University of Virginia Main Campus

NSF Awards · FY 2025 · 2025-06

With the support of the Chemical Catalysis Program and the Chemical Mechanism, Function, and Properties Program of the Division of Chemistry, Professor T. Brent Gunnoe of the Chemistry Department at the University of Virginia is studying the development of new catalysts for hydrocarbon functionalization, which enables conversion of chemicals derived from natural gas and petroleum into higher value products. Current methods for hydrocarbon functionalization typically involve multi-step and energy-intensive processes. New catalysts that provide more direct and efficient routes for hydrocarbon functionalization can advance the chemical and energy sectors. While there are opportunities to increase energy efficiency of large-scale chemical processes, there are also substantial scientific challenges. In this project, Professor Gunnoe's group will develop new understanding of how to perform selective and energy-efficient catalytic chemical transformations that are essential to the goal of using natural gas and other fossil resources in a more energy-efficient manner. Catalytic small molecule functionalization using molecular transition metal complexes often involves catalytic cycles that feature changes in the formal oxidation state of the metal. It is common that proposed catalytic cycles involve a transition metal catalyst that mediates a series of bond-breaking and bond-forming steps while cycling through at least two different redox states. Hence, kinetic access to multiple oxidation states is critical to rapid catalysis. However, accessing multiple formal oxidation states can be particularly problematic for catalytic hydrocarbon functionalization reactions using low valent metals for C–H bond-breaking under conditions that include an oxidant, because the presence of an oxidant can lead to formation of more stable, higher valent redox state(s). The research effort will focus on three objectives. Objective 1. Understand the impact of capping arene ligands on hydrocarbon activation and functionalization reactions that involve redox changes at the transition metal. Objective 2. Understand and develop hydrocarbon activation and functionalization with bifunctional Cu complexes. Objective 3. Understand and develop new small molecule activation with bifunctional second and third row transition metal complexes. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2025 · 2025-06

As robots become increasingly integrated into society, enabling rich, adaptive social interactions through physical actions is essential. Despite significant advances in human-robot interaction (HRI), current computational models are designed for specific types of interactions, making it difficult to generalize across different social and task-based scenarios. Research funded by this Faculty Early Career Development (CAREER) award attempts to address this limitation by incorporating shared representations and Theory of Mind (ToM) reasoning into robotic decision frameworks, enhancing their ability to interpret, predict, and respond to humans in a wide-range of physical-social interactions. This project looks to make fundamental contributions to HRI with potentially important applications to autonomous driving, healthcare, service, and home assistance applications. This research aims to develop new computational frameworks that support a broad range of cooperative and non-cooperative physical interactions by integrating three key components: 1. Shared Representations for Physical Social Interaction – modeled as a symbol-grounding process, optimizing motion prediction and inverse planning to connect human actions and intentions to the robot’s understanding of the scene. 2. Higher-Order ToM Reasoning – enabling robots to estimate human intentions at multiple cognitive levels, dynamically adapting to real-time task and motion changes. 3. Scalable Evaluation Framework – automating large-scale testing of novel interactions in simulation, followed by deployment on physical robots for real-world validation in household tasks. This project looks to also advance robotic social intelligence in AI education. It will develop open-source libraries and course materials that integrate psychological principles (e.g., ToM and social perception) into undergraduate AI and graduate robotics curricula and research mentorship. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2025 · 2025-06

This project uses long-term data to predict how coastlines will change in response to environmental change. Nearly half the world’s population lives on the coast. Even more humans depend on coastal resources. These resources are threatened by sea-level rise, warming air and water temperatures, and changes in the frequency and strength of storms and rainfall. The best way to understand how and why coasts are changing in response to these threats is to use long-term ecological data. This project will use long-term experiments and models to predict how future environments will affect coasts and their resources. The research will be done at the Virginia Coast Reserve Long-Term Ecological Research (VCR LTER) site. The results will benefit society by improving coastal management that will protect shorelines, increase biodiversity, and produce seafood. VCR LTER research builds on prior research and addresses three themes that test fundamental theories of ecosystem state change and spatial ecology: 1) mechanisms and consequences of state change within ecosystems; 2) connectivity and coupled dynamics between ecosystems; and 3) landscape-scale dynamics of ecosystem function, synchrony, and stability. VCR LTER research will identify biophysical feedbacks that either maintain or facilitate transitions in ecosystem states--including disturbance, recovery and restoration--and threshold responses to environmental drivers. The project will develop mechanistic models, calibrated and validated with short- and long-term data, and will use these to project state change. Additionally, the consequences of state change and cross-scale interactions for ecosystems attributes will be investigated, focusing on biodiversity, organic matter, nutrient cycling, and carbon sequestration. Collectively, this research increases the current and predictive understanding of how coastal ecosystems and their functions respond to environmental drivers. Cross-site and synthesis studies enhance the impact of the site-based research and contribute to broader knowledge on ecosystem transitions in response to long-term trends and variation in the environment. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2025 · 2025-05

This three-year REU Site: Multi-Scale Systems Bioengineering and Biomedical Data Sciences focuses on combining experimentation with computational analyses to discover the mechanisms of disease and identify new treatments and cures. The number of Americans with cardiovascular disease, obesity, diabetes, and cancer is increasing because these diseases are complex and affect each person differently based on their genetics, lifestyle, environment, and access to healthcare. Identifying effective therapies requires a workforce of biomedical researchers able to deploy state-of-the-art experimental protocols in wet-lab environments and analyze the resulting data sets using sophisticated algorithms and computational models. Ten undergraduates each summer will be recruited to participate in state-of-the-art systems bioengineering and data science research. REU students will engage in research projects about new biological mechanisms of disease and novel therapies. By equipping students with the skills for conducting and combining wet-lab research with computational analyses, this REU Site will generate alumni who are prepared for further graduate education and/or successful careers in biomedical industries. The goal of this REU Site is to recruit and educate young scientists from a variety of STEM backgrounds, and provide them with the skills, confidence, and mentorship necessary for successful careers in systems bioengineering and biomedical data science. Participants will engage in hands-on collaborative and immersive experimentation and computational analyses, working closely with their faculty and graduate student mentors. Professional development sessions include career advising, training in research ethics and scientific communication, and ongoing mentorship after completion of the summer program. Advances in the biomedical sciences and medicine will increasingly depend upon the application of rigorous and quantitative engineering-based approaches to characterize and interrogate biological systems and the resulting “big data” and a workforce that can successfully address these challenges. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2025 · 2025-05

1. Non-Technical Summary A bottlebrush polymer consists of a long linear backbone densely grafted with many relatively short linear side chains. Recent advances have shown that mechanical, physical, and biochemical features can be independently encoded into the molecular architecture of bottlebrush polymers. Nevertheless, as for any polymers, it is of foundational importance to understand the molecular structure of bottlebrush polymers. A widely accepted view is that strong steric repulsion among highly overlapped side chains pre-strains the bottlebrush backbone. However, recent breakthroughs suggest that for a bottlebrush polymer with highly incompatible backbone and side chains, the backbone does not have to be pre-strained; instead, it can fold into a cylindrical core with all grafting sites on its surface, thereby reducing interfacial free energy. This so-called foldable bottlebrush polymer provides a new building block for the development of polymer-based soft (bio)materials. This project seeks to establish the deterministic relationships between molecular architecture, mesoscopic conformation, and macroscopic properties of foldable bottlebrush polymers and networks, as well as to demonstrate the application of foldable bottlebrush polymers as a new class of resins for multi-material additive manufacturing. The resulting knowledge, materials, methods, and tools will positively impact polymer science and engineering, drug delivery, tissue engineering, soft robotics, and advanced manufacturing. By providing opportunities for interdisciplinary research and organizing local scientific activities, this program will train students from diverse backgrounds to be next-generation scientific leaders in polymers and soft materials. Through outreach interactions with local high schools, the PI will leverage additive manufacturing demonstrations to attract students to STEM fields. 2. Technical Summary The research goal of this project is to establish the foundational science of foldable bottlebrush polymers and networks. Guided by a new molecular theory, foldable bottlebrush polymers with prescribed molecular architecture parameters will be designed, synthesized, and characterized to elucidate their deterministic molecular architecture-mesoscopic structure-nonlinear mechanical property relationships. Based on the understanding of individual foldable bottlebrush polymers, a constitutive law will be developed to describe the remarkable nonlinear stress-strain behavior of foldable bottlebrush polymer networks. Finally, the foldable bottlebrush concept will be exploited to develop photocurable resins of dramatically different mechanical properties but crosslinked by the same chemistry. Using a customized additive manufacturing platform, these resins will be transformed into functional multi-material three-dimensional structures. These research activities will develop new knowledge for designing, methods for creating, and technologies for processing foldable bottlebrush polymers and networks. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2025 · 2025-05

Riverside Geometric Group Theory Workshop (RivGGT) 2025 will be held on May 9–12, 2025 at the University of Virginia in Charlottesville, VA. This conference will focus on emerging research in geometric group theory, with a special emphasis on training early-career researchers and graduate students. It builds on the success of the 2023 and 2024 workshops, offering an in-depth, expository format through a series of lectures that explore cutting-edge topics in geometric group theory. Geometric group theory is a dynamic area of mathematics, contributing to the understanding of various mathematical structures by studying groups through their geometric properties. The RivGGT 2025 conference provides a platform for young researchers, especially graduate students, to engage with these topics in a deeper and more substantial way. The format includes three series of four lectures by early-career researchers, each focused on a major area of recent breakthroughs. This conference will significantly contribute to professional development by providing participants with comprehensive introductions to active areas of research, making the content accessible to a broad audience. It also fosters collaboration and mentorship, aligning with the National Science Foundation’s mission to promote the progress of science and advance national welfare by investing in future generations of scientists. The conference will center on recent advances in geometric group theory, with lectures from Harrison Bray, Dídac Martínez-Granado, and Jenya Sapir. The focus will be on geometric and dynamical techniques applied to areas such as hyperbolic groups, Teichmüller spaces, and geodesic currents. For instance, Sapir’s work will cover the behavior of closed geodesics on surfaces and its connections to counting problems in hyperbolic geometry. Bray will explore geometric structures on manifolds through dynamical and analytic methods, while Martínez-Granado will present generalizations of geodesic currents to hyperbolic groups acting on R-trees. Graduate student talks will introduce these minicourses, and there will be ample opportunities for participants to share their ideas and foster collaborations. Proceedings from the conference will be published. Webpage: https://sites.google.com/view/rivggt25/home This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2025 · 2025-03

Studies of early monumental architecture have traditionally focused on the scale of buildings and their final configuration, often employing a quantitative perspective. This doctoral dissertation project integrates additional analytical frameworks to assess the emergence of monumental works as a process that articulates different levels of analysis over time. The research sheds new light on one of the most critical periods in history when large, monumental constructions flourished before the rise of states, offering a new case study on how societies achieved monumentality as a process unrelated to top-down state-sponsored works. The project provides training for a graduate student in scientific methods of data collection and analysis, and builds capacity for future scientific research. This doctoral dissertation project evaluates the emergence of monumental architecture, the associated social practices, and the role that competition and corporate organizations played during the construction of ritual spaces. This period saw the regional rise of massive pyramidal complexes with an enigmatic U-shaped architectural pattern. Using archaeology and geospatial analysis, a monumental U-shaped ritual complex is studied to understand its architectural design, spatial organization, and construction practices over time. Four questions guide this research: (1) What types of activities took place in the different buildings, and how did different social groups participate in them? (2) how did the structures change over time? (3) what role did the structures play in existing interregional interaction and exchange spheres? and (4) on a regional scale, how does the distribution of U-shaped structures reveal patterns of societal organization? To address these questions, the research conducts excavations in different site areas, analyzes the different cultural assemblages, examines the changes in the architecture and constructive techniques, and compiles a geospatial database of other U-shaped structures. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2025 · 2025-03

NON-TECHNICAL SUMMARY This CAREER award supports computational and theoretical research aimed at designing peptide-functionalized surfaces with tunable nano-scale patterns. Controlling how surfaces interact with water is critical for engineering many useful properties. For example, modulating surface stickiness plays an important role in designing surfaces to serve as artificial tissue scaffolds, inducing water evaporation is valuable for manufacturing electronic components, and controlling selective surface interactions is important purifying drugs. A powerful strategy for altering surface properties is attaching molecules with desirable properties to the surface. A major challenge with designing such molecules, however, is that they often change their behavior when tethered to a surface. The goal of this project is to understand how molecules change their behaviors when tethered to a surface, and how these molecular behaviors control how the surface interacts with water. By coupling molecular dynamics simulations with deep learning models, this project will explore these challenges both by asking fundamental scientific questions about molecular interactions and by developing a rapid predictive tool aimed at quantitatively predicting surface-water interactions. The fundamental understanding and predictive tools gained from this project will enable the design of new surfaces for applications ranging from semiconductor manufacturing to water desalination. Alongside these scientific goals, the PI will implement an education plan aimed at giving undergraduate students cross-disciplinary training in both molecular and scientific skillsets. Specifically, a series of application-oriented case studies will be developed aimed at helping students practice computational skills in scientific and engineering classes. These case studies will be designed based on research from the above scientific project as well as collaborations with industrial and academic experts. Additionally, the PI will create free, publicly available educational materials to help other educators adapt case studies to be implemented across diverse classroom settings. These case studies will serve as a valuable tool for developing undergraduate students with strong computational and scientific skills. TECHNICAL SUMMARY This CAREER award supports theoretical/computational studies of peptide-functionalized surfaces with tunable nano-scale patterns. Surface functionalization is a powerful way to control hydrophobicity, and selective adsorption and plays a central role in many engineering applications such as protein purification, tissue culture, drug delivery, semiconductor manufacturing, and water desalination. Peptides offer a promising strategy for modulating surface properties because they are chemically diverse and straightforward to synthesize on solid supports. Previous research has shown that combinations of weak, noncovalent interactions between neighboring peptide-derived ligands on a surface can lead to the formation of ordered, nanoscale patterns, whose properties can be controlled through small changes to ligand structure. In this way, peptide surface functionalization presents a promising opportunity for designing nanoscale, patterned surfaces with tunable interfacial properties. This project focuses on developing the fundamental understanding and deep learning tools required to engineer peptide-functionalized surfaces with desirable properties. In Aim 1, a hypothesis-driven study of a library of peptide-functionalized surfaces will be performed to connect peptide characteristics with surface patterns. In Aim 2, a simulation study will be conducted to elucidate how surface patterns control hydrophobicity, focusing on patterns that combine charge, hydrophobicity, and uncharged hydrophilic groups. While these methods will establish concrete molecular design rules for peptide-functionalized surfaces, they are too computationally expensive to be used to explore surface design space. Therefore, in Aim 3, a deep learning model will be developed for rapidly predicting the hydrophobicity of functionalized surfaces to enable the design of new surfaces with hydrophobicity as a design objective. In parallel, the PI will pursue a synergistic education plan centered on repurposing our research efforts to give undergraduate students cross-disciplinary training in the computational and molecular sciences. Specifically, a repository of educational case studies will be developed that showcases how computational techniques can be used to solve molecular problems based on research from the PI’s lab, academic collaborators, and industrial collaborators. These case studies will be made publicly available and easy to implement in a variety of settings by creating thorough documentation, testing the materials with other instructors, and disseminating the materials using established education networks. The PI will additionally develop an annual summer research program to enable one undergraduate student to perform a subset of the proposed research plan using the skills developed through these case studies. STATEMENT OF MERIT REVIEW 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 · 2025-02

Cloud systems are the backbone of modern societies. Ensuring their robustness is paramount. Despite significant efforts to improve cloud availability, today cloud systems become increasingly inadequate at managing emerging failure modes. One example is silent semantic failures, which violate system semantics without generating error signals. The current practice relies on statistical methods to analyze system metrics (e.g., logs, resource usage, I/O), often leaving silent semantic failures undetected and causing huge damage. This gap calls for a paradigm shift from merely monitoring metrics to verifying execution during failures at runtime. However, state-of-the-art runtime checking solutions require high manual effort for writing semantic checkers, a time-consuming and error-prone process even for small programs. Consequently, few of these solutions were actually adopted in production systems. This absence leads to jeopardized stability of critical infrastructure and significant economic losses. The overall objective in this proposal is to develop a framework which provides runtime assurance for detecting, diagnosing, mitigating and preventing cloud failures with low developer effort. The central hypothesis is that the bottleneck of manual effort arises from isolation between runtime checkers and other system components. The project’s novelties are leveraging automated reasoning to systematically extract insights from existing system resources, elevating checker construction from isolated and labor-intensive work to continuous and integrated activities throughout cloud development. The project's broader significance and importance are in its potential to greatly reduce the impact of cloud failures, minimizing financial losses and enhancing service availability to a new standard. Leveraging the PI’s prior experience, the project will deploy and validate the developed techniques in collaboration with partners including Microsoft and Amazon. The specific aims of this research include synergistic thrusts for handling cloud failures end-to-end. First, timely detection is the first step in dealing with failures; thus, the project synthesizes semantic checkers from test cases for detecting ongoing silent failures. Since the failure root cause often hides in numerous services, the project models system dynamics and helps developers verify their failure hypotheses. This project further proposes a systematic approach to mitigate failures without side effects on production services. It uses a novel dry-run execution on transformed system codes to verify the consequences of failure mitigation which reduces action risks. Lastly, the project deploys prevention mechanisms that verify system execution against triggering conditions of past failures to avoid recurrence of similar incidents. 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 · 2025-02

Protein-based therapeutics have revolutionized the treatment of diseases with historically poor prognoses, including many oncological, neurological, and infectious conditions. These advanced therapeutics are produced in engineered cells. However, the cells also produce unwanted impurities alongside the therapeutic, including infectious viruses, unwanted proteins and DNA, and misformed products. These impurities must be removed to ensure patient safety and treatment efficacy. Most current separation methods are not designed to purify these emerging therapeutics, creating bottlenecks in drug discovery and manufacturing pipelines. More effective separation methods could accelerate development and accessibility of these medicines and reduce costs for patients and pharmaceutical companies. This project will design innovative, biology-inspired adsorptive separation materials using peptides, a type of biopolymer, to purify new medicines from contaminants. Knowledge gained about the materials will be used to build an engineering toolbox to optimize the design of highly efficient separation materials and predict their performance in manufacturing. The project will also help train a domestic workforce equipped to tackle the challenges associated with manufacturing these novel therapeutics. An annual summer workshop for engineering students will be established, offering hands-on training in biomanufacturing processes and cutting-edge modeling tools to facilitate rapid and reliable design of these critical systems. This project will leverage rationally designed short peptides to advance the molecular understanding of protein adsorption onto functionalized surfaces and establish design rules linking the chemistry and architecture of mixed-mode peptide ligands featuring synergistic interaction modes to protein adsorption behaviors. Using these materials, the investigator will determine how factors such as grafting density, ligand flexibility, ion type, and the spatial arrangement of charge and hydrophobic chemical groups influence protein adsorption in chromatographic systems and other functionalized surfaces. Furthermore, this project will quantitatively examine how these ligand properties dictate which protein surface characteristics govern interactions in non-specific adsorption systems. Taken together, these insights will enable the identification and synthesis of a small set of orthogonally selective mixed-mode chromatographic resins capable of efficiently purifying a wide range of protein therapeutic modalities. Additionally, this project will establish a predictive process design tool by creating a new model for studying and tracking individual host cell protein transport and adsorption in these materials, enabling full in silico design and optimization of separation processes for new therapeutics without extensive model calibration. Beyond its implications for chromatographic separations and streamlining biomanufacturing workflows, this work will enhance the understanding of mixed-mode surfaces in broader applications, including drug delivery, biosensing, and biomaterials engineering. Further, this research program will establish a summer workshop series, Chromatographic Approaches for Manufacturing Protein Biologics (CAMPBio), designed for undergraduate engineering students. This annual, week-long program will combine lab and classroom-based learning to teach the theory and hands-on application of modeling and process development for non-traditional protein therapeutics, developing a workforce equipped to solve the manufacturing challenges associated with widening therapeutics pipelines. The data and models developed through this research and CAMPBio will be leveraged in interactive projects in chemical engineering courses at the University of Virginia, exposing students to manufacturing processes for new therapeutic modalities. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2025 · 2025-01

Large-scale high-throughput prevalence and diagnostic testing is essential for the containment and mitigation of pandemics. The testing bottleneck in the COVID-19 pandemic has led to a resurgence of interest in group testing, where several people's biological samples are mixed together and examined in a single test. When the rate of infection in the population is low, this method can significantly reduce the total number of tests per subject and increase the throughput of the existing testing infrastructure. However, traditional group testing has the following limitations: First, efficient group testing based methods for the estimation of prevalence have been largely overlooked in the literature. Second, traditional group testing usually assumes that the testing results are qualitative (positive versus negative), not quantitative (providing viral load information). Third, the theoretical study of group testing rarely takes practical constraints, such as the sensitivity of the pooled tests and the dilution effect, into consideration, which hinders the applicability of the testing schemes in practice. The goal of this project is to overcome these limitations of traditional group testing and design advanced pooled testing strategies for efficient prevalence tracking and accurate infection diagnosis. It will develop optimized pooled testing strategies with strong theoretical performance guarantees yet feasible and cost-effective in practice. The proposed research is organized in three research thrusts as follows. Thrust 1 aims to design effective sampling and testing algorithms to estimate the prevalence in communities and track its evolution, under scarce testing resource constraints. Thrust 2 focuses on the design of optimized pooling and decoding algorithms for compressed sensing based (COVID-19) virus diagnostic testing. Thrust 3 validates the accuracy and efficiency of the proposed pooled testing through experiments on anonymized COVID-19 samples. This project bridges group testing and online learning, the two largely disconnected areas, with the objective to effectively allocate limited testing resources for efficient prevalence tracking. Such integration leads to novel sampling strategies, broadens the paradigm of group testing, and advances the state of the art of online learning. Moreover, the proposed compressed sensing based diagnostic testing leverages quantitative measurements provided by advanced testing technologies, which can significantly increase test throughput, reduce the number of needed tests, decrease the consumption of scarce reagents, and provide results robust against observation noises and outliers. The rich compressed sensing theory provides possible approaches to the rigorous mathematical certification of the correctness of the decoded results. Besides, the clinical constraints on pooled testing also lead to novel problem formulation and theoretical characterization, enriching the study of compressed sensing. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2025 · 2025-01

As we begin to explore novel paradigms for next-generation spectrum access and management, there is growing consensus that non-exclusive spectrum sharing strategies will play a key role in realizing a compelling vision for Spectrum Era 4. Every non-exclusive use system is subject to unintended and unpredictable interference; even exclusive-use systems can suffer from interference due to misbehaving or malicious agents. Finding more effective ways to deal with interference is of paramount importance on the road towards Spectrum Era 4 wireless prominence, which is needed to sustain the US technological and intellectual leadership and to support a thriving US economy. This project will develop a fundamentally new paradigm for efficient and reliable communication in the presence of unpredictable interference. It will improve the performance, resilience, and reliability of wireless systems operating in shared, congested, and contested spectrum bands. The project specifically aims to address mitigation and autonomous recovery from harmful interference, service degradation or denial – key new spectrum capabilities that will empower Spectrum Era 4 systems. The research draws from wireless communications theory and practice (including software radio programming and experimentation), statistical machine learning, signal processing and linear algebra. The findings will make impactful contributions to shared-spectrum wireless communications by introducing novel interference-immune communication modalities, with broader impacts on the above constituent disciplines. The project will offer exciting educational opportunities and added value in terms of spectrum workforce development. Communications engineers with solid theoretical training and hands-on software radio skills are highly sought-after in Northern Virginia and other wireless industry hubs around the US. As part of this project, the PI will also work to identify and motivate students for undergraduate research, and to support his department’s broadening participation plan through the Allyship program that he helped co-found in 2020. The PI recently proposed a very simple and practical method for (re)using spectrum occupied by a legacy service (e.g., analog or digital broadcast). The idea is to use repetition coding in a way that induces a common 1-D signal subspace at the multi-antenna receiver, while interference is different and spans distinct subspaces. Thus the signal of interest can be uniquely recovered using subspace intersection, implemented via canonical correlation analysis (CCA). This signal alignment approach has been demonstrated to work under adverse time-varying interference, without any coordination or channel state information. Signal alignment is well-positioned to address key Spectrum Era 4 challenges, including autonomous recovery from service degradation, denial of service, security and privacy concerns. The project is designed to leverage signal alignment for effective communication in congested and contested environments, with particular focus on the following synergistic thrusts: 1) Streaming Time-Frequency Signal Alignment: Leveraging the algebraic structure of CCA and the shift structure of streaming data to enable computationally lightweight time-frequency signal alignment under unpredictable (e.g., intermittent) and potentially harmful interference; 2) Signal Alignment for Ultra Reliable Low Latency Communications (URLLC): Building on surprising insights obtained recently by the PI to boost CCA performance in the sample-starved (short packet) regime; 3) Full-rate Signal Alignment: Avoiding repetition which halves the transmission rate, this thrust will design and study the performance of signal alignment strategies that operate close to full-rate; and 4) Software radio experiments and validation: Judicious experiments will be conducted at UVA and the COSMOS PAWR testbed at Rutgers/WINLAB to validate the research in practice. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2024 · 2024-12

STEM learning is a function of both student level and classroom level characteristics. Though research efforts often focus on the impacts of classrooms level features, much of the variation in student outcomes is at the student level. Hence it is critical to consider individual students and how their developmental systems (e.g., emotion, cognition; relational, attention, language) interact to influence learning in classroom settings. This is particularly important in developing effective models for personalized learning. To date, efforts to individualize curricula, differentiate instruction, or leverage formative assessment lack an evidence base to support innovation and impact. Tools are needed to describe individual-level learning processes and contexts that support them. The proposed network will incubate and pilot a laboratory classroom to produce real-time metrics on behavioral, neurological, physiological, cognitive, and physical data at individual student and teacher levels, reflecting the diverse dynamics of classroom experiences that co-regulate learning for all students. The Incubator aims to establish and pilot, for eventual expansion, a new laboratory design for research on learning in classrooms that captures and processes information available on multiple systems that influence student learning. Activities include: 1) engaging experts across diverse relevant fields to integrate collective scientific, technical, and applied capacity; 2) incubating a ‘laboratory’ design for real-time sensoring of learning-relevant information at individual student and teacher levels and for teacher, student, and peer interactions; 3) developing and testing prototypes (equipment, software, draft protocols for data collection, human subjects participation, data management, data sharing and IP), and building network website capability; and 4) developing the plans to deploy this laboratory in multiple sites with plans to provide training, collect and distribute data, and develop applications for implementation and evaluation. The public availability of data produced by this type of research laboratory will advance understanding of how individual students learn and how classroom experiences support their learning. This information will foster breakthroughs in personalized education that harness the learning potential of all students, including those from historically marginalized populations. This project is supported through a partnership with the Bill & Melinda Gates Foundation, Schmidt Futures, and the Walton Family Foundation. Funding is also provided by the Discovery Research preK-12 program (DRK-12) program. The Discovery Research preK-12 program (DRK-12) is an applied research program that seeks to significantly enhance the learning and teaching of science, technology, engineering, and mathematics (STEM) by preK-12 students and teachers. 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 funded projects. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2024 · 2024-12

Gravitational waves, a prediction of general relativity, are produced during the mergers of strongly gravitating objects, such as black holes and neutron stars. The NSF Laser Interferometer Gravitational-Wave Observatory (LIGO) facilities detect the waves from these mergers almost daily during operations. The North American Nanohertz Observatory for Gravitational Waves (NANOGrav), also an NSF-supported collaboration, has detected a background of gravitational waves from many supermassive black holes throughout cosmic time. These detectors opened new windows onto the universe, provided novel insights into the nature of gravity, and impact fields beyond gravitational physics. This project will develop data analysis methods and perform new theoretical calculations to search for as-of-yet-undiscovered predictions of general relativity and identify new relativistic phenomena that could be observed through gravitational wave measurements by LIGO and NANOGrav. The PI will carry out the work in collaboration with students, who will learn transferable quantitative skills when conducting the research. The project also has a closely related educational component that involves creating new visualizations of the warped space around colliding black holes and accompanying recorded video explanations of the visualizations. This will help students learn about the LIGO discoveries and teachers to convey these results to their students. The project aims to use gravitational waves from black-hole and neutron-star mergers to understand the infrared properties of the gravitational interaction in general relativity (gravitational wave memory effects) and to probe the nature of dark matter around these systems when it is present in high densities. The primary research efforts of the project can be summarized in terms of four main goals, which are (i) to determine the prospects for pulsar timing arrays to detect the memory effect from intermediate or extreme mass-ratio inspirals, which have not been systematically studied before; (ii) to compute analogs of the memory effect in electromagnetism and Yang-Mills theories, to highlight the similarities with and the unique differences from gravitational-wave memory effects; (iii) to perform simulation studies to determine the optimal method for searching for the gravitational wave memory effect in LIGO and Virgo data; and (iv) to model the gravitational waves from compact objects surrounded by dense distributions of dark matter and infer constraints that can be placed on the dark-matter cross-section, assuming there are non-gravitational interactions between nuclear matter and dark matter. The educational goals of the project are two-fold: (a) The PI and undergraduate students will make visualizations of the curved space around black-hole mergers, which illustrate how gravitational waves are generated and help convey the project's results in simpler terms. The PI will record video explanations of the visualizations with descriptions at three different levels: for high-school level students, for advanced undergraduate or beginning graduate students, and for experts who might use the visualizations in teaching. (b) This project also involves training undergraduate and graduate students to learn analytical, data analysis and numerical techniques highly valued in quantitative 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 2024 · 2024-11

The University of Virginia will organize and host the 2025 Summer Program in Astrophysics (SPA), from June 23 to August 1, 2025. The topic of the 2025 program will be “Cool Frontiers: Exploring Dust and Ice in the Cosmos.” The SPA is a unique graduate and postdoctoral training program which brings together world-class scientists with a wide breadth of technical skills and research interests, together with about fifteen graduate students to solve topical outstanding problems in astrophysics. The SPA operates in the summer for six weeks and has been hosted by various institutions world-wide over the past fourteen years. The program begins with a one-week workshop on a featured topic with morning introductory lectures by invited faculty and afternoon contributed presentations on state-of-the-art research by our long-term participants. The remaining five weeks are devoted to student research, supplemented by daily seminars and discussions. Astrophysical dust and ice play a critical role in the formation and evolution of planets, stars, and galaxies. Many questions about their properties, origin, and evolution remain. With access to forefront observational data, e.g., ALMA and JWST, which are providing unprecedented spatially and spectrally resolved views of dust and ice in different contexts, as well as theoretical models on all scales incorporating self-consistent, detailed treatments of the important physical and chemical processes, participants will work on projects that not only address fundamental and long-standing questions about dust and ice, but also ultimately have the potential to advance our understanding of stellar and planetary astrophysics, galaxy evolution and cosmology. By design, the SPA trains a large number of graduate students and postdoctoral researchers, providing them with close mentoring by leading scientists in the field. The students acquire research skills during the program, while the postdocs gain mentoring and leadership skills. The program is mindful of diversity, and has an excellent track record for training female scientists in particular. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2024 · 2024-10

The forthcoming 6G cellular network infrastructure is anticipated to reclaim part of the 7-24 GHz upper mid-band (FR3) spectrum to strike a balance between the high capacity of millimeter-wave and wide coverage of the sub-6 GHz bands. However, the prohibitive channel probing overhead and spectrum sharing with various incumbent upper mid-band users (e.g., radar and satellite transceivers) remain major challenges. The objective of this project is to harness innovations in generative AI to overcome these limitations of 6G radios. The PI team explores the design of a generative channel modeling framework to tame the channel probing overhead and enable efficient interference management and spectrum coexistence between directional transmitters in the 6G upper mid-band. The project outcome will likely inform the beyond 5G/6G standardization and adoption through collaboration with industry partners. The project impact is further extended by creating a new curriculum focusing on “generative AI for 5G and beyond,” engaging underrepresented researchers, and disseminating open-source experimental hardware and software for 6G FR3 experimentation. The proposed research innovates a generative channel cartography framework that can predict the spatial channel distribution (i.e., length and angles of dominant propagation paths) across unseen locations. The framework enables unique 6G FR3 use cases, including efficient directional interference management and spectrum coexistence. Classical channel estimation methods heavily rely on real-time probing and largely ignore the channel's intrinsic structures originating from the sparsity of the multipath environment. This project advances generative models to encapsulate the channel prior, enabling directional beam/interference prediction conditioned on few-shot channel samples across locations. The resulting models can be broadly used to facilitate other types of networks, such as massive MIMO and mmWave networks facing the prohibitive channel probing overhead. This framework is applied to resolve the 6G FR3 networking problems through 3 thrusts: (1) Designing a cross-band generative diffusion model to predict FR3 interference maps using co-located FR1 measurements to maximize the spatial reuse and potentially reduce the interference management overhead. (2) Developing a generative NeRF which embeds prior knowledge of the physical environment into generative models, to predict the sparse multipath channels and efficiently estimate 6G-to-incumbent interference without deploying extensive spectrum sensors. (3) Developing a large generative model pre-trained on massive ray-tracing simulations to reconstruct the spatiotemporal channel dynamics with few-shot prompts. At runtime, it performs in-context learning from few-shot channel samples to enable dynamic beam and interference management for FR3 links. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2024 · 2024-10

Graphs play a pivotal role across diverse domains, including social networks, and natural language processing. While Graph Neural Network (GNN) training systems have facilitated training on large static graphs, real-world applications (e.g., social media) often involve large-scale, and time-varying dynamic graphs (LTGs) and Dynamic GNN (DGNN) models have emerged to tackle them. Therefore, there is a need to reduce computational resources and training time of such models, especially considering that tackling DGNN training on LTGs is more formidable than GNNs due to the vast scale and distinctive time-varying graph characteristics. This project’s novelties are new effective training methods tailored for DGNNs on LTGs. The project has several areas of broader significance and importance. The project generates critical insights into the challenges of achieving highly scalable and efficient DGNN training on LTGs for different applications, and advanced approaches for tackling this challenge. Moreover, the project provides thorough training of students, and collaborative research opportunities for participating graduate, undergraduate, and K-12 students and faculty, with research results disseminated and integrated into courses. To achieve highly efficient DGNN training on LTGs, this project endeavors to create innovative approaches for graph partitioning, sampling, caching and training. The project comprises four distinct tasks: 1) a comprehensive analysis of the characteristics of DGNN training on various types of LTGs; 2) LTG partitioning and caching before training; 3) LTG sampling and caching during training; and 4) efficient DGNN training on LTGs. This project impacts different applications and the science and engineering fields by improving the performance of many of their required tasks. It serves the system community as a vehicle to conduct further research and experiments, and advance the state-of-the-art. Finally, this project has potential to yield social-economic benefits to organizations requiring efficient DGNN execution on LTGs. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2024 · 2024-10

To feed the world’s growing population, food production must increase by an estimated 70% by 2050. Achieving food security by minimizing supply fluctuations and adjusting to the growth in food demand presents many challenges that will require major adjustments in current agricultural practices, most importantly in controlled-environment agriculture (CEA). Current CEA facilities consume substantial energy, hence making this technology energy-hungry and preventing their wider adoption. This interdisciplinary CPS project intends to build a networked CPS together with advanced data analytics and integrated renewable energy and energy storage aiming at reducing the dependence on utility grid and hence energy cost, while optimizing crop production efficiency. This project led by Clemson University brings together a team from agricultural sciences, control systems and computing/data science to create a networked system for CEA, with the goal of improving crop growth and yield while minimizing the energy cost; it enables self-adaptation and autonomy of CEA and advances the frontier of core CPS research. The research results will be integrated into the undergraduate and graduate curriculum development at the institutions involved with students trained on interdisciplinary research and education. The PIs’ partnership with K-12 schools and CEA growers will be leveraged to educate students, mostly from underrepresented groups, and practicing engineers on the development and deployment of CPS technologies in CEAs. This project builds a novel system for multi-scale, cooperative and autonomous sensing, control and renewable energy management to address several fundamental challenges of complex CEA systems, a key step towards fully autonomous and net-zero-energy CEA. The hierarchical structure of this project exploits inter-dependencies of crop physiology, energy systems and environment to advance research in CEA systems aiming at enhancing their resilience. This project outcomes enable a paradigm shift in a number of areas including: (1) integration of photosynthesis models with real-time biophysical measurements for optimizing environmental parameters; (2) automatic monitoring of the crop growth and environmental conditions using advanced AI-guided image and sensor data analytics; (3) automated robot-assisted data collection using novel control approaches for optimal distribution of mobile manipulators over large CEAs with safety guarantees; (4) devising novel stochastic control tools to manipulate environmental parameters to facilitate photosynthesis for each crop species and growth stage. The tight interaction of controllable physical systems with autonomous biological systems and the environment provides an intriguing problem space that can be also useful for a broad range of other cyber-physical systems. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2024 · 2024-10

The digital revolution has led to a rapid expansion of available data sources. Data integration methods such as record linkage are critically important in generating actionable insights from the combination of data sources in a cost-effective manner. Record linkage merges multiple files by identifying matching records associated with the same entity. Without unique identifiers, this process is often error-prone, bearing the risk of false and missed links, which can compromise data analysis based on the merged file. Building on established implementations of record linkage, this project develops software in popular data science programming languages that addresses potential errors and uncertainty in data analysis performed post-linkage. The development of the software is driven by the needs of federal statistical agencies, researchers and practitioners in health services, and other stakeholders seeking to harness record linkage to unravel the potential of their data. The resulting additional capabilities for linked data analysis support informed decision-making in public administration and health services and enable savings in data collection and human labor. The project provides research opportunities and support for graduate students and delivers educational materials on the record linkage and analysis pipeline, thereby contributing to workforce development and the training of future data scientists. Existing software for record linkage is almost entirely dedicated to aspects pertaining to the creation of linked files, leaving a gap in supporting downstream tasks. This project fills this gap by providing software supporting post-linkage data analysis, an evolving subject with various open problems requiring novel statistical and computational tools. Guided by use cases from several application domains, the investigators intend to unify and refine state-of-the-art approaches to post-linkage data analysis in primary and secondary analysis settings given information of varying degrees about the underlying linkage process. The project develops scalable, robust and user-friendly implementations of these approaches embedded in a modular software that is envisioned to ease burdens for users of linked data, enable the validity of analyses based on such data, and propel advances in the field of data integration. The latter is achieved by generating capabilities for validation, benchmarking, and inspiring new avenues of methodological research. This Office of Advanced Cyberinfrastructure award is jointly funded by the Division of Social and Economic Sciences (SES) in the Social, Behavioral and Economic Sciences Directorate (SBE). This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2024 · 2024-10

This planning award supports development of a community partnership addressing food, energy, and water (FEW) security concerns in rural Alaska. The PI team will meet with community members both virtually and in person to build trust and discuss the local challenges to FEW security in a remote rural context. Through a series of workshops, meetings, and consultations, the PI team and community members will explore issues such as infrastructure, the built environment of the village, and social factors that affect security and resilience. This participatory project lays the foundation for a community-based study of food, energy and water security. With planning support, the PI team will form an advisory board and conduct a series of engagement activities to build a relationship with the community and help the researchers better understand residents’ needs and concerns. A series of formal and informal meetings and workshops will be held, providing residents with multiple opportunities to participate. Two workshops will focus on perceptions of food, energy and water among children ages six through nine. Informed by these activities and the advisory board, the PI team will develop a place-based challenge-solution matrix, which will be refined through community input. The final matrix will be distributed to community members as a poster. This matrix will be the basis for a community-centered research proposal addressing the most salient FEW questions identified during the planning process. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2024 · 2024-09

Black disabled students encounter systemic challenges in K-12 education such as being overrepresented in special education categories of behavioral and intellectual disabilities while facing harsher disciplinary consequences compared to other students. These challenges impact their opportunities for meaningful STEM learning. A key avenue to counter these disparities is through high school mathematics teacher coaching encompassing knowledge of the interactional nature of racism and ableism in teaching and decision making. Therefore, this project aims to develop and test a theoretical coaching framework that addresses challenges while advancing conceptual mathematics learning and high school mathematics instructional practices. Using qualitative participatory methodology, this project will involve establishing and sustaining an authentic partnership with a cohort of Black disabled high school students. Their voices, knowledge, and experiences will be central in informing the development of this project’s coaching theoretical framework. The research team will support students’ learning, developing, and enacting ways to counter racism and ableism, advance conceptually oriented mathematics instructional practices, and impact instruction to improve students’ experiences and learning opportunities. Students will have opportunities to convene to share their experiences, and mathematics teachers will participate in professional development opportunities to support working with students as well as piloting and developing the coaching model. This project will contribute to both theory and practice in mathematics education as well as produce positive impact to the lives of Black disabled 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 2024 · 2024-09

Notebooks are general-purpose programming platforms widely used in machine learning (ML), artificial intelligence (AI), data science, and data analytics across almost every science and engineering field. Despite supporting a wide diversity of disciplines, a dominating application of production Notebook workloads is interactive ML training (IMLT). To guarantee high interactivity, modern Notebook services typically allocate and reserve GPU resources for actively running Notebook sessions. These Notebook sessions are long-running but characterized by intermittent and sporadic GPU usage. Consequently, during most of their lifetimes, Notebook sessions do not use the reserved GPUs, resulting in extremely low GPU utilization and prohibitively high cost. This project aims to build a new Notebook platform solution for IMLT workloads to address these issues. The success of the project will provide an efficient and interactive Notebook platform that significantly reduces GPU resource wastage. The project will advance understanding in large-scale cluster computing systems and gain insights into achieving high carbon efficiency and sustainability of large-scale GPU computing infrastructure. The integrated educational plan will create a new, versatile, educational platform. This will include new pedagogical tools, new courses, as well as a proof-of-concept carbon-efficient Notebook service built on energy-efficient computers. This initiative aims to provide graduate and undergraduate students with multidisciplinary research and education experiences and develop outreach activities for K-12 students. This proposal rethinks resource management for large-scale Notebook IMLT workloads by designing a novel, resource-efficient, and sustainable cyberinfrastructure called REITOS. The research is organized around several key research thrusts: (1) REITOS will develop distributed Notebook algorithms that replicate the Notebook kernel state for high availability and high interactivity. (2) Distributed Notebooks will enable a new way for oversubscribing and dynamically sharing significantly fewer GPU resources. (3) REITOS proposes new GPU cluster scheduling algorithms to dynamically preempt or migrate Notebook processes in cluster setups. (4) The project will establish a sustainable REITOS community by developing and maintaining a healthy GitHub community project that will encompass the entire REITOS ecosystem. The potential contributions of this project are multi-fold: REITOS will enable new capabilities that are urgently required by existing Notebook platforms, such as high availability and efficient GPU sharing. The proposed research will create new cyberinfrastructure techniques as standalone, reusable modules that will be adopted by independent applications. REITOS will be deployed to active Notebook user communities at multiple facilities through community outreach and collaborations. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2024 · 2024-09

This research team will study planet formation by characterizing the carbon monoxide (CO) gas in disks that surround new-born stars. This volatile gas may stick on grains and pebble-sized solids far away from the star within the disk, moving with them as they drift towards the star, where they may be released due to its heat. Using infrared spectroscopy to sense CO in the inner disk and millimeter images to sense CO in the outer disk, the team will probe, respectively, the planet-formation region and where most of the disk material exists as in a ‘reservoir’. Three graduate students will be trained in telescope observing and data modeling at the PhD level, and two undergraduate students will be involved in high level research. Calibrated datasets and open-source codes will be made publicly available. A new course on radio astronomy for undergraduate astronomy majors will be developed. There will be broader outreach to local middle and high schools through summer programs and campus visits, and to the community at large through public events. Recent observations and simulations suggest that CO chemistry is being impacted by dynamical and chemical processing. Patterns between the derived CO column densities in the terrestrial planet forming zone (e.g., < 5 au) and in the outer bulk gas reservoir probed at ALMA-wavelengths will be sought. The program will use data from world-class ground-based observatories: the Atacama Large Millimeter Array (ALMA) in Chile; and the Keck observatory and the Infrared Telescope Facility in Hawaii. The collaboration leverages the strengths of the three participating institutions to provide the most complete picture to date of the volatile content of protoplanetary disks. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2024 · 2024-09

Nontechnical Description Effective cooling and heat dissipation are essential for managing heat in microelectronics. This is particularly important as microelectronics continue to shrink and pack more processing power into smaller spaces. Just consider how hot a mobile phone can get in normal use. Thomson cooling is a solid-state cooling method that was proposed by Lord Kelvin in 1850, but it has not been commercialized due to the limited cooling power observed in most materials. Recent measurements have revealed promising materials with a strong thermal response due to thermally induced magnetic and structural phase changes. These findings suggest the potential to design highly efficient Thomson coolers. This project establishes a new paradigm for Thomson materials and provides fundamental insights that will guide the future design of Thomson materials for electronic cooling. Additionally, investigators aim to co-design hybrid modules that can be used at low temperatures as an alternative to helium-based refrigeration. This addresses the current helium shortage and reduces related costs. Educational initiatives involve engaging graduate and undergraduate students, as well as organizing hands-on workshops and classes for elementary and middle school students. These initiatives aim to introduce students to the principles of thermodynamics and materials science. Technical Description Investigators will study the Thomson effect systematically in thermally induced phase transitions across two distinct classes of materials: 1) a subset of Heusler compounds with promising characteristics as both magnetocaloric and thermoelectric materials during their first-order magneto-structural phase transitions. 2) Transition metal dichalcogenides, investigated during structural (e.g., from 2H to 1T) phase transitions and electronic phase transitions (e.g., charge density wave transitions). At the phase transition point, numerous transport properties undergo simultaneous changes, enabling the development of multifunctional materials. The investigators focus on gaining fundamental understanding of electronic and phononic characteristics, and their relationship to the structural changes in the materials, using a combined theoretical and experimental approach with an iterative feedback loop. Through these studies, the PIs aim to identify material metrics relevant to the design and coefficient of performance of Thomson coolers under both continuous and transient operation, as well as hybrid structures integrating Thomson coolers with magnetocaloric or Ettingshausen coolers. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2024 · 2024-09

The question of how galaxies form and change is an extremely complex one. The resolved stars and gas of the Milky Way and the Andromeda galaxy allow us to infer many important aspects of galaxy evolution for larger galaxies. For the more numerous and smaller dwarf galaxies, however, the picture of how they formed and evolved is much less clear. Two exceptions, the Large and Small Magellanic Clouds (MCs), serve as unique laboratories for observing galaxy evolution processes that occur in low mass galaxies. The investigators will use new observations and existing datasets to map the Magellanic Clouds in a search for the fossil evidence of past interactions of the MCs with one another or with smaller systems they have accreted. The team will will create astronomy lesson plans and resources with the Apsáalooke (Crow) tribe that explore Native and Western science, and they will help deliver the lessons at many outreach events. The team will contribute to a summer STEM research programs at UVa for students at college-level, identified as a primary attrition point in the STEM career path for the URM community. The team will support professional development of early career scientists, including two graduate students and six undergraduates. The investigators will use cutting-edge observational datasets to map the Magellanic Clouds and their periphery and search for the fossil evidence of past interactions of the MCs with one another or with smaller systems they have accreted. By mapping the kinematical and chemical abundance patterns across the Clouds they will look for spatial variations that are benchmarks in their elemental abundance and motions dynamics and/or provide key signatures to the origin of known and potentially new substructures among the stars and gas in the extended periphery of the MCs. 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.