University of Vermont & State Agricultural College

NSF Awards · FY 2026 · 2026-09

This Faculty Early Career Development Program (CAREER) award will support research to create innovative strategies for controlling vibrations in critical structures such as bridges, buildings, and aircraft, where uncontrolled vibrations can lead to severe damage, safety risks, and economic losses. Current approaches often fail to detect and manage these vibrations in time. This project will establish a novel system capable of both localizing vibrations to prevent their spread and measuring their intensity with high precision, enabling timely intervention. The system will use layered materials engineered to interact in unique ways, inspired by quantum wave propagation features observed in nanoscale heterostructures, to reveal how material differences and long-range interactions influence elastodynamic wave behavior. These new findings will advance fundamental knowledge of vibration control and drive innovations for safer infrastructure and aerospace technologies, promoting national health, prosperity, and security. The CAREER project also integrates education by mentoring students in applying quantum principles to engineering dynamic control solutions, developing a graduate course on quantum-inspired vibration manipulation, and engaging K–12 learners to inspire future engineers. Additionally, interactive vibration design tools will be created to involve the public and improve solutions through feedback, creating a self-reinforcing cycle where education and research advance together, breaking traditional barriers between fields. This research addresses fundamental challenges in dynamic control—specifically vibration mitigation, scattering-free waveguiding, and broadband edge states—by investigating elastodynamic wave propagation in composite metastructures composed of dissimilar layered materials with inter- and intralayer long-range couplings. By reinterpreting elastodynamic waves through the lens of quantum wave behavior and employing quantum-inspired analysis techniques, the study aims to uncover novel elastodynamic wave properties and develop advanced nonlinear dynamic control strategies. These strategies will draw inspiration from quantum wave propagation and interaction phenomena observed in nanoscale heterostructures, enabling effective vibration suppression under extreme operating conditions while incorporating real-time sensing capabilities. Experimental validation and characterization of these wave properties will be achieved using 3D printing and laser-based and image-correlation techniques, supported by advanced three-dimensional structural design using deep-learning algorithms for phonon dispersion prediction, mode tracing, and geometry optimization. The outcomes will establish new principles for wave manipulation in engineered structures and provide transformative approaches to vibration control in critical infrastructure and aerospace systems. Furthermore, the theoretical frameworks developed may inversely inspire discoveries in condensed matter physics, fostering cross-disciplinary breakthroughs. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2026 · 2026-08

This project improves understanding of how trees store, move, and use carbon by focusing on sugar maple (Acer saccharum). Sugar maple is an important tree species in northeastern North America and it is the foundation of the region’s rapidly growing maple syrup industry. Trees play a vital role in Earth system function by storing carbon, yet key aspects of how carbon is allocated within living trees—especially during periods without leaves—remain poorly understood. Sugar maple offers a unique natural system to address these knowledge gaps because sap extraction removes stored carbon during late winter. This creates an opportunity to directly observe how trees react to removal of some internal carbon reserves. This research advances fundamental knowledge about a longstanding question in plant physiology while supporting sustainable land management, rural economies, and STEM education. The project integrates research with education and public engagement focusing on sugar maple trees in a region with significant maple syrup production. Educational activities engage audiences ranging from K–12 students to professional maple producers through hands-on field experiences, a mobile sugarhouse, a new university course, undergraduate research opportunities, and continuing education programs. The project actively supports workforce development for a growing, forest-based industry. This project advances NSF’s priorities in Biotechnology. The goal of this project is to develop a detailed understanding of within-tree carbon cycling in sugar maple across seasonal and interannual timescales. The research pursues three integrated objectives: (1) quantifying seasonal patterns of carbon pools and fluxes using physiological measurements of processes, such as of growth and the evolution of sap composition and nonstructural carbon reserves; (2) experimentally testing controls on carbon allocation through field manipulations that alter carbon sources and sinks, including controlled sap extraction; and (3) developing a predictive simulation model of carbon transport and allocation within trees based on a transport-resistance framework. The project combines existing long-term datasets and novel observations with field experiments, enabling rare insight into carbon cycling of mature trees. Results are highly relevant to the maple sugaring industry. Together, these efforts will advance ecophysiological theory on carbon allocation in perennial plants, provide a template for studying other temperate tree species, and strengthen links between basic tree physiology, forest management, and the sustainability of the maple sugaring industry. 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 · 2026-03

Open and accessible research data is essential for scientific progress, informed policy, and public benefit. Yet many researchers and communities, especially in rural states, lack the infrastructure, skills, and support needed to share and reuse data effectively. This project, led by the Vermont Research Open Source Program Office (VERSO) at the University of Vermont (UVM), addresses these gaps by strengthening open-data infrastructure and cultivating a regional culture of open science. It expands UVM’s institutional data repositories, so research outputs become more findable, accessible, and reusable (FAIR) for scholars, state agencies, nonprofits, and the public. The project also continues the annual Vermont Open Data Summit, bringing together researchers, government, industry, and civic organizations to build skills and collaborate around open data. Through partnerships with state agencies, regional planning bodies, and the Leahy Institute for Rural Partnerships, the initiative links university capabilities to real community needs. The Vermont Research Open Source Program Office (VERSO) at the University of Vermont proposes a three-year effort to build sustainable, multidisciplinary FAIR (Findable, Accessible, Interoperable, Reusable) research data management practices and open science capacity across Vermont. The project will expand UVM’s open data infrastructure by increasing Dataverse storage beyond its current one-terabyte limit, establishing standard operating procedures for data ingestion and curation, and providing researcher outreach, onboarding, and individualized data management consultations. The enhanced repository will support Digital Object Identifier assignment, metadata standards, and interoperability with external systems. VERSO will launch the annual Vermont Open Data Summit, a two-day regional event that strengthens open-data literacy and collaboration among academic institutions, government, nonprofits, and industry. The summit will feature keynotes, workshops including an adapted NASA Open Science 101 curriculum that the team has published, and a participant-driven unconference, with proceedings openly published via GitHub and Jupyter Books. VERSO will continue supporting community-driven open data projects through the Open Research Community Accelerator (ORCA), which engages interdisciplinary student teams in Agile-managed open data and open-source software projects with public-sector and community partners. Project outcomes will be tracked using repository metrics and summit metrics. Together, these efforts provide a replicable model for advancing FAIR data practices at rural land-grant institutions nationwide. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2026 · 2026-02

Liquids composed of polymers exhibit unusual flow properties. These liquids contain long, flexible molecular chains that induce surprising behaviors in flow. Even when flowing slowly, they can generate a chaotic motion called elastic turbulence. When flowing rapidly, they can generate another chaotic state called elasto-inertial turbulence. These unusual flow behaviors are seen in common materials such as saliva, mucus and tree sap. They also are observed in advanced manufacturing processes, inkjet printing, energy production, and food manufacturing. Most mathematical models of these flows do not reproduce what experiments show, which limits their use in predicting and controlling polymer liquid flows. This joint project between NSF and UK's EPSRC will combine laboratory experiments, computer simulations, and machine-learning tools to construct models that capture the flow behaviors of polymeric fluids. Results will improve understanding of complex flows, support the development of more energy- and cost-efficient processing technologies, and improve the design of new materials. Undergraduate and graduate students will be trained in fluid mechanics and data science. Computational and data-analysis tools developed in the project will be shared with the scientific community. The proposal aligns with NSF priorities by supporting artificial intelligence/machine learning tools for advanced manufacturing. This project will close long-standing gaps between theoretical predictions and experimental observations of viscoelastic flows. The project will focus on three interconnected goals. First, the team will integrate two-dimensional experiments and simulations to achieve quantitative agreement in statistics, flow structures, and dynamical features. Machine-learning methods will be developed to infer optimal model parameters and to reconstruct polymer stress fields directly from experiments – an essential but historically inaccessible quantity that limits the accuracy of constitutive models. Second, the project will investigate the differences and universality of elastic-turbulent states in flows with streamline curvature and in parallel shear configurations, spanning both 2D and 3D geometries. Third, the project will explore how increasing inertia leads to the transition from elastic turbulence to elasto-inertial turbulence to examine whether these states share underlying mechanisms. Together, these efforts will expand the fundamental understanding of viscoelastic flows, delineate the parameter space in which chaotic flow arises, and generate high-fidelity datasets and modeling approaches that can be applied broadly. The project’s outcomes, including computational tools, improved models, and interdisciplinary training, will strengthen U.S. research capacity in fluid mechanics and support technological advancement in polymer processing and material design. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2026 · 2026-01

This ExLENT Explorations Track project aims to serve the national interest by expanding workforce opportunities across critical sectors such as semiconductors and microelectronics, advanced manufacturing, and clean energy systems. Built on a cross-sector partnership among two state universities, industry leaders, and nonprofit organizations, this initiative responds to pressing workforce challenges in a rural region critical to the nation's innovation economy. Although the region is home to companies at the forefront of emerging technologies, it faces workforce shortages and rising demand for college-educated employees. In response, this project engages recent high school graduates who are not pursuing college in a year-long experience that includes a residential summer practicum, paid internships, and ongoing mentorship. Importantly it also engages high school students through innovative programming that raises awareness of career opportunities in Vermont’s growing innovation economy. Together these efforts help create clearer pathways into high-growth technical careers and strengthen the region's future workforce. The project advances three interconnected goals: 1) to establish a robust statewide collaboration that blends academic rigor, hands on skills training, and mentorship in high demand technology fields; 2) to ensure participants gain both the confidence and practical knowledge needed to pursue further education in meaningful career advancement; and 3) to create a shared vision for expanding educational and career options for young Vermonters who face challenges in transitioning to college or full-time employment. To achieve these goals, the initiative offers participants a curated gap year experience that includes a residential summer practicum, year-long paid internships with Vermont based companies operating in semiconductor and microelectronics advanced manufacturing sectors, and mentorship from both their host employers and faculty or staff affiliated with the state's two public universities. The project also develops new programming that directly engages at least 200 current high school students by building awareness of Vermont's emerging technology sector. Evaluation focuses on tracking participant engagement, measuring progress into college and careers, assessing growth in college and career readiness, and examining the barriers students face prior to entry, including the extent to which the program helps mitigate these. Findings from the work will be disseminated through multiple channels aimed at reaching diverse audiences including stakeholders such as policymakers, educational leaders and industry partners through reports, publications and presentations. Overall, this effort supports a coordinated, research-informed model for expanding opportunity in emerging technology fields and strengthening the regional talent ecosystem. The NSF ExLENT Program, supported by the NSF TIP and EDU Directorates, seeks to support experiential learning opportunities for individuals to increase their interest in and their access to career pathways in emerging technology fields. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2026 · 2026-01

This IRES project supports groups of U.S. students to engage in summer international research experiences to investigate organic and biomaterials for energy innovation in experimental research labs at Yamagata University (YU), Japan. A cohort of six undergraduate students and one PhD student travels to Japan each summer for a nine-week research experience guided by a host professor at the Faculty of Science (Kojirakawa Campus) or the Faculty of Engineering (Yonezawa Campus). Pre-program preparation activities and a one-week orientation session at the University of Vermont (UVM) provide laboratory, Japanese language, and culture training necessary for the students to transition smoothly into the research groups in Yamagata prefecture. The host site at Yamagata University provides a safe, but culturally unique experience to broaden the global perspectives of the IRES participants while training them in cutting edge research projects within world-class facilities. A series of cultural and outreach events guide the student participants towards growth as global citizens. The interdisciplinary research team of faculty and mentors allows student participation from STEM fields spanning physics, chemistry, biology, mechanical engineering, electrical engineering, materials science, and computer science. The project builds on a decade-long research collaboration between UVM and YU. Soft materials like organic semiconductors, polymers, biomaterials, and composite nanomaterials offer transformative potential for energy innovation. This project trains participating students with discrete, fundamental materials science research projects within the space of biomimetic and nanomaterials for energy applications. Examples include photonic nanostructures like those found in shells, gemstones, insects, and lizards, light harvesting inspired by plant-based pigments, and electrochemical conversion and storage of energy. Interdisciplinary collaboration in these areas may ultimately yield new technologies for energy conversion and storage that are not assembled but grown at little to no cost, and not recycled as high-tech components but decomposed. The projects are fundamental in nature but ultimately use-inspired to prepare students for innovation in materials for clean energy applications. 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 characterize the developmental, physiological, biochemical, and transcriptional mechanisms that underlie chilling tolerance in some grasses, and to use this information to improve the thermal tolerance of specific cereal crops. Cold temperatures in the US cause millions of dollars of damage to crops every year. This is particularly true for crops domesticated from tropical species, such as the grass cereals corn, sorghum, and millet. One reason that tropics-derived cereals fare best in warm environments is their special type of photosynthesis known as “C4". Photosynthesis is the process by which CO2 is converted into sugars, and it requires the diffusion of air through open pores, such that plants lose water when conditions are dry/hot. C4 cereals and their relatives have repeatedly evolved a trick whereby CO2 is concentrated in cells, allowing their pores to close and conserve water without greatly reducing photosynthetic sugar production. This strategy works well when temperatures are high, but it comes at the cost of poor tolerance to colder temperatures or "chilling tolerance". The aim of this project is to better understand how some C4 grasses have been able to circumvent the tradeoff between C4 photosynthesis and cold tolerance with an eye to designing C4 cereals that thrive under a range of temperatures. The first step is to determine the number of origins of chilling tolerance in the hundreds of C4 cereal grasses, and then to determine how chilling-tolerant C4 grasses are physiologically and genetically distinct from their chilling-sensitive counterparts. Finally, C4 enzymes will be strategically targeted for modification in corn and millet with the aim of increasing the chilling tolerance of these species. Beyond the goal of translating this research into designing chilling-tolerant crops to improve US agriculture, this project will provide excellent training opportunities to post-doctoral fellows, undergraduate and graduate students and will generate rich resources for the broader scientific community. Complex traits have evolved repeatedly across all domains of life, but it is still something of a mystery as to how this can occur. This proposal seeks to explore the mechanisms underlying convergent trait evolution by focusing on independent origins of chilling tolerance in the C4 grass clade Panicoideae: Paniceae, containing maize, sorghum, and millets among its roughly 1,500 members. The first objective is to characterize an integrated measure of chilling tolerance across the subfamily, reconstruct its number of origins, and determine the degree to which different developmental, physiological, biochemical, and transcriptional responses to cold correlate with its independent origins. The second objective is to focus specifically on the evolution and biochemistry of typically cold-sensitive enzymes of the C4 photosynthesis pathway where increased abundance and/or activity below 15 C have been implicated in more chilling-tolerant plants. The third objective is to select a subset of chilling-tolerant enzymes to express in corn and Setaria viridis to test for modifications towards chilling tolerance at the whole plant level. Beyond answering questions about how convergent traits can evolve, expected outcomes of the project are a better understanding of the magnitude of changes required to engineer chilling tolerance, the number of evolutionary paths that have been taken to this trait, and specific paths to increased chilling tolerance in corn and its relatives. This award is co-funded by the BIO-IOS-Physiological Mechanisms and Biomechanics (PMB) program and the BIO-DEB-Systematic and Biodiversity Science (SBS) program. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2025 · 2025-10

This NSF CAREER project aims to design algorithms and computational tools which rigorously verify the performance of Machine Learning (ML) models built for use in electric power systems. The project will bring transformative change by giving power system engineers the next-generational computational tools they will need to guarantee that ML models cannot have disastrous impacts on the grid. This will be achieved by fusing recent advances from the fields of ML verification and power system optimization, thus capturing the organic connection between the scalable verification approaches emerging from the ML community, and the secure and optimal operation of large-scale power systems. The intellectual merits of the project include a synergized modeling framework capable of verifying over physics-based grid models fused with ML-based control technologies, custom tree search algorithms which search for elusive adversarial inputs, and self-supervised learning routines which accelerate verification. The broader impacts of the project aim at training and engaging the next generation of data scientists and engineers who will be responsible deploying safe ML technologies across the US power grid. This will be achieved through (1) the release of an open-source modeling toolbox, GridVerification.jl, which provides access to the algorithms developed in this project, (2) a verification competition which will entice students and young researchers into the exciting field, and (3) a new course, titled "Safe ML for Engineering", which will give engineering students the tools they need to ensure that ML technologies deployed in safety-critical engineering applications can be safely verified. The computational challenge of verifying large-scale ML models constrained by nonlinear power flow physics is immense and growing. However, state-of-the-art ML verification tools are orders of magnitude behind oncoming industry needs. Using dual cone projections, this framework will exploit a suite of convex relaxation tightening advances that the power flow community has designed over decades and port them directly into the ML verification problem. Tunable bounding hyperplanes will smoothly rotate about nonlinear manifolds of the dual space, enabling optimally tight relaxations. Concurrently, Monte Carlo Tree Search algorithms will sequentially look for increasingly damaging adversarial inputs, and self-supervised learning agents will learn how to accelerate hard verification problems. 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 NSF project aims to design and evaluate honeybee-inspired virtual electric peer-to-peer networks to enable efficient and resilient control of distributed energy resources. These resources include electric vehicles, heat pumps, electric water heaters, and battery energy storage systems at the distribution level. Existing electrical infrastructures are undersized to handle increasing loads, and a lack of effective coordination among these resources further exacerbates this challenge. Inspired by the decentralized coordination mechanisms observed in honeybee colonies—where energy (food) is exchanged among members in a process called trophallaxis—this project will develop a bio-inspired cyber-physical system where distributed resources (“bees”) and storage systems (“hive”) dynamically allocate energy. By applying principles from collective insect behavior, this research seeks to transform energy coordination, benefiting grid operators and consumers alike. The intellectual merits of the project include novel mathematical models based on trophallaxis, development of bio-inspired control strategies, and validation through virtual testbeds and real-world demonstrations. The broader impacts include advancing non-wire alternatives that enhance grid resilience, improving access to electricity services, and fostering interdisciplinary knowledge exchange between biology, computing, and engineering. Additionally, the project will provide publicly available open-source software, engage students through an undergraduate design competition, and disseminate findings through workshops and outreach initiatives. This project will address existing technical challenges in energy coordination by translating honeybee trophallaxis into mathematical models and integrating them into an innovative cyber-physical framework. It will develop predictive models for uncertain building loads and energy behaviors using stochastic transfer learning. Additionally, it will create bidirectional biology-technology knowledge transfer frameworks to inform control-oriented models across multiple system layers. New bio-inspired control methods will be designed to optimize peer-to-peer energy sharing and grid operations. The project will leverage virtual testbeds using Python and GridLAB-D for rigorous evaluation, with experimental demonstrations conducted at the University of Colorado Boulder’s microgrid. By combining expertise from biology, computer science, and engineering, this research will generate novel strategies for resilient, adaptive, and efficient grid operations. 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 Faculty Early Career Development (CAREER) award supports studying the relationship between microscopic structure (‘microstructure’) and macroscopic function of cartilage when forces are applied that are similar to what is experienced in everyday life. Cartilage health is important to human health, as the main feature of osteoarthritis is cartilage loss. Osteoarthritis is a painful disease that affects about 30 million adults in the United States. Once a person loses cartilage, there is no treatment to regrow cartilage. Currently, little is known about cartilage microstructure, forces, and function, especially from measurements made in young and middle-aged adults before osteoarthritis usually begins. While new medical imaging tools provide an exciting opportunity to see changes in microstructure before cartilage loss, how much altered microstructure relates to cartilage function is still unknown. Proposed research will improve our current scientific understanding of cartilage and help patients at risk for osteoarthritis in the future. Community outreach events will engage and inspire middle and high school students from Vermont and include hands-on activities that show how changing what a material is made of impacts its function. The research project investigates the links between quantitative magnetic resonance imaging metrics of cartilage microstructure, experimentally derived function of articular cartilage, and statistical modeling of bone shape. The studies supported in this project employ in vivo experiments, in situ testing, and in silico computational simulations. In vivo experimentation will leverage recently developed quantitative magnetic resonance imaging metrics of microstructure in cartilage, as assessed in a traditional ‘unloaded’ state, and bone shape to accurately predict changes with loading and subsequent relaxation. In situ testing will assess the capacity of quantitative magnetic resonance imaging metrics to predict the results of viscoelastic tests. In silico computational simulations will be developed and benchmarked for improving subject-specific computational models of joint mechanics by updating biphasic material properties with image-based measurements of articular cartilage microstructure. Overall, this research work will generate comprehensive and novel understanding of cartilage microstructure, response to load (with subsequent relaxation), and statistical shape modeling of bone to-date, advancing knowledge of multiscale structure-function relationships in articular cartilage by combining imaging experiments, biomechanical tests, and computational simulations. The benchmarked computational framework has the potential to transform subject-specific modeling in articular cartilage, which has broad implications for the field of biomechanics and computational modeling. 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 funds construction of IceCore, a graphics processing unit (GPU)-focused, high-performance computing (HPC), enhancing critically needed research computing capacity in Northern New England (NNE). IceCore will enable research of national significance conducted by scientists across NNE, including projects enhancing the explainability of machine learning, infectious disease simulation, hi-resolution mapping of neural circuitry, and the computational design of living robots. Managed by the Vermont Advanced Computing Center (VACC) at the University of Vermont (UVM), IceCore fulfills the unique needs of smaller institutions while catalyzing continued growth in research infrastructure. Access to IceCore will improve the ability of NNE institutions to recruit and retain new faculty, staff, researchers, and students, building their institutional capacity. IceCore will also serve as a powerful educational platform that will train the next generation science and technology workforce for a region whose growth has been critically limited by a shortage of workers with the necessary skills for a digital and computational economy. IceCore will increase the computational capacity of UVM’s GPU cluster by nearly two orders of magnitude to over 100-petaflops, accelerating large-scale computing research and facilitating a broad set of important scientific investigations. Crucially, it will also offer improved HPC access for rural health network research spanning Vermont and Upstate New York (UVM Health Network), as well as enhanced compute capacity for State Agencies that harness the VACC. The presence of a local cluster powered by 64 NVIDIA GPUs provides the UVM research community and the region at large with locally supported advanced computing capabilities, high-speed access, flexible scheduling policy, and customized training. The scientific and technical team at the VACC is committed to training the next generation of data scientists in the region by broadening the number of researchers and students able to access and work with IceCore. This commitment is exemplified by a holistic cyberinfrastructure ecosystem as well as new training workshops focused on GPU computing and machine learning frameworks, courses, and partnerships with NSF-funded undergraduate training initiatives. IceCore will be leveraged to develop cross-sector partnerships with industry and community organizations across NNE to provide computational resources and establish workforce development initiatives that are urgently needed in this rural area. This project is jointly funded by Office of Integrative Activities (OIA) and Office of Advanced Cyberinfrastructure (OAC). 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

With the support of the Chemical Synthesis Program in the Division of Chemistry, Dr. Brewer of the University of Vermont is studying new reactions to prepare organic structures. These fundamental studies are important because discovering new ways to prepare organic molecules enables advances in many related fields of research including drug discovery and materials science. A key focus of this work is developing a new ring opening reaction that will produce a synthetically useful class of compounds, called encarbamates, that have received minimal attention from chemists and the studies being conducted under this award will enable their use in a variety of syntheses. This research will provide new routes to molecular scaffolds that contain nitrogen atoms, which take advantage of a more stable and easier to isolate intermediate than other systems. This is important because nitrogen containing compounds are ubiquitous in chemistry and common in medicines and this research has the potential to impact the field of medicinal chemistry. This funding will also impact STEM workforce development as the students who work on this project will receive training in synthetic organic chemistry and will learn to conduct mechanistic studies, reaction optimization and development studies, and will become adept at compound characterization and structure elucidation. Professor Brewer will also be engaged in student training and outreach programs. This research is a fundamental investigation into the reactivity of oxazolidinones in the presences of an activating agent and base to prepare N-vinyl carbamates through a putative azomethine ylide intermediate. Importantly, the ring opening of oxazolidinones provides a method to form 2-(N-acyl)amino-1,3-dienes, which have not received thorough study from the synthetic community because there are few convenient and reliable ways to prepare them. This research addresses that gap by providing the synthetic community with a new way to access these intermediates. These diene products will be studied as reaction partners in Diels-Alder cycloaddition reactions to give cyclic N-vinyl carbamates. These studies will benchmark the reactivity of these dienes in cycloaddition reactions and provide insight into the scope of these reactions. These fundamental studies will make 2-(N-acyl)amino-1,3-dienes more useful to the synthetic community and ultimately this research will provide novel and efficient ways to prepare structurally-complex biomedically-relevant compounds, or basic but important molecular scaffolds that contain amines. 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 Faculty Early Career Development (CAREER) award supports research studying living tissues as new types of adaptive materials. Unlike traditional materials, living tissues are made of responsive and adaptive cells. They can change their structure or coordinate their movements in response to mechanical or chemical signals. These abilities make living tissues dynamic and flexible. They also lead to complex mechanical transitions in processes like wound healing, embryonic development, and cancer invasion. This project will investigate how cellular adaptive responses to the environment influence the larger-scale behavior of tissues. It will create a link between cellular biology and material properties, providing insights into treating related diseases. Moreover, this connection can advance the understanding of living materials and inspire the design of new materials to replace damaged tissues. The PI’s educational and outreach activities aim to foster interdisciplinary studies between fields like physics, biology, and materials science, and broaden the views of the next generation of scientists. This research project investigates the mechanics of living tissues as adaptive materials by incorporating cellular adaptability into shape-based tissue models. Cellular adaptability, such as epithelial-mesenchymal transitions and coordinated cell movements, is a key factor distinguishing living tissues from traditional soft materials. The research will explore how these adaptive behaviors influence phenomena like jamming and glass transitions, rheological responses, and emergent instabilities on the tissue level. The findings are expected to advance understanding of biological processes involving these phenomena, such as wound healing and cancer invasion. They will also provide insights into the interplay between cellular sensing and responding mechanisms and emergent tissue-level behaviors. This project will develop a new adaptive tissue model to integrate biological and soft matter physics perspectives. Using this model, computational and theoretical studies will produce experimentally testable and clinically relevant predictions for tuning tissue-level mechanical properties as needed. These predictions will support future collaborations between theoretical and experimental researchers, while the open-source computational tools developed through this work will facilitate further studies in tissue mechanics and adaptive materials. The outcomes may also inspire the development of bio-mimic materials with self-healing and adaptable properties, broadening the scope of biomechanics. This project is jointly funded by Biomechanics and Mechanobiology (BMMB) Program in the Division of Civil, Mechanical, and Manufacturing Innovation (CMMI) and the Established Program to Stimulate Competitive Research (EPSCoR). This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2025 · 2025-08

This project investigates what role environmental factors have in mediating the connections between human activities and the development of place attachment for these activities. The results of the research contribute to a generalizable theory of attachment to places and what role environmental conditions have in influencing the psychology of place connections. Broader impacts include the wide distribution of the research findings to stakeholders who are positioned to foster business and economic development opportunities within communities. The project provides STEM training opportunities for undergraduate and graduate student researchers, and for an early-career researcher. This research builds a generalized theory of the influence that external pressures have in influencing attachment to place. It takes a mixed-methods approach, including mapping geographic patterns of human activities, to analyze place attachments to natural landscapes. The research findings are applicable to explain human activities and connections to places in communities that are experiencing externally driven changes (e.g., natural disasters, land use changes, shifts in weather patterns) and how those changes impact place attachment. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2025 · 2025-07

This Engineering Research Initiation (ERI) award supports research that focuses on design and control of legged robots to adeptly navigate unstructured terrains, and especially subterranean environments, thereby promoting the progress of science, and advancing prosperity and welfare. Recently, robotic technologies have enabled the automation of many tasks that were once dangerous for humans. However, robots are still not advanced enough to navigate many natural terrain types that are either unsafe or not reachable by humans, such as rubble at disaster sites, or sandy surfaces on other planets. In particular, current robotic technologies have largely focused on surface locomotion, and few robots are able to navigate vertically underground, which restricts potential applications, such as grain monitoring for agriculture and soil sampling in remote locations. This research project looks to address this critical gap by integrating techniques in mechanical design and controls to uncover strategies for legged robots to navigate under the surface of loose, sandy substrates. This research seeks to generate knowledge of interaction mechanics within granular media and enable novel robotic capability below ground. Furthermore, this work seeks to expand robotics education in the state of Vermont by engaging graduate students in research, developing new undergraduate robotics curriculum, and fostering strong K-12 robotics programming. This research aims to elucidate fundamental principles of legged robotic locomotion in subterranean environments, through mechanism design, terramechanics modeling, motion planning, and feedback controls approaches. The project specifically focuses on legged robots due to their potential for multi-modal locomotion. The research looks to first characterize anisotropy in compliant appendage designs with simple actuation and control schemes, with the goal of understanding principles of mechanism design for granular settings. These designs will be implemented on a self-burrowing robot, which will serve as an experimental platform. Using this platform, high-level motion planning will be implemented to determine desired trajectories of the robot given goal positions within the substrate. The work will then incorporate reduced order models for granular media, such as Resistive Force Theory, to determine optimal sequences of appendage gaits which result in the desired trajectories. These model-informed approaches look to generate strategies for closed-loop control which incorporate state estimation subsurface. This project aims for both the design and control approaches to result in more intelligent navigation in a largely unexplored regime. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2025 · 2025-07

This I-Corps project focuses on the development of a multifunctional, uncrewed aircraft system (UAS) to monitor water quality and detect harmful algal blooms (HABs). HABs in freshwater bodies pose significant threats to both public health and the environment. These blooms, often fueled by excess nutrients such as phosphorus, can produce toxins that affect water quality. The neurotoxins released by certain bacteria species can contaminate water supplies, posing serious risks to human health. The rapid growth of these blooms can disrupt local ecosystems, harm biodiversity, and impact agriculture, tourism, and recreation. Timely monitoring of HABs and water conditions is critical for taking prompt action to protect public health and water safety and security. Current in-situ and remote sensing methods are limited by factors such as high labor intensity and cost, inefficiency, and an inability to provide real-time monitoring. This sensing system facilitates the identification of nutrient sources and conditions that promote algal bloom formation, helping to inform targeted strategies for mitigating or preventing future outbreaks. Furthermore, by tracking HABs and measuring water condition parameters, this technology allows researchers and policymakers to evaluate the effects of these events on aquatic ecosystems, including potential changes in species composition and ecosystem services. This solution has the potential to enhance sensing efficiency, reduce cost, expand coverage, and deliver more comprehensive monitoring results. This I-Corps project utilizes experiential learning coupled with a first-hand investigation of the industry ecosystem to assess the translation potential of the technology. This solution is based on the development of an uncrewed aircraft system (UAS) that combines the benefits of both in-situ and remote sensing, enabling real-time monitoring of harmful algal bloom (HAB) distribution, movement, and water conditions. The technology is equipped with multiple onboard sensing devices including multispectral imaging cameras, and can measure temperature, total dissolved solids, turbidity, potential of hydrogen, and other factors in real-time, using wireless data transmission. Additionally, this solution features a novel multi-tube water sampling structure, allowing for the collection of water samples from multiple locations to enable advanced laboratory analysis to identify cyanobacteria and characterizing HABs. The multispectral imaging camera enhances the system’s ability to visually monitor water conditions across a wide region, facilitating the tracking of HAB distribution and movement. Furthermore, the system incorporates a geographical information system to record spatial and temporal water condition data, generating maps for comprehensive water and HABs monitoring and management. This UAS-based water sensing system offers better monitoring of water conditions with increased versatility, efficiency, and precision in data collection and analysis. 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 focuses on an investigation of the effects of relative humidity on organic new particle formation in the atmosphere. The hypothesis is that water modifies and/or accelerates chemical processes that increase the instantaneous concentrations of oxidized, low volatility products and rapidly favors the formation of new particles. An integrated chemical/physical description of enhancements in particle formation due to relative humidity will be developed through environmental chamber research to help bridge the gap between modeled and measured organic aerosols in the atmosphere. The goals of this effort are to: (1) explore the relative humidity sensitivity of new particle formation for representative atmospheric organic aerosol precursors at atmospherically relevant mixing ratios of volatile organic compounds, humidities and organic seed aerosol loadings; and (2) use n-butanol, cyclohexane and hydrogen peroxide as hydroxyl radical scrubbers to identify the roles of different oxidative pathways in relative humidity sensitivity of organic particle formation. Systematic laboratory studies will be undertaken to measure organic particle formation (rates, yields, geometric mean diameters), as well as chemical profiles of freshly nucleated organic nanoparticles. This project will provide research experience and mentorship to graduate, undergraduate and high school 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 · 2025-06

Life cycle assessment (LCA) is a methodology that evaluates the environmental impact of a product or system over its lifetime. The existing methodology is not well-suited for systems whose characteristics vary in time, which are called dynamic systems. This project will combine LCA with models of dynamic systems to improve predictions of environmental impact. The project will use renewable energy hubs, a timely and economically important application, as a prototype to demonstrate new computational methods for analyzing environmental impacts of complex dynamic systems. The project will build a data exchange interface connecting LCA with other modeling environments. It will provide an environmental tool to determine the quantity and quality of energy used by these systems over time. It will also provide a teaching method to promote critical thinking about complex systems connected with the environment. Results from the project will help evaluate and minimize the environmental impact of energy technologies that affect the lives of millions of people every day. To advance dynamic life cycle assessment and close a modeling gap in multi-energy systems, this project will integrate life cycle analysis and system dynamic modeling in a bidirectional co-simulation framework. This will enable an accurate, numerically efficient, and accessible evaluation of environmental impacts of renewable energy hub systems. The research team will first develop a flexible data exchange platform based on the Functional Mockup Interface and interconnect Brightway and Modelica environments. For the demonstration, the team will investigate the accuracy and numerical performance of energy- and exergy-based functional units for life cycle assessment of renewable energy hubs. An interactive web module will be developed and deployed for understanding complex energy systems and the environment. The module will be evaluated for student cognitive and affective learning outcomes. The project will advance computational methods for studying environmental impacts of complex dynamical systems with tangible results in energy applications. Additionally, this project will provide publicly available open-source software and educational modules for undergraduate students. The module will be integrated into a core undergraduate course in Civil and Environmental Engineering at the University of Vermont. 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

With the support of the Chemistry of Life Processes (CLP) program in the Division of Chemistry, Professor David Punihaole of the University of Vermont is studying the development of new chemical imaging methods to investigate protein folding in living cells. Proteins must fold into specific 3D shapes to function correctly, but most current tools can only observe this process in test tubes, not within cells. To overcome this challenge, this project seeks to create a technique called Fast Raman Relaxation Imaging (FRReI), which will track protein folding in real time inside cells. This technique seeks to transform the current understanding of how cells regulate protein folding, preventing misfolding linked to diseases and genetic disorders. The project also includes an educational program that partners with the Library of Congress to train undergraduate students in advanced imaging techniques. This program seeks to equip students with valuable skills for future careers in science and technology. This project seeks to develop Fast Raman Relaxation Imaging (FRReI), a novel technique to directly monitor protein folding structural dynamics in living cells. The specific research objectives of this project are: (1) Build and validate the FRReI platform by incorporating an IR laser line into a stimulated Raman microscope; (2) Create novel Raman probes to attach to proteins for sensitive monitoring of folding dynamics; and (3) Use FRReI to study how different cellular environments, like the cytosol and mitochondria, influence protein folding. Success in this project could provide transformative insights into how cells regulate protein folding through mechanisms like post-translational modifications, molecular crowding, spatial compartmentalization, and chaperones. 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

University Of Vermont & State Agricultural College (UVM) will undertake an institutional self-assessment to investigate systemic barriers that lead to the attrition of some STEM faculty. The ADVANCE Catalyst project will result in a five-year STEM faculty equity plan tailored to the UVM context and institutional data that will guide institutional actions to address any issues identified during the grant. This project will identify gaps in campus-data infrastructure, collection, and analysis and collect qualitative data to accurately capture the experiences STEM faculty at UVM. The institutional self-assessment and five-year strategic plan will set a foundation to improve equity for STEM faculty at the institution. This work will benefit STEM disciplines as well as non-STEM disciplines due to the interconnected nature of institutional policy. Results of the Catalyst project will be regularly communicated with the UVM community. The project is expected to add to our understanding of STEM faculty equity issues at rural institutions of higher education in EPSCoR jurisdictions. This project is jointly funded by NSF ADVANCE and the Established Program to Stimulate Competitive Research (EPSCoR). The NSF ADVANCE program is designed to foster gender equity through a focus on the identification and elimination of organizational barriers that impede the full participation and advancement of diverse faculty in academic institutions. Organizational barriers that inhibit equity may exist in policies, processes, practices, and the organizational culture and climate. ADVANCE "Catalyst" awards provide support for institutional equity assessments and the development of five-year faculty equity strategic plans at academic, non-profit institutions of higher 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 2025 · 2025-03

Through this project, a workshop addressing reproducibility of research in homogeneous catalysis will be conducted. The workshop is led by Dr. Rory Waterman of the University of Vermont with co-organizers Drs. Jillian Dempsey (University of North Carolina), Abigail Doyle (University of California, Los Angles), and Aaron Sadow (Iowa State University). By convening experts from the academy, industry, U.S. funding agencies and national laboratories, and the broad homogeneous catalysis research community, assessment of the challenges for reproducible homogeneous catalysis will be performed. From that assessment, actionable opportunities to address these challenges will be proposed as well as suggestions to enhance the training of new researchers and professionals in catalysis to strengthen the domestic catalysis workforce. The wealth of discoveries in homogeneous catalysis—a central tool for industries including pharmaceuticals, consumer goods, agriculture, and energy—must remain transferable to allow further advances and economic growth. Ensuring that there are no inherent barriers to this knowledge transfer is key to solving a variety of current and future challenges, creating economic and employment opportunity, fostering new discovery, and preparing the next-generation science workforce. The Workshop in Reproducibility of Homogeneous Catalysis, supported by the Divisions of Chemistry and Chemical, Bioengineering, Environmental and Transport Systems, is planned for late 2025. This workshop will bring together at least 50 students, early-career researchers, and senior-level technical experts, selected by a scientific advisory committee that includes representatives from leading scientific journals and societies as well as academia and industry. Work in the fall will leverage community input through virtual and in-person meetings in the prior spring and summer. The workshop products will consist of a report followed by one or more journal articles that will be concurrently available in open-access format that summarize findings, challenges, and recommended best practices for homogeneous catalysis and training next generation STEM professionals. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2025 · 2025-01

This project aims to advance space exploration by improving how an assembly of systems collaborate in extreme environments like the Moon or Mars through the design of smart data-driven mechanisms capable of overcoming unexpected changes in the component systems and their sensors. By using smaller and smarter robotic systems, the proposed research seeks to replace complex and heavy rovers with multi-vehicle platforms that permit more efficient and resilient exploration. By partnering with NASA's Jet Propulsion Laboratory (JPL), the project contributes to the current push to return to the moon and the exploration of earth-surrounding celestial bodies enabling the establishment of a permanent human presence. In this vein, the project will demonstrate the efficacy of the proposed methodologies on state-of-the-art testing platforms and drive innovations in robotic technology that could benefit other fields like disaster response, environmental monitoring, and national security. Additionally, the research supports education and diversity by training the next generation of engineers and scientists in advanced robotics. This collaboration with experts at JPL will also strengthen U.S. leadership in space technology while fostering scientific progress in the national interest. The outcomes will contribute to the NSF’s mission to promote the advancement of science, national health, prosperity, and defense. The proposed project aims to enhance the performance and resilience of multi-agent systems (MAS) in space exploration by developing data-driven robustification mechanisms in collaboration with experts at NASA’s Jet Propulsion Laboratory (JPL). The primary goal is to improve the fault tolerance of MAS formations, consisting of multiple autonomous vehicles capable of distributively managing complex tasks in harsh and/or unknown environments. A novel model-free (MF) approach, based on the ultralocal model (ULM), will be used to simplify the control dynamics of heterogeneous agents (e.g., hoppers, hedgehogs, and tumbling robots) collaborating on tasks that when combined fulfill a common mission interest while maintaining performance. This method will enable real-time adaptation and robust control of a MAS by leveraging group redundancy and sensing capabilities to overcome structural and sensor failures. Additionally, integrating these methodologies into the high-level MAS controller may introduce new types of outliers or faults, which the proposed resulting algorithms are designed to mitigate. The project's key objectives are: (1) establishing a link between robust statistical methods and MAS resiliency against faults, (2) developing self-tuning control mechanisms to optimize formation dynamics and reduce control effort, and (3) validating these techniques on cutting-edge platforms at JPL. The research has the potential to significantly advance MAS technology for space missions, enabling more efficient and reliable exploration of celestial bodies, particularly the moon. Ultimately, this project will help position the University of Vermont as a leader in autonomous systems research for space exploration and foster long-term collaboration with JPL. 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

Organizations shape and depend on cooperation among members, but the volatility of cooperation can cause organizations to fail. Co-operative organizations are especially dependent on cooperation, but also generate unique social benefits. Understanding how co-operatives persist on volatile patterns of cooperation can clarify the general role of cooperation in organizational change and survival. This project examines how cooperation dynamics can drive organizational outcomes through a collaboration with a network of consumer co-operatives. The project will benefit participating co-operatives with data-driven organizational health reports, and benefit society at large with a cooperation management toolkit for any type of organization. This research can have profound impacts on the ability of organizations to grow and maintain cooperation and may contribute to strategies for growing cooperation to address pressing societal challenges. The project seeks to improve our understanding of organizational evolution by testing a novel dynamical theory of organizational change based on altruistic stress - the psychological stress of unreciprocated cooperative effort to advance group goals. The research team develops a model of a generic organization, which specifies the relationship between tasks, cooperation, altruistic stress, institutions and organizational outcomes. This model is tested with a rich multilevel organizational dataset. Building on a collaboration with a network of consumer co-operatives, the research team combines surveys, behavioral cooperation measures, organizational history data, and complete multi-year economic interaction networks across a large sample of co-operatives in the network. To assess the effect of altruistic stress on organizational change, the research team employs multilevel Bayesian analysis. In addition, network methods are used to describe the structural features of cooperation networks associated with organizational tipping points. Results will improve our understanding of the mechanisms that drive organizational cooperation, survival, and evolution. 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

How an organism responds to environmental stress has been a central question in evolutionary biology since its inception, but climate change has turned this interest into an imperative. Prior research has focused on individual species’ response mechanisms from the perspective of adaptation and acclimation, but there is a recent appreciation for how species interactions are also central to climate resilience. An organism’s interactions with its microbiome are one of these interactions, which have been recently identified as a key component of thermal resistance. We know very little about the microbiome of the most abundant animal on the planet, copepods, which may present an important yet unexplored component of their evolutionary and ecological dynamics under climate change. Copepods are experiencing massive die-offs due to increasing ocean temperatures, placing marine food webs and commercially-important fish stocks, such as cod, at risk. How the copepod microbiome changes and perhaps even mediates responses to thermal stress is unknown and precisely what the proposed project will ascertain. The proposed research will generate foundational data on the copepod microbiome and its link to thermal resilience. The insights gained will provide an additional diagnostic tool for assessing the impacts that coastal communities and fisheries may face under climate change. Furthermore, the project will generate opportunities for historically-excluded undergraduate students to participate in research, and develop an art-science communication module for the University of Vermont's Department of Biology. These initiatives will broaden the scope of participation within STEM and equip the next generation with new tools for sharing their science and engaging the larger community in the protection of marine ecosystems. This project explores how thermal evolutionary trajectories are shaped by the microbiome in the emerging model system, the copepod Acartia tonsa. The study will sample A. tonsa from its southernmost population in North America, Key Largo, Florida, and experimentally evolve these organisms at an elevated temperature with and without a probiotic treatment. The project PI has chosen this particular population, as it experiences a monthly mean temperature of 25C, as compared to more northern sites experiencing temperatures closer to 18C, thus yielding interesting insights on thermal adaptation. This project has three main objectives: 1) Determine the microbial composition of A. tonsa’s North American southernmost population and its variation across generations experiencing elevated temperatures and/or probiotics, 2) Assess the fitness consequences of A. tonsa experiencing elevated temperatures and determine if adding a previously identified putatively beneficial bacteria can rescue observed fitness costs, and 3) Link the functional potential of A. tonsa’s microbiome to the functions of the host’s loci under selection to determine if there is a synergy between host and microbiome response pathways. This project provides an unparalleled opportunity to consider how the host and its microbiome in tandem may shape or even offset each other’s responses to thermal stress, providing key understanding into the multiple factors involved in thermal resilience that will bolster marine ecosystem management. 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

Commonly known as the “spotted wing Drosophila,” Drosophila suzukii has become one of the most captivating cases of rapid worldwide invasion. Originally from southeast Asia, this fruit fly was first recorded in California in 2008 and has since spread to 48 of 50 states in the US, with parallel expansions occurring in Europe. In addition to its intriguing invasion biology, D. suzukii is a significant pest that causes up to $500 million in annual losses to US agricultural efforts. The proposed work will create long-term collaborations with local farmers and naturalists across Vermont and Kentucky to understand the genetic, physiological, and ecological underpinnings of D. suzukii’s success as an invasive species in the context of a rapidly changing world. Of particular interest to the work is D. suzukii’s capability to develop into summer-specialized and winter-specialized “morphs,” a physiological feature that plays a key role in their success and hardiness as invaders. The PIs will combine physiological experiments, genomics, and computer simulations to predict how these traits will evolve under various climate change projections. They will focus on the capacity of the fly to expand its habitat into northern latitudes as colder winters, an ecological delimiter for D. suzukii, continue to weaken due to climate change. They will also develop a summer science module for K-12 students focused on horticulture, invasive species, and climate change, and lesson plans from these modules will be published in peer-reviewed science education journals. The project will train multiple undergraduate interns, two graduate students, and a postdoc. Global climate change has introduced novel stressors to many habitats, and it is unclear which species may emerge as winners or losers in a changing world. To date, most efforts in climate change biology have focused on traits important for coping with extreme heat events. Yet, winter temperatures are warming twice as fast as summer temperatures in North America, and the evolutionary consequences of more heterogeneous winters remain understudied. This is a critical knowledge gap given that species distributions are often limited by minimum winter temperatures. Quantifying factors that shape winter biology is critical for predicting organismal responses to changing climates. This proposal investigates the relative contributions of plasticity, local adaptation, and seasonal adaptive tracking in fine-tuning key overwintering traits in D. suzukii. The PIs will use flies from Vermont and Kentucky to quantify genetic variation in cold tolerance, overwintering survival, and post-winter reproduction, as well as the reaction norms of these traits in summer/winter seasonal morphs. They will use whole genome resequencing to determine whether D. suzukii has persistent overwintering populations and the extent to which genetic structure is shaped by adaptive tracking. Lastly, they will create a novel set of simulations to explore how plasticity, local adaptation, and adaptive tracking evolve in a metapopulation that experiences fluctuating stressors. They will also incorporate projections into our simulations to predict how reaction norms for overwintering traits evolve in response to climate change. As the species of study is a major agricultural pest, the work is relevant to the bioeconomy. This project is jointly funded by Integrative Ecological Physiology (IOS/IEP) 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.