University of Colorado at Boulder

NSF Awards · FY 2026 · 2026-10

People, organizations, and automated systems all need to base their decisions on information. But good information is often broadly distributed, expensive to obtain, and difficult to piece together. This project lays out the theoretical foundations for this problem and how to solve it. First, it will study how to evaluate quality, correctness, and economic value of information. This part also studies the design of loss functions in machine learning and includes writing an educational text. Second, the project will study how different pieces of information interact, both economically and algorithmically. Third, it will design systems that incentivize people to provide good information and aggregate it into useful outputs. These systems will be based on the above principles: valuing information economically and also processing it algorithmically. The high-level goal of the project is for these system designs to be useful for private and public organizations. Potential applications include data marketplaces, gathering public health information, and forecasting. The first part of the project studies the design of proper scoring rules and proper loss functions in decision making contexts. It includes writing a monograph on the field of information elicitation, given a loss function, what types of predictions and information can be provided by minimizing it, and vice versa. In a decision-making context, scoring rules and loss functions each define a value of information. The first part also investigates the relationship between value of information, good decision making, and accurate predictions, focusing on properties such as strong convexity of the utility and/or loss. The second part investigates the complexity of sequentially communicating and aggregating multiple pieces of information for decision making. This problem is related to classical communication complexity but explored in a fully Bayesian context with success measured by expected utility. Here, the project uses a classification of information structures into substitutes and complements based on value of information to a decisionmaker. The goal is to relate the economic structure of information, such as its substitutability, to the communication complexity of aggregating that information. Finally, the third part investigates the design of incentive-compatible mechanisms in which strategic agents provide private information and aggregate it into accurate predictions. This part's goal is to solve several open problems relating to information markets, including the computational complexity of finding a Nash equilibrium in such markets and the (im)possibility of designs with good properties when signals are complements. The project will consider how all of these principles can be used to design data marketplaces, prediction markets, public information dashboards, and other applications. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2026 · 2026-09

Quantum computers have the potential to transform critical sectors such as drug discovery, materials engineering, logistics, and energy infrastructure by solving complex decision-making and resource allocation problems, commonly known as optimization problems, that challenge even the most powerful classical supercomputers. Nations worldwide are investing heavily in the development of large-scale quantum systems capable of running increasingly sophisticated programs. However, realizing practical value from quantum computing requires more than advances in hardware; it demands new approaches that coordinate hardware and software so these systems can operate reliably, efficiently, and securely. Rather than viewing useful quantum computing as dependent solely on fully error-corrected, general-purpose quantum computers designed to run a wide range of tasks, this award advances a scalable and complementary path centered on quantum optimization as a unifying and strategically important application area. By establishing design principles that align software and hardware around optimization workloads, the project seeks to accelerate the transition of quantum technologies from experimental demonstrations to practical computing tools while helping shape the architecture of future scalable systems. The integrated education plan will prepare the next generation of quantum engineers through research-based undergraduate courses, partnerships with regional colleges that have historically had limited access to quantum research, and accessible training resources for industry. Through its educational and workforce development pathways, the project will expand who contributes to and benefits from the rapidly growing field of quantum technology. This project redefines the quantum computing stack by aligning its layers around a single high-impact application: optimization workloads. A central innovation elevates the application layer into the system-level design process, enabling architecture-aware restructuring of computational problems at the application level prior to execution. This shift unlocks three capabilities: reducing quantum error-correction overhead by embedding partial resilience into problem instances, improving efficiency on modular and distributed architectures through topology-aligned mappings, and enabling privacy-preserving quantum optimization on shared or untrusted platforms. Because these transformations operate at the problem level, the resulting application-layer techniques remain adaptable to emerging quantum hardware technologies, computational models, and algorithms as quantum systems evolve. Building on this foundation, the project co-designs the full computing stack, spanning application transformations, compilation, error correction, and hardware, to enable reliable and scalable quantum optimization. At the device level, the project develops application-aware techniques that adapt hardware behavior to problem structure, reducing communication overhead, improving fidelity, and shortening execution time. Together, these efforts establish foundational system design principles for trustworthy and scalable quantum optimization and inform the architecture of next-generation quantum computing systems. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2026 · 2026-07

Using modified short strands of genetic material, known as oligonucleotides, to treat disease is revolutionizing medicine. Many different types of oligonucleotides are being used to treat common and rare diseases. These therapeutics remain active and stable in the body. Some pass through patients into wastewater systems. However, there are no tools available to track these genetic materials in water systems or to guide wastewater treatment approaches. This project will develop methods to detect these new pharmaceuticals in wastewater, study how they break down, and design treatment systems to remove them. The project will also train engineers in molecular biology skills needed to manage genetic material in the environment through a new undergraduate course, industry workshops, and a graduate student podcast series. Together, these efforts will prepare engineers to manage the increasingly important intersection of biotechnology and water infrastructure. This project will advance a new conceptual framework and analytical methodology for monitoring short, modified oligonucleotides in the environment. The project will develop advanced quantitative polymerase chain reactions optimized for wastewater. These tools will identify how structural modifications of the oligonucleotides drive persistence and how molecular hybridization influences fate in direct wastewater bioassays. Unlike conventional small-molecule fate models, this research will emphasize biomolecular interactions, specifically hybridization with environmental nucleic acids. Hybridization can act as a barrier or enhancement to degradation, representing an underexplored fate pathway within wastewater. The project will couple these experimentally derived fate parameters with pharmaceutical use data to model sewer network concentrations and evaluate activated sludge treatment performance, supporting better management of these biotechnology byproducts in the built environment. Education activities will leverage this research infrastructure directly: a freshman bio-design course will use project-developed assays as teaching tools, industry workshops will disseminate modeling frameworks for assessing infrastructure vulnerability, and graduate researchers will develop communication skills while producing podcast content on the interface between biotechnology and the environment. This integrated approach will establish a framework for anticipating how emerging bioproducts enter and persist in water systems and will support advances in biotechnology 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 project provides funds to support student participation in the Student Research Workshop (SRW) at the 64th Annual Meeting of the Association for Computational Linguistics (ACL), to be held July 2-7, 2026, in San Diego, California. Natural language processing is a critical area of computer science that enables machines to understand and generate human language, powering everyday technologies such as virtual assistants, translation services, and accessibility tools. By supporting doctoral students in attending this premier international conference, this project helps develop the next generation of researchers who will advance these technologies. The workshop gives students the opportunity to present their research, receive expert feedback, and build professional networks that will shape their careers and lead to new collaborations. These interactions broaden awareness of cutting-edge research across institutions and foster diversity within the field. The SRW provides a structured forum for doctoral students to present both research papers and thesis proposals to an audience of peers and established researchers. Student presenters receive detailed feedback on their methodology, experimental design, and research direction at a critical stage of their graduate careers. The workshop complements the main conference program, which features advances in areas such as machine learning for language understanding, dialogue systems, information extraction, summarization, and multilingual processing. Through a combination of oral presentations, poster sessions, and mentoring activities, the SRW enables students to refine their dissertation research, identify gaps in their approaches, and situate their contributions within the broader landscape of computational linguistics. 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

People increasingly rely on artificial intelligence to answer questions, generate content, and support learning. Yet most of these systems still live on flat screens, even when the task itself takes place in the physical world. That gap matters when someone is repairing equipment, learning hands-on skill, conducting a scientific experiment, working on a construction site, or creating something with physical materials. This project studies a new kind of augmented reality that can interpret a person’s physical surroundings, generate useful visual and interactive content for that situation, and place that content directly into the real physical environment. The goal is to make artificial intelligence more naturally present in everyday settings where people need guidance, feedback, or creative support in the moment. Potential uses include job training, design work, and science education. The project also contributes new educational materials in augmented reality and artificial intelligence, research training for undergraduate and graduate students, and outreach activities for younger learners. This project establishes a new concept called Generative Augmented Reality (Gen AR) as a framework for combining computer vision, large language models, and generative models within interactive augmented reality systems. The research develops methods for extracting context from the physical environment, generating content that fits the current task and setting, and embedding that content into the user’s view in ways that remain understandable and controllable. It also creates a general development platform so researchers, developers, and learners can build new Gen AR applications without starting from scratch. The project evaluates these systems in areas such as hands-on skills training, creative design, and science learning, and uses human-subject studies to examine user agency, trust, understanding, and privacy. The expected result is a set of technical methods, design tools, and human-centered guidelines for building augmented reality systems that use generative artificial intelligence responsibly and effectively. 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

Artificial intelligence models typically reduce to optimization problems: find the best solution according to a problem-specific metric. Sometimes, the nature of the problems means that the standard calculus-based tools cannot be applied. This setting is known as gradient-free optimization, and is particularly relevant for small businesses, academic research groups, and public-sector organizations that lack large-scale computing infrastructure yet still need to fine-tune machine learning models or optimize complex simulations. This project develops new mathematical and computational tools that make gradient-free optimization dramatically more efficient by exploiting hidden low-dimensional structure in these problems. This will lower the computational barrier to entry for a broad range of users. The project will train PhD students in these interdisciplinary methods, produce openly available software, and develop instructional materials connecting linear algebra to modern deep learning. Gradient-free optimization (GFO) has deep theoretical foundations, yet remains poorly understood in high dimensions. This project will establish mathematical and algorithmic tools that break worst-case GFO barriers by exploiting structure in matrix-valued gradients. Algorithms for objective functions whose gradients exhibit various kinds of low intrinsic dimensionality, such as sparsity, low rank, or sparsity-plus-low-rank will be developed. These gradient estimation techniques will be wrapped into modern algorithms such as muon which involve matrix factorizations, projections, or solving linear systems in the Fisher information matrix. Drawing upon computational linear algebra, novel techniques for performing these matrix operations in the gradient-free setting will be provided. 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

This collaborative project focuses on an enzyme called CDK7, which is critical for all physiological processes (e.g., human development, growth, maintenance of normal cell function). Aberrant CDK7 activity contributes to developmental disorders and cancer; thus, understanding how CDK7 works has broad health implications and will advance molecular therapeutics. CDK7 controls how the human genome is decoded by RNA polymerase II (RNAPII), an enzyme that "reads" specific sections of DNA, which ultimately determines cellular function. RNAPII must start and stop reading at precise sites on DNA to avoid pathogenic outcomes (e.g. developmental disorders or cancer). CDK7 is known for its ability to direct RNAPII to specific "start" sites on DNA. This scientific team recently discovered that CDK7 controls RNAPII function at the "stop" sites as well. Furthermore, this discovery linked CDK7 function to cell stress responses (e.g., to elevated body temperature or viral/bacterial infections) and therefore has broad implications for biotechnology and biomedicine. In this project, the team will define how CDK7 performs the newly discovered functions in human cells, under normal and stress conditions. The outcomes are expected to yield fundamental insights about how cells respond to and recover from stress, and the knowledge will improve anti-cancer therapeutics and strategies to mitigate chronic stress. The project will also engage trainees from high school to graduate levels and foster the next generation of scientists to help maintain America's competitive edge in the biological, computational, and biomedical sciences. The experimental and educational plans are multi-disciplinary, involving computer science, mathematics, biochemistry, biophysics, cell biology, 3D animation, and science education. Objective 1 seeks to uncover how CDK7 kinase function controls RNAPII termination and/or RNA 3'-end processing, using a combination of transcriptomics, proteomics, in vitro biochemistry, smTIRF microscopy, cell biology and computational methods. Objective 2 probes whether common cellular responses to environmental stress mimic CDK7-inhibition conditions, and how CDK7 contributes to transcriptional stress responses. The results could reveal a completely new set of functions for CDK7, an important kinase that is conserved throughout eukaryotic evolution. Objective 3 addresses educational and training goals. The team will develop educational modules that address content gaps in undergraduate biochemistry textbooks and online resources, engage in outreach to local high schools, and train PhD students to become independent scientists who contribute to society in meaningful ways. 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

Modern cyber-physical systems — such as autonomous vehicles, medical devices, and power grid controllers — must operate safely despite uncertainty in their environments, models, and decision-making components. A key challenge in ensuring their safety is predicting all possible future behaviors of these systems to verify that unsafe conditions are avoided. Doing so, in practice, allows us to encounter unforeseen and potentially dangerous behaviors in these systems. This problem, known as reachability analysis, is central to the design and certification of safety-critical systems. However, existing methods struggle to scale to complex, nonlinear systems and are especially challenged when systems incorporate artificial intelligence (AI) components. This project addresses a fundamental gap by developing new mathematical and computational tools for analyzing such systems under uncertainty. The proposed advances will improve the ability to provide strong safety guarantees for emerging technologies, particularly AI-enabled autonomous systems that are increasingly deployed in domains critical to national health, economic prosperity, and public safety. By enabling more reliable verification of these systems, the project contributes to safer transportation, more robust medical technologies, and resilient infrastructure. In addition to its research contributions, the project will support education and workforce development through open-source software, benchmark problems, and a summer school on cyber-physical systems. These activities will help train students in an interdisciplinary area spanning control theory, computer science, and applied mathematics, while broadening participation in this field. Overall, the project advances the science of cyber-physical systems and supports the national interest by improving the safety and reliability of technologies that are central to modern society. This project develops a novel framework for reachability analysis of nonlinear cyber-physical systems, including systems with learning-enabled components. The central idea is to decompose reachability into two interacting problems: constructing accurate approximations of system trajectories and computing rigorous bounds on the error between these approximations and the true system behavior. The project introduces new classes of trajectory approximations that extend beyond standard polynomial (Taylor series) methods to include rational function (Padé) approximations and Fourier-based representations. These approximations provide improved accuracy over longer time horizons and for oscillatory dynamics. In parallel, the project develops scalable techniques for bounding approximation errors by exploiting properties of mixed monotone dynamical systems, which provide mathematically sound over-approximations of reachable sets under uncertainty. The framework is further extended to systems with neural network controllers by combining trajectory approximation with neural network verification techniques that bound their input-output behavior. The proposed methods will be evaluated on numerous benchmark problems and applied to representative cyber-physical system testbeds, including autonomous systems, medical devices, and power networks. Expected outcomes include new theoretical foundations for nonlinear reachability, algorithms that improve scalability and precision, and prototype software tools. These contributions advance the state of the art in safety verification for complex dynamical systems and enable more reliable deployment of AI-enabled cyber-physical systems. Broader impacts of this work are enhanced by the development and release of open-source software tools, benchmarks and a proposed interdisciplinary summer school on cyber-physical systems. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2026 · 2026-06

The science of science and innovation is an interdisciplinary field that uses quantitative and computational methods to understand how scientific research is conducted, communicated, and translated into societal impact. We are holding the 5th annual ICSSI at the University of Colorado Boulder from June 29 to July 1, 2026, with a pre-conference Open Data Hackathon on June 28. The conference is organized around three themes that are unified by the rapid emergence of artificial intelligence: (1) the future of science policy, (2) the future infrastructure of the scientific ecosystem, and (3) the scientific workforce. ICSSI 2026 will advance the science of science and innovation by convening researchers and practitioners to present, debate, and synthesize cutting-edge work on the dynamics of scientific discovery, communication, and impact. The conference’s three thematic panels—on scientific infrastructure, the scientific workforce, and science policy—address foundational questions in the field at a time of rapid change driven by AI. The presentations and poster will showcase the latest methods and findings from across the discipline. The pre-conference Open Data Hackathon will produce shareable community data resources while training the next generation of scholars in AI-augmented research workflows. By bringing together researchers from disparate fields—spanning economics, sociology, computer science, information science, management, and more—with policymakers and funders at multiple levels, ICSSI catalyzes new research directions and collaborations that would not emerge from any single disciplinary venue. 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

This NSF-BSF project will find better ways to keep water clean without using chemicals like chlorine. Most water systems use chlorine to kill germs. It is effective against many, but not all, germs. Furthermore, it leaves chemicals in the water. This project will test two types of light to clean water – ultraviolet (UV) light and antimicrobial blue light (aBL). UV light can kill germs quickly without leaving chemicals behind, but it doesn't stop germs from growing back later. aBL can damage germs in a different way that may help keep the water clean for a much longer time. Engineers from the University of Colorado and Tel Aviv University will work together to find the ideal mix of these two types of light to treat water. The project outcomes will be safer, chemical-free ways to provide clean water. This collaborative research project will investigate integrated ultraviolet (UV) and antimicrobial blue light (aBL) disinfection technologies to improve water quality in decentralized water infrastructure such as household storage tanks, community water tanks, rainwater harvesting systems, and piped distribution systems. While UV disinfection is already widely used in centralized water treatment and point of use devices due to its proven microbial inactivation capability, its lack of disinfectant residual allows for microbial regrowth. aBL presents a complementary approach to UV in that it excites light-sensitive chromophores inside the microbes, generating reactive oxygen species that deliver damage to multiple cellular components causing permanent inactivation; yet these mechanisms and their potential to benefit water quality is not well studied. Therefore, this project will: (1) identify the most effective combinations of aBL and UV wavelengths for virus and bacteria inactivation across different water quality conditions; (2) evaluate how optimized UV and aBL conditions affect microbial regrowth, dark repair, and photoreactivation in water systems; and (3) investigate how continuous or intermittent application of UV and/or aBL influences microbial community composition, diversity, and biofilm development in storage and distribution systems, in comparison to conventional chlorine disinfection. A variety of pathogens and pathogen surrogates will be evaluated across diverse water sources drawn from the US and Israeli environments to enable testing of treatment efficacy in representative conditions. 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 will improve the fabrication precision of volume additive manufacturing by controlling residual stress through improved CAD tools, mechanical modeling, and materials. Volume additive manufacturing is a relatively new polymer additive manufacturing method that projects hundreds of three-dimensional light fields into a container of photosensitive resin. These light fields overlap within the resin, solidifying the part by exceeding a total exposure threshold in the desired shape. This process is orders of magnitude faster and provides better material uniformity than traditional layer-by-layer processes. However, parts fabricated by volumetric exposure have lower stiffness before post processing and are thus subject to greater deformation, reducing shape accuracy. This project will improve part fidelity by studying residual stress development, focusing on one volumetric manufacturing architecture, parallax volume additive manufacturing. Improved dimensional accuracy and reduced stress will extend the applicability of volumetric additive manufacturing to meet the needs of high precision, high volume industrial production such as high bandwidth electronic connectors. The hundreds of images projected into the resin container are found by solving a very large inverse problem using the mathematics of computed tomography. To make this problem computationally tractable, current algorithms ignore the inevitable stresses that develop during polymerization and post-processing steps. This project will build a finite element model as a digital twin, comparing this to the Virtual Volumetric Additive Manufacturing model created at Lawrence Livermore National Laboratory. Simplified models of viscoelasticity will be implemented to find a minimal description of the fabrication process. This model will be implemented in a new image generation algorithm that provides greater computational efficiency by representing fields in basis sets developed for computer image generation. These algorithms solve an analogous problem of mapping three-dimensional radiance fields to two-dimensional images with orders of magnitude efficiency gains. Those gains will be exploited here to incorporate more complex materials models while maintaining tractable computational cost. To validate this process, custom resins will be formulated with distinctive stress development characteristics. These will compare step to chain growth monomers to manipulate polymerization shrinkage and covalent adaptable networks to relax stress during post processing. The expected outcome of this program is a computational tool that optimizes image sets for final shape after post processing, significantly improving shape fidelity relative to current tools which optimize only for monomer conversion during exposure. 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

The weight of thick ice sheets from the last ice age depressed the land surface and pushed the underlying mantle away. Since the melting of these ice sheets, the land has been rebounding upwards as the underlying mantle returns. This process affects plate motions and seafloor spreading rates. This project will determine how glacial forcing affects the San Andreas fault and earthquake hazards. The project will also assess how ongoing rapid deglaciation in Greenland affects volcanic eruptions in Iceland. Project results will determine how glacial uplift and mantle convection processes interact with each other. This project will promote society's understanding of Earth hazards and computing for geoscience research. It will also build the STEM workforce of the future. Dynamic processes determine Earth’s long-term thermal history and large-scale plate tectonics on timescales of many millions of years, while glaciation cycles operate on timescales of 40,000 to 100,000 years. These competing processes represent significant mass movement on Earth’s surface in the past few million years. Glacial isostatic adjustment (GIA) due to glacial cycles affects global sea-level change, erosional and depositional processes, tectonics, and Earth’s rotation. Recent research established an interplay between tectonic processes and GIA, as regulated by mantle viscosity. This project will develop a mantle viscosity model that is consistent with the observations of sea level, GIA-induced crustal motion and gravity change, structures of subducted slabs, and geoid anomalies above subduction zones. Mantle convection and GIA models will be computed to predict the present-day geoid, convective slab structures, relative sea level, and crustal motions. This will lead to a unified mantle viscosity structure to examine the effects of GIA on plate motions at mid-ocean ridges and across the San Andreas fault system. This project will further develop the open-source software packages for modeling mantle convection, viscoelastic deformation, and tidal deformation on global and regional scales. 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

Converting renewable or waste materials to useful products is important for energy security and U.S. manufacturing. Upgrading plant-based materials and plastic wastes often requires converting small molecules into larger molecules. The larger molecules can be used in various products, including aviation fuel. The required reactions use catalysts to speed up product formation and suppress byproduct formation. Unfortunately, many catalysts do not perform well. Their short lifespan also limits their practical use. Recent studies show that dissolving small-molecule reactants in solvents improves catalyst performance. However, the reasons for the improvements are not well understood. This project will combine laboratory experiments and computer modeling to determine how solvent properties influence key reactions. It will establish design principles for use of solvents or other chemicals to improve catalyst efficiency. The outcomes will enable more efficient production of chemical products from domestic sources. The research will be carried out by a team of researchers who will learn to communicate and work with a cross-disciplinary team. Students at graduate, undergraduate, and high school levels will be trained in research skills. Solvents are widely known to affect rates and selectivity in heterogeneous catalysis. Changes in solvation environment can increase the rate of aldol condensation from lighter carbonyl compounds. This also decreases condensation rates for heavier molecules, leading to improved resistance to deactivation from carbonaceous deposits. The project will develop a framework for understanding these changes in relative reaction rates (i.e., selectivities) for aldol condensation on TiO2 catalysts. Experimental kinetic studies will benchmark computational models, which in turn will suggest new solvent combinations to further improve selectivity. Solvation effects will be probed using vapor phase condensation within catalyst mesopores. Controlling the extent of pore condensation will enable kinetics measurements in the presence and absence of a solvating environment. This methodology facilitates comparisons between experiment and theory by providing experimental information on how the addition of a solvent environment perturbs surface chemistry at the gas-catalyst interface. In the first phase, researchers will study acetaldehyde self-aldol condensation and develop models for a relatively simple reaction that is strongly impacted by solvents. These models will be expanded to include mixed reactant systems, including both acetaldehyde/acetone and acetaldehyde/ethanol systems. Mixed systems have been shown to exhibit complex reaction kinetics due to changes in surface intermediate populations. These studies are therefore designed to determine the sensitivity of solvent effects to surface intermediate concentrations. Finally, a similar approach will be used to investigate how catalyst materials can be designed to exhibit transfer of the promotional solvent effects to the catalyst surface. The material modifications will be achieved by depositing ligands with different chemical functionalities within the TiO2 mesopores. Cumulatively, the goal of this research is to better understand the role of solvent functional groups — and the impacts of tethering those groups within catalyst pores — in directing the reactivity of oxygenates. Because solvent effects are ubiquitous in catalysis, the proposed research will help develop methods to effectively model them and, in turn, predict the effects of solvent design. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2026 · 2026-03

This grant supports a two and half day interdisciplinary Convergence Workshop on the Mechanics of BioNetworks, MechNet’26, which will take place at the University of Colorado Boulder, 11-13 May 2026, bringing together approximately 40 senior speakers and a broad group of about 30 early-career researchers in biophysics, mechanobiology, and mechanics. The grant provides funding for travel awards for researchers, including Ph.D. students and postdoctoral fellows, to facilitate participation and participation. The workshop will feature keynote lectures, invited talks, flash presentations, poster sessions, and structured breakout discussions centered on the unifying concept of networks. These activities are designed to foster sustained cross-disciplinary interaction and to identify shared challenges and opportunities at the interface of biology, mechanics, and physics. The specific objectives of this workshop are: 1) to advance the scientific foundations of network mechanics and shape a coherent research agenda for multiscale, adaptive, and living networks, and 2) to promote interdisciplinary collaborations, professional networking, and career development for early-career scientists. MechNet’26 focuses on the unifying concept of networks to bridge the gap between biophysics, mechanobiology, and mechanics. By bringing together leading theorists, experimentalists, and computational scientists, the workshop will identify unifying principles, critical open problems, and promising methodological directions in the mechanics, dynamics, and adaptation of biological and soft-matter networks. The intellectual merit lies in advancing these scientific foundations and fostering integration across disciplines to guide future NSF investments in convergence science, most notably between active matter physics, mechanics, and mechanobiology. The workshop will have broad impacts on workforce development, interdisciplinary education, and community building by engaging and broadening participation to generate momentum in the field. Primary outcomes will include a community-informed white paper with actionable recommendations, a peer-reviewed review and perspective article, and a potential special journal issue to consolidate emerging results and strengthen this community. 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

High latitude wildfires are increasing in frequency and intensity. Smoke and black carbon from Canadian wildfires is often transported to and deposited on the Greenland Ice Sheet, which then forces these ice-covered areas to absorb more solar energy, making them melt faster. Historically, wildfire intensity peaks later in summer, however 2023 has been marked by unprecedented early-season fires across Canada, although these events are projected to become more likely in future climate warming scenarios. Capturing data on black carbon deposition, as well as quantifying the impacts of this deposition on snow reflectance and melt, will lead to a better understanding of localized impacts of the early season smoke deposition on seasonal snowmelt. These valuable data will also help reduce uncertainty in future climate model projections of the effects a warming climate has on the Greenland Ice Sheet. This research will explore the impacts of the early onset, record-setting wildfires across Canada and quantify the wildfire-derived black carbon deposition on the Greenland Ice Sheet and the greater Arctic cryosphere. The radiative forcing attributable to the presence of black carbon on the cryosphere is one of the least constrained variables of global climate models, so a high level of uncertainty exists among modelled smoke deposition, concentrations of black carbon in snow, and the weathering crust on the surface of the ice sheet. This unique wildfire season provides an opportunity for model verification. The study will provide the first verification of the 1/3rd degree smoke deposition output from the Navy Aerosol Analysis Prediction System (NAAPS) global aerosol model, which provides high-resolution black carbon deposition as an output. The output has yet to be verified with ground observations of snow, so the project team will collect and analyze snow and water samples for refractory black carbon. The modelled black carbon deposition on the Greenland Ice Sheet from this year will also be compared to the previous 23-years of the model record. Darkening of snow and ice surfaces will also be explored with satellite remote sensing, as well as the role of the increased deposition of black carbon on snow albedo reduction, radiative forcing, the timing of seasonal snowmelt, as well as overall seasonal meltwater yield. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2026 · 2026-01

Fires are growing faster across the West—with a 250% increase in just the past two decades. While fast fires only represent 3% of all wildfires, they are the most destructive and deadly because they challenge suppression efforts and compromise evacuation routes. Fire science and policy need to expand the focus from 'megafires' to also address 'fast fires.' To tackle this urgent threat, this effort will build a Fast Fire Network to develop a new framework for ‘fast fire risk’, leveraging expertise across disciplines in the natural, social, data, and computer sciences, hazards and risk analysis, and engineering. This novel framework will utilize big data and next-generation AI models centered on the three fundamental elements of risk: hazard, exposure, and vulnerability. Given recent fast fire disasters, such as the Paradise, Marshall, and Lahaina wildfires, which took over a hundred lives and destroyed tens of thousands of homes, there is a critical need to improve risk estimates of who and what is potentially in the fire’s path. This work will contribute to better protecting American lives and property, as the pace of wildfires increases. Building upon the Environmental Data Science Innovation and Impact Lab’s (ESIIL) data synthesis approach, this team will empower the Fast Fires Network, a community of over 200 people, to build critically needed solutions to faster-moving wildfires. ESIIL, an NSF-funded synthesis center, is experienced in building large science teams that hold a broad spectrum of ideas and perspectives, while also offering advanced cyberinfrastructure (CI) tools for seamless data integration via CyVerse. Within the ‘fast fire risk’ framework, three cross-sector and cross-discipline Incubator Working Groups and Stakeholder Forums on fast fire hazard, exposure, and vulnerability will advance the science around: i) what are the best metrics on fire speed and what are the biophysical and built environment drivers; ii) what western US towns have potentially compromised evacuation during a fast fire; and iii) what are the social and structural attributes that increase potential home losses. A Fast Fires Hackathon in Year 1 will incubate ideas and kick off data integration and AI model development. A closing Fast Fires Solutions Summit in Year 5 will solidify an adaptation menu of risk-reduction approaches. This effort will employ a novel data-infused translation model, where possible solutions are tested in real-time using collaborative cloud-based analytics. Overall, the Fast Fire Network will address the increasing pace of wildfires with science-backed resilience solutions. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2026 · 2026-01

Cellular wireless communications are an integral part of modern life, with the demand for high data-rate communications continuously increasing. To support this need, the cellular infrastructure in the United States uses a combination of legacy frequency bands from 4G standards as well as newly allocated spectrum for 5G/6G systems. As a result, a cellular base station must support a variety of frequency ranges and standards, often simultaneously in the case of carrier aggregation. Conventional industry solutions use a single radio-frequency (RF) hardware chain to serve each band, for example using separate dedicated hardware for the two Advanced Wireless Services 1 (AWS-1) bands of 1805-1880 MHz and 2110-2155 MHz even though these frequencies are relatively close to each other. The RF front-end hardware therefore exists in duplicate or triplicate, depending on the range of frequencies to be covered. If a single transmitter could operate instead over the entire frequency range of 1805-2155 MHz, this would cut in half the size, cost, complexity, and capital investment for the cellular base stations. At the same time, a successful broadband solution must not incur any energy efficiency penalty, as a reduction of even five percentage points is prohibitive in terms of operating costs and the consequent need for heat removal. This efficiency requirement precludes the use of existing hardware solutions for bandwidth extension, which are well-known to exhibit a bandwidth-vs-efficiency tradeoff. This project will develop alternative solutions involving both new hardware design and machine learning (ML) algorithms to address these challenges. The project will also train an engineering workforce able to solve challenges from a multidisciplinary perspective. The aim of this project is to address the growing demand for high-power RF transceiver front-ends capable of operating over multiple bands without incurring any energy efficiency penalty through the co-design of RF transceiver hardware and signal processing software. The majority of previous efforts addressing the bandwidth-vs-efficiency tradeoff have approached this problem from a purely circuit design perspective. Similarly, linearization of, and signal generation for, cellular RF transceiver front-ends falls squarely in the domain of signal processing design. The research of this project, on the other hand, approaches the challenge from the basis that a true wideband and efficient solution cannot arise from a narrow technical approach, but instead requires a cross-disciplinary solution incorporating both hardware design and artificial intelligence (AI)/ML-assisted signal processing that can control the data signals to be transmitted in a way that optimizes both spectral efficiency and energy efficiency. The new research approach of this project eliminates the artificial boundary between hardware and software in conventional designs which typically pre-determine the signal generation strategy for a dual-drive power amplifier. By allowing the signal processing and separation to be fully two-dimensional, an additional degree of design freedom is enabled. The project exploits this new 2D design space for both hardware design and signal processing to optimize bandwidth and efficiency. Because of the complexity of the function space, optimization tools are employed. The end goal is a co-design of hardware and signal processing. Beyond these technical goals, the project supports US workforce development for the wireless industry by training RF engineers with critical interdisciplinary skills in both hardware design and AI/ML tools for the next-generation wireless communication systems. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2026 · 2026-01

This project will support the continued access of U.S. researchers to the CLOUD consortium at CERN to study the chemistry and physics that drive new-particle formation and growth in Earth's atmosphere. CLOUD is a unique state-of-the-art 26 cubic meter stainless-steel chamber with precise control over temperature and relative humidity. There is no chamber facility within the USA with these capabilities. Because these particles influence cloud formation and how energy moves through the atmosphere, this research helps improve our understanding of weather behavior and atmospheric conditions. Experiments over the next three years using the CLOUD chamber will include a focus on developing molecular descriptions of new particle formation and advancing parameterizations for use in scientific models. These experiments will include mixtures relevant to the upper atmosphere, with sulfuric and nitric acids, ammonia and other bases, and iodine oxidation products. The CLOUD experiments often lead to advances in instrumentation and provide an ideal test bed for new instruments. Students and early career researchers will be integrated into an international collaborative network of students and senior researchers and have the opportunity to work with some of the most highly regarded scientists worldwide in the study of aerosols and atmospheric chemistry. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2025 · 2025-11

Grassland wildfires increasingly threaten human life and property in regions of the USA and worldwide. Managing fuels is a key way to reduce grassland fire risk. However, risk management strategies developed for forests do not translate to grassland systems, which are not as well studied. Grassland fuels vary across the year, vary from year to year, and change dramatically across the landscape. The critical knowledge gaps around how grassland fuel structures vary across space and through time make it difficult to evaluate methods for effectively reducing wildfire risk. In addition, approaches that allow scaling on-the-ground fuel measurements to characterize landscape-scale parcels are needed to assess risk and prioritize mitigation. In this project, a collaborative network of researchers, land managers, and fire practitioners work to fill these knowledge gaps and build the capacity to coordinate across organizations and regions in the Southern Great Plains of the United States. This project aims to fill several critical gaps in understanding grassland wildfire risk. The project forms and coordinates a collaborative network of partners interested in grassland wildfire that range from researchers to land managers and fire practitioners across regions of the Southern Great Plains. In addition, this project organizes coordinated data collection on grassland fuel variation and how the attributes of fuel influence fire behavior. This dataset enables effective scaling from on-the-ground fuel characteristic measurements to landscape scales important for land management planning and risk assessment. This network of field sites provides the ability to test patterns in grassland fuel characteristics across different climates, plant communities, and cultural fire contexts. Together, these advances enable better representation of grassland fuels in large scale fuel models and ultimately improve fire behavior modeling and risk assessments. 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

________________________________________________________________________________________________ Part I: Non-technical Summary The Antarctic Peninsula is one of the most rapidly warming regions on the planet. This 5-yr time-series program will build on an ongoing international collaboration with scientists from the Chilean Antarctic Program to evaluate the role of temperature, light absorbing particles, snow-algae growth, and their radiative forcing effects on snow and ice melt in the Western Antarctic Peninsula. There is strong evidence that these effects may be intensifying due to a warming climate. Rising temperatures can increase the growth rate of coastal snow algae as well as enhance the input of particles from sources such as the long-range transport of black carbon to the Antarctic continent from intensifying Southern Hemisphere wildfire seasons. Particle and algae feedbacks can have immediate local impacts on snow melt and long-term regional impacts on climate because reduced snow cover alters how the Antarctic continent interacts with the rest of the global climate. A variety of ground-based and remote sensing data collected across multiple spatial scales will be used. Ground measurements will be compared to satellite imagery to develop novel computer algorithms to map ice algal bloom effects under changing climates. The project is expected to fundamentally advance knowledge of the spatial and temporal snow algae growing season, which is needed to quantify impacts on regional snow and ice melt. The program also has a strong partnership with the International Association of Antarctic Tour Operators to involve cruise passengers as citizen scientists for sample collection. Antarctic research results will be integrated into undergraduate curricula and research opportunities through studies to LAPs and snow algae in the Pacific Northwest. The PI will recruit and train a diverse pool of students in cryosphere climate related research methods on Mt. Baker in Western Washington. Trained undergraduate will then serve as instructors for a local Snow School that takes middle school students to Mt. Baker to learn about snow science. Resulting datasets from Antarctica and Mt. Baker will be used in University classes to explore regional effects of climate change. Along with enhancing cryosphere-oriented place-based undergraduate field courses in the Pacific Northwest, the PI will recruit and train a diverse pool of undergraduate students to serve as instructors for the Mt. Baker Snow School program. This award will advance our understanding of cryosphere-climate feedbacks, which are likely changing and will continue to evolve in a warming world, while also increasing under-represented student engagement in the polar geosciences. Part 2: Technical Summary Rapid and persistent climate warming in the Western Antarctic Peninsula is likely resulting in intensified snow-algae growth and an extended bloom season in coastal areas. Similarly, deposition of light absorbing particles (LAPs) onto Antarctica cryosphere surfaces, such as black carbon from intensifying Southern Hemisphere wildfire seasons, and dust from the expansion of ice-free regions in the Antarctic Peninsula, may be increasing. The presence of snow algae blooms and LAPs enhance the absorption of solar radiation by snow and ice surfaces. This positive feedback creates a measurable radiative forcing, which can have immediate local and long-term regional impacts on albedo, snow melt and downstream ecosystems. This project will investigate the spatial and temporal distribution of snow algae, black carbon and dust across the Western Antarctica Peninsula region, their response to climate warming, and their role in regional snow and ice melt. Data will be collected across multiple spatial scales from in situ field measurements and sample collection to imagery from ground-based photos and high resolution multi-spectral satellite sensors. Ground measurements will inform development and application of novel algorithms to map algal bloom extent through time using 0.5-3m spatial resolution multi-spectral satellite imagery. Results will be used to improve snow algae parameterization in a new version of the Snow Ice Aerosol Radiation model (SNICARv3) that includes bio-albedo feedbacks, eventually informing models of ice-free area expansion through incorporation of SNICARv3 in the Community Earth System Model. Citizen scientists will be mentored and engaged in the research through an active partnership with the International Association of Antarctic Tour Operators that frequently visits the region. The cruise ship association will facilitate sampling to develop a unique snow algae observing network to validate remote sensing algorithms that map snow algae with high-resolution multi-spectral satellite imagery from space. These time-series will inform instantaneous and interannual radiative forcing calculations to assess impacts of snow algae and LAPs on regional snow melt. Quantifying the spatio-temporal growing season of snow algae and impacts from black carbon and dust will increase our ability to model their impact on snow melt, regional climate warming and ice-free expansion in the Antarctic Peninsula region. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2025 · 2025-10

This NSF project aims to design and evaluate honeybee-inspired virtual electric peer-to-peer networks to enable efficient and resilient control of distributed energy resources. These resources include electric vehicles, heat pumps, electric water heaters, and battery energy storage systems at the distribution level. Existing electrical infrastructures are undersized to handle increasing loads, and a lack of effective coordination among these resources further exacerbates this challenge. Inspired by the decentralized coordination mechanisms observed in honeybee colonies—where energy (food) is exchanged among members in a process called trophallaxis—this project will develop a bio-inspired cyber-physical system where distributed resources (“bees”) and storage systems (“hive”) dynamically allocate energy. By applying principles from collective insect behavior, this research seeks to transform energy coordination, benefiting grid operators and consumers alike. The intellectual merits of the project include novel mathematical models based on trophallaxis, development of bio-inspired control strategies, and validation through virtual testbeds and real-world demonstrations. The broader impacts include advancing non-wire alternatives that enhance grid resilience, improving access to electricity services, and fostering interdisciplinary knowledge exchange between biology, computing, and engineering. Additionally, the project will provide publicly available open-source software, engage students through an undergraduate design competition, and disseminate findings through workshops and outreach initiatives. This project will address existing technical challenges in energy coordination by translating honeybee trophallaxis into mathematical models and integrating them into an innovative cyber-physical framework. It will develop predictive models for uncertain building loads and energy behaviors using stochastic transfer learning. Additionally, it will create bidirectional biology-technology knowledge transfer frameworks to inform control-oriented models across multiple system layers. New bio-inspired control methods will be designed to optimize peer-to-peer energy sharing and grid operations. The project will leverage virtual testbeds using Python and GridLAB-D for rigorous evaluation, with experimental demonstrations conducted at the University of Colorado Boulder’s microgrid. By combining expertise from biology, computer science, and engineering, this research will generate novel strategies for resilient, adaptive, and efficient grid operations. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2025 · 2025-10

Recent developments in energy generation technologies have presented new challenges to power systems concerning their control and stability. Many such challenges stem from the fact that new technologies are integrated with power systems through power electronic inverters that demonstrate multiple time-scale dynamics, forced oscillations from digital control, and a potential for increased computational and communication burdens in control policies. To ensure the continued safe and reliable supply of power to consumers in the face of these challenges, the proposed work will develop computationally efficient methods for the analysis and control of power networks through a novel framework for voltage and frequency control that leverages unique properties of inverter-based resources (IBRs). The anticipated results comprise an innovative approach to wide-area control design that avoids massive computation or communication requirements and enables a wide-spread adoption of emerging technologies in power generation moving forward. The intellectual merit of this proposed project includes: (1) development of a theory of scalable control design for large-scale, multi-time-scale systems, which will advance power systems along with applications including cyberphysical systems or biological networks; (2) identification of critical communication links and sensor measurements in power networks with high IBR penetration; and (3) simulation of large-scale power networks and open-source validation models to allow for transparency in research and enable consistent testing of methods. The broader impacts of the proposed project include (1) PhD students training and research opportunities to undergraduate students; (2) the development of a new course on Wide Area Control in Power Systems, with the goal of public availability through Coursera; and (3) K-12 outreach to introduce power networks and controls concepts to middle school students. Technical methods and approaches utilized in this project are as follows. Control policies that leverage IBRs to achieve voltage and frequency stability will be optimally designed, facilitated by the development and validation of a reduced-order model of IBR dynamics. A general framework for distributed control design with minimal communication will be developed leveraging frameworks of multi-time-scale transfer functions, sparsity promoting optimal control design, and numerical optimization approaches. The performance of derived distributed wide-area control (WAC) policies for power networks will be validated through electromagnetic transient (EMT) simulations of the full nonlinear voltage and frequency dynamics. Case studies will provide comparisons of WAC policies for networks with high IBR shares and validation of the design approach. 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

Responding to education and workforce demands for supporting the growth of the chip industry in the country, the team from Northern Virginia Community College's SySTEMic program and the team from University of Colorado Boulder's PhET Interactive Simulations project intends to develop an essential linchpin between education and industry. The aim of the project is to establish a regional professional learning community of well-qualified 8th through 10th grade teachers, who are intended to be at the forefront of integrating chip industry content and evidence-based practices into secondary education classroom instruction. The project is built upon partnerships among teachers, industry representatives, discipline-based educators, and instructional designers that place content expertise and experience in the physical sciences and advanced technologies at the center of project activities. Partners include the Arlington Public Schools, regionally based industry partners (STACK Infrastructure and Micron Technology), nanotechnology industry networks (such as the Micro Nano Technology Education Center funded by the Advanced Technological Education program, the STEM Education Innovation Center, and the Associated Universities Incorporated), the Maryland STEM Ecosystem, the Women's Aerospace Network, and other local educational agencies. The regional focus on secondary education teachers allows and supports a comprehensive professional development effort for teachers. This includes field-based capacity building experiences with industry partners; development of leadership skills; co-design of interactive PhET simulations and lesson plans for teaching the fundamental science principles behind the chip industry and data center operations; participation in summer institutes; and opportunities to teach peers (train-the-trainer). The overarching goal of the project is to establish a regional professional learning community of grade 8-10 physical STEM teachers to integrate chip industry content and high impact workforce practices with classroom instruction. In addition, the project expects to answer the following questions: (a) What opportunities can teachers leverage to develop chip industry-integrated teaching resources for grades 8-10 physical science? and (b) How can a professional learning community of teacher leaders advance the adoption of industry-integrated resources in physical science classrooms? Along with the project team, fifteen teachers (2 Master Fellows, 13 Fellows) will form the regional professional learning community. The 3-year professional development opportunity aims to address nationally recognized and regionally significant education and workforce needs for chip manufacturing and operations. Project feedback, monitoring, and a mixed methods evaluation will document and assess two project components essential to the success of the project. The project intends to examine an approach to leveraging existing and recent opportunities to develop and implement chip industry-integrated teaching resources that focus on new and existing PhET materials. Additionally, it also intends to investigate the effectiveness of establishing a professional learning community of teacher leaders to advance the adoption of high-quality industry-integrated resources and instructional strategies in 8th through 10th grade physical science classrooms. The project has the potential to enhance practice, to contribute to the knowledge base, and to understand the role of professional development in integrating traditional and emerging STEM disciplines and skills into secondary education classrooms. This project is funded by the Advanced Technological Education program and is supported in part by funds from Intel Corporation under the ETSTE DCL. The program focuses on the education of technicians for the advanced-technology fields that drive the nation's economy. 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 · 2025-10

The NSF ECR Building Capacity in STEM Education Research (BCSER) program contributes to the NSF mission (42 U.S. Code Chapter 16) by building the capacity of the US STEM education research workforce to design, propose, and implement high quality STEM education research. The BCSER Institutes for Methods and Practices in STEM Education Research (IMP) track supports institutes that provide participants with training and support to advance the participants' knowledge, skills, and competencies in STEM education research including in the use of cutting-edge methodological techniques. Institute participants include investigators at any stage in their career development. This institute's focus is on building capacity in STEM education research by sustaining and expanding a novel, problem-led, and quality-focused approach to interpretive research design. This project extends the impact of the first ProQual institute by training 48 scholars and providing web-accessible case study examples of key elements of the ProQual approach. The ProQual approach reconceptualizes research design as a structured, design-based process, helping STEM scholars overcome epistemological and methodological barriers in educational research. This BCSER IMP project is providing training to approximately 48 STEM faculty interested in retooling to become STEM education researchers during the lifetime of the institute. The participants engage in a suite of activities to learn how to approach STEM education research as a design problem and to gain qualitative and mixed-methodology skills to undertake their own research project. Through an innovative 4-step program, participants develop research competence while engaging in a community of practice that fosters long-term knowledge exchange. The incorporation of "ProQual-in-a-Box" resources further extends these benefits beyond direct participants, enhancing dissemination and adoption. This expansion of the first ProQual Institute will strengthen STEM education by increasing the quality of interpretive STEM research that is designed and conducted by faculty and postdocs with technical backgrounds in STEM fields. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.