University of California-Davis

NSF Awards · FY 2026 · 2026-09

The project will provide travel and subsistence support for American and US based mathematical scientists at all career stages to enable them to participate in the program on Mathematics of Many-Body Entanglement that will take place at the Isaac Newton Institute for Mathematical Sciences, Cambridge, UK, September 1 - December 18, 2026. The program includes three workshops and one school with each up to 100 participants, with workshops on `Dynamics of Entanglement’ (September 1-4, 2026), on `Structure of entanglement’ (November 2-6, 2026), and on `Complexity of entanglement’, December 14-18, 2026. A School on Tensor Networks, will take place October 26-30, 2026. In addition, on November 11, 2026, the Isaac Newton Institute will host an event about how to create a startup in the quantum science and technology sphere. Facilitating the participation of US quantum science researchers in this top level program will help grow and strengthen the US workforce in quantum information science and increase the involvement of mathematical scientists. It will do so by sharing the newest insights and most promising research directions among participants and by building new relationships internationally. Such interactions are essential for the country’s competitive edge in quantum science and technology. Understanding and controlling quantum entanglement in many-body states sits at the core of quantum information science and key to its applications. This is a formidable problem both from the mathematical as well as the engineering point of view. This program has its major focus on the mathematics underlying quantum entanglement which draws from a broad swath of areas, including Tensor Networks, Geometric Invariant Theory, Operator Algebras, Complexity Theory, Statistical Mechanics, Quantum Field Theory, Random Matrix Theory, Commutative Algebra, Functional Inequalities, Fusion Categories and Hopf Algebras. The program will create exciting opportunities to develop new mathematics as well as the potential to create new interconnections between different areas of mathematics related to this application domain. The website for the overarching program is https://www.newton.ac.uk/event/mmb/. That page also contains links to further information about the three individual workshops, the school, and the startup event about entrepreneurship in quantum information science and engineering. 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

The conference "Statistics Beyond Euclid: Functional Data, Random Objects and AI" will be held on November 13–14, 2026, at the University of California, Davis. Modern scientific data is increasingly captured in complex, non-traditional formats—such as the evolving shape of a virus, the connectivity patterns of a social network, or the continuous movement recorded by a wearable health monitor. Unlike simple numbers or coordinates, these "non-Euclidean" objects do not follow standard geometric rules, rendering traditional statistical tools ineffective for making accurate predictions or quantifying uncertainty. This project supports a landmark conference that brings together world-leading statisticians and artificial intelligence (AI) researchers to develop a new mathematical language for these data types. By integrating rigorous statistical reasoning with modern AI, the conference aims to create reliable methods for analyzing complex structures, ensuring that breakthroughs in technology are grounded in mathematical rigor. These advancements are vital for progress in diverse fields, from biomedical imaging to climate modeling. Furthermore, the project serves a critical national interest by providing travel support and mentorship to graduate students and early-career scientists, ensuring that the next generation of the American workforce is prepared to lead in the rapidly advancing landscape of data science and AI. The conference "Statistics Beyond Euclid: Functional Data, Random Objects and AI" addresses the urgent need for foundational statistical methodology for data residing in general metric and geometric spaces, such as probability distributions, covariance matrices, manifolds, and functional trajectories. While modern deep learning architectures and large language models (LLMs) offer unprecedented computational power, they often lack the rigorous framework necessary to handle structured, non-Euclidean data or to provide valid uncertainty quantification. This project facilitates an interdisciplinary exchange among researchers in functional data analysis (FDA), metric-space statistics, and machine learning to address these challenges. Key focal points include extending FDA beyond Hilbert-space formulations, developing Fréchet regression and inference for object-valued outcomes, and creating geometry-aware AI architectures that respect the intrinsic properties of the response space. By exploring emerging frontiers such as causal inference for random objects and conformal inference in metric spaces, this conference aims to establish a robust theoretical and methodological bridge between classical statistical theory and modern AI applications for complex data structures. The conference website is: https://anson.ucdavis.edu/statisticsbeyondeuclid/. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2026 · 2026-07

This Faculty Early Career Development (CAREER) project seeks to determine how hawks change the shape of their wings and tail during flight to initiate and control turning maneuvers. Raptors maneuver with apparent ease in cluttered environments, such as forests or cities, exceeding the capabilities of comparably sized uncrewed aerial vehicles (UAVs). Lateral maneuvers like turning, rolling, or banking can be initiated by asymmetric changes in the shape and position of the wings or tail, such as folding one wing but not the other. This project will identify which wing and tail changes are used by hawks to turn in flight and quantify how effectively these changes control the resulting maneuvers. The project serves the national interest by promoting the progress of animal flight science and developing biomechanical insights to support avian rehabilitation. Furthermore, the results will inform the design of more maneuverable UAVs that contribute to securing the national defense by facilitating improved disaster-response and urban operations, contributing to Biotechnology priorities. By embedding the research within a collaborative ecosystem at the Center for Animal Locomotion and Innovation, the project strengthens STEM education and workforce development by training engineers, scientists, and veterinarians to communicate and collaborate across disciplinary boundaries. The research outputs will be directly integrated into public educational programming at the California Raptor Center, an online visual series on bird flight, a peer mentoring program across engineering, biology, and veterinary medicine, and undergraduate course curriculum modules that encourage a transdisciplinary approach in research and education. This project combines in vivo motion capture of red-tailed hawks (Buteo jamaicensis), wind-tunnel testing of a morphing wing-tail model, and coupled flight-dynamics modeling to connect biology, aerodynamics, and maneuvering performance. High-resolution imaging facilities will produce time-resolved measurements of dynamic wing-tail configurations employed during hawk turning maneuvers. These morphing actions will then be characterized with wind tunnel measurements of aerodynamic forces and moments to inform the development of a flight dynamics model of a maneuvering hawk. The output data and models will allow the identification of functional relationships between wing and tail control actions and the resulting flight maneuvers. The developed framework will provide a method to test hypotheses about avian and UAV maneuverability while advancing a fundamental understanding of how birds navigate complex environments. An Artificial Intelligence/Machine Learning regression model to predict bird wing joint angles from peripheral wing shapes alone will be developed. Together, the integrated research and education activities will address a fundamental question of how birds perform lateral maneuvers and train the next generation of engineers and scientists to address complex challenges at the interface of biology and engineering. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2026 · 2026-06

All forms of movement require an awareness of where the body and limbs are in space, an internal sense called proprioception. Proprioception requires the activity of specialized sensory neurons, referred to as proprioceptors, that detect changes in muscle movement and force. Proprioceptors transmit this sensory information in the form of electrical signals to the spinal cord. These electrical signals serve as the neural code that guides movement to ensure its accuracy and efficiency and are created by specialized proteins called sodium channels. During early development, as different motor skills, such as walking, are acquired, the proprioceptive sensory system undergoes dynamic changes that enhance movement accuracy and precision, thus making movement more effective. It is unclear, however, how changes in sodium channel function and expression contribute to the maturation of proprioceptor electrical signaling. This knowledge is critical understand how our proprioceptive system develops. Moreover, there are thousands of sodium channel mutations that have been identified in human patients with neurodevelopmental disorders. Notably, these disorders often include severe motor deficits as clinical manifestations; however, these deficits have traditionally been attributed to sodium channel dysfunction in the brain. This work challenges this notion and highlights the critical role of sensory systems for appropriate neurodevelopment. Importantly, this project will also provide educational opportunities to undergraduate researchers from local California State Universities through hands on, paid, summer research experiences, which will be an extension of our successful We Are UC Davis pathways to PhD program. This project advances NSF’s priorities in Biotechnology. This project investigates the cellular and molecular mechanisms that given rise to a mature functional phenotype in mammalian proprioceptors. Proprioceptors are specialized sensory neurons that initiate proprioceptive signaling, which allows for the awareness of body and limbs in space by detecting changes in muscle length and tension. Proprioceptors are highly excitable and rely on three functionally distinct sodium channel (NaV) subtypes to transmit sensory information about muscle movement to the spinal cord. Despite the essential nature of proprioception to all daily activities, how NaVs drive proprioceptor function is poorly understood. The goal of this project is to unravel the complex expression and functional dynamics of each NaV subtype in mammalian proprioceptors across development. This has previously been technically challenging, due to the lack of genetic tools to selectively target proprioceptors, which in turn has hampered progress towards understanding the roles of different ion channels in the proprioceptive sensory system. To overcome this technical barrier, this project will leverage a novel CRISPR/Cas9 intersectional genetic and viral approach developed by the Griffith lab. By combining sensory-neuron specific viral delivery of single-guide RNAs with spatially restricted Cas9 expression, this work will, for the first time, allow for selective targeting of sodium channels in proprioceptors with temporal precision. By combining this approach with behavioral analyses, varied electrophysiological approaches, and quantitative image analysis, the research will analyze the temporal dynamics of sodium channel localization and function in proprioceptors during postnatal development. Collectively, this work will advance knowledge of mammalian proprioception by uncovering the developmental timing of NaV function and localization that drive proprioceptor signaling. 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 · 2026-05

Understanding the structure and function of the tissues that transport water and sugars in plants—xylem and phloem—is essential for predicting and managing the future of both agricultural and natural ecosystems and how they respond to stress. Yet, there are large gaps in current knowledge about how these tissues are organized, how they interact, and how they respond during drought. This project will test hypotheses for how the coordinated anatomy and physiology of leaf carbon and water transport determines the growth, drought resilience, and geographic distribution of plant species, for a wide range of species of herbs, shrubs, and trees from across the US. By combining measurements of xylem and phloem function with state-of-the-art mechanistic models at different scales (leaf, whole plant and ecosystem), the project team will generate fundamental discoveries and resolve how the leaf carbon and water transport systems contribute to whole plant and ecosystem function. This project will benefit the American public more broadly by creating unprecedented databases for leaf structure and function and plant responses to drought, by training undergraduate students in methods of research, data analysis, and writing, and—in collaboration with artists—by developing workshops to transform scientific research into creative public engagement, combining lectures, demonstrations, and hands-on activities, including creation of visual pieces and augmented- and virtual-reality experiences. Further, the project will include outreach to the grape and wine industry, highlighting new discoveries, as the interaction between sugar and water transport in grapevine leaves strongly influences grapevine stress responses and wine quality. The goal of this research is a mechanistic understanding of the variation in leaf xylem and phloem traits and their coordination and dynamics during drought, and implications at tissue, organ, plant and ecosystem levels. First, the project will break new ground in establishing how leaf sugar and water transport are integrated physiologically, and how this integration influences growth at leaf, plant, and ecosystem scales and adaptation across climatic niches. In particular, the project will resolve how leaf carbon and water transport anatomy and flow rates are coordinated within and across species, how they determine maximum rates of gas exchange and growth, how they vary with other functional traits, and how they adapt to environmental conditions. Second, the project will provide new resolution of drought impacts on leaf sugar and water transport across scales, including on ecosystem carbon and water fluxes. The project will clarify drought responses—how, and in what sequence, the sensitivities of the leaf xylem-phloem complex influence the responses and resilience of leaf gas exchange, leaf expansion, plant growth (and, if unrelieved, plant mortality), and ecosystem functions. The project team will generate physiological and functional trait data and model products that will be of value to a wide range of scientists from physiologists to ecologists, the training of many undergraduate and graduate students, innovative art/science workshops, and an unprecedented understanding of leaf xylem and phloem structure, dynamic transport, and implications for drought tolerance, adaptation and ecosystem function. 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 · 2026-05

This project supports long-term research in rangeland ecosystem to better understand the relationships between livestock, wildlife, and other stressors impacting ecosystem resilience. Researchers will study the impact of multiple factors on competition and coexistence of livestock with wildlife, and the stability and resilience in a savanna rangeland community in the face of drought, fire, and other environmental stressors. This research provides a unique and essential baseline for the conservation, management, and restoration of rangelands including those in the United States, which lost most of its large herbivores more than 10,000 years ago, but where efforts are underway to reintroduce species similar to those lost. This project will fosters the career development of a strong research team of early career researchers and graduate students and outreach to stakeholders. The use of molecular techniques and remote sensing technology to evaluate the impact of herbivory, drought, and fertilization will improve rangeland management practices from targeted approaches to the landscape scale. This proposal is to support years 31-35 of the Kenya Long-term Exclosure Experiment (KLEE), a controlled replicated experiment examining the separate and combined effects of livestock, wildlife, and fire on each other and on their shared savanna landscape. Although it is becoming increasingly clear that loss of native fauna (“defaunation”) can have far-reaching effects on ecosystems, experimental studies to evaluate these effects remain rare. KLEE uses semi-permeable barriers to create six replicated treatments comprised of different combinations of 1) cattle, 2) meso-herbivore wildlife, and 3) mega-herbivores (elephants and giraffes). This project provides a unique opportunity to understand how interactions between defaunation and multiple pulse and press disturbances affect ecosystem stability and function. After 30 years, the six herbivore treatments support distinct (but still diverging) plant communities, providing powerful opportunities to 1) analyze long-term data in the context of community and ecosystem resilience and stability, and 2) analyze new experimental layers and additional response variables that, along with our previous core long-term data, allow us to assess community resilience under multiple disturbance stressors, including herbivory (three guilds), drought, fire, fertilization, heavy grazing, and termites. The project will continue to add to and explore this rich data set. The decadal proposal also included an ambitious plan to implement experimental reversals of several KLEE treatments in the second five years to test dynamics related to the efficacy of rewilding, the reversibility of rangeland degradation, and the stability of alternative ecological states in general. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2026 · 2026-04

This conference is the fifth annual program-wide meeting in the National AI Research Institutes Program, advancing U.S. leadership in artificial intelligence research. The Summit for AI Institutes Leadership (SAIL 2026) is an NSF-sponsored conference organized and executed by the program’s hub activity, the AI Institutes Virtual Organization (AIVO). The conference gathers the leaders and other key personnel from all AI Institutes to foster community building of those Institutes and other related activities into a network of collaborating organizations conducting knowledge exchange, growing their own competencies, and engaging with the broader public. The conference will take place November 2-4, 2026, in San Diego, CA. This conference aims to maximize the value of the AI Institutes as a flagship national AI investment. The conference delivers on the intent of NSF and its funding partners to continue to nurture the AI Institutes into a fully cohered national program, resulting in synergy across the constituent institutes that is greater than the sum of its parts. This gathering builds upon the successes and lessons from the previous SAIL events (2022 through 2025) and continues a successful record of establishing SAIL as the cornerstone event for the National AI Research Institutes program. The conference program addresses the needs of AI Institutes in various stages of their lifecycle, from fully established to newly awarded AI Institutes. This greatly enhances knowledge transfer among all. The program includes knowledge exchange about education and outreach, project management, computing and research infrastructure, communications, workforce development, and ethics. The conference is comprised of a balance of community-moderated panels with plenary sessions and other program-wide community building. A day of workshops prior to the main conference allows the program’s special interest groups to hold smaller community workshops around topics of interest withing a specialized area, and across institute boundaries. The plan also calls for a substantial involvement of graduate student participation. Professional recordings and archives will maximize post-event impact both within the AI Institutes community and publicly to promote ongoing collaboration and knowledge dissemination to sustain and grow the impact of federally funded AI research. 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

The ability to build or repair biological structures inside the human body without open surgery has long been a goal of medicine and manufacturing, with the potential to reduce infection risk, recovery time, pain, and healthcare costs. Existing bioprinting approaches rely on light or heat to solidify materials, which limits their use to shallow or externally accessible regions because these energy sources cannot penetrate deeply into biological tissues. Ultrasound, by contrast, can travel through the body safely and precisely, offering a fundamentally new pathway for non-invasive bioprinting. Recent advances have shown that ultrasound can trigger material solidification at depth, but a critical barrier remains: the lack of scientific understanding needed to ensure that this process can be performed safely in the presence of living cells. This EArly-concept Grant for Exploratory Research (EAGER) project addresses that barrier by focusing on the cell-level safety of ultrasound-driven bioprinting. By identifying how ultrasound exposure, material chemistry, and protective strategies interact to affect cell survival, the research advances fundamental knowledge at the intersection of physics, chemistry, and biology. The outcomes will inform the responsible development of non-invasive manufacturing technologies with broad implications for regenerative medicine, minimally invasive therapies, and advanced manufacturing. The project also contributes to workforce development by training students in interdisciplinary research spanning biomaterials, ultrasound physics, and cell biology, supporting the national interest in scientific leadership and innovation. The goal of this EAGER project is to establish a predictive, cell-level safety framework for ultrasound-induced bioprinting by systematically de-risking the mechanical, thermal, and chemical effects experienced by living cells during material solidification. The research focuses on in vitro and ex vivo systems and deliberately excludes tissue-scale translation to concentrate on fundamental cellular mechanisms. The approach integrates multiphysics modeling of acoustic pressure, cavitation behavior, and sonothermal heating with experimental studies of bioink formulation, polymerization chemistry, and cell encapsulation strategies. Candidate inks will be engineered to balance polymerization efficiency with reduced radical generation and thermal exposure, while encapsulation architectures will be evaluated for their ability to shield cells from mechanical stress and transient heating. Cell viability, membrane integrity, and stress responses will be quantitatively assessed to define safe operating windows for ultrasound exposure. The expected outcomes include validated design rules for cell-compatible inks, quantitative exposure limits, and mechanistic insights into how ultrasound-driven polymerization can transition from damaging to constructive at the cellular level. These results will provide the foundational knowledge required for future advances in non-invasive bioprinting and ultrasound-based manufacturing technologies. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2026 · 2026-02

Many aquatic organisms use a technique called metachronal rowing to swim. They use their paddle-like appendages to row in a coordinated rhythm, starting from the rear and moving toward the front of the organism. Metachronal rowing is observed in organisms that range in size from single cells to large crustaceans such as shrimp and krill. This project will use experiments and computational modeling on live animals and metachronal rowing vehicles to explain why this swimming technique works regardless of organism size. By examining how animals of different sizes optimize their swimming appendages, this research will help design underwater vehicles that can efficiently operate over broad ranges of sizes and speeds. Undergraduate and graduate students will receive cross-disciplinary training in fluid mechanics, robotics, and scientific computing. New summer camps on bio-inspired engineering will be developed for high school students. This project will elucidate the fluid dynamic principles that enable thrust and lift generation by metachronal rowing across an astounding seven orders of magnitude in paddle-scale Reynolds number from strongly viscous (0.01) to inertially dominated (10,000) flow regimes. Previous studies of metachronal rowing have considered tethered paddling without body motion for a limited Reynolds number range. Flow visualization and force measurements will be performed on state-of-the-art dynamically similar remotely operated vehicles to examine the flow physics, swimming performance and scalability of metachronal rowing across the biologically relevant range of Reynolds numbers. Computational simulations will be conducted to examine fluid-structure interaction in untethered metachronal swimming and evaluate the cost of transport for varying Reynolds number, paddle geometry and kinematics. The integrated experimental and computational approach will be used to test whether differences in the mechanical design and paddling kinematics of natural metachronal swimmers can facilitate efficient locomotion in their specific flow regime. The findings can guide the development of new bio-inspired autonomous underwater vehicles that provide efficient propulsive performance across broad ranges of sizes and speeds. 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

Winter is the fastest warming season in the northern hemisphere. For millions of the world’s seasonally-frozen lakes, this warming means shorter and thinner ice cover and changing patterns of snow accumulation on the ice. Because ice and snow affect many fundamental physical, chemical, and biological properties of lakes, changes in winter conditions can disrupt lake ecosystems and the services they provide to humanity. Until recently, lake scientists paid relatively little attention to winter, meaning we know very little about how lakes work when covered by ice and snow and how winter conditions affect the rest of the year. This leaves scientists ill-prepared to predict how changing winters will impact lakes or to mitigate negative impacts. This study addresses this “winter knowledge gap” and develops a predictive understanding of how winter conditions affect the ecological populations, communities, and food webs of diverse types of lakes. Along with intensive studies of lakes by the core project team, the investigators are also recruiting researchers from dozens of institutions to expand sampling to many additional lakes. This ‘Team Science’ approach will train many aquatic scientists in specialized winter sampling methods, empowering other scientists to include studies of winter conditions in their research programs. It will develop a network of winter-hardy aquatic researchers with the goal of advancing understanding of year-round ecosystem function in the face of climate change. The project provides education and training opportunities for multiple graduate and undergraduate students and a postbaccalaureate researcher. This study combines two approaches: 1) detailed seasonal studies of ecological processes in 12 lakes by the project’s investigators; and 2) research across at least 60 other lakes by a network of collaborators. In the first part of the effort, the investigators are focusing on 12 lakes with contrasting water quality characteristics and winter severity. The lakes are being instrumented with continuously-recording temperature, light, and oxygen sensors. The investigators are also studying water, bacteria, phytoplankton, and zooplankton throughout the year to determine how plankton populations and communities evolve through seasons in different lake types. Using stable isotopes and fatty acid analysis, the investigators are assessing the way food web structure changes across seasons and the production and cycling of organic matter. For the second part of the study, the investigators are recruiting a network of researchers to collect samples from at least another 60 lakes. These collaborators are being trained in winter research methods and are provided with sampling kits and instructions for sample collection. Their samples are being analyzed with samples from the core set of 12 lakes, ensuring compatibility of results. Collaboration between the co-PIs and their network is allowing for broad participation in interpretation of data and testing of hypotheses about the way winter severity interacts with water quality to affect lake ecology. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2025 · 2025-10

This project aims to serve the national interest by expanding access to high-quality, adaptable STEM learning materials through the development and large-scale deployment of Open Educational Resources (OER) and integrated social annotation technologies. This Level 2 Institutional and Community Transformation project addresses the ongoing challenge of limited access to affordable and pedagogically effective instructional materials, particularly in large educational systems. At the core of the project is the enhancement and deployment of Nota Bene (NB), a web-based annotation platform that enables students and instructors to engage in contextual conversations directly alongside the textbook content - the digital version of marking up physical textbooks. By analyzing these in-line comments, instructors can identify confusing, outdated, or incomplete material, and students can contribute insights or suggest improvements that reflect their learning needs. This project will integrate Nota Bene into the LibreTexts platform, one of the most widely used OER repositories, with the aim of transforming passive reading into an interactive and collaborative STEM learning experience. The project's goals are to establish social annotation as a core practice in STEM education, to create a scalable infrastructure for collaborative OER development, and to generate actionable knowledge on how learner-instructor interactions through annotation drive content improvement and learning outcomes. Specifically, the project will (1) redesign and expand the NB platform to enhance usability for different types of STEM courses and institutions nationwide, (2) develop and implement workflows for peer review and open pedagogy that embed continuous feedback loops into OER lifecycle management, and (3) build and operationalize CalOPEN, a statewide hub supporting OER creation, adoption, and innovation within California's higher education systems, serving as a replicable national model. The project employs design-based research to iteratively improve the Nota Bene platform and its pedagogical integration, coupled with large-scale learning analytics to examine patterns in annotation use, feedback quality, and their correlation with student learning gains. Assessment and evaluation will be multi-faceted, involving external evaluators who will conduct surveys, interviews, system usage analysis, and performance assessments to measure impact on student engagement, instructor practices, and content quality. The NSF IUSE: EDU Program supports research and development projects to improve the effectiveness of STEM education for all students. Through the Institutional and Community Transformation track, the program supports efforts to transform and improve STEM education across institutions of higher education and disciplinary communities. 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 Noyce Track 2 project aims to serve the national need to address lagging achievement scores in STEM education by preparing highly-qualified STEM teachers who can effectively leverage artificial intelligence (AI) to advance student learning. AI holds significant promise for enriching learning through its ability to personalize learning experiences, provide timely feedback and support, and help teachers identify and address student needs. This project plans to support one cohort of 12 STEM professionals (i.e., biological & physical science, computer science, engineering, mathematics, and statistics undergraduate majors) to become 7-12th grade teachers through an innovative program that combines research-based pedagogies with strategic AI integration. First, participants are expected to complete an integrated post-baccalaureate teaching credential and MA program while gaining hands-on experience using AI tools in high-need schools. The project is designed to provide participants training in, and conduct teacher research on, AI applications for advancing student learning. Finally, participants are scheduled to host an "AI for Advancing Learning in STEM Education" conference to share their research and practical strategies with other educators to help build a broader community of teachers skilled in leveraging AI to create supportive and engaging learning environments. The proposed project components intends to prepare highly-qualified STEM professionals to become highly-effective 7-12th grade STEM teacher leaders who have a strong understanding of the practical applications, challenges, and successes of leveraging AI to advance student achievement and success in STEM. This project at the University of California, Davis, includes partnerships with the high-needs Twin Rivers Unified School District, and the non-profit organization Wicket who is dedicated to empowering STEM learners via technology. Project goals include recruiting one cohort of 12 STEM professionals (i.e., biological & physical science, computer science, engineering, mathematics, and statistics undergraduate majors) to become 7-12th grade teachers in high-need school districts, providing training in research-based pedagogies and the strategic use of AI to support these practices, and cultivating these teachers as leaders who can effectively leverage and advocate for the use of AI to advance student learning in their schools and beyond. This project seeks to utilize an iterative evaluation process. The evaluation plan is designed to draw on the following project goals: (1) recruit one cohort of 12 highly-qualified STEM professionals to become 7-12th grade STEM teachers serving high-need schools, (2) provide comprehensive training in research-based pedagogies early in the project, while developing teacher fellows who are AI-ready, and (3) cultivate a cadre of teacher leaders who can effectively leverage and advocate for the use of AI to advance student learning in their schools, the region, and nationally. Through surveys, interviews, and an array of teaching artifacts, this project can offer new knowledge about approaches for recruiting, preparing, and supporting STEM teachers to use AI to advance student learning - insights particularly valuable given the novelty of AI technologies and the need to understand their use in education. Broader impacts include addressing the shortage of highly-qualified STEM teachers while building capacity among educators to effectively leverage AI to support and advance learning. Project findings plan to be disseminated through publications and conference presentations to inform both research and practice in STEM education. This Track 2 Teaching Fellowship project is supported through the Robert Noyce Teacher Scholarship Program (Noyce). The Noyce program supports talented STEM undergraduate majors and professionals to become effective K-12 STEM teachers and experienced, exemplary K-12 teachers to become STEM master teachers in high-need school districts. It also supports research on the effectiveness and retention of K-12 STEM teachers in high-need school districts. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2025 · 2025-10

2318717 (Cobian). In recent years, high-severity wildfires have severely damaged forested mountain watersheds that store carbon, supply western-states’ water, and provide many co-benefits. While periodic low- to medium-severity wildfires are a natural part of western U.S. forests, too much of the area is burning at high severity owing to excessive fuel loads and a warming climate. Therefore, a critical goal of forest management is often the introduction of practices designed to reduce the risk of high severity fires. These practices, known collectively as forest restoration, involve thinning and removing or masticating the less-fire-resistant, smaller trees, while leaving larger, more widely spaced trees. A common restoration approach is applying mechanical thinning followed by mastication which changes the forest-canopy structure and surface-fuel loading and characteristics. While it is known that changing the arrangement and density of trees and other vegetation through mastication affects fire behavior, there is a lack a quantitative understanding of how attributes of masticated fuels affect subsequent fire severity. The goal of this project is to develop metrics of the impact of fuel treatments on fire behavior across a range of spatial and temporal scales. This project will contribute to improved understanding of how surface-fuel attributes, resulting from management decisions, influence fire behavior and severity and thus forest biomass carbon storage. Mechanical thinning followed by mastication changes the forest-canopy structure and surface-fuel loading and characteristics. While these changes can impact fire behavior significantly, their sensitivities to the type and degree of mastication are not well understood. The three main goals of the study are: (1) To establish the importance of heterogeneous and multiscale surface-fuel loadings and attributes, following mechanical fuel treatments, on the temperature, flame length, and spread of fire as these fuels burn; (2) To develop and assess predictive tools for wildfire severity and spread in the forest given these different fuel attributes, under contrasting micro-meteorological conditions; and (3) To demonstrate carbon-storage and related benefits from different fuel treatments designed to reduce the occurrence and impacts of high-severity wildfires. The study will use bench-scale combustion experiments, field measurements involving controlled burns in thinned and masticated stands in a mixed-conifer forest, and modeling of wildland fire and carbon storage. Results from experiments, modeling, and assessment of field-scale control burns will be used to identify key drivers of fire severity in ground fuels following thinning. Primary contributions of the study will be improved understanding of how surface-fuel attributes, resulting from management decisions, influence fire behavior and severity and thus forest biomass carbon storage. Results will improve modeling of flame length, fire spread and carbon balance and storage in the litter, mineral soil, and deeper regolith. This research will engage decision maker and stakeholder partnerships with organizations that are advancing landscape restoration, providing much-needed improvements in projection of how their investments may affect wildfire severity. 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 doctoral dissertation research investigates the impacts of digital innovations on eldercare in aging societies. Researchers specifically test the extent to which digital tools and an eldercare industry can transform the quality and capacities for elder care. Investigators specifically study the use of a wide range of digital platforms and smart technical devices such as wearable sensors, telecare and telemedicine, big data platforms for health monitoring and prediction, and smart home technology. Broader impacts of this research include training of a graduate student and dissemination of findings at industry conferences, through podcasts, and in public venues that focus on elder-care practices. Research findings will contribute to understanding the intersections between technological innovations and society. The data will inform the future management of eldercare and aging and responds to research priorities in the science of artificial intelligence through the study and usage of computers and software capable of intelligent behavior and their impact on society. The research also contributes to the translation of findings into novel use-inspired technologies to improve elder care 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 2025 · 2025-10

As population growth and urbanization accelerate worldwide, the demand for infrastructure that is both resilient and resource-efficient is becoming increasingly urgent. Concrete, the most widely used human-made material on Earth, plays a central role in meeting this demand but faces escalating challenges related to aging when exposed to the elements, material scarcity, and the limitations of current construction methods. This United States/Saudi Arabia workshop brings together leading researchers from both nations to identify transformative solutions for next-generation concrete materials and systems. Both the United States (US) and the Kingdom of Saudi Arabia (KSA) are leaders in research and technology transfer related to advanced cementitious materials. However, both countries face important challenges linked to the availability of quality materials, aging infrastructure, and the growing frequency and severity of extreme natural events. By fostering international collaboration and integrating expertise across materials science, construction engineering, and structural engineering, the workshop advances the national interest by promoting scientific progress and laying the foundation for innovative infrastructure that enhances public welfare, defense readiness, and economic resilience. A particular focus on mentoring early-career researchers and building global research networks supports the development of a skilled, internationally engaged STEM workforce. Technically, the workshop will bring approximately 20 US researchers to Riyadh to collaborate with counterparts from the KSA in four key research areas: (1) resource-efficient construction, (2) durability and resilience, (3) innovative materials and construction methods, and (4) next-generation cementitious binders and mixture design. The role of artificial intelligence, automation, and machine learning in accelerating innovation will be integrated into each focal area. The workshop will identify critical knowledge gaps and catalyze new directions in cementitious materials research, advancing a platform for interdisciplinary collaboration. Outcomes will include a joint research agenda, a publicly available workshop report, and a digital platform to support ongoing collaboration and proposal development aligned with the priorities of NSF and KSA’s Research, Development, and Innovation Authority. 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

Methane is a greenhouse gas. It is made in landfills and wastewater treatment facilities. Certain microbes can grow on methane. These microbes can also produce biodegradable polymers. However, separating these polymers from the cells that produce them and then purifying them is difficult and expensive. One promising biodegradable polymer, PHA, is the focus of this project. To reduce the cost of purification, methane-consuming bacteria that produce PHA will be genetically engineered to accomplish two objectives. One is to cause the cells to automatically break open after producing PHA. The second is to reduce the amount of protein produced to simplify purification. This could significantly improve the affordability of PHA. Educational and workforce development programs will prepare students to join the biomanufacturing industry. The overarching goal is to significantly reduce the downstream processing (DSP) costs associated with methane-derived polyhydroxyalkanoate (PHA) production through advanced genetic engineering of Type II methanotrophic bacteria. Two specific bacterial strains will be targeted for these genetic modifications: the well-established model strain Methylosinus trichosporium and the Mango Materials’ proprietary strain (str. mngo). Genetic modifications will target pathways to facilitate easier recovery of PHAs from the fermentation process. The project employs iterative Design-Build-Test-Learn (DBTL) cycles supported by proteomic analyses, techno- economic analysis (TEA), and life cycle assessment (LCA). The goal is a reduction of 5–10% in capital expenditures and up to 15% in operational expenditures. This research will yield robust genetic engineering tools and engineered strains, while also significantly advancing fundamental scientific understanding in synthetic biology, metabolic engineering, and microbial physiology, particularly in non-model organisms that have traditionally been challenging to engineer. A detailed case study and a specialized industry workshop will facilitate the dissemination of a generalizable framework for DSP-focused genetic engineering, directly benefiting the broader biomanufacturing community. This project is being jointly supported by ENG/CBET/CBE and the BioMADE Manufacturing Innovation Institute. 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

Understanding events, such as who did what to whom, when and where, is one of the fundamental human activities to learn about the changing world. The answers to these questions underpin the key information conveyed in the overwhelming majority, if not all, of language-based communication. However, current research paradigm suffers from several shortcomings in extracting event knowledge from the open world scenarios. In these scenarios, knowledge extraction from data is limited to a few large domains (e.g., news or biomedical) or common languages (e.g., English, Spanish and Chinese), because of the heavy reliance on the human effort to contextualize data. This includes creating large-scale manual annotations or defining the schematic templates for a few target event types. This project aims to lay the foundation and establish new paradigms for open world event knowledge extraction by developing new and more efficient algorithms to extend the extraction capability to the wide range of scenario, while requiring minimal human effort. This foundation should provide extensive coverage of different event types and be easily adapted to emerging scenarios. The success of this project will directly benefit users of the intelligent information access systems. For applications that analyze emerging and trending topics and events, such as natural disasters, protests and disease outbreak, success of the proposed research will not only provide an accurate and abstractive summary and easy access of each topic for humans, but also allow analysts to better discover the participants of the events, the cause, effects and temporal orders among them, and help discover more insights. The technical aims of the project are divided into three thrusts. Thrust 1 develops schema-guided event extraction approaches. This is done by leveraging the knowledge from the complex target event schema, such as the event type structures (i.e., type name and argument roles), hierarchy and temporal/causal/part-whole relations among the event types, which provide valuable guidance, especially when there is few to no annotations available. While event annotations for most of the domains and scenarios are not existing and extremely expensive and time-consuming to obtain, the large-scale unlabeled in-domain data are usually accessible. Thus, Thrust 2 will further develops a suite of more efficient and novel self-training strategies to make use of the large-scale unlabeled data through self-supervision. In practice, there is even no event type schema available to most of the domains and scenarios, such as natural disaster or disease outbreak. Manually defining an event schema with high coverage is extremely challenging and time consuming as it requires background knowledge in both linguistics and the target domain, and humans need to manually examine a large amount of in-domain data to determine the salient event types. Considering these challenges, Thrust 3 further explores novel solutions to automatically deduce the target event schema, including event types, the roles of their participants, as well as their relations from the raw text and extract their event mentions accordingly. 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

Landslides are a significant natural hazard that threaten both human safety and infrastructure. They can cause billions of dollars of damage and thousands of deaths and injuries annually. To reduce the impact of landslides on people and infrastructure it is important to improve our understanding of landslide dynamics and the processes controlling this diversity of behaviors. This CAREER project leverages a unique seismic dataset collected along an active landslide near Yakima in Washington State, the Rattlesnake Ridge landslide. The project will detect and analyze small earthquakes that occurred as the landslide developed and use these to better understand how the landslide evolves and what drives its failure. Because the landslide occurred on land from the Yakama Nation, the project will work closely to inform the Tribal Council and others about results from this research. The project will conduct field trips for students at Heritage University and the Tribal School to understand landslides in the region. The project also supports a graduate student. The Rattlesnake Ridge landslide is a hillslope located in Union Gap, WA that has rapidly destabilized over the last few months. Cracks in the ridge were first identified in late October of 2017 and over a two month period the 3 million cubic meter mass detached from the adjacent hillside and began accelerating downhill. The future behavior of the slide is still uncertain and concerning. This project will perform a comprehensive seismic analysis of data collected from this event. The dataset is rich in small magnitude seismicity. The research plan consists of earthquake detection, location, cross-correlation to determine which events repeat, comparison of the spatiotemporal distribution of seismicity with geodetic data recording slide motion, and local precipitation. The results of this analysis will be synthesized estimate the stresses acting on the slide body and address why the Rattlesnake Ridge landslide is slowing down while other nearly-identical slides in the region have failed catastrophically. Behavior of seismicity at the slide will provide insight into how repeating earthquake sequences on tectonic faults evolve over large displacements, a proxy for long timescales. The results of the analysis will constitute a unique case study that will provide insight into landslide character and failure mechanisms helping to evaluate underlying hazard. This proposal is supported by the Geophysics program, The Tribal Colleges and Universities Program and the Prediction of and Resilience against Extreme Events (PREEVENTS) 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

Reinforcement learning (RL) is an area of machine learning where agents learn from interacting with environment to determine actions. RL tools have been widely used in many different engineering systems such as power grid, wireless communications, and autonomous driving etc. A common goal in these decision-making tasks is to determine an optimal policy that minimizes the expected total discounted cost, which is also named risk-neutral approach. Although the risk-neutral approach is quite popular, it does not take the tail of the distributions of the cost into consideration. In the tail of the distribution, the cost may be prohibitively high, even though the probability of happening is low. In safety-critical applications, it is important to consider these rare but consequential events. Given the potential drawbacks of risk-neutral approach in safety critical applications, there is a pressing need to systematically study the risk-sensitive approach, in which one designs decision policies that take the risk into consideration. The goal of this project is to develop a unified framework for the design of efficient algorithms for risk-sensitive RL by systematically employing a class of risk measure named coherent risk measures and develop efficient algorithms that could be implemented in engineering systems. Even though a multitude of risk measures have been extensively studied in the literature and successfully applied to RL, existing work face the following challenges: 1) Most of these applied risk measures are not coherent; 2) While there is an increasing demand for a broader range of choices in risk measures to better align with individual risk preferences in complex scenarios, there is a lack of unified framework that enables efficient design of risk-sensitive RL algorithms tailoring to users’ different choices of risk measures suitable for their applications; and 3) There may be model uncertainties and model shifting in practical applications, but existing risk-sensitive RL algorithms are not robust to such scenarios. To address these challenges, this project is focusing on two interconnected thrusts. In the first thrust, the research team aims to develop a unified framework for the efficient design of risk-sensitive RL using coherent measures tailoring to users’ different choices of risk measures suitable for their applications. In the second thrust, based on the developed framework, the team aims to design robust risk sensitive RL algorithms that are robust to model uncertainties and model misspecifications. 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

Barrier island breaches have occurred during many tropical storms, constituting a major mechanism for tidal inlet formation, dune and beach erosion and development. Thus, they represent a major challenge to coastal management. The current understanding of the fate, physical processes, and impacts surrounding new and evolving breaches is limited due to the lack of comprehensive longitudinal studies capturing the breaching event and post-breaching evolution on monthly and annual time scales in a holistic and transdisciplinary manner. To address the current gaps in knowledge and data, this EArly-concept Grant for Experimental Research (EAGER) study investigates the development of two barrier island breaches from their original formation over multiple seasons and years, and their potential impacts on coastal management and infrastructure systems. The "high-risk high-outcome" study is expected to reveal new insights into the roles of hydrodynamics, land coverage, and geomechanical sediment properties on barrier island breach evolution, as well as into the impacts of these newly formed inlets on coastal infrastructure systems. It looks to unravel the importance of barrier island breach data collection for informed coastal management, planning, engineering design, and decision-making in coastal regions affected by storms. The data are expected to become a benchmark data set that will serve the wider coastal research community for calibration and validation of numerical and physical models and the development of new concepts, relationships, and theories regarding the geomorphological evolution of storm-induced barrier island breaches, local hydrodynamics, surrounding sediment and land-use conditions, coastal infrastructure, and the built environment. Midnight Pass breach in Venice, Florida, and Milton Pass breach in Englewood, Florida, opened during the 2024 sequence of Hurricanes Helene and Milton and are located in the same geological and meteorological region. The two inlets will be investigated with focus on post-breach geomorphodynamics driven by small-scale variability in hydrodynamics, sediment properties, geomorphology, vegetation, and anthropogenic influences from engineering actions and land use. The study seeks to leverage and extend the interdisciplinary field data collections following the storms and in 2025, complementing the effort with analyses and initial application to existing numerical models. The project intends to also test and assess newly emerging instrumentation and cross-disciplinary data collection strategies for storm-related geomorphodynamics and infrastructure system performance research. The study seeks to build on and strengthens an interdisciplinary network of natural hazards sciences and engineering researchers. 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

Growing a strong science, technology, engineering, and mathematics (STEM) workforce and providing workforce training for citizens in the United States is a national priority. Research has demonstrated that participation in educational opportunities while in carceral settings leads to substantial positive impacts, including reducing the likelihood to return to prison. Ninety-five percent of all people in carceral settings will eventually be released, so providing STEM educational opportunities related to workforce can improve success when they return to their communities. However, not much is known about citizen science in carceral settings and what might motivate individuals to participate in STEM. Previous work has investigated motivation for educational opportunities while incarcerated, examining pre-carceral factors and in-prison influences. Expanding the reach of citizen science will provide insight into the motivations of incarcerated adults to pursue STEM opportunities and how their science identity may be formed or changed through these experiences. This project will provide evidence-based understanding into why incarcerated individuals pursue citizen science and how their STEM identities may change as a result. As motivation and identity are critical factors to workforce and lifelong learning in STEM, this project will use a mixed-methods approach, aligned with Self-Determination Theory, Volunteer Functions Inventory, and science identity literature. This project will provide evidence-based understanding into why incarcerated individuals pursue citizen science and how their identities with respect to STEM may change as a result. It will quantitatively evaluate the intrinsic and extrinsic motivations of participants, qualitatively evaluate development and drivers of their identity with respect to science, and examine how these change as a result of participation. This study may provide insight into these relationships for settings where people are typically less likely to identify with STEM. These findings will contribute to the success of educational systems seeking to improve outcomes for individuals, providing benefit for their communities and society. This project is supported by NSF's STEM Education Postdoctoral Research Fellowship (STEM Ed PRF) Program. The STEM Ed PRF Program aims to enhance the research knowledge, skills, and practices of recent doctorates in STEM, STEM education, education, and related disciplines to advance their preparation to engage in fundamental and applied research that advances knowledge within the field. 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 Integrating Research and Practice project aims to make lifelong environmental learning and stewardship a reality by using a Design-Based Research (DBR) approach to collaboratively adapt the existing California Naturalist (CalNat) certification program for the context of prison gardens. The study of science learning outcomes will investigate the impacts of two main innovative program features: 1) co-developed hands-on rigorous naturalist training with incarcerated participants, and 2) participation in and contributions to authentic environmental science research. This research-practice partnership offers a unique opportunity to examine combined effects of doing real science, pursuing a CalNat certification, and engaging in outdoor garden spaces on participants' science identities, and what resources, facilitation, and other supports enhance this work. The project is designed around the following research questions: How do 1) hands-on field ecology training and 2) participation in environmental science research influence participants' a) self-efficacy toward science and environmental issues, and b) science identity? The project addresses these research questions through the case of a California Naturalist certification program implemented in a prison garden setting. The project will study the two main learning outcome areas through observations and interviews to discern what aspects of the program most strongly influence participants' learning outcomes, including but not limited to design features. The DBR approach, involving iterative cycles of design and investigation, will focus on integrating practitioner experiences and research theory to design a prison-based CalNat program, and assess its impacts, then collaboratively refine the design and re-assess it. The co-design process incorporates four major steps: 1) co-design with incarcerated participants a CalNat certification program centered around prison gardens; 2) implement the co-designed program in two iterations; 3) study the self-efficacy and science identity of participants; and 4) develop a model of effective practices for implementing naturalist programs state- and nation-wide. This project is funded by the Advancing Informal STEM Learning (AISL) program, which seeks to advance new approaches to, and evidence-based understanding of, the design and development of STEM learning in informal environments. This includes providing everyone multiple pathways for accessing and engaging in STEM learning experiences. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2025 · 2025-09

There are four forces in nature: the gravitational force, the electromagnetic force, the weak nuclear force, and the strong force. Quantum Chromodynamics (QCD) is the theoretical framework that describes the strong nuclear force. The fundamental particles that interact via this force are the quarks and gluons that build up protons and neutrons, which in turn build up the nuclei that are at the cores of all atoms. This project aims to study the nature of matter at very high temperatures (about a million times hotter than the sun’s core), where the particles within the matter interact via the strong nuclear force. At these high temperatures, nuclear matter reaches a phase called the Quark-Gluon Plasma (QGP). The primary goal of this project is to achieve a better understanding of the nature of matter governed by strong force interaction. This will allow better explanations of the early phase of the big bang when the universe underwent a transition from a QGP to hot gas of neutrons and protons. This transition occurred prior to the formation of light nuclei, big-bang nucleosynthesis (ten minutes after the start of the big bang), or the formation of atoms (one hundred thousand years later). High energy collisions of nuclei are also dominated by strong force interactions. The primary activity of this project is the study of high energy nuclear interactions produced at two major accelerator facilities: the Relativistic Heavy Ion Collider (RHIC) and the Large Hadron Collider (LHC). At RHIC, the UC Davis Nuclear Physics Group (UCD NPG) will study the thermodynamic properties of the QGP. At the LHC, the UCD NPG will use heavy-quark bound states to study the properties of the QGP at the highest available temperatures. Potential benefits are a better understanding of the universe, a better understanding of the shielding needed to protect from high energy space radiation, and the important training of the next generation of scientists to tackle major technical challenges and to work in large international collaborations using the latest data tools and techniques. The goals of the project include determining the nature of the transition from a QGP phase to a hot hadronic gas, advancing our understanding of the temperatures produced in the hottest collisions available, and advancing our understanding of the magnetic properties of the QGP. The scope of this project involves studies at RHIC and at the LHC. At the energy range covered by RHIC and the fixed-target program of the STAR experiment, from 3 – 200 GeV, the UCD NPG will study the phase diagram of QCD matter. Analyses of hadron spectra and baryon-number fluctuations will be used to characterize the QCD environment at each measured energy. At the top energy available at the LHC, the UCD NPG will study heavy-quarkonium states with the CMS experiment. 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

Given the national priority for America's leadership in science, there is a need to strengthen the quality of teaching and learning in science classrooms. This conference brings together researchers, practitioners, curriculum developers, and policymakers to chart the future of curriculum-based professional development (CPBL) in science education. CBPL is an approach that uses high-quality curricular materials as a catalyst for teacher learning. Historically, efforts to improve classroom learning outcomes have focused on high-quality curricular materials--written to support students for learning beyond rote recall to fundamental understandings. These new materials have been designed so that their use would lead to shifts in teacher instruction. Because the scope, sequence and teaching strategies in these materials are research-based, these materials represent a key leverage point for translating research to practice. Presently, the field is not clear about how teachers learn from these well-designed materials and what other supports might be necessary. The need to understand how teachers learn from them is made more poignant by the advent of open source, because several new high-quality curricula in science are made freely available and come without traditional professional development support. The conference aims to address pressing questions about how high-quality materials can drive teacher learning, how materials should be designed to support teacher learning trajectories, how CBPL can promote high quality science education, and what organizational supports are needed for successful implementation. Through structured collaboration among stakeholders, this gathering will consolidate existing work and generate concrete plans for advancing both research and practice in ways that honor teacher professionalism while supporting student learning in science. The conference employs a four-phase structure to maximize its impact on the field. In Phase 1, commissioned white papers from leading scholars map the theoretical terrain of CBPL and identify critical areas for advancement. Phase 2 involves careful participant selection and meeting design to ensure productive engagement across perspectives. During Phase 3, the conference engages approximately 55 participants in analyzing current practices, identifying shared commitments and assumptions, conducting gap analyses, and developing action plans for moving the field forward. Activities alternate between whole-group sessions examining white paper themes and small-group work focused on specific dimensions of CBPL. Phase 4 focuses on dissemination through a special journal issue that combines the commissioned papers with additional manuscripts emerging from the conference. Throughout all phases, the work is guided by an experienced planning committee and advisory board representing key stakeholder groups in science education. This systematic approach is supporting scholarship that advances understanding of how CBPL can support teacher learning and transformation of science education practice. 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 2025 · 2025-09

In this project, funded by the Chemical Mechanism, Function, and Properties Program of the Chemistry Division, Professor Dean Tantillo of the Department of Chemistry at the University of California, Davis is using modern computational chemistry methods to explore the limits of reactivity principles for organic reactions of fundamental and synthetic interest. The overall goal is to provide fundamental understanding of factors that control reactivity, including non-statistical dynamic effects, at a level useful for predicting laboratory reaction outcomes. In addition to the fundamental importance of the mechanistic models uncovered through this research, the project will be used to train students in multidisciplinary approaches to mechanistic chemistry and expose them to careers that employ such techniques. Educational resources will be developed and widely distributed. This research will advance knowledge in mechanistic organic chemistry through the construction of new mechanistic models, including those based on non-statistical dynamic effects that allow for the control of selectivity in the face of post-transition state bifurcations, and those that predict the feasibility of both dyotropic reactions with transition metal-based migrating groups and homopericyclic reactions. The principles uncovered will likely transform how organic chemists predict mechanisms and product selectivity for reactions of structurally complex organic and organometallic molecules. 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.