University of Colorado at Boulder

NSF Awards · FY 2025 · 2025-10

Wildland fires are expected to increase in prevalence in the coming decades and will have a significant and growing impact on human health, safety, and property. The impacts on air quality can be enormous and extend over very large distances, and human lives and property are affected at the wildland urban interface. An important contributor to the rapid spread of many large fires is the ignition of fresh fuel by firebrands that break away from burning vegetation and structures. Firebrands can rapidly ignite fresh fuel over short distances or can be lofted high into the air and cause spotting ignition many kilometers ahead of the fire front. To accurately predict the spread and impact of large fires, reliable computational models are therefore required for firebrand generation, transport, deposition, and ignition. Of these processes, firebrand generation is generally the least well understood, limiting the accuracy and reliability of computational tools used to predict large fires. Firebrand generation depends on physical, chemical, and mechanical processes spanning scales from meters (the size of typical shrubs and trees) to millimeters and below (the scales at which combustion and pyrolysis occur). Due to this complexity, there is currently no physics-based simulation capability that can predict firebrand formation from initial heating to subsequent fracture, through to near-field transport and deposition. In the proposed project, a physics-based predictive capability will be developed for firebrand generation, which will enable more accurate simulations of fire spread in both controlled and catastrophic settings across a range of scales. This is a highly interdisciplinary problem that requires expertise in a wide range of areas, including fluid and solid mechanics, combustion, and computational modeling. The proposed project will involve a coordinated computational and experimental effort to add structural modeling and overset meshing capabilities to a multiphase multi-region adaptive mesh solver, then validate the solver using carefully controlled experiments in a tiltable wind tunnel called the WindCline. The solver will be tested by comparing model results with experimental data on the generation of firebrands from pile burns in Boulder County, CO. Ultimately, the new solver will allow the exploration of a wide range of firebrand generation scenarios, informing the development of models for landscape-scale simulations of fire spread. 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 improving curricula for teaching quantum concepts in physics and many other disciplinary contexts. Quantum science and engineering is a fast-growing, high-priority field for national security and technological innovation. Developing the workforce for it is important for America's economic future. Topics in quantum science and engineering are taught in an increasingly broad array of courses across many disciplines and educational levels. Yet it remains difficult for many students and educators to access effective, engaging learning tools. This project aims to develop and modernize a suite of research-based computer simulations, along with adaptable, field-tested teaching materials, for teaching quantum concepts. These free resources will support a broad audience of students and educators, helping prepare learners from all backgrounds for future opportunities in quantum science and engineering. This work is supported as a Level 3 project in the Engaged Student Learning track of NSF's Improving Undergraduate STEM Education (IUSE: EDU) program. PhET interactive simulations are based on extensive education research and encourage students to learn through exploration and discovery in a game-like environment. In this project, a team of investigators at the University of Colorado at Boulder and California State University, Fullerton, will redesign and modernize at least six PhET sims covering a variety of core quantum topics, including quantum wave interference, the photoelectric effect, quantum bound states, double wells and covalent bonds, band structure, and quantum tunneling and wave packets. Each sim will be paired with research-based teaching materials, including in-class activities, guiding questions, tutorials, and homework exercises. The investigators intend to build a broad community of educators and learners to support the ongoing use and improvement of the materials. They will study how undergraduate students learn with these tools across a range of institutions, disciplines, and backgrounds. Their education research will address questions such as the following: How do students reason about quantum concepts while using sims, and how does this vary by background and STEM discipline? How do the sims, coupled with the other teaching materials, affect students' learning and engagement across different educational levels, departments, and institutions? How do students' backgrounds and mathematical preparation impact their learning using these tools? How do faculty use PhET resources in their teaching, and what helps support effective uses? The NSF IUSE: EDU program supports research and development projects to improve the effectiveness of STEM education for all students. Through the Engaged Student Learning track, the program supports the creation, exploration, and implementation of promising practices and tools. 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

Ambiguous language is a common part of communication. It means using vague words or phrases that can be interpreted in multiple ways depending on the context. This project addresses how a question answering system might handle ambiguous questions about images where it is unclear which part of an image a question refers to. For example, if someone asks “What is the medicine?” while looking at an image showing several pill bottles, a system should identify all relevant parts of the image and provide answers for each so that a person receives the full picture and can resolve ambiguities later. Instead, current visual question answering (VQA) services typically provide people with one answer per question and do not explain their reasoning process for choosing the answer. This limits a person’s ability to verify whether the desired interpretation was made. The possible repercussions from VQA services providing incomplete information can be grave, inflicting adverse personal, social, professional, legal, and financial consequences to VQA service users. The researchers will develop a socio-technical solution to address the need for innovative solutions that empower people to recognize when there is question ambiguity, and then resolve it. The project introduces the first back-end AI model that can specify every plausible image region that could be the focus of a question's language paired with natural language answers derived from those regions.  The project will establish effective interaction designs within a user-facing tool that empowers people to recognize and resolve focus ambiguity in visual questions.  Progress will be measured by evaluating the proposed AI model on a benchmark dataset and examining real users’ experiences with this model when embedded within a larger VQA system. User studies will focus on blind individuals, since they are the current dominant end-user for VQA services. More generally though, project success will benefit all VQA service users, whether visually impaired or sighted. 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

Informal learning environments such as museums, libraries, and community organizations are important settings that provide innovative learning experiences to develop young people's interests and skills with science, technology, engineering, and mathematics (STEM) and computing. Yet despite the increasing educational opportunities, there are barriers to meaningful participation for all youth. Research suggests that attention to the structural conditions of learning environments can advance understanding of what enables or hinders youths' successful learning experiences with STEM and computing. These structural conditions not only include materials such as tools and space but also human capital (e.g., informal educators, staff, and volunteers) which plays an instrumental role in designing learning experiences. As new STEM and technology-based opportunities emerge, educators working in informal learning environments must navigate and re-design the material, social, and knowledge infrastructures of their organizations and communities. This can include curating new materials, engaging in professional development, navigating institutional resources and policies to implement these opportunities, and connecting more effectively with community organizations and leaders who invite youth and families to these experiences. The decisions that educators make and how they implement them have a consequential impact on youth and families' access and engagement with these learning experiences. However, these important efforts tend to go unseen as much of the research and development within informal STEM and computing experiences focus on the immediate experiences and outcomes of learners. This project focuses research on the supports that informal educators in STEM and computing need to further realize the full participation of all youth and families. This award will engage collaborators across three informal settings: a museum, a library system, and a community-based organization, each with its own structural affordances and constraints as well as networks of local and/or national community partners. Specifically, the project team will investigate how educators across these settings navigate their structural conditions as they attempt to incorporate new computational tools and learning opportunities for youth and families in their organizations and communities. This project will involve three phases: (1) an ethnography to examine the types of infrastructures and educators' emergent strategies to negotiate those infrastructures; (2) critical reflection with educator partners related to the structural resistances and possibilities of their settings and communities; (3) design and development of resources for informal educators to think systematically about the ways they collectively redesign and navigate their infrastructures towards full participation for all youth and families with computing. The findings and resources from this project will be shared widely with other STEM informal learning educators and organizations through professional networks and gatherings as well as with other researchers through academic venues such as publications and conferences. This CAREER award 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 with 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

Our Universe is dynamic: everything in the sky moves or appears to move. The Universe expands and accelerates, gravity pulls galaxies together, the Milky Way Galaxy moves through the Universe, the Solar System orbits the center of the Milky Way, and the Earth orbits the Sun. On top of all these motions, there are small ripples in spacetime due to Gravitational waves that arose in the early universe or were created by supermassive black holes throughout its history. In this project, scientists at the University of Colorado plan to disentangle all of these motions in order to detect and characterize the stretching and squeezing of spacetime by gravitational waves. These are new cosmological probes of our dynamic Universe. As part of this project, the team will bring novel research techniques and results into the classroom and the public domain. The team will also create and distribute visualizations that will communicate the broad ideas of gravity and cosmology to the public and to K-12 schools nationwide. Astrometry, or the measurement of the positions of celestial bodies, has entered a new era of accuracy and precision, and it is now possible to detect proper motions at the level of 1 microarcsecond per year. This project will use extragalactic proper motions to address fundamental questions about gravitational waves and our dynamic Universe. A layered approach will disentangle our motion from cosmic motions and from apparent motions caused by a rippling spacetime. This program will use current and upcoming Gaia astrometry to detect and characterize the low-frequency stochastic gravitational wave background in three dimensions spanning nHz to attoHz frequencies. The team will assess the source of the pulsar-detected stochastic background, determine whether the background is isotropic, unpolarized, transverse and traceless, and if not, will measure its anisotropy, polarization, or vector or tensor qualities. The team will also make the first measurement of the secular extragalactic parallax, the real-time growth of large-scale cosmic structures, and the secular aberration drift perturbations that may arise from the Hubble expansion and dark energy. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2025 · 2025-09

The renewal of the Institute for Student-AI Teaming will continue to directly address the urgent national need to develop a world-leading, next-generation artificial intelligence (AI) workforce. The Institute will pursue a transformative new vision for 21st century K-12 classrooms that uses AI to promote student success in STEM--where all students experience authentic learning by co-constructing knowledge, making discoveries through inquiry, and developing their interests. This work reframes the role of AI in education, pioneering a future where AI serves as a social, collaborative partner that helps students and teachers make learning more effective and engaging. The Institute will push the capabilities of AI to support small groups with knowledge sharing and uptake, collaboratively solving complex problems, and working through uncertainty and differences in ideas to engender "mental leaps" during STEM learning. The broad research agenda will focus on developing, testing, and scaling AI Partners and their integration in innovative STEM curricula supporting AI education through hands-on student-centered investigations. Research will be situated in authentic school contexts and developed in close partnerships with students, teachers, and school districts to examine the impacts of AI-enhanced pedagogies on learners' collaboration skills as well as their STEM knowledge and capabilities. A dedicated Nexus Hub will work with more than 25 organizations to amplify the Institute's translational impact--connecting research, education systems, and industry to accelerate innovation and workforce readiness. The Institute will advance the new science of student-AI teaming through foundational and use-inspired research contributions. Foundational advances in multimodal, multiparty, situated awareness in authentic classroom environments will yield AI models that can autonomously monitor unfolding collaborative discourse at multiple levels - understanding the content, the conversational dynamics, gestures, and other communicative signals - and learn optimal AI control strategies grounded in the learning sciences. New frameworks of trustworthy AI in education will support collaborative learning involving complex knowledge sharing and negotiations among learners, teachers, and AI Partners. These frameworks will extend beyond existing notions of privacy, security, and transparency to address relational privacy, longitudinal trust calibration, and humans-in-the-loop, empowering students with informed agency over their classroom AI. Co-design with educators and youth will produce a semester-long sequence of problem-based units designed to develop students' AI literacy, along with curriculum-linked professional learning that prepares educators to implement AI-enabled curricula with integrity. Case studies on school-wide implementations will develop deeper understanding of school practices and conditions that shape uptake and effective implementation of AI-enabled curricula. 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

NONTECHNICAL SUMMARY Electrons are very light and move very fast in materials: if one tries to drive them out of their equilibrium steady state, for example by using an applied voltage, they reorganize rapidly and establish a new steady state. Until very recently, experimenters were incapable of measuring materials on fast-enough timescales to see how electrons behave when they are far out of equilibrium before they reach a new steady state. This restriction to studying electrons in materials at or near equilibrium has fundamentally limited understanding of the physical mechanisms governing charge and heat conduction, magnetism, and other topics. In the past decade, developments in ultrafast laser technology have made it possible to study far-from-equilibrium systems of electrons. Apart from isolated examples, however, there is no theoretical framework for interpreting such experiments, i.e., for understanding what they can tell us about the underlying quantum mechanical dynamics of the electrons, or how we can use this knowledge to guide the search for materials with new functionalities. This is the theoretical gap that the present project aims to fill. This project has two main thrusts. First, for broad classes of systems — such as superconductors and thin wires — the researchers will develop explicit predictions for out-of-equilibrium behavior based on state-of-the-art theoretical descriptions. In materials that exhibit exotic phenomena, there are often multiple theoretical models that agree on what equilibrium behavior looks like, but in general make distinct predictions for out of equilibrium. The researchers will use out-of-equilibrium dynamics as a way for distinguishing between these various models. Also, many fundamental aspects of the physics of complex quantum systems have largely been studied with computer simulations because they have no nontrivial implications for near-equilibrium dynamics. Out of equilibrium, however, such phenomena appear to have concrete and testable implications; the researchers will establish what these implications are, and how they can be unambiguously identified in present-day experiments. This project aims to establish a theoretical framework to exploit new experimental capabilities to yield new insights into quantum materials. Alongside the research, the educational and outreach component of this activity includes the training of students in this new field and the organization of conferences. In addition, the researchers intend to write a monograph on nonequilibrium quantum dynamics, intended to make the dramatic developments that have taken place in this field over the past two decades accessible to a broad audience. TECHNICAL SUMMARY Most current experimental probes of quantum materials employ linear response. Recent experimental developments enable interrogation of materials through nonlinear response, offering a wealth of opportunities for materials characterization, and for resolving fundamental questions that could not be unambiguously settled through linear-response techniques. However, exploiting this opportunity requires a theoretical foundation, which is currently absent. The researchers will develop the necessary theoretical foundation for applying nonlinear response techniques to correlated quantum materials. The research consists of three principal thrusts. The first thrust will address both clean and disordered superconductors to investigate how nonlinear response may be used to probe intrinsic lifetimes of excitations, as well as how to use nonlinear response to resolve fundamental questions regarding energy localization. The second thrust will consider one-dimensional systems at low temperatures. Specific problems include how nonlinear response may be used to probe disorder-driven localization in Luttinger liquids, how nonlinear response may be used to characterize the spin incoherent Luttinger liquid (with and without disorder), and nonlinear corrections to Luttinger liquid physics. The third thrust aims at developing a crisp means of characterizing chaos, addressing formal theoretical concepts like eigenstate thermalization, and understanding how nonlinear response could be used to gain insight into strange-metal phases without good quasiparticles. In addition to the research, this project will train students in this new field, and disseminate the key developments through conference organization and summer-school lectures. Furthermore, the researchers intend to write a monograph on quantum dynamics. Quantum dynamics has witnessed dramatic advances over the past two decades, yet these advances are scattered across a wide literature. A monograph that collects the key developments in one place will play a key role in making this field accessible to a broad audience. 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

Too often, valuable STEM education research knowledge and products fail to reach classrooms. Improving the translation and diffusion of education research knowledge requires improving the understanding of how to map the movement of people, ideas, and products along a continuum between basic research and practice. Until recently, such mapping of a field was time-consuming and technically difficult. This study will develop and evaluate an AI-assisted approach for mapping complex relationships in educational research in ways that are both meaningful and efficient across a large number of studies. The study will characterize the movement of people and knowledge across a sample of approximately 2,200 K-12 STEM research projects supported by NSF through the ECR and DRK-12 funding programs between 2013-2027. Ultimately, this study's findings will contribute to improved strategies for organizing education research to enhance connectivity across study types, topics, and researchers. The blend of broad AI-assisted approaches with more qualitative network analyses will offer a methodological framework that can be applied to other fields of education research, enabling broader insights into how scientific knowledge grows, evolves, and informs practice, especially as it moves from fundamental research towards more applied research and development and eventually studies of interventions at scale. The study will characterize the movement of people and knowledge across a sample of approximately 2,200 K-12 STEM research projects supported by NSF through the ECR and DRK-12 funding programs between 2013-2027. The researchers will use AI-assisted text, network, and bibliometric analyses. In Phase 1, they will use human-in-the-loop natural language processing to retrieve data (e.g., project information, abstracts, publications) from publicly available sources to build a dataset that captures key characteristics of the projects — such as researchers, study types, focal topics, and related publications. Additional data, derived from surveys, interviews, and study documents, will support refinement and validation of computer-assisted methods. This will ensure that human expertise and insight is combined with computer-based efficiency at scale through iterative cycles of computer and human coding, interpretation, and model refinement. In Phase 2, they will conduct network analysis to visualize how study characteristics, people, knowledge, and products are interconnected across the sample, revealing large-scale patterns and disconnects, evolution over time, and pathways for idea diffusion. They will apply bibliometric mapping techniques to analyze related publications, examining collaboration networks (e.g., relationships among authors), conceptual networks (e.g., connections between study characteristics), and citation networks (i.e., how articles cite one another). The findings have the potential to advance theories of research diffusion and translation, inform future study designs, and guide the strategic approaches to accelerating the impact of K-12 STEM research on teaching and learning. This project is jointly funded by the Translation and Diffusion (TD) program that supports research that advances the science of translation and diffusion between research and practice in STEM education, the EDU Core Research (ECR) program, which supports fundamental research that generates foundational knowledge to advance the research literatures in STEM learning and learning environments, the Innovative Technology Experiences for Students and Teachers (ITEST) program, which supports projects that build understandings of practices, program elements, contexts and processes contributing to increasing students' knowledge and interest in STEM and information and communication technology careers, and the Directorate for Technology, Innovation and Partnerships (TIP) which advances use-inspired and translational research in all fields of 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 2025 · 2025-09

Much of the Arctic is underlain by perennially frozen ground known as permafrost. Over the last few decades, the Arctic is thawing and destabilizing riverbanks and affecting infrastructure, water quality, and fish habitat. Additionally, a significant portion of the United States' natural resources and national security interests are contained within river corridors in Alaska. Arctic and Subarctic Federal, State, and Tribal governments need advanced knowledge and tools to identify and assess more accurately riverbank erosion vulnerability and risk in order to guide local decision-makers. Phase-1 of this work includes an interdisciplinary team of physical and social scientists, land managers, engineering design firms, stakeholders and land owners at local, tribal and federal levels. This team is well positioned to integrate advanced research techniques with community needs to document and forecast ongoing landscape and river changes, and enable the development of pragmatic solutions to protect investments in infrastructure. This project is poised to make an impact with science that informs public policy; increases partnerships between local community members, academia, industry, non-profit, and government sectors; and develops an American workforce in interdisciplinary applied science. This project will develop new state-of-the-art approaches to critical and immediate environmental threats to communities and infrastructure in Arctic Alaska. Solution strategies include: 1) information-based tools for decision making including river-erosion forecasting tools and watershed monitoring networks; and 2) physical solutions to changing rivers including community scale infrastructure to mitigate erosion and siltation and watershed scale solutions. The project will leverage recent advances in Earth science including satellite imagery and novel sub-pixel and machine-learning techniques for change detection, theoretical advances in permafrost erosion and mud transport prediction, low-cost sensor networks for autonomous monitoring of water quality, high-throughput microbial sequencing-as-sensing techniques, and collaborative cyberinfrastructure for watershed monitoring. Solutions will be used to forecast river erosion to protect important infrastructure, increase the ability to mitigate physical risk once identified, and manage water quality for human health and aquatic life. The modeling tools can be broadcast into the future, aiding in decision making that will minimize long-term damage and costs. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2025 · 2025-09

The Douglas-fir tussock moth and the spongy moth are insect pests that defoliate forests in North America, causing millions of dollars of damage every year, but damage would be far worse if not for the mortality caused by insect-killing viruses. Models that could predict how and when insect viruses will protect forests from defoliating insects would be invaluable for protecting forests. The creation of accurate models is hampered by the computational difficulties of using data to create realistic models, and by the logistic difficulties of collecting sufficient data to determine the best models. The investigators have recently developed a new class of interpretable machine learning algorithms that can discover the best mathematical models directly from data, even if the data are sparse and noisy, as ecological data usually are. In this project, the investigators will advance these methods to work with insect host-pathogen data. The ultimate goal is to rapidly provide robust, evidence-based models for guiding the management of pests of American forests. This project will foster a variety of inter-disciplinary mathematical biology and quantitative ecology research experiences for graduate and undergraduate students. Students in high school and university communities will be trained through the project outreach activities. The goal of this work is to advance Weak form Scientific Machine Learning (WSciML) theory and methodology, expanding its capabilities in model discovery and parameter inference to answer critical questions in disease ecology, with applications in the use of pathogens to control pest insects. The central premise of the research is that faster and more robust parameter estimation algorithms and automated model discovery methods will dramatically enhance the usefulness of general models of host-pathogen dynamics for guiding the microbial control of forest pests, as well as enabling accelerated scientific discovery more broadly. This project builds on a close collaboration between the investigators, whose collective research expertise spans applied mathematics, computational statistics, disease ecology, and forestry. The project aims to transform parameter inference and equation discovery from forward-solver discretizations, which take months of computing time, to data-driven weak form computations, which take seconds to minutes of computing time. Modern weak-form methods are superior in accuracy, robustness, and computational efficiency, but have not been sufficiently developed to be of practical use in ecology. The WSciML methods that are developed will be tested by using them to understand how host and pathogen variation drive the spread of insect pathogens, thereby testing whether WSciML can handle the sparse observations, non-Gaussian errors, and other problems that have prevented the effective use of insect pathogens for protecting forest health. 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

Honeybee colonies must solve difficult group challenges: locating food, sharing it effectively, and adjusting as their environment changes. This project explores how bees use simple behaviors—like following scents, watching each other, or pausing to share food—to accomplish these tasks as a group. Researchers will film bees in both lab and natural environments, then use these observations to understand how bees make group decisions without a leader. The team will build mathematical models to describe these behaviors and test them using detailed video recordings and new technologies for tracking bee movement and food sharing. The goal is to uncover the basic rules that guide effective group behavior and resource sharing. Understanding how bees coordinate as a group has far-reaching benefits. These lessons can be applied to design better systems for managing teams, guiding robots, or improving logistics—especially when conditions are uncertain or constantly changing. The project supports federal education goals by offering hands-on learning for high school students, developing classroom materials for underserved communities, and connecting young learners with real-world science through public outreach events. Students will participate in research and data analysis, helping them build skills in science, technology, and problem-solving. By combining insights from biology and mathematics, this project advances both scientific understanding and the practical design of systems that rely on teamwork, communication, and adaptability. This project develops and analyzes multiscale mathematical models of decentralized search and resource exchange in social insect groups, with a focus on honeybee foraging behavior. The investigators will characterize how local behavioral rules—such as reorientation driven by chemical gradients or neighbor alignment—shape group-level efficiency in food discovery and redistribution. Using velocity-jump stochastic processes, asymptotic analysis of first-passage problems, and data-informed simulations, the team will derive encounter rates, transfer dynamics, and order statistics for motile agents under variable interaction regimes. The research plan is structured into four aims: (1) model the dynamics of encounters at fixed resource sites; (2) incorporate multisensory communication to examine search optimization; (3) analyze mobile agent-based food sharing via trophallaxis; and (4) validate models in natural hive environments with fluctuating resource inputs. Experimental observations will include high-resolution video of bees in controlled arenas, infrared tracking in observation hives, and quantification of food exchange using fluorescent labeling. The models will address adaptive features such as variable pheromone emission, stopping behavior during transfers, and reorientation based on local conditions. Model validation will involve parameter inference from empirical data and comparison of predicted and observed collective dynamics. This integrated modeling–experiment framework will establish general principles for information-guided resource allocation in decentralized systems and contribute new theoretical tools for understanding biologically informed search and exchange processes. 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

Space weather events create complex phenomena in the ionosphere above the polar regions, for which data collection and spatial coverage are sparse. This project aims to deploy Ground Navigation Satellite System (GNSS) receivers to several sites in the Antarctic. The new ground stations will form an array that will monitor activity in the Earth’s ionosphere. High-latitude observations from the Southern Hemisphere are essential in improving the understanding and global forecasting tools of the geospace community, with implications for fundamental science as well as industry. In October 2020, Congress passed a space weather bill that “sets forth provisions concerning improving the ability of the United States to forecast space weather events and mitigate the effects of space weather” (PROSWIFT Act, S.881). The project funded under this award represents a step toward addressing this need. The data products will be made available on publicly accessible repositories used by researchers worldwide to store and disseminate data on the upper atmosphere. The project will also include significant educational initiatives in space science and satellite navigation. The polar ionosphere is home to many complex processes, which reflect dynamic coupling between the solar wind, magnetosphere and the upper atmosphere. As we enter an active new solar cycle and approach the solar maximum, the polar ionosphere increasingly experiences intense space weather events. GNSS signals propagating through the polar ionosphere offer a unique opportunity to enable passive observations of space weather effects. Currently, the sparsely distributed GNSS receivers in Antarctica generally do not take full advantage of the large number of multi-constellation, multi-frequency bands GNSS signals now available. The objectives of the project are to: improve observability of ionospheric plasma irregularities and their dynamics over Antarctica; provide data to enable comparative studies of northern hemisphere and southern hemisphere ionospheric responses to space weather events; augment GNSS radio occultation (GNSS-RO), reflectometry (GNSS-R), LEO satellite POD receiver, and plasma in situ measurements to localize irregularities over Antarctica; and support the efforts of the broader space sciences community conducting high latitude ionosphere and space weather research. The planned GNSS receiver array will improve observations of ionospheric irregularities and scintillation in terms of accuracy, spatial and temporal resolution. The data collected by the new ground instruments will be processed and prepared for broader community access. The products will then be analyzed in conjunction with existing ground-based instruments, GNSS-RO, GNSS-R, and other LEO satellite measurements to study patterns of ionosphere irregularities in both hemispheres. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2025 · 2025-09

The Boulder Solar Alliance (BSA) REU site program introduces students to authentic research in solar and space physics in an interdisciplinary environment. The REU site begins with a one-week immersive summer school, or “Boot Camp,” where students learn the fundamentals of solar and space physics and essential programming skills. After Boot Camp, the students work on authentic research projects with individual mentors drawn from the scientists at the consortium institutes across Boulder, Colorado. Throughout the summer, weekly professional development sessions facilitate students learning key career and research skills while also having the opportunity to network with peers and other professionals. The 10-week program culminates in a symposium where each student communicates their results through an oral presentation, as well as a poster or manuscript of their findings. The primary goals of this program are 1) Introduce students to authentic research in solar and space physics, particularly those with limited access to research opportunities at their home institutions, 2) Provide access and training on valuable professional skills in the sciences— including written summaries, oral presentations, independent research, software development, and proposal writing and 3) Provide a rich, supportive environment where long-term professional relationships can develop, including relationships between participants and their mentors, between the participants within the cohort, and between the current participants and former BSA REU alumni. 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

Tomorrow's domestic STEM workforce demands that students bring the ability to explain real-world phenomena and solve problems collaboratively. In many school districts, a significant gap persists between this ambitious vision and the realities of current instruction. One promising approach to bridge this gap is the use of high-quality instructional materials (HQIM), which have been shown to improve science teaching and learning. However, school systems often face serious challenges in selecting, adopting, and implementing these materials in ways that lead to consistent implementation across classrooms and lasting change. This project will establish a research-practice partnership between the University of Colorado Boulder and the Weld RE-4 School District in Colorado to better understand and address these challenges. The project will generate new understandings that support the translation of research on how curriculum can improve teaching and learning into practice for a whole school district, and yield insights into how school districts navigate organizational dynamics and competing priorities during curriculum adoption. The project will also result in a practical toolkit to guide other districts through similar efforts. This work supports NSF's mission by strengthening the foundational infrastructure of science education in ways that prepare students with the knowledge and skills necessary for future innovation, economic competitiveness, and flourishing. By improving the quality and consistency of science instruction across school systems, the project contributes to building a robust, future-ready STEM workforce. Through a research-practice partnership between the University of Colorado Boulder and the Weld RE-4 School District, this project will develop and investigate a novel methodological approach--improvement-oriented curriculum adoption--designed to help school systems implement HQIM in ways that are context-sensitive and improvement-driven. This process aims to help district leaders allocate resources strategically, build teacher capacity, and sustain the use of HQIM over time through continuous improvement cycles. Anchored in improvement science, this design-based research will unfold through iterative cycles of testing, feedback, and adaptation as the district pilots, selects, and scales new instructional materials in secondary science. The project is intended to: advance understanding of the organizational dynamics and tensions districts must navigate when adopting HQIM; contribute practical tools and processes that can guide other systems through similar transformations; and mobilize knowledge by producing and disseminating a publicly available implementation toolkit to support future district-led or researcher-supported efforts. The partnership work will include regular meetings with district leadership to identify and leverage resources available and needed to support a curriculum pilot as well as district goals for the pilot to guide data collection. Pilot data will be analyzed and interpreted in collaboration with the district to inform the district-wide roll out of the curriculum. By enabling more effective curriculum decisions at the district level, it strengthens the infrastructure needed to prepare students with the scientific knowledge and practices required to thrive in a rapidly changing, innovation-driven economy. The HQIM that this project helps districts adopt reflect extensive prior NSF investment in learning sciences, inclusive pedagogy, and cutting-edge science content. As such, increasing their effective use is itself a key mechanism for realizing the broader impact of that research. 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

The majority of buildings use energy-intensive Heating, Ventilation, and Air Conditioning (HVAC) systems to maintain healthy and conformable spaces inside. The goal of this project is to develop lichen-inspired surfaces that are energy-efficient, capable of removing indoor pollutants, and maintain comfortable indoor humidity levels. In nature, lichens are complex communities of microbes that can absorb moisture and contaminants in the air with sunlight as their primary energy source, making them an ideal candidate for reducing the energy cost of maintaining indoor air quality. Yet, natural lichen is very slow-growing and is difficult to grow indoors. This work uses synthetic biology to engineer industrial microbes to create lichen-inspired surfaces on various building materials (wood, stone, brick, concrete). The project will study how these lichen-inspired surfaces remove pollutants and control humidity levels to enhance indoor air quality. Indoor environmental quality (IEQ) is a central determinant of human health and quality of life in the modern world, and maintaining it consumes 40% of the energy in the US. The goal of this project is to determine fundamental design principles for sustainable bioactive surfaces that improve IEQ. This work is inspired by lichens, a symbiotic consortium of cyanobacteria, fungi, and other microbes. Their resilience to environmental fluctuations and capacity to colonize building materials without exogenous inputs make them a promising material to generate sustainable bioactive surfaces. Additionally, their inherent capacity to buffer moisture and accumulate pollutants in the air makes them well suited to improving IEQ. However, their slow growth and the inability to engineer their biology have limited both the understanding of their material properties and bioactivity, as well as their application as a tool to enhance IEQ. This project will develop lichen-inspired consortia using engineered co-cultures of experimentally tractable and fast-growing microbes to address these challenges. The capacity of these consortia to generate surface coatings that can enhance indoor air quality will be determined by engineering the bioactivity and material properties of lichen, characterizing the capacity of lichen-inspired consortia to colonize nutrient-free materials, and characterizing the bioactive functions of these consortia. 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

Research in social psychology has been at the forefront of studying and using technological advancements. From computational and statistical modeling, to fine grained measurement, to new methods for data collection, and to the discovery of bio-psycho-social mechanisms, the field of social psychology is well positioned to integrate and advance research on artificial intelligence and biotechnology and other technologies in ways that accelerate scientific discovery. The conference convenes experts in social psychology to reflect on the impact of Artificial Intelligence, biotechnology, and other emerging technologies for research and education. The conference begins with a series of talks highlighting the influence of recent technological advancements on the field of social psychology. The conference then moves into a larger group discussion, reflecting on the future of psychology research and education. The conference provides a forum for attendees to consider how these technological tools can best be used to inspire new research questions and influence the teaching of social psychological courses. The conference also sparks discussion surrounding the contribution of psychology to understanding human interactions with these technologies. The conference is widely promoted and is accessible to many social psychologists, including early-career scholars. The discussion and results from the conference are shared broadly. The conference creates new knowledge for how new technologies have transformed the way psychologists approach and conduct their science, and situates social psychologists to lead the way in advancing the science of human-technology interactions. 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

Modern machine learning has made a great impact in our society, but it faces challenges such as the requirement for large amounts of computational cost and data. This project explores how the strange features of quantum physics called quantum correlations, can be used to design more efficient machine learning models based on quantum computing. The research aims to build quantum machine learning models enhanced by quantum correlations with less cost and complexity compared to classical models, including those widely used in industry. Meanwhile, the project will show that the enhancement is not only theoretical but also useful for real-world problems by establishing a foundation for why learning human-generated data like natural language can benefit from quantum correlations. Ultimately, the project aims to adapt these models to near-term quantum experiments, paving the way toward building practical quantum machine learning systems. In addition, the research will be integrated with the education and training of both graduate and undergraduate students, along with outreach activities connected to the quantum industry. This research investigates how to build potentially practical quantum machine learning systems through the following three steps. First, the investigator explores the connection between quantum contextuality (a typical form of quantum correlation) and tools from optimization theory, such as the Sum-of-Squares hierarchy, to demonstrate the enhanced expressive power of quantum models compared to classical ones and to pinpoint the origin of this advantage as quantum contextuality. Second, based on a Bayesian interpretation of these tools and supported by experimental results from cognitive science, the investigator aims to explain why quantum contextuality is useful for capturing certain correlations in human-generated data, making the first step not only theoretical but also practically relevant. Third, the investigator will design quantum machine learning models for real-world problems that can be naturally implemented in near-term quantum experiments, on either analog or digital quantum devices. This is possible because quantum contextuality is both commonly present in quantum devices and useful for machine learning as shown in the first two steps. The resulting models will be tested through numerical simulations and collaborations with experimental groups, laying the groundwork for practical quantum advantage in machine learning tasks. 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

Bacteria commonly swim through complex biological fluids like mucus, playing a crucial role in health and disease, from infections in the lungs to microbial imbalances in the gut. Understanding how bacteria move through biological fluids is the first step toward developing new ways to cure and prevent such infections. Many mathematical tools describing how microorganisms swim through fluids like water were developed in the 1950s-1970s. These foundational theories continue to be used today. However, mucus is a far more complex and challenging environment than water. It is composed of macromolecular proteins (mucins) that confer it viscoelastic properties, simultaneously flowing like a fluid, yet capable of recoiling like elastic solids. Mathematical tools for studying bacterial locomotion through such complex biological fluids are lacking. This research will combine mathematics, computer simulations, and laboratory experiments to create a more comprehensive picture of this process. It will first investigate the fluid mechanics of propulsion through complex fluids using a single bacterial flagellum. This will be followed by a study of how multiple flagella bundle together, a standard feature of many bacteria like E. coli. Finally, the collective behavior of large groups of bacteria in fluids like mucus will be investigated. Knowledge so gained will be instructive in the design of new medicines, the prevention of dangerous infections of mucosal surfaces, and in the management of stubborn biofilms. The research focuses on bacterial flagellar propulsion in mucus, and in a better-controlled anisotropic, viscoelastic fluid: a lyotropic liquid crystal (LC). Using mathematical modeling and analysis, numerical simulations, and experiments, this project will address three interconnected problems. First, a novel slender body theory will be derived from first principles, alongside controlled experiments, to quantify the forces, flow fields, and resulting dynamics of individual bacterial flagella within a nematic LC environment. Theories will be tested against full numerical simulations of Ericksen-Leslie and Beris-Edwards model LC fluids. The first aim will be extended to encompass the coordinated behavior of multiple flagella forming helical bundles, a key aspect of bacterial locomotion. Finally, the emergent behavior and dynamics of many bacteria interacting within LCs will be modeled and analyzed, bridging the gap between individual flagellar mechanics and population-level phenomena. The expected outcomes include significant advances in our understanding of general fluid-structure interactions in complex biological media. The mathematical machinery developed will be applicable to a wide range of nearby problems in biology and engineering and will illuminate new mechanical aspects of evolutionary biology. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2025 · 2025-09

The Quantum Education and Policy Summit is a workshop meeting designed to foster strategic coordination across the national quantum education and workforce development landscape. Education and workforce development in quantum information science (QIS) is important because it enables cutting-edge research in several areas of science and engineering, and fosters talent that will be needed in many high technology industries of the future. The summit will convene educators, administrators, and workforce leaders to identify challenges, share best practices, and reduce duplication of efforts in building the quantum talent pipeline. The Quantum Education and Policy Summit is scheduled for August 6-8, 2025, in Washington DC. Speakers will be drawn from the quantum education community with a focus sharing a range of perspectives across industry, education, and workforce development and highlight specific, successful models from the US and international partners. This event will bring together faculty and colleagues from a network of NSF-funded Quantum Leap Challenge Institutes (QLCI) and other efforts aligned with the National Quantum Initiative to explore pathways to realize added value from coordination. 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

Many educational innovations show promise in small studies but never reach widespread classroom use. This study will examine co-design as a mechanism for transforming foundational research into usable interventions, and scaling and sustaining those interventions within diverse K-12 systems. Co-design is a collaborative process whereby teachers, researchers, and developers work together to design and test STEM innovations to improve teaching and learning outcomes. Drawing inspiration from medical research, where translation occurs from "bench to bedside" (Type I) and from bedside to widespread and sustainable practice (Type II), this project will study how co-design supports both the creation of usable tools and their successful integration into schools. Although co-design is increasingly used in federally funded education research, there has been no large-scale study of how it works in practice or contributes to the broader use and long-term sustainability of STEM innovations. Through a multi-method study of 100 NSF-funded STEM education projects, the team will examine outcomes of co-designed innovations, refine what is meant by co-design, identify the conditions that support and impede its success, and develop models of effective co-design. The project will advance the broader literature on theories and models of translation in education and in so doing help ensure that innovations actually reach and benefit all students. This study will examine co-design as a mechanism for transforming foundational research into usable interventions, and scaling and sustaining those interventions within diverse K-12 systems. The study will focus on a sample of 100 projects funded by the NSF DRK-12 and ITEST programs between 2013-2024 that engaged in co-design to develop STEM education innovations. In Phase 1, the team will use human-in-the-loop natural language processing to analyze project abstracts and construct a typology of design challenges, products, and contexts. In Phase 2, the team will survey researchers and educators from sampled projects and analyze public reports to identify common co-design components and model relationships between co-design practices and outcomes of scale and sustainability. In Phase 3, the team will conduct multiple case studies--including interviews and artifact collection--to refine descriptive and explanatory models and integrate insights about challenges and adaptive strategies in co-design. Outcomes will include: (1) empirically grounded models of co-design practices and their mechanisms for supporting translation, scale, and sustainability; (2) factors associated failure of co-design to achieve these goals; and (3) practical guidelines for deciding how and when to use co-design and for avoiding pitfalls that limit co-design's effectiveness. The project will advance the science of translation by providing foundational evidence on how co-design can be structured to increase the reach and lasting impact of STEM education innovations. This project is jointly funded by the Translation and Diffusion (TD) program that supports research that advances the science of translation and diffusion between research and practice in STEM education, the Innovative Technology Experiences for Students and Teachers (ITEST) program, which supports projects that increase students' knowledge and interest in STEM and information and communication technology careers, and the Directorate for Technology, Innovation, and Partnerships (TIP) which advances use-inspired and translational research in all fields of 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 2025 · 2025-09

In terrestrial plant communities, woody plants are overtaking their herbaceous (non-woody) relatives. This is happening in plant communities globaly. In tundra ecosystems, the environmental causes of this have been studied extensively, but less is known about the potential influence of biologically-influenced interactions on woody plant abundance and growth rates. This project evaluates the extent to which associations between plants and root-soil fungi contribute to shrub expansion in alpine tundra. Studies will take place at the Niwot Ridge Long-Term Ecological Research (LTER) site. This project integrates field and modeling experiments across different spatial scales to understand plant-soil feedbacks that may result in shrub expansion into alpine ecosystems. This project will engage K-12 students and educators in partnership with "CU Science Discovery" at the University of Colorado, train undergraduate students through project-based research, mentor and train a graduate student on the practice and science of running ecosystem-scale simulations using computers. This project will investigate the role of mycorrhizal associations in shaping density-dependent threshold dynamics and how these feedbacks shift across topographically heterogeneous terrain. The main objectives include (1) quantifying heterogeneity in rates of woody plant expansion using remote sensing and supervised image classification, (2) assessing biotic and abiotic drivers of woody expansion, including mycorrhizal colonization rates, communities, and soil biogeochemistry by pairing remote sensing analysis with field surveys, (3) investigating the role of mycorrhizal symbionts in mediating shrub growth rates and gross nitrogen transformations by transplanting individual shrubs with known mycorrhizal symbionts across a shrub density, and (4) integrating field surveys and experiments into modeling experiments to quantify changes in shrub growth and expansion rates under future scenarios of environmental change, and examine how density-dependent feedbacks shift across topographically complex alpine landscapes. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2025 · 2025-09

The collective motion of stars in the cores of galaxies reveals the presence of supermassive black holes, generally millions to billions of times more massive than the Sun. Gravitational waves can deliver a kick to orbiting stars when two supermassive black holes collide during galaxy mergers, reorganizing stellar orbits into a lopsided, eccentric disk around the recoiling black hole. This is precisely what is observed in the nucleus of our nearest major galaxy neighbor, Andromeda, which suggests that Andromeda might harbor a recoiled black hole that has returned to the galaxy’s nucleus through dynamical friction. A 3-year research program led by investigators at the University of Colorado at Boulder will rigorously test this hypothesis. The team will compare computer simulations with observations of the Andromeda nucleus to determine merger event rates and characteristics of high-energy transients following a merger. The investigators will implement a series of Sensory-Friendly planetarium experiences at the University of Colorado Boulder. Guidelines and activities will be developed in collaboration with the Autism Society of Boulder County and the Speech, Language, and Hearing Clinic at the University of Colorado Boulder. These events, accessible to all, would broaden theater participation to neurodiverse children, opening the full dome experience and wonders of the night sky to all families. The investigators will use theoretically motivated initial conditions for stellar distributions, designing a fiducial model to explore the Andromeda major merger event 2-4 billion years ago: a million solar mass merger with recoil kick magnitude of 300 km/s. Simulations will track 10^6 particles that interact only with the central body. The simulation will be paused immediately following the recoil kick after calculating new orbits. In the region where apsidal alignment is strongest, the investigators will select 10^4 stars and switch them to being massive particles, allowing them to evolve the system forward in time (10 million years) until reaching equilibrium. These high-resolution N-body simulations will explore stellar dynamics, particularly the influence of lopsided stellar disks on the dynamical friction timescale for recoiling black holes to return to their galactic centers. Conclusively identifying the Andromeda nucleus as the nearest recoiled black hole system would allow its use as a laboratory to probe the physics of gravitational wave recoil kicks, supermassive black hole growth, galaxy evolution, and stellar dynamics. Furthermore, this research could provide a blueprint for identifying recoiled supermassive black holes in other local galaxies. 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

Research Project Management (RPM) is the structured planning, coordination, and oversight of research activities to help faculty researchers and institutions achieve their goals more efficiently and effectively. Although project management is widely used in other sectors, RPM remains underrecognized and inconsistently applied across U.S. research institutions. This project, Shaping the Future of Research Project Management: A Survey on Practice and Needs, brings together a national team of experts from multiple institutions to explore RPM further. The goal is to better understand how RPM is currently used, how it is perceived within the research community, and what future interventions would be most impactful to institutions of all shapes and sizes. In the future, survey data will be used to identify unique challenges and opportunities, ultimately supporting the development of targeted RPM tools and training development. This work supports the National Science Foundation’s mission by promoting the progress of science, strengthening the research workforce, and enhancing the national research infrastructure. The findings will be used to shape new programs and policies that boost research productivity, and then share them widely with the public, educators, and research professionals. While project management is widely used in other sectors, it is underutilized in research and inconsistently applied across institutions. This project will generate actionable data that can inform a scalable national model for RPM integration. This planning project will design, pilot, and distribute comprehensive surveys to assess the incidence, perceptions, and applications of RPM across U.S. research institutions. Using rigorous survey design and statistical analysis, the project will identify key trends, challenges, and best practices with comparative insights between various institutions to assess how RPM is understood and used across diverse research settings. The primary objective is to develop a comprehensive understanding of RPM practices and needs. Expected outcomes include: (1) enhanced understanding of RPM structures and challenges across institutions; (2) data-driven recommendations for developing targeted RPM related interventions, training programs, and resources; and (3) academic contributions to the growing literature on RPM. These outcomes will support the formalization of RPM as a recognized field and provide a foundation for future research and capacity-building efforts. Findings will be shared through publications, presentations, and webinars to ensure accessibility and impact across the research community and the public. 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

Arctic sea ice plays a crucial role in regulating global climate. Accordingly, monitoring sea ice conditions and mapping its properties, such as type, extent, and concentration, are important for climate monitoring as well as marine navigation and near- or off-shore operations. This project will address data availability and bottlenecks in manually producing maps of sea ice by applying artificial intelligence (AI) methods to the problem. The project will introduce novel AI driven methods to effectively learn from existing label data, despite its irregularities, in order to automate sea ice classification. The intellectual merits of the proposed project are novel weakly supervised techniques that are aware of irregularities with existing label data for sea ice classification and provide methodological solutions to address these irregularities for improved sea ice classification performance. For Ill-formed Labels (i.e., coarse polygon-level annotations that fail to reflect spatial detail at the pixel-level required for typical methods) the sea ice classification problem will be reformulated and addressed with polygon-level data as a multi-instance, multi-label proportion learning problem. For Inaccurate Labels (i.e., uncertain annotations due to human subjectivity, ambiguous concentration ranges, or class trimming protocols) confidence-aware sample selection and uncertainty quantification will be embedded directly into model training via a hybrid strategy that combines loss correction. For Inadequate Labels (i.e., lack of sufficient pixel-level annotations needed to train supervised models effectively) a spatiotemporal multi-teacher knowledge distillation framework will be introduced that uniquely integrates foundation models and task-specific models to guide a task-specific student model for sea ice classification through meta-learned teacher weighting. 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

Arctic regions are experiencing warming air, rising ocean temperatures, and reduced sea ice cover. This increases the occurrence of harmful algal blooms in northern latitudes. These blooms consist of high concentrations of toxic algae in coastal marine waters that poison marine life and pose significant threats to human health. The toxins can cause stomach pain, headache, and rashes as well as more serious problems like liver damage, seizures, cardiac arrhythmias, and death. Knowledge of the impact of harmful algal blooms on Arctic populations and marine ecosystems is limited. Once relatively immune to such blooms, Arctic coastal waters are becoming increasingly susceptible to their presence. This research undertakes exploratory research to investigate impacts of harmful algal blooms on Arctic peoples and marine ecosystems. Western Greenland was chosen for the pilot due to its seasonal sea ice cover and the calving of ice bergs from continental glaciers, both of which can host toxic algae. Here local populations subsist primarily on marine mammals and sea life (i.e., seals, whales, fish, and shellfish), all of which, under the right environmental conditions, can contain algal bloom toxins. The project team is composed of scientists who are experts in Arctic environmental science, social scientists, and medical professionals who are well acquainted with Greenland health issues and the associated data. It also includes significant interaction with the local communities to learn from their experience. Broader impacts of the work include an improved understanding of the impacts of toxic algae on northern populations, critical fisheries, other marine food sources. There is also the likelihood that results of the work can be translated to other populations in the northern latitudes. This project advances knowledge across the fields of environmental science and human health in northern latitudes. It draws on data as diverse as indigenous knowledge, coupled natural/human systems, eco-dynamics, historical ecology, food security and resilience theory. The work involves data and statistical analysis of environmental condition records from the present back to 1777; data from satellite remote sensing and ocean hydrographic, salinity, and temperature data; marine ecosystem and biology data and extensive stakeholder engagement; state-of-the-art gridded ocean data sets and local health and hospitalization records. It involves scenario-building for identifying the impacts of harmful algal blooms in northern latitudes. This approach will improve understanding of how different environmental factors work together to trigger algal toxin-related health problems and perhaps help devise mitigations to reduce human health risks in rapidly evolving northern climates and the associated marine ecosystem responses. The work falls withing the context of the One Arctic One Health initiative, initiated during the U.S. Chairmanship (2015–2017) of the Arctic Council which was designed to strengthen regional knowledge sharing and establish knowledge hubs and coordination for health concerns in the Arctic member states. Project tasks involve (1) establishing baselines for algal bloom environmental factors; (2) generating a detailed analysis of potential occurrences of north latitude harmful algal blooms from the past to present; (3) identifying health issues that can be traced to the presence of algal neurotoxins and the consumption of different marine species (fish/marine mammals). 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.