromolecular processes related to light and temperature perception and integration. Using experiments that mirror natural conditions, they will use quantitative PCR (qPCR) in light- and temperature-sensing tissues and bulk RNAseq in the rest of the brain over a 24 h cycle to determine how variation in temperature impacts daylength-entrained molecular rhythms (Obj 1). They will use single-nucleus RNAseq and RNAscope to determine the cell populations and brain regions that are most vulnerable to climate-change-relevant mismatches between temperature and daylength (Obj 2). They will use electroretinography to assess how temperature and daylength experience combine with acute temperature conditions to impact visual sensitivity and spatial resolving abilities (Obj 3). Addressing these objectives will identify nervous system components that may be vulnerable or resilient to cue mismatches in a widespread and adaptable model pollinator species. This project is supported jointly by the Division of Integrative Organismal Systems in the Directorate for Biological Science of NSF and the Kavli Foundation. 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

Mobile applications (apps), though useful, can create privacy risks for their users by leaking sensitive information from SMS text messages, location data, contacts, and photos, etc. to the app developers or to data brokers. This is a critical problem since both the number of mobile applications and people's use of them have grown greatly over the last decade. This expansion of choices and uses makes it hard for people to find apps that meet both their functional and privacy needs. Recommender systems, which provide personalized suggestions based on user ratings, usage, and other data, are often used in other domains to help people decide between many choices. However, applying existing recommendation methods to mobile apps can expose users to options that may appear useful but pose substantial privacy risks. This project introduces a new genre of recommender systems that selects a small set of candidate apps that might work for people based on their functional needs; analyzes the way those apps access, transform, and share data; and combines those rankings with people's privacy expectations to suggest apps that best meet individual people's needs. Further, the system will be designed to clearly communicate the privacy risks involved with suggested apps. Together, the work will promote safer, more trustworthy mobile experiences while advancing people's understanding of privacy online. The project will achieve this goal by grounding conflicting information about app behavior, such as discrepancies between app metadata and findings from static software analysis, through simulated user interactions with apps. First, new static analysis techniques will be developed by introducing novel program slicing methods for mobile apps that emphasize user-interpretable actions, incorporate permission awareness, and account for critical code in life cycle methods, event callbacks, and inter-component communication. Next, a multi-step deep reinforcement learning framework will be designed to simulate user interactions with apps under different configurations, enabling estimation of true app behavior in realistic settings. Finally, an interactive conversational recommendation system will be created to integrate privacy considerations through targeted interventions on app aspects derived from users' historical interactions and to refine recommendations based on insights from grounded app behaviors. This approach will enhance user safety while maintaining satisfaction by effectively balancing privacy and functionality. 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 5-year Noyce Track 2 project aims to serve the national need of preparing highly qualified STEM teachers. This project plans to support ten Teaching Fellows (TFs) by providing financial support for their master's degree as well as a yearly stipend and strategic STEM activities during their first four years of teaching. The project intends to prepare highly talented graduates from B.S. / B.A. programs in mathematics and science as well as career changers (who have a STEM undergraduate degree in the areas of Mathematics, Chemistry, Biology, Physics, or Earth Science). It is proposed that TFs are anticipated to complete the Master’s of Arts in Teaching (MAT) program in Secondary STEM Education at the University of Kentucky (UK), which is a one-year fulltime program. Additionally, providing strategic STEM activities and support mechanisms to guide TFs through their first four years of their teaching career are integrated into the project design. This proposed project is expected to enable high-achieving prospective teachers to become secondary STEM teachers with extensive expertise in active learning instruction. This 5-year project at the University of Kentucky includes partnerships among UK's College of Arts & Sciences, College of Education, Fayette County Public Schools (a high-need school district), and local businesses and informal STEM learning entities. Project goals include: (1) Recruit a population of ten TFs in the STEM disciplines to become secondary STEM teacher leaders and increase the number of master's graduates in secondary STEM Education. (2) Assist the UK Noyce program's TFs in obtaining secondary teaching positions in high-needs Kentucky schools. (3) Retain the population of STEM TFs as they graduate from our MAT STEM program and become in-service teacher leaders in high-needs schools for at least four years. (4) Implement the MAT Secondary STEM Education program curriculum as a collaborative effort between UK partners and UK College of Arts and Sciences and College of Education in order to equip TFs in one year with a strong content preparation pathway to STEM teacher certification. (5) Provide TFs with carefully designed experiences that enable them to engage in face-to-face and virtual learning communities that promote pedagogical content knowledge, STEM research skills, stress coping skills, and the design and implementation of research-based curricular units. (6) Assist TFs in their pursuit of National Board Certification by their fourth year of teaching. This project plans to implement an iterative evaluation strategy. Evaluation of the project will be guided by several evaluation questions focusing on recruitment, impact of preparation and induction support. The project aims to disseminate findings that may enhance knowledge and practice within 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

Coastal freshwater ecosystems are well-known for being biologically diverse and they provide important services to humans worldwide. With continued global warming, these coastal systems are at risk of undergoing dramatic environmental changes associated with rising seas. Future sea-level rise scenarios suggest either a gradual or a rapid upland migration of marine waters, yet the response of freshwater systems to these novel environmental conditions is unknown. Establishing an understanding of how ecosystems respond to marine water inundation is difficult to constrain using only modern observations. The low-lying freshwater ecosystem in eastern Guatemala, which is made up of two interconnected lakes and several important wetlands (known as the Izabal/Golfete system), has undergone two significant environmental changes during the recent past, one associated with a rapid and a second with a gradual inundation by marine waters. These two historical natural experiments provide an unparalleled opportunity to investigate how the Izabal/Golfete system responded to different degrees of environmental stress. This project will constrain these changes using sedimentological, geochemical, biological, and genetic methods. We aim to reveal how the environment and biota responded to these two scenarios of marine water inundation, providing crucial information to assess how this and other at-risk ecosystems will respond to future sea-level rise. We aim to provide essential data for managers and entities to safeguard these important biological hotspots, establish strong international relationships, and engage with local communities and governmental and educational institutions in the US and Guatemala. Future sea-level rise models suggest that marine flooding of coastal freshwater ecosystems will increase in frequency, yet the response of these biologically-diverse systems to different degrees of marine inundation is unknown. This project will use the Izabal/Golfete system, a freshwater ecosystem in eastern Guatemala, to assess how variations in marine inundation affected the environment and its functional diversity. Our study is therefore in an unrivaled position to make contributions to our understanding of how ecosystems function and respond to marine flooding events. We will do this by collecting sediment cores, surface sediment, fish, and water samples and generate high-resolution time series of environmental and biological changes using sedimentological, inorganic and organic geochemical, micropaleontological, and genetic data. The combination of datasets will allow us to model functional diversity through temporally different environmental stressors and transitions, allowing us to understand and forecast the response of freshwater ecosystems to marine inundation events. Finally, the highly integrative, multi-institution, and international nature of this project will be of significant benefit to the participating students, will allow us to establish several outreach programs in US and Guatemalan schools and museums, and will provide a foundation for understanding the impacts of potential change to the regional system in eastern Guatemala and other similar systems worldwide. 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

Error-controlled lossy compressors are widely used to manage the large amount of data produced by scientific applications. Still, they may produce undesired compression artifacts that distort both raw and post hoc data analytics. This project aims to bridge the gap by developing a novel learning-driven framework to mitigate artifacts produced by scientific lossy compressors. The success of this project is expected to improve the integrity and quality of lossy-compressed scientific data significantly, thus facilitating the use of existing lossy-compression frameworks for efficient data storage, transmission, and analytics in scientific applications. This contributes to scientific discoveries in a broad range of domains, including climatology, cosmology, fusion energy science, and X-ray ptychography, as well as multiple aspects of research and education in advanced cyberinfrastructure. This project addresses the artifact issue by leveraging recent scientific data compression and deep-learning advancements. In-depth investigations are conducted to generically characterize the compression artifacts produced by scientific compressors on both raw data and post-hoc analysis. This aims to improve the understanding of data quality and establish a benchmark for artifact mitigation. Next, deep learning models are designed to tackle artifact mitigation on both raw data and features of interest, with specifically designed transfer learning to reduce training costs. The quality of the recovered data is improved by fusing model outputs tailored to preserve different features. Finally, the quality of the recovered data is validated through tailored uncertainty quantifications, and the performance of the framework is investigated through careful optimization and parallelization. Integration into state-of-the-art error-controlled lossy compressors and incorporation with real-world scientific applications are expected to advance multiple scientific data management tasks, including data storage, I/O, and transmission. 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 goal of this project is to investigate the integration of computational thinking (CT) into elementary school curricula by studying how teachers develop expertise in integrating CT activities that align with interdisciplinary standards and existing curricula. This project addresses the critical need for science, technology, engineering, and mathematics (STEM) literacy by investigating effective models of professional development (PD) that improve STEM teaching and learning. Leveraging an asset-based approach, the project will provide opportunities to broaden participation in computer science education through building a community of practice for teachers and designing CT-infused curricula. To ensure that all teachers have the necessary support to participate in new pedagogical practices of increasing complexity, the project will utilize a collaborative teacher PD and mentorship structure in which teachers will work closely with peers and project team members to prepare, teach, and reflect upon lessons through multi-year participation. The research will document how teachers come to understand CT and take up these practices in their classrooms. The Discovery Research preK-12 program (DRK-12) seeks to significantly enhance the learning and teaching of STEM by preK-12 students and teachers, through research and development of innovative resources, models and tools. Projects in the DRK-12 program build on fundamental research in STEM education and prior research and development efforts that provide theoretical and empirical justification for proposed projects. Over four years, this project will directly impact 135 teachers in multi-year participation in professional development and approximately 12,000 students indirectly in their classrooms. This project will initially focus on schools in South Carolina, especially in rural districts. The project will be guided by three primary research questions: 1) How do preK-5 teachers integrate CT into their disciplinary teaching?; 2) What are the personal learning trajectories that teachers move through as they work towards CT integration?; 3) What are the barriers and accelerators for teachers’ implementation of CT-infused lessons? This project is grounded in the idea that incremental, sustainable, and substantial teacher learning comes when teachers are fully invested in the cycle of learning and have opportunities to build and strengthen their communities of practice. In reflective PD sessions held throughout the academic year and summer, teachers will explore, co-design, implement, and refine student learning activities that integrate CT into existing content area standards and curricula. The CT activities, which will include standards-based lesson plans, assessments, and supplemental resources, will offer both plugged and unplugged components, so that students and teachers have opportunities to understand the conceptual underpinnings of computational thinking with and without computers. A minimum of 120 lessons and related pedagogical materials mapped to core-content standards will be developed and made freely available to the public on the project website. Using a mixed methods approach, this study will document the trajectories through which elementary teachers move as they begin to integrate CT into disciplinary teaching, as well as barriers and accelerators faced as they integrate CT into disciplinary teaching. Quantitative survey methodologies and multimodal analysis of narrative interviews, classroom video, teacher reflections, and pedagogical artifacts will be used to analyze teachers’ sense making about CT over time and the uptake of new pedagogical approaches in their teaching. Ultimately, this study will produce a set of empirically-tested learning trajectories that can be used to support teachers in integrating CT into their classrooms. As a means of expanding the community of practice, sharing resources, and disseminating project findings to a nationwide audience, the project also incorporates a virtual PD component to support PD access to elementary school teachers from underrepresented and underserved schools across the United States. 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

Lake Tanganyika is renowned for its biodiversity, but the spectacular life in this vast and ancient ecosystem is threatened by warming temperatures in ways that are not well-understood. As one of sub-Saharan Africa’s most prolific inland fisheries, a healthy Lake Tanganyika is critically important to the nutrition of four developing nations. If global warming alters internal processes that affect the fish production in Lake Tanganyika, then the food security for millions of people will suffer. Moreover, the impacts of environmental change on the characteristics of different groups making up Lake Tanganyika’s open water and lake floor communities, as well as interactions among these groups, are unknown. This project aims to study the response of Lake Tanganyika’s food web to several different scenarios of climate change using sediments, fossils, and genetic tools. The results of the project will reveal how aquatic organisms, particularly economically valuable fish, respond to changes in temperature and precipitation within large tropical lakes. With this information, fisheries and ecosystem managers will be better equipped to safeguard food resources and biodiversity in their areas of responsibility. Finally, this project will include strong international partnership to train students, conduct workshops and develop materials for public audiences. This project will use Lake Tanganyika’s high-resolution sedimentary record to set up a series of historical experiments to track functional biodiversity lake-wide. This framework integrates geochemical, fossil, and genomic tools to assess open water and bottom-dwelling community structures and functions under different scenarios of climate change, as well as the physical and physiological responses of key organisms to these changes. Because the hydroclimatic conditions of the Holocene are underrepresented in historical data, this approach provides the opportunity to evaluate the consequences of environmental change for Lake Tanganyika’s food web in a way that was previously impossible to know. In addition, the project will identify shared and divergent responses to climatic fluctuations across the lake’s diverse fauna, and link these responses to trait-based understanding of community assembly and functioning. This work holds potential for predicting changes in biodiversity amidst severe climatic uncertainty in large tropical lakes. 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) is drastically changing how we approach problems and the ways we craft solutions. AI awareness and competency are critical for the next generation workforce to stay competitive in the global economy. Improved AI literacy also leads to more responsible use and adoption of AI in society. While many institutions have increased their offerings of AI and AI-related courses, it is a challenge to ensure these are scalable and relevant for students who have had limited exposure or experiences in AI. To enable all institutions to better serve all students interested in AI, this IUSE: CUE Pathways project will develop undergraduate AI curricula that is appealing and approachable for students with a range of experiences in and knowledge of AI and computing technologies. The project will also be tested at community colleges and 4-year institutions with a range of resources, personnel, and access to technology to ensure wide adoptability across institution types. To design AI curricula that can appeal to all students so that every student interested in AI can have an opportunity to take AI courses, the project identifies five principles in the design that allow for technical competency regardless of the student’s background in AI or computing in general. These five principles include a zero-background entry point, a non-programming option, a discipline-specific application, and an invitation to go deeper. The collaboration among four institutions of different types explores how to adapt programs to the needs of different institutions while still serving each institution’s student population. The project develops a comprehensive evaluation plan that can be used to evaluate these principles and the programs created using them. The project is expected to reveal new ways to provide effective AI education to the widest possible population of undergraduate students. 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

Ancient environmental DNA (aeDNA) is transforming the scientific study of past biodiversity dynamics and the effects of past climate change and intensifying human land use. aeDNA methods permit the detection of past species by recovering ancient fragments of DNA from sediments and matching these fragments to genetic libraries of known species. Because many species preserve poorly and are thus invisible to traditional paleontological approaches, aeDNA is enabling the study of past biodiversity dynamics at an unprecedented combination of taxonomic extent and resolution; in principle the entire tree of life can be studied. aeDNA so far has been at an early stage of research, focusing on methodological refinements and discoveries at individual sites. As the number of aeDNA research teams and records rapidly grows worldwide, the next-stage scientific opportunity is to integrate these many records and thereby study biodiversity responses to past environmental change at regional to global scales. Achieving this global synthesis requires building both the advanced data platforms that can support aeDNA data sharing and the community of experts who will provide and curate this data. This award will enable the next generation of global-scale biodiversity research at unprecedented taxonomic resolution, coverage, and temporal extent, powered by 1) the integration of aeDNA data into a linked open ecosystem of paleoecological and bioinformatic resources and 2) building a closely interlinked social infrastructure that ensures high data quality, social trust, and alignment of informatics development with scientific priorities. To achieve this data integration, the data schema, curatorial systems, and data-sharing systems of the Neotoma Paleoecology Database will be extended to support aeDNA data and metadata. Linking services will be built between Neotoma and standard bioinformatic resources (NCBI/EMBL, GBIF, ORCID), so that aeDNA-based taxonomic inferences are provenanced back to standard authorities and can be regenerated as reference databases improve. To build social infrastructure and a data governance system, the project establishes a Council of aeDNA Stewards and holds annual workshops to advance data governance and metadata norms. Multiple training workshops are held for early career aeDNA researchers and virtual workshops employ a platform-agnostic Docker-based system to minimize barriers to access. Protocols and recommendations will be published in Protocols.io and peer-reviewed journals. The project develops multiple venues for engaging with high-school, college-level, and graduate-level students interested in learning more about how aeDNA can be used to study life’s responses to past environmental change. 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

Batteries play a tremendous role in our society and economy. From portable electronics and sensors to hybrid and electric transportation to our nation’s electrical grid, batteries provide an important way to store energy across large differences in scale. However, there is continued need to build better, safer batteries so that they can store more energy, be charged and discharged more times, and be assembled with cheaper and more readily available components. Addressing these challenges starts with the materials inside of the battery. This project seeks to design new battery components that are made from earth abundant materials dissolved in water. These materials can be cheaper and safer than those found in conventional lithium-ion batteries, but they are not currently as powerful. Moreover, as these materials are made from mixtures of different chemicals at different concentrations, the space for design is tremendous. To aid in the search through this complex chemical space, this project will develop and deploy robotic experiments and machine learning models to rapidly vary component combinations, record their properties, and make predictions as to new systems to explore. Promising materials will be tested in batteries to evaluate how they perform. Through this project, researchers will be trained as new battery scientists who understand both the chemistry and engineering of emerging battery science, and they will learn how to develop and use artificial intelligence to expedite discovery. The results of this investigation will help guide efforts to enhance our nation’s energy economy and support energy security. Water-based battery chemistries offer abundant, non-toxic, and non-flammable solutions to energy storage challenges. The goal of this project is to design and analyze redox electrolytes with large concentrations of redox-active, earth-abundant, ligand-metal coordination complexes capable of storing multiple electron equivalents in the metal and surrounding ligand framework. The overarching hypothesis is that a framework for the co-design of redox electrolytes can be derived from systematic and concerted molecular synthesis and experimental characterization coupled with autonomous materials formulation, electrochemical characterization experiments, and development of aligned machine learning (ML) models. Solubility and stability will be regulated by the chemistry imbued to the metal complexes by sulfonation of the redox-active ligands and through judicious formulation of the aqueous electrolyte. Coupled with the human-centered electrolyte formulation and electrochemical characterization, autonomous formulation and electrochemical experiments and high-throughput computations will be implemented to expand the redox electrolyte chemical space being explored. A publicly accessible data infrastructure will be developed and released, and the data will be used to develop ML models to identify and optimize co-design principles for redox electrolytes. This project will also train professional scientists in a multidisciplinary project combining experimentation, computation, automation, data infrastructures, and artificial intelligence (AI), guiding our nation’s energy economy and supporting energy security. 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 project investigates the economic, social, and demographic processes that allowed the success of some cities while others in the immediate area declined. Processes that operate in ancient cities are not fundamentally different from those operating in modern cities. Therefore, findings have the potential to impact urban planning and issues affecting contemporary cities. Scholarship on urbanism suggests a number of mechanisms that boost the growth and sustainability of cities, including the creation of neighborhoods, the development of marketplaces, and enhanced efficiencies that come from increasing scale and higher settlement density. Archaeology provides valuable insights about the growth and success of cities because the actions of parts of the population whose lives are often not recorded in written histories are nevertheless preserved in the material record. Findings are disseminated to improve the public’s understanding of science and the scientific method. This archaeological site provides a unique opportunity to address several major questions regarding urbanism and economics. Settlement scaling theory proposes that increasing density transforms cities into social reactors that enhance productivity. Thus, household economic indicators coupled with demography may also illuminate urban success. If urban growth rates are fast enough to indicate migration into the city, the degree to which newcomers assimilated or maintained distinctiveness impacts urban viability. The project uses lidar, a form of aerial laser scanning that sees through dense tropical vegetation and reveals thousands of ancient houses, to create the first systematic and detailed map of the residential areas of the city. Excavations in a representative sample of households provide the first robust understanding of how regional and domestic economies intersected. 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 impact and severity of individual weather events can be shaped at the neighborhood scale by unique local environmental features such as buildings, tree cover, pavement or nearby bodies of water. These features influence temperature, humidity and wind, potentially amplifying weather effects and leading to localized extremes like strong winds, elevated surface temperatures, and poor air quality. This Leading Engineering for America's Prosperity, Health, and Infrastructure (LEAP-HI) award supports research investigating development of a new system that combines atmospheric measurements and simulation to deliver accurate and actionable weather forecasts at the neighborhood scale. The system looks to be designed to improve routine predictions by accounting for fine-scale environmental effects that are often unresolved in current weather forecasting models. Central to the measurement system are uncrewed aircraft systems (UAS), which offer a proven advantage in high-resolution sensing of atmospheric conditions. The UAS-based observations look to feed into a high-resolution nested numerical weather prediction model enhanced with model adaptation and machine learning. This approach should allow the model to continually adjust and minimize prediction errors, resulting in more accurate, fine-grained forecasts of localized weather variability. The project brings together a multidisciplinary team with expertise in fluid dynamics, computational science, machine learning, atmospheric science, microscale modeling and autonomous UAS operations. Once fully developed, the integrated system intends to equip decision-makers and emergency responders with neighborhood-level weather insights to better prepare for and respond to extreme events. The project emphasizes stakeholder engagement, workforce development, and engineering education and outreach to help train the next generation of engineers equipped to address climate-related challenges. This research seeks to address key challenges in producing accurate operational forecasts at micrometeorological scales. Specifically, inaccuracies in the boundary conditions that drive microscale predictions can lead to significant errors in simulating flow fields and turbulence in the lower atmosphere. To mitigate this, the project looks to initialize the microscale model with a high-resolution parameter map of boundary conditions constructed using offline machine learning. The initial focus will be on estimating surface roughness length scales (which governs surface fluxes) and inflow conditioning parameters (such as perturbation scale and magnitudes). These parameter maps will include localization functions that are adapted via retrospective cost adaptation using UAS measurements to optimize agreement between model predictions and physical observations. Since these maps and localization functions are expected to slowly vary over time, the system intends to reduce reliance on continuous UAS measurements. Anticipated scientific contributions include: (i) a comprehensive computational framework for high-fidelity microscale forecasts; (ii) a novel methodology for integrating UAS observations into microscale atmospheric models; and (iii) innovative use of machine learning to improve the representation of boundary conditions that drive microscale dynamics. 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 Major Research Instrumentation (MRI) award supports the acquisition of a modular servohydraulic actuator system at the University of Kentucky, enabling controlled static and dynamic testing of structural components for transformative, cross-disciplinary research. The system’s versatility opens new pathways for experimental investigations into seismic simulation, impact loading, and full-scale testing of engineered wood products crafted from sustainably harvested hardwood species native to the region. Spanning three colleges and six departments, this initiative will catalyze research in infrastructure resilience, advanced structural design, and sustainable materials - bridging Engineering, Architecture, and Agriculture. The platform seeks to enhance additive manufacturing research at the component level and provide experiential learning opportunities for undergraduate and graduate students, strengthening the pipeline for innovation in construction technologies. The system will be a shared resource, accessible to academic and industry collaborators across the region and nationally. The actuator system comprises three independently operated servohydraulic actuators with flexible orientation options, enabling a range of test configurations. Its integrated data acquisition system supports 64 channels, allowing researchers to simultaneously monitor multiple sensors across varied experimental setups. Research facilitated by this system will include studies of strengthening techniques for concrete and steel beams, such as Carbon Fiber Reinforced Polymer (CFRP) rod panels and Near Surface Mounted (NSM) Fiber Reinforced Polymer (FRP) strips and rods as well as performance evaluations of hybrid materials like Ultra-High Performance Concrete (UHPC) combined with conventional concrete. The system will also support testing of 3D-printed concrete elements, Large-Scale Additively Manufactured (LSAM) components, Expanded Polystyrene (EPS) fill materials, and Cross Laminated Timber (CLT) panels developed from sustainably sourced hardwoods. This instrument will significantly expand experimental capabilities and foster innovation in resilient infrastructure and sustainable material 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 2025 · 2025-09

Bovine Respiratory Disease (BRD) is an infection of the respiratory tract in cattle that compromises the welfare of the calf. It is estimated that BRD costs the industry $800-$900 million dollars a year. Precision technologies have the potential to monitor calves’ behavioral information to help detect diseases, such as BRD. However, the science of inferring BRD using these technologies is just at its infancy. Existing works often rely on expensive precision technologies, preventing widespread adoption. Furthermore, these approaches adopt simplistic inference solutions, are trained and tested on individual farms, and cannot provide explainability of model predictions. To address this gap, this project develops CalfHealth, a comprehensive framework that adopts innovative sensing technologies to enable the cost-effective and explainable detection of BRD in dairy calves. This can have profound implications for improving the profitability of farmers and calf welfare. In addition, this project will have a significant impact on the community through innovative education and outreach activities. These include: (i) field experiments and participatory workshops with relevant stakeholders, including farmers, veterinarians, companies, and consumers; (ii) interdisciplinary research experience for undergraduate and graduate students; (iii) wide dissemination of the project outcomes through high-quality publications; and (iv) demonstrations to future students at the E-Day of the College of Engineering of the University of Kentucky. CalfHealth is based on a novel multimodal learning framework that exploits accelerometer sensors to model calves' behavior using a fine-grained attention mechanism and fuses it with data regarding respiration rate, acquired by a Wi-Fi sensing system, through cross-attention mechanisms. To effectively adapt the detection framework to diverse farms and environmental conditions, the project adopts zero-shot and few-shot active learning approaches. Furthermore, CalfHealth enhances explainability, interaction with technology, and the ability to explore what-if scenarios. To this purpose, CalfHealth exploits language models combined with a feature attribution approach to develop an interactive chatbot for farmers. This project also accelerates the adoption of CalfHealth by using state-of-the-art economic experiments and qualitative methods to assess the behavioral and technological factors influencing farmers’ acceptance of precision technologies aimed at detecting BRD in calves. Additionally, comprehensive behavioral interventions are tested to enhance the farmer-chatbot interaction and increase farmers’ trust in CalfHealth. Finally, extensive validation on several farms is performed, including closing the loop by testing the benefit of early intervention for cattle identified by CalfHealth. 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 proposal aims to create a safe and reliable way for a person and a robot to work together. It will focus on robotic teleoperation in harsh conditions. While a human can guide robots in challenging situations, autonomous motion planning can handle both obstacle avoidance and fault tolerance. Having human-robot shared control in place, can make humans and robots work together more effectively. This research can improve space exploration, ocean exploration, and the nuclear industry. The educational goal is to boost STEM participation. We will do this through mentorship and role models. To achieve this aim, we will offer well-designed activities for all student levels. This includes research opportunities, K–12 outreach programs, and workshops. Undergraduate and graduate students involved will receive special training and technical support. They will contribute to the national workforce in robotics engineering. This project proposes three research activities. (1) Create a new teleoperation method for humanoid and non-humanoid robotic arms. This method will help these robots mimic human arm motions in real-time. (2) Design three new autonomous motion planning algorithms. These algorithms help avoid obstacles and ensure fault tolerance. They do this by predicting possible joint failures and reacting to real failures. (3) Build a shared control framework. It will blend the motion from the human operator with the autonomous planner. We will use a weighted arbitration system. This system considers prediction uncertainties and the operator's preferences. Furthermore, this project proposes four educational activities. (1) The PI will team up with two local high schools. We will provide demos, robotics talks, and research opportunities. Also, these two high schools encourage students to do lab research at a university. (2) The PI will provide research opportunities for undergraduate students through an Undergraduate Research Fellowship (URF) program. (3) The PI will integrate research activities into the Introduction to Robotics course and develop new computer projects based on this research. (4) The PI will work with the IEEE Robotics and Automation Society to organize workshops and give guidance in robotics research for master's and Ph.D. students. 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 project develops a method, based on cryptographic protocols, that can be used in emerging radio spectrum markets so Federal agencies can acquire spectrum quickly when needed without disclosing information that may compromise their missions. Radio spectrum is a vital resource for wireless communication and sensing. Market approaches such as the Pay-As-You-Go (PAYG) model and the Spectrum Bux currency have been proposed to more efficiently use the congested radio spectrum. Since Federal agencies are a significant fraction of the spectrum ecosystem, Federal agency use of these emerging market mechanisms is necessary for the market mechanisms to provide the desired overall benefits for the nation. However, Federal agency missions sometimes require accessing spectrum without delay; most market designs require pre-registration and other slow steps. Agency missions sometimes require protecting information about sensitive operations; most market designs disclose the identity of the buyer to the seller so the seller can enforce payment terms. The cryptographic methods developed in this project overcome these constraints and thereby help emerging radio spectrum markets succeed. The architecture supports policy adaptation and microeconomic experiments to inform future spectrum policy and market design decisions. The project also helps educate the next-generation spectrum workforce. The core of the project is development of novel protocols based on cryptographic credentials and zero-knowledge proof (ZKP) technologies. The research effort has three primary thrusts. Thrust one creates a ZKP-capable spectrum credential system to enable efficient attribute-based authentication for spectrum access. This allows users with valid spectrum credentials to request access without prior registration or disclosure of sensitive identifiers. Thrust two develops a secure and auditable Spectrum Bux payment system in the PAYG model. The payment system enables asynchronous settlement that guarantees the band manager receives payment after a user successfully obtains a spectrum access assignment, without disclosing private details about the user. Thrust three develops a simulator for studying the interactions between markets using the new credential and payment systems, user and agency pricing strategies, service-level agreements and other contract types, and Federal spectrum policy choices such as rules for protecting spectrum incumbents. The simulator is used for microeconomic experiments under diverse scenarios, ultimately identifying optimal policy and pricing strategies for specific spectrum-sharing contexts. The outcomes of this research will be made publicly available online, including publications, tutorials, and open-source 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 · 2025-09

The simplest atoms, those composed of only two elementary particles, are optimal systems for testing fundamental theories of physics in high precision experiments because they avoid additional complications which arise in three-, four- or many-body systems. Testing laws of nature with these “simplest atoms” requires making experimental measurements of observable properties with a high level of precision, followed by detailed comparison with correspondingly exhaustive calculations using fundamental theoretical principles. This project addresses several theoretical challenges in this field, including calculations of the precise energies of stationary states of muonium and positronium as well as calculations of various mechanical properties of loosely bound states in quantum electrodynamics. Results will be useful in the search for new physics beyond the Standard Model as well as for comparisons with processes in nuclear and particle physics, including the physics of heavy charmonia and pentaquarks. The results of this research also will be used in teaching graduate courses. The project will develop methods for calculation of high order three-loop spin-independent corrections to energy levels of muonium and positronium in high precision quantum electrodynamics. With the help of these methods, all hard three-loop spin-independent corrections to the energy levels in muonium and positronium will be calculated. A second goal of the project is the calculation of the two-loop electron mass as a matrix element of the trace of the energy-momentum tensor (including both the anomalous and non-anomalous terms). A third focus of the project is research on the properties of one-loop matrix elements of the energy-momentum tensor trace in loosely bound multiscale states, like those in muonic hydrogen. Research on the properties of the energy-momentum tensor of bound states has the potential to provide a new perspective on the calculation of radiative corrections to the energy levels of hydrogen, muonium and positronium. 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

Water infrastructure in the U.S. faces many challenges for long-term sustainability. In Kentucky, particularly in the Appalachian region, water distribution systems, which transport water from the point of treatment to the point of consumption, are unique and complex. The various challenges include topography of the region, insufficient revenue from a declining customer base, and decreasing numbers of licensed operators – the people who work daily to make sure water safely gets to customers. Collectively, these deficiencies have led to unsustainable water infrastructure systems, both in terms of the physical infrastructure and the workforce required to operate it. This project seeks to advance the sustainability of physical water workforce pipelines while strengthening new and existing pipelines for individuals to join the water workforce. This work will identify what drives the sustainability of water distribution systems while simultaneously engaging the workforce who could use this information for decision-making. This work is timely due to unprecedented investments in U.S. infrastructure, which should be capitalized on by prioritizing sustainability to ensure long-term benefits for the physical infrastructure and the workforce needed to design, operate, and manage it. This project will develop sustainable pipelines – advancing U.S. water infrastructure sustainability by (1) integrating sustainability assessments into water distribution system planning and management and (2) preparing a sustainability-minded workforce for the nation’s water infrastructure. The first goal – to integrate sustainability assessments into water distribution system planning and management – will be addressed by developing life cycle environmental and economic models for water distribution systems and leveraging Kentucky Infrastructure Authority’s existing datasets to evaluate hundreds of systems, identify sustainability drivers, and develop a state-level screening assessment for informed decision-making. The second goal – to prepare a sustainability-minded workforce for the nation’s water infrastructure – will take a multi-level approach to water workforce development by engaging new entrants to the workforce through partnerships with prisons in Kentucky and training the current and future workforce in sustainability thinking. This project will advance fundamental understanding of drivers of sustainability for water distribution systems while addressing unique challenges for water distribution systems in Kentucky. Ultimately, this work can increase U.S. economic competitiveness by providing a path to consider long-term sustainability when investing the billions of dollars planned for drinking water infrastructure. This project is jointly funded by the ENG/CBET Environmental Sustainability program 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.