University of Utah

NSF Awards · FY 2026 · 2026-09

This project will build new connections between research frontiers in different fields of mathematics while providing innovative training opportunities for graduate and undergraduate students. The research areas developed will mix contributions by graduate students, undergraduates, and senior faculty by focusing on problems that admit entry points for researchers at different levels. The project will also develop specific new training programs, including an online bridge to advanced mathematics program for undergraduate freshmen and sophomores and a distinguished lecture series emphasizing interactions between the lecturer and local graduate students. The project focuses on research problems in three areas: p-adic harmonic analysis, periods of algebraic and analytic varieties, and probability in lambda rings. These areas connect to central problems in modern number theory, especially those arising from the study of the cohomology of algebraic and analytic varieties and the Langlands program. The project will develop new techniques that can be applied to important open problems in these fields such as the Fontaine-Mazur conjecture, which generalizes Wiles’ work on Fermat’s Last Theorem, and the Birch and Swinnerton-Dyer conjecture and its generalizations linking special values of L-functions to arithmetic. The tools that will be developed in the study of p-adic harmonic analysis and periods of algebraic and analytic varieties lie at the interface of geometry, analysis, and number theory, while the tools developed for the study of probability in lambda rings lie at the interface of probability theory, number theory, and commutative algebra. This project will thus elucidate new connections between these different areas of mathematics. Moreover, by focusing on problems that are accessible at different levels, and by developing targeted training opportunities for graduate and undergraduate students, the project will integrate the discovery of new mathematics with the development and mentoring of the next generation of US researchers in mathematics. 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

The Great Salt Lake (GSL) is vital to Utah’s economy, contributing over $1 billion annually through mineral extraction, brine shrimp harvesting, and recreation. However, the lake has reached historically low water levels due to upstream water diversions for agriculture, industry, and municipalities. As the lake shrinks, the newly exposed lakebed is emitting wind-blown dust containing harmful heavy metals like arsenic and lead—byproducts of past industrial activity. This toxic dust threatens public health, agriculture, and ecosystems, with risks that extend far beyond the lake itself. This project will shed light on the role that dust plays in depositing heavy metals into ecosystems and onto important crops including corn and alfalfa. As metals accumulate in plants, they may ascend the food chain into livestock, predators, and ultimately humans, with a variety of negative health outcomes. Therefore, the results of this study will have direct implications for the health of ecosystems and communities both within the Great Basin and around the world. The results of this research be shared with communities that may be directly impacted by increased dust emission, by leveraging partnerships with local and state agencies and non-profit organizations in outreach through their regular programming such as fact sheets, newsletters, and community presentations. The research will be integrated with education activities by building and distributing soil test kits for students to use within their local communities, and by engaging local K-12 teachers in hands-on research through teacher internships. As the Great Salt Lake continues to shrink and emit more dust, native and agricultural plants may act as vectors of metal contamination, locally and regionally. As such, this study will utilize a combination of greenhouse and field-based sampling and atmospheric modeling to evaluate the risk to humans and ecosystems posed by GSL dust deposited on key native plants and agricultural crops through: 1) Assessing the extent to which plants take up these heavy metals through root and foliar (leaf) uptake, 2)Evaluating differing plant bioaccumulation among taxa key to the Utah economy, 3) Determining the impact of GSL-sourced dust on plants in the Great Basin region, and 4) Identifying potential source regions of dust that are impacting plants. The project will also analyze strontium, neodymium, and lead isotope ratios in plant tissues to determine the ability of these isotopic signals to determine soil and/or foliar dust compositions or “fingerprints”. Through geochemistry and atmospheric modeling, the research will assess the sources and transport pathways of dust from the GSL lakebed and other regional dust emissions areas and quantify the dust contribution to regional soils. The data generated by this project will contribute to environmental and health-related planning and serve as a new tool for understanding heavy metal (re)distribution during natural processes expedited by environmental change and human activity. These insights are essential for addressing the immediate consequences of the lake’s decline and for predicting more severe impacts in the future, as continued drought and future water management practices could expose more lakebed and increase the risk of toxic dust emissions. 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

Medication adherence is the process by which patients take their medications as prescribed. Medication nonadherence is a major problem, resulting in over 100,000 preventable deaths and more than 100 billion dollars in preventable healthcare costs every year in the United States. Many fundamental and clinically important questions surround nonadherence, including (1) why some drugs are "forgiving," in that they maintain efficacy despite nonadherence (missed doses, late doses, etc.) whereas other drugs are decidedly not forgiving, and (2) how the deleterious effects of nonadherence can be mitigated by designing robust dosing regimens and identifying remedial protocols for whether patients should skip or take late doses of medication. This project uses mathematical modeling and analysis to answer these questions. This project has strong potential to improve societal well-being via improved pharmacotherapy outcomes. These improvements will arise both from nonadherence mitigation for existing medications and a new quantitative understanding of drug forgiveness allowing for identification and design of robust medications. This project will develop the STEM workforce through training junior researchers. Medication nonadherence is challenging to study because (a) clinical trials that force patients to skip doses may be unethical, (b) nonadherence is erratic (i.e. patients do not miss doses in regular, deterministic patterns), and (c) there are many competing factors to consider (adherence rates, dose timing, drug absorption, drug half-life, etc.), and it is difficult to disentangle their individual contributions. For these reasons, mathematical modeling is well suited to study medication nonadherence. Importantly, there is an extensive literature of published, empirically parameterized and validated mathematical models of how specific drugs move through and affect the body (pharmacokinetic and pharmacodynamic models). This research takes these established models and subjects them to stochastic drug input. This stochastic drug input models medication nonadherence, such as randomly missed doses or random late doses. Quantifying drug forgiveness, designing dosing regimens, and evaluating remedial protocols thus involves determining how this stochastic input flows through the deterministic dynamical system. For instance, understanding drug forgiveness requires quantifying how the topology and kinetics of a dynamical system can either dampen or magnify stochastic inputs. From the standpoint of biology and biotechnology, this project transforms the understanding of medication nonadherence and offer innovative methods to alleviate this persistent public health problem. 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

Tensors are fundamental data structures underpinning applications in scientific/engineering computing and machine learning. High-dimensional sparse tensors contain many zero elements and thus require specialized data representations and optimized algorithms. Robust support for high-performance sparse tensor computations is currently lacking, for example, for the challenging sparse tensor operator graphs arising in quantum chemistry. Both within a single machine and for distributed execution across multiple machines, there is a pressing need for software infrastructure that accelerates software development and the scale/performance of distributed scientific computations on sparse tensors. This project will build such an infrastructure to help scientists, especially in the fields of Quantum Chemistry and Machine Learning, to achieve (1) high performance, (2) reduced effort for software development, and (3) performance portability for distributed sparse tensor computations. The project makes contributions along multiple directions: (1) Multi-Level Intermediate Representation (MLIR) integration: integration with the popular MLIR compiler infrastructure, to enhance sustainability and dissemination; (2) data structures and algorithms for sparse tensors: novel hash-based data representations for sparse tensors, together with corresponding efficient parallel and adaptive algorithms for tensor operators; (3) optimizations for tensor operator graphs: new operator fusion optimizations for graphs of tensor operators, to reduce memory and increase performance; (4) distributed tensors: data structures and efficient operations to enable high-productivity development of distributed sparse tensor algorithms, together with compiler support to automatically generate implementations of distributed sparse tensor operators with minimized data movement costs; (5) engagement with domain scientists to achieve and sustain infrastructure impact. 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

Municipal wastewater treatment plants often use the activated sludge process, which relies on groups of bacteria to remove harmful pollutants from wastewater. These bacteria break down organic matter and ammonia into safer substances, helping protect rivers, lakes, and human health. To keep the bacteria alive and working, air is pumped into the water to provide oxygen. However, this aeration step uses a large amount of energy—about 50–90% of the electricity used in treatment—because oxygen does not transfer efficiently from air into water. This problem becomes even harder when treatment plants try to handle larger water flows or meet stricter cleaning standards. This project will study the use of pure oxygen instead of air to support bacterial growth more efficiently. Researchers will also operate small laboratory reactors to encourage the formation of dense bacterial granules, which can treat wastewater faster. The project will support STEM education by training college students and providing research experiences for undergraduate and K–12 students, while also working with industry partners to apply the results in real systems. There are two major challenges associated with using pure oxygen (pure-Ox) in densified wastewater treatment plants (WWTPs): first, the process kinetics and operational strategies for sludge densification in activated sludge systems using pure-Ox are not well developed, creating a significant knowledge gap; second, activated sludge processes using pure-Ox typically fail to achieve nitrification, the biological oxidation of ammonia to nitrite and nitrate. Efficient nitrogen management is one of the 14 grand challenges identified by the National Academy of Engineering, and WWTPs that discharge into sensitive water bodies are increasingly required to control nitrogen more effectively. This project aims to address both challenges through a set of integrated research objectives, providing new tools for process intensification in space-limited urban treatment plants and improving nitrogen management in existing oxygen-fed systems. The project will pursue three objectives: (1) evaluate the feasibility of sludge densification supported by pure oxygen, (2) investigate the molecular mechanisms responsible for nitrification inhibition using functional gene expression analysis and advanced analytical chemistry under both open and closed reactor configurations, and (3) examine the ecophysiology of microbial communities in flocs, granules, and biofilms, and link microbial structure and function to process performance using ecological theory. The central hypothesis is that sludge intensification through granulation-based densification with pure oxygen can achieve high treatment rates while enabling effective nitrogen removal. Compact, densified granules are expected to protect nitrifying bacteria from low pH and other operational stresses associated with pure-Ox systems, thereby improving overall treatment performance. 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 award supports participation at 2026 ICM Satellite Conference on Moduli of Galois Representations and the p-adic Local Langlands Correspondence, a research conference in mathematics that will be hosted by the University of Utah Department of Mathematics the week of August 10-14, 2026. It will provide a unique training opportunity for US mathematicians by bringing together leading experts in several rapidly-developing areas of number theory to describe the newest advances in the field. In addition to traditional research talks, the conference will feature lightning talks and poster sessions for early-career participants to share their research, and will be accompanied by a series of online survey lectures to prepare attending graduate students before the conference begins. More precisely, this research conference will focus on recent advances in modularity, moduli of Galois representations and L-parameters, the categorification of the Langlands correspondence, the p-adic local Langlands correspondence, the cohomology of Shimura varieties, and other nearby topics in number theory and arithmetic geometry. In recent years, these areas have seen enormous advances and conceptual reformulations based on new tools such as the Emerton-Gee stack and categorical local Langlands, bringing within reach central problems and questions that were previously inaccessible. The conference program will communicate some of the most exciting recent developments in these areas, with an emphasis on those topics that are ripe for future work by the next generation of US mathematicians. The conference website is available at: http://math.utah.edu/~howe/australian-direction.html 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

Correct compilation is essential to scientific computing, as it provides the bridge from high-level algorithms in source code to computing machinery. LLVM is a critical backbone in this process, powering compilers and infrastructure for languages including Julia, Python, and Fortran. However, the engineering of LLVM has focused on improving general-purpose code, with relatively little attention given to the particular needs of scientific computing. The central question of this project is: Can compiler infrastructure be redesigned so that scientists can express and statically enforce the complex physical and mathematical constraints that define their work? For example, scientists interested in proving positivity for an advection-diffusion-reaction system should be able to do so statically, though the use of tools that lower the burden of proof. The project's novelties are methods to incrementally synthesize domain-specific and program-specific static analyses. The project's impacts are to integrate correct static analyses into a widely-used compiler, thereby enhancing the work of a broad community of scientists. Ultimately, the goal of this research is a compiler development process in which scientists specify what they want and tools generate the how automatically. This research consists of three technical thrusts toward a new, synthesis-based foundation for compiler construction: (1) synthesizing analyses one transformer at a time, through stochastic search and SMT-based verification; (2) creating semantic MLIR dialects for scientific computing domains, namely floating-point numbers and tensors; and (3) developing project-specific techniques to strengthen correctness claims. A key idea is to model traditionally-manual compiler components as compositions of semantic program transformers: small programs that summarize how instructions behave or how they can be replaced without changing observable behavior. These transformers are learned from examples, specifications, or profiling data, using a combination of symbolic reasoning and abstract interpretation. 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

Tiny particles such as microplastics, pathogens, and other contaminants are increasingly found in groundwater, raising concerns for drinking water quality and ecosystem health. Predicting how these particles move underground remains a major scientific challenge because existing models do not accurately represent how particles behave in natural soils. This project seeks to improve understanding of how very small particles travel through soil and groundwater, which is essential for protecting water resources and improving cleanup strategies. The research will also support education, workforce development, and public awareness. By improving the ability to predict how contaminants spread, this project advances the national interest in safeguarding water quality, public health, and environmental sustainability. This project investigates how nanoscale surface features and repeated particle–surface interactions control particle attachment and transport in porous media. The research integrates laboratory experiments, surface characterization, and modeling to quantify how particles attach after multiple encounters with sediment grains. A new predictive framework will be developed that incorporates nanoscale attraction and interception history into transport models. The work combines theory, simulations, and machine learning to improve prediction of particle retention and mobility across 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 · 2026-05

Non-technical Description The interaction of chiral materials and devices with circularly polarized light creates new opportunities for applications in sensing, imaging, computing, and quantum photonic technologies. However, current chiroptical platforms face challenges, including limited understanding and electrical control of key chiroptical responses, a narrow range of material choices, manufacturing processes that are challenging to scale, and the lack of fast, accurate simulation and design tools that can incorporate chiral materials. To address these challenges, this project develops a scalable and electrically reconfigurable chiroptical platform based on one-dimensional chiral quantum materials and reconfigurable photonic materials, together with a high-performance solver for fast simulation and inverse design. Carbon nanotubes (CNTs) and nonvolatile phase change materials (PCMs) serve as example material systems. The resulting heterostructure and solver can be extended to a broad range of materials and device structures to explore fundamental chirality-induced phenomena, enable functional devices, and serve a broad community. This project also integrates research with education through community building, multilevel student training, curriculum development, and outreach, helping cultivate future engineers and scientists in semiconductor optics and optoelectronics. Technical Description This project aims to address current limitations in the practical deployment of the proposed chiroptical heterostructure and solver, including weak chiroptical responses, binary and one-way electrical reconfigurability, and the solver’s inability to handle chiral materials, complex structures, and experimental variations. Specifically, this project combines molecular and structure-induced chiralities from strong excitons in twisted aligned semiconducting CNT enantiomers, increases the number of stacking layers with low-loss PCMs, and creates planar Moiré cavities to enhance chiroptical responses. In addition, the small-sized cavities controlled by aligned metallic CNT films enable reversible and multilevel reconfigurability. Further, this project develops a high-performance chiral rigorous-coupled-wave-analysis solver powered by graphics-processing-unit-accelerated simulation and differentiable inverse design, supporting general bi-anisotropic materials, two-dimensional periodic patterns of arbitrary shapes, and robust design in experiments. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2026 · 2026-04

This award supports participation in the first annual Western Dynamics Conference that will take place on May 8-10, 2026 at the University of Utah in Salt Lake City, Utah. The conference will focus on the recent developments in dynamical systems. The goal of the conference is to bring together the growing dynamics community across universities in the western United States and Canada. This meeting is designed to advance high-quality research in dynamical systems, strengthen scientific collaboration—particularly within the western United States—and broaden participation by engaging early-career researchers and students. By creating opportunities for interaction among mathematicians at different stages of their careers, the conference will help cultivate the next generation of researchers in the field. The theory of dynamical systems provides important tools for the modeling, analysis, and prediction of complex phenomena across mathematics and the sciences. Addressing key challenges in the field increasingly requires ideas and methods from multiple areas of mathematics. The conference program highlights many areas of dynamical systems and its connections with other areas of mathematics, such as geometry, combinatorics, and number theory. The conference features talks by graduate students and early-career mathematicians, complemented by presentations from a selected group of leaders in the field. This combination will ensure that participants are exposed both to cutting-edge advances and to the diverse perspectives of the next generation of researchers. More information can be found on the workshop website https://www.math.utah.edu/wdc/ 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

Diagnostic testing is challenging when the targeted compounds are present in extremely small amounts. Sophisticated laboratories are required to detect low levels in samples, which makes the tests expensive and limits their availability. If the signal from targeted compounds could be amplified, some tests could become available in homes and smaller clinics. This project will make detection easier by amplifying signals using simple chemical reactions. The project will demonstrate that one chemical reaction can generate several new reactive molecules, which can then trigger more reactions. As the reactions continue, the signal will grow rapidly until it becomes easy to detect optically. This technology may support a wide range of applications, including at-home disease tests, environmental and food safety monitoring, and rapid response tools for public health and security. The project outcomes will make advanced diagnostic capabilities more accessible, affordable, and practical in everyday settings. A central challenge in biosensing and diagnostics is that many target analytes are present at extremely low concentrations, requiring advanced instrumentation for detection. This requirement leads to high costs and limits many assays to specialized laboratory settings. Exponential signal amplification offers an alternative strategy by enhancing detection sensitivity while reducing instrumental requirements, but existing approaches largely rely on enzymes and complex reaction conditions. This project will develop a purely chemical approach to exponential signal amplification for biosensing applications. The core concept is a self-propagating chemical reaction in which a single triggering event generates multiple reactive species, producing an amplification cascade that can be coupled to an optical readout. To ensure compatibility with biologically relevant molecules, the design relies on rapid and selective bioorthogonal dissociative reactions. The project will establish the principles governing this chemical amplification strategy and evaluate its performance in biosensing formats. The approach will be implemented for the detection of protein and RNA analytes, enabling assessment of sensitivity, specificity, and practical applicability. The project outcomes are expected to introduce a new class of enzyme-free amplification methods with broad relevance to biosensing, diagnostics, and biological research. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2026 · 2026-03

Non-Technical Summary: This project will develop a new class of flexible, lightweight materials that can actively change how they interact with light and electronic signals. Many modern devices, from medical sensors to communication systems, use materials whose properties are fixed once they are manufactured. This research aims to overcome that limitation by creating materials that can be electrically “reprogrammed” in real time. The approach combines well-established plastics that conduct electricity with small, “handed” molecules that twist light in unique ways. When mixed together, these components can produce materials that respond to electrical signals by altering how they absorb and emit light or how they control the flow of electronic information. The new materials developed in this work have the potential to support future quantum technologies by enabling low-power devices that control light and electron spin, key ingredients for quantum communication and information processing. To accelerate discovery, the project will employ robotic laboratory systems guided by machine learning and artificial intelligence (AI), allowing the research team to rapidly test and analyze thousands of material combinations. This “self-driving lab” approach dramatically speeds up scientific progress while training students in automation, programming, and artificial intelligence. Educational activities will include hands-on spectrometer-building kits distributed to schools and universities, broadening access to scientific tools. By advancing scientific knowledge, developing future researchers, and enabling new technological opportunities in AI and quantum science, this project promotes the progress of science and contributes to national prosperity and welfare. Technical Summary: This research will establish molecular-level design rules for chirality transfer in conjugated polymer systems by integrating automated thin-film fabrication, Bayesian optimization, and multiscale structural and chiroptical characterization. The project investigates how noncovalent interactions between achiral conjugated polymers and chiral small molecules generate circular dichroism, circularly polarized luminescence, and spin-selective transport. Aim 1 employs high-throughput robotic platforms coupled with AI and machine learning to map processing–structure–chirality relationships across polymer–additive libraries, identifying key chemical and processing parameters that maximize chiroptical responses for quantum science applications. Aim 2 combines X-ray scattering, nanoscale imaging, and chiroptical spectroscopy to elucidate the supramolecular mechanisms that govern chirality transfer and determine the roles of molecular packing, phase behavior, and local interactions. Aim 3 explores chemical and electrochemical doping as a reversible external stimulus to modulate chiroptical properties and induce chiral polaron formation, including in situ circular dichroism spectroelectrochemistry to track doping-dependent optical signatures. Together, these efforts will produce a mechanistic framework linking additive chemistry, polymer structure, and external stimuli to dynamically tunable chiroptical functionality in conjugated polymers, informing the design of reconfigurable materials for photonic, spintronic and quantum 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 · 2026-02

With the support of the Chemical Catalysis program in the Chemistry Section, Professor Qilei Zhu of the University of Utah is studying new light-driven chemical strategies for the efficient synthesis and production of value-added chemicals from readily available hydrocarbon feedstocks. Carbon–hydrogen bonds are among the most common yet difficult chemical connections to manipulate, and improving control over these bonds would enable more efficient routes to medicines, advanced materials, and other important chemicals. These advances are expected to reduce hazardous waste, lower energy and material costs, and accelerate access to novel value-added products and materials. Beyond fundamental discovery, the project will support the training of undergraduate, graduate, and postdoctoral researchers in modern catalysis strategies and spectroscopy tools, while expanding outreach activities that introduce K–12 students and community audiences to scientific research and career pathways in chemistry. With the support of the Chemical Catalysis program in the Chemistry Section, Professor Qilei Zhu of the University of Utah is studying photocatalyst-controlled radical reactions enabled by electron donor–acceptor complex formation to achieve unprecedented selectivity in carbon–hydrogen bond functionalization. The project will establish catalyst-directed approaches for the selective cleavage and modification of strong aromatic and aliphatic carbon–hydrogen bonds in the presence of more labile ones, enabling deuteration, alkylation, heteroatom incorporation, and asymmetric deracemization. These studies will integrate synthetic method development with mechanistic investigations using spectroscopy and electrochemical analysis to elucidate the roles of noncovalent interactions, excited-state electron transfer, and multi-electron redox processes in controlling radical reactivity. The anticipated outcomes include new catalytic paradigms that override intrinsic thermodynamic selectivity in carbon–hydrogen functionalization, generalizable strategies for late-stage molecular editing, and mechanistic insights that will broadly impact photocatalysis, radical chemistry, and asymmetric synthesis. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2026 · 2026-02

Keeping urban areas vibrant and productive relies on redeveloping older, underused or neglected neighborhoods. To guide redevelopment efforts, planners must understand how changes affect economic and social activity. However, there is little information on how urban changes affect the temperature, humidity and air quality of local environments. This project will close this knowledge gap by studying the redevelopment of a city block in Salt Lake City. The team will use measurements and numerical models to examine how the redevelopment affects the local environment. The data and results from numerical models will help urban planners understand how different types of urban change affect human comfort, pollution exposure, and building energy use. The results will also help evaluate the economic and social tradeoffs associated with these changes. Furthermore, this project will foster collaboration between city officials, residents, and students at the University of Utah. This project will create a new understanding of transport processes in geometrically complex environmental systems. This will be accomplished by using a paired physical and digital twin control-volume approach through a longitudinal study of a neighborhood-scale project. Field and modeling studies will center on the `Fleet Block' project in downtown Salt Lake City. Data collection will start after the razing of existing buildings and continue through new construction and occupation. A high-resolution measurement array consisting of a centralized eddy-covariance tower and 16 low-cost stations will be deployed around the city block. New transport models will be created, tested, and used to identify components of the renewal process that strongly affect the environment and to generalize the findings beyond Salt Lake City. The experiment will measure the volume integrated energy, moisture, and scalar (particulate and ozone) budgets over a range of length scales. Additionally, a new Lagrangian transport model for conserved scalars will enable high-resolution longitudinal evaluation of urban landscape changes. 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

Understanding how mixtures of solid particles and fluids, such as those found in avalanches, volcanic flows, and river sediments, initiate and how fast they propagate is vital for natural hazards and improving engineering models of Earth systems. These particle-laden flows are complex because the solids and fluids interact in diverse ways: sometimes the mixture acts like a solid, other times like a liquid, and often something in between. This project brings together geoscientists, computer scientists, and engineers to develop new models that better capture these behaviors by combining laboratory experiments, advanced numerical simulations, and artificial intelligence (AI). By using AI methods that are designed to be transparent and interpretable, this work not only enhances scientific understanding but also helps build public trust in AI-driven tools. The findings will support a broad range of geoscience applications and improve forecasts of events that can impact lives and infrastructure. Educationally, the project supports a vertically integrated training model, where postdocs mentor graduate and undergraduate students in a collaborative, hands-on research environment. The team will also create publicly accessible AI tools, YouTube tutorials, and organize quarterly seminars to disseminate their advances in AI for geosciences. These efforts will help prepare a new generation of researchers skilled in both scientific computing and Earth science. This project aims to discover and validate an elasto-viscoplastic continuum rheology for dense granular suspensions under varying stress conditions, relevant to natural and engineered geophysical flows. Three central scientific questions guide the research: (1) how to represent stochastic force chains in a continuum framework, (2) how to define a rheology accounting for competing fluid–particle and particle–particle interactions, and (3) how to incorporate nonlocal and memory effects in stress evolution formulated using integro-differential equations. The approach integrates laboratory experiments, discrete element simulations, and interpretable machine learning. A novel by-design interpretable AI framework will be developed to discover analytical integro-differential equations for the rheological model, while physics-informed operator learning with Kolmogorov–Arnold networks will enable its reduced-order surrogate modeling for GPU-based numerical solvers. The resulting models will be deployed in a large-scale application involving melt extraction from crystal-rich magmas. Open-source software and educational content will support broad dissemination. Collectively, this project advances both geoscientific understanding and AI methodologies for modeling multiscale, memory-driven 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 award will fund research to address a grand challenge in the geophysics community: deep-focus earthquakes, which are earthquakes that originate at depths greater than 70km below the Earth’s surface. Although there is no consensus on the processes that cause the sudden release of energy that results in a deep-focus earthquake, there are three agreed-upon mechanisms that could occur individually or together during a deep-focus earthquake event. These mechanisms are 1) the transformation of minerals from one phase to another, 2) the dehydration of rocks, which causes them to become easier to fracture, and 3) the formation of a small molten band caused by thermal heating during shearing. This research project will critically examine each of these mechanisms under the extreme temperatures, pressures, and deformation rates present in the Earth’s mantle. Multiple factors underlie the need for updated earthquake hazard models, including aging infrastructure, continued concentration of population centers, and the fact that supply chain interruptions at one location can have substantial economic impacts well beyond the earthquake-affected region. This project is anticipated to have significant implications for updating earthquake hazard models. Education and outreach efforts will focus on community education, the engagement of citizen scientists, and addressing the scarcity of science education resources in rural counties through the creation of portable teaching toolboxes and science programming for K-12 youth aimed at sparking a passion for science. This research project will advance Pressure-Shear Plate Impact (PSPI) experimentation to achieve the temperatures (up to 2500 K), pressures (>30 GPa), and slip rates (> 1m/s) that occur over the span of depths at which deep-focus earthquakes originate. State-of-the-art PSPI apparatus will be leveraged to generate novel data sets to interrogate prevailing hypotheses as to the cause of deep-focus earthquakes. These data sets include: 1) identifying the onset and defined kinetics of the temperature and pressure-dependent shear-induced phase transformations of silicates (i.e., olivine to wadsleyite or ringwoodite) as a function of deviatoric stress at seismic discontinuity depths; 2) Quantified rheological properties of hydrous peridotite that reveal the relationships between bound water (wt. % H2O), subducted slab thermal parameters, and the emergence of eased fracture embrittlement; 3) An envelope of rate-and-state friction measurements that assess fault weakening (and strengthening) mechanisms under constant and variable normal pressure with the aim of determining whether molten shear band formation is a spontaneous or triggered process. The data captured has strong transformative potential for our understanding of the dynamic processes occurring within the Earth’s mantle, which are currently inferred from sparse surface formations, seismology measurements, or experiments that are unable to simultaneously match subducted slab temperatures, pressures, or slip rates. Anticipated Tranformative Impact: Disaster prevention and mitigation 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

Computational infrastructures have become a foundational enabler of scientific discovery across a range of critical domains, including seismic imaging, air quality monitoring, epidemiology, drug discovery, and nuclear engineering. As a result, ensuring the security of these infrastructures is paramount. Scientific computing ecosystems rely heavily on open-source software to develop, port, deploy, schedule, and manage computational codes. However, the open-source model inherently exposes projects to software supply chain threats. Real-world incidents have shown that adversaries can exploit this model by injecting malicious code into compromised repositories, thereby affecting downstream users. Even in the absence of malicious actors, open-source components may contain latent vulnerabilities that introduce significant security risks. Although extensive research has been conducted on understanding and mitigating software supply chain risks, their implications in the high-performance computing (HPC) context remain largely understudied. HPC environments—including their infrastructures, applications, and operational models—present distinct characteristics and challenges that may render conventional security approaches ineffective. This award, HPCSafeChain, addresses a pressing question: How can current security techniques be applied or adapted to confront software supply chain threats in the HPC domain? To this end, this project undertakes the following three key tasks. (1) Risk Characterization and Taxonomy Development: the HPCSafeChain project systematically identifies security risks specific to HPC software supply chains, analyzes the underlying challenges in mitigating them, and constructs a comprehensive taxonomy tailored to HPC. (2) Testbeds and Benchmarks: leveraging the constructed taxonomy, HPCSafeChain develops realistic testbeds and an attack benchmark to rigorously assess the effectiveness and limitations of existing security tools within HPC settings. (3) Technique Adaptation and Enhancement: HPCSafeChain investigates how existing software supply chain security techniques can be refined or extended to address the unique requirements and operational constraints of HPC environments. This project offers valuable insights into the security challenges faced by real-world HPC systems and creates distinctive research and educational opportunities for both undergraduate and graduate students. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2026 · 2026-01

This project aims to advance how porous materials such as foams and energy-absorbing materials are modeled and designed by integrating quantum computing with classical simulation tools. These materials play a critical role in various industries, from healthcare to aerospace, and understanding their failure mechanisms is key to improving safety, performance, and sustainability. The research combines cutting-edge technologies of quantum computing, which enables faster and more efficient problem solving, along high-resolution experimental imaging, which reveals how these materials behave under stress. By developing new modeling tools that are both more accurate and computationally scalable, this work supports scientific discovery, promotes technological innovation, and helps prepare the next generation of engineers and scientists. The project will also provide open-source tools and training opportunities in quantum-enhanced simulation to a broader research community. This project develops a hybrid quantum-classical finite element method (FEM) framework for modeling fracture in heterogeneous porous materials. The methodology integrates quantum solvers for partial differential equations (PDEs) with topological data analysis (TDA) to extract key structural features from porous microstructures, which are then used to inform the homogenized FEM model. High-resolution in-situ mechanical and fracture tests on porous structures will be performed to provide ground-truth data for validation. The final deliverable is a reproducible and extensible computational platform that unifies data-driven microstructural analysis with scalable, accurate simulation of deformation and fracture in porous solids. This research advances the state-of-the-art in multiscale modeling and enables broader application of quantum computing in engineering mechanics. 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 · 2025-12

Strengthening wildfire resilience requires accurate modeling and a deep understanding of collective human behavior during wildfire evacuations. In particular, there is a critical need for simulation models that can realistically capture how civilians, incident commanders, and public safety officials make protective action decisions during wildfires. However, existing simulation models face fundamental limitations that often cause low prediction accuracy and insufficient capacity to support effective decision-making during wildfire response. Therefore, this project aims to develop a convergent framework for next-generation wildfire evacuation simulation that features realistic Artificial Intelligence (AI) agents powered by psychological theory-informed large language models (LLMs), reinforcement learning, and multi-modal datasets. This research is a transformative step toward improving the behavioral realism, prediction accuracy, and decision-support capability of wildfire evacuation simulation models. This project will also lead to generalizable simulation methods, promote teaching, training, and learning, strengthen partnerships, and support wildfire resilience through broad dissemination and open-access tools. Despite progress in wildfire evacuation simulation models, key challenges remain in accurately modeling and understanding the decision-making processes by incident commanders and public safety officials, realistically modeling human behavior in wildfire evacuations, and adequately representing diverse populations and their dynamic, complex interactions. LLM-based agents could help address some of these limitations, though they bring their own issues with hallucination and generalizability. To tackle the above research challenges, this project develops a novel convergent framework for learning-based simulation of collective human behavior during wildfires. Specifically, the objectives of this research are to: 1) Extend and enrich the Protective Action Decision Model (PADM) for civilians as well as incident commanders and public safety officials; 2) develop psychological theory-informed LLM agents for protective action modeling; 3) generate a realistic, context-aware synthetic population to serve as the critical input for the simulation platform; 4) develop the learning-based simulation platform to integrate complex interactions among various agents and predict collective human behavior at the community level under various scenarios (e.g., fire spread, warning, infrastructure damage); and 5) test and validate the convergent simulation framework with various case studies across the U.S. The research outcomes will enable wildland-urban interface (WUI) communities to better predict wildfire impacts, manage risks, and develop life-saving strategies. 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

Integrated Sensing and Communication (ISAC) is a transformative technology that unifies sensing and communication functions within a single system, significantly enhancing efficiency, cost-effectiveness, and performance. By sharing key resources such as spectrum, power, and hardware, ISAC not only reduces infrastructure costs but also improves spectrum utilization, minimizes interference, and alleviates congestion in increasingly crowded wireless environments. This integration enables real-time environmental awareness and faster decision-making, which are essential for applications such as autonomous vehicles, smart cities, Internet of Things networks, and industrial automation. Despite its promise, ISAC systems face major challenges due to the dynamic and complex nature of the wireless medium, particularly in multi-user scenarios, and the limitations of Radio-Frequency (RF) circuitry, which impact both sensing accuracy and communication reliability. This project introduces a novel ISAC system, Integrated Sensing and Telecommunications for Intelligent Connection and Transmission (INSTINCT), to address key challenges in joint communication and sensing. It advances multi-dimensional signal processing (MSP) across the delay, Doppler, and wavenumber domains, while ensuring compatibility with standard wireless protocols. A central innovation is the use of wavenumber-delay-Doppler domain signal processing, where range and velocity information naturally reside. INSTINCT further enables continuous-aperture phased multiple-input multiple-output (CAP)-MIMO, a reconfigurable sub-aperture architecture that enables dynamic, simultaneous communication and sensing capabilities. Key research contributions of this project include: (1) Developing reconfigurable RF hardware that seamlessly integrates communication and sensing via spatially adaptive apertures; (2) Creating electromagnetic-informed channel models and optimal signaling strategies tailored for CAP-MIMO systems; (3) Designing multi-dimensional waveforms and analyzing the impact of synchronization errors, RF impairments, and multi-user interference in the wavenumber-delay-Doppler domain; (4) Developing domain-informed progressive neural network architectures for joint beamforming and phase shift design in communication and radar sensing using reconfigurable hardware; (5) Designing a practical dynamic spectrum sharing framework using learning-based spectrum activity sensing for INSTINCT. 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 of STEM Education Research (BCSER) program contributes to the NSF mission 42 U.S. Code Chapter 16 by building the US workforce undertaking STEM education research. The BCSER Individual Investigator Development in STEM Education Research (IID) track supports individual investigators who are new to STEM education research to develop foundational skills and gain practical experience to advance STEM education knowledge through mentored professional development and pilot research activities. STEM education research generates the knowledge, theories, and understandings on which viable strategies for improving STEM education and workforce outcomes are based. This project will teach students to use generative artificial intelligence (AI) tools in STEM as learning partners aimed at facilitating deep, self-regulated learning in organic chemistry courses. The study will compare GAI-enhanced instruction to traditional learning environments to understand student mastery of a STEM learning in undergraduate organic chemistry courses. This BCSER IID project will allow the PI to develop foundational skills and gain practical experience in designing and implementing cutting edge STEM education research using innovative methods and tools. The PI will develop new expertise in implementing cutting edge STEM education research using modern methods and tools, as wells as using generative AI tools and statistical analyses in STEM education research. The PI will work with experts in evidence-based STEM teaching methods and interdisciplinary quantitative methodologies. 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

Computer graphics uses a range of techniques to create and manipulate both 2D and 3D images. Graphics users have to balance quality and timing when creating or adjusting images. This project addresses this tradeoff with both hardware and software techniques to accelerate ray tracing - a technique for computer graphic rendering that generates tremendously realistic images by simulating the physics of how light interacts with physical objects. While ray tracing is the gold standard for rendering realistic images where the time taken to do the rendering is less important than the quality of the final result, interactive rendering (games, data visualization, virtual reality, etc.) rely on a different technique, rasterization. Rasterization is well-supported by commercial graphics processing units (GPUs), but does not accurately represent realistic optical effects such as shadows, reflections, refraction, global illumination, glossy/specular materials. Improving the performance of ray tracing can bring its power to generate highly realistic images to bear on a whole new range of important problems that must run at interactive rates. Despite the clear advantages of ray tracing, virtually all GPUs are designed primarily to accelerate rasterization. The ray tracing cores included in some GPUs automate the ray traversal and intersection routines, but data movement remains a fundamental problem to improving performance. This project targets the data movement problem with ray tracing by compressing ray data and reordering the related computations. It investigates methods for coalescing rays that access the same memory block so that they may be processed together, thereby reducing repeated and unstructured memory accesses. This involves keeping track of the rays in flight and interrupting ray traversal to prevent premature memory access before they can be coalesced and handled efficiently. One specific technique being explored is reduced precision data representations with specialized hardware support and optimized storage of triangles within treelets. This project is also exploring alternative scene data representations other than bounding volume hierarchies (BVHs). While BVHs are known to provide good performance with traditional ray tracing on existing hardware, custom hardware could favor different representations. Therefore, the project explores both software/algorithmic modifications to ray traversal and special-purpose hardware designs targeting the modified traversal and data movement patterns. A modular and parallel cycle-level hardware simulator is developed to test the novel hardware designs, identify the bottlenecks, and iteratively improve them. This hardware simulator will also be publicly released and maintained as a part of this project. 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 Adaptive Spectrum Sharing and UsE (ASSURE) project investigates a new method for cellular (5G/6G) and radar systems to share a spectrum band, and it explores a general architecture for fine-grained spectrum sharing. The increasing use of wireless systems of all types is creating congestion in the limited available radio spectrum. The congestion can be reduced through sharing between services that previously required exclusive allocations. One example is the 2.7-3.1 GHz band used by federal radar systems. The radars in this band include airport surveillance radars and weather radars. Previously deployed radar-communications spectrum sharing approaches require shutting down the communications system when a radar is in interference distance. Since airports and weather radars are scattered widely across the US, use of such a sharing approach in the 2.7-3.1 GHz band would greatly limit cellular operations. The ASSURE project investigates different spectrum sharing approaches, in which the communications system operates safely within interference range of the radar by controlling transmission frequencies and times in a fine-grained manner to avoid the radar beam. If successful, the project will help advance the US as a leader in current, emerging and future spectrum use and wireless systems. The ASSURE project includes multiple research tasks. (1) Design and develop server software called Spectral Intelligence & Control (SIC) that transforms incoming information about users of a spectrum band into orchestration and control plans enabling safe fine-grained spectrum sharing. (2) Develop new site-specific and transceiver-specific interference models, expressed in IEEE spectrum consumption model format, for analysis of sharing opportunities. (3) Create OpenRAN rApp and xApp software modules for 5G cellular systems to interface to the SIC and control fine-grained spectrum sharing. (4) Develop radar operational mode detection methods based on spectrum sensors, for use when the radar operator does not share the information directly. (5) Develop monitor nodes to place near radar antennas to detect interference from cellular operations. (6) Use the building blocks from the previous tasks to implement sharing between 5G cellular and NEXRAD weather radars, running experiments using the University of Utah POWDER testbed. If experimentation in the 2.7 GHz band is not feasible, time-synchronized real-time sensing of a nearby NEXRAD radar (KMTX) together with over-the-air 5G experiments at 3.3 GHz will be performed, and then post-processed to evaluate whether interference would have occurred if both had been operating in the same band. 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 addresses the growing challenge of data movement in high-performance computing systems with diverse processors, memory, storage, and networks. These systems are critical for national-scale efforts in drug discovery, materials science, energy research, and large-scale artificial intelligence and machine learning. Applications such as molecular dynamics, graph neural networks, and particle-in-cell simulations generate large data volumes that must be moved efficiently. Data transfers often limit performance rather than computation. This project develops tools to reduce data movement time and energy, improving throughput, efficiency, and scientific productivity. It empowers researchers and developers to scale workflows on complex HPC systems while fostering collaboration among academia, industry, and national laboratories to transition ideas into practical solutions for exascale platforms. The project also advances national interests by enabling scalable AI and simulation workflows and engaging students in systems research for next-generation infrastructure. The project develops a unified framework to reduce data movement overheads in heterogeneous high-performance computing systems. It integrates three core components: a cross-layer monitoring and learning framework that characterizes data transfer patterns and predicts contention; a heterogeneity-aware data movement scheduler that coordinates bandwidth usage across computation, memory, storage, and interconnect resources; and a collaborative caching and prefetching architecture that anticipates future data needs across workflows. The framework treats data movement as a first-class task, parallel to computation, and uses analytical and machine learning techniques to reduce interference and improve overall throughput. The research is validated through representative workloads on petascale and exascale systems, including simulations and machine learning pipelines. Results will provide generalizable strategies for optimizing data movement in next-generation scientific computing environments. 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

With the support of the Chemical Catalysis program in the Division of Chemistry, Professor Matthew Sigman of the University of Utah is studying the reactivity, mechanism, and synthetic application of heteroleptic catalysts. During this project, the Sigman group will investigate heteroleptic metal complexes catalysts that require two disparate ligand scaffolds for function on a single metal. A broad goal of the Sigman lab is the fundamental understanding of relationships between catalyst structure and function. In modern catalysis, ligands, and the catalysts they support, have evolved in complexity to meet synthetic demands. As such, the Sigman group has been at the forefront of applying data science in chemistry to better understand intricate structure-function relationships, contributing to advancements in reactivity and optimizations. By applying a unified set of data science tactics, including the use of physically meaningful molecular descriptors, this project will provide the broader community with new strategies for reaction development and enhanced methods for chemical synthesis. Ultimately, this work will enable independent tuning of each ligand's role to achieve novel selectivity and reactivity. This work is highly collaborative, which will not only provide robust professional development opportunities for the trainees involved but also enable our science to reach broader audiences. The Sigman group highly values training the upcoming generation of chemists. As part of this work, the group will dedicate time towards training incoming students and scholars in the field of data science in addition to developing and sharing open-source educational/science tools for the at-large community. With the support of the Chemical Catalysis program in the Division of Chemistry, Professor Matthew Sigman of the University of Utah is studying new strategies for organometallic reaction development while advancing methods for chemical synthesis. This will be achieved through the exploration of heteroleptic catalytic systems, in which two distinct ligands (with non-additive impacts on reactivity) are coordinated to a single metal center. Additionally, the integration of modern data science tools with rigorous synthetic data collection will help decode intricate structure-activity relationships. These relationships will be probed using physically interpretable molecular descriptors, carefully designed datasets for high-throughput experimentation, machine-learning optimization, and data analysis employing contemporary physical organic techniques. It is expected that this work will be most applicable to catalysis that proceeds via transition metal species with high oxidation states, and as a result will yield higher selectivity (site-, atropo-, and enantio-) in addition to novel reactivity with alternative metals. It is anticipated that the approach and strategies developed during this project will be broadly applicable to complex catalytic systems with non-intuitive reactivity patterns. 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.