Board of Regents, NSHE, obo University of Nevada, Reno

NSF Awards · FY 2026 · 2026-10

This project establishes a Research Experiences for Undergraduates (REU) site at the University of Nevada, Reno, to provide undergraduate students nationwide with immersive research experiences in the field of edge intelligence. Edge intelligence, where data collection, processing, and analysis are performed close to the data source rather than on centralized cloud-based systems, is a rapidly advancing paradigm critical for applications such as autonomous vehicles, smart cities, industrial automation, and healthcare systems. However, deploying artificial intelligence (AI) on resource-limited edge devices presents substantial challenges in accuracy, efficiency, and robustness, with significant implications for public health, national security, and economic competitiveness. By engaging students in hands-on research and equipping them with the skills to address these challenges, this project aims to develop a highly skilled STEM workforce capable of advancing practical AI technologies. The program will foster sustained interest in research and encourage participants to pursue graduate studies and careers in STEM. Through the development of accurate, efficient, and trustworthy AI systems, the project will contribute to technological innovation, strengthen public trust in AI, and support societal well-being and national security. This REU site develops undergraduate research capacity in edge intelligence by leveraging the interdisciplinary expertise of University of Nevada, Reno faculty in AI, robotics, and cyber-physical systems. The research is structured around three core thrusts in edge-based machine learning (ML) systems: (1) algorithms and architectures for resource-constrained devices, (2) scalable and robust learning in heterogeneous edge environments, and (3) resource management for energy-efficient and sustainable operation. Representative projects include accelerating federated learning in heterogeneous wireless networks, developing memory-efficient training of large language models for edge devices, designing decentralized federated learning with workload balancing, federated fine-tuning of vision models for wildfire detection, and collaborative multi-robot deep reinforcement learning. Guided by experienced faculty and graduate student mentors, students will engage in cutting-edge research and contribute to the development of ML systems for real-world applications such as smart Internet-of-Things devices, autonomous vehicles, and robotics. The program is complemented by training in research methodology, scientific communication, and career development, preparing participants for graduate study and careers 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.

NSF Awards · FY 2026 · 2026-08

Life on Earth has been challenged repeatedly by periods of catastrophic change that shift the structure and function of communities and ecosystems. The consequences of environmental upheaval have traditionally been studied by paleontologists reconstructing the appearance and disappearance of species in the fossil record. That approach has revealed much about extinction as a process, but has left questions unanswered about the properties of species that lead to persistence. Contemporary changes in the abundance of wild plants and animals provide biologists with the opportunity to track and study natural populations responding to environmental fluctuations that include extremes of weather and drought. This project builds on one of North America's longest-running observational studies of insect populations by continuing data collection at six sites in the Sierra Nevada Mountains of Northern California and Nevada. Encompassing more than 500 species of butterflies and moths, researchers are investigating habitat use by adult butterflies and by caterpillars to better understand direct and indirect effects of temperature and precipitation on insect populations. Results from this work will advance the use of artificial intelligence (AI) in forecasting insect populations, and will continue to inform our understanding of the health and stability of pollinators and other insects that are crucial for national health and prosperity. Project participants interact with the public through talks, field days, and a novel forecasting website, as well as with local school groups and teachers, supporting science education in urban and rural communities. The coming years of this project represent the completion of a decadal plan to advance and expand upon fifty years of research in a dynamic system that has played an important role in our understanding of insects in the Anthropocene. Ongoing work with this long-term dataset suggests that the impacts of environmental extremes, including drought, are in some cases as important as the effects of habitat loss and degradation through pesticide accumulation and other processes; additional discoveries include organismal traits that mediate abiotic effects in ways that are population-specific and predictable. In addition to observations of adult butterflies that have been recorded for decades, other lines of information being gathered include phenology of plant communities and fine-scale environmental data on microsites associated with caterpillar occurrence. Heterogeneous lines of information are being integrated into a statistical modeling framework that will take advantage of neural networks and other approaches in artificial intelligence (AI) to forecast insect populations, with real-time, publicly available model validation. Outcomes from this project will include interdisciplinary tools for prediction with heterogeneous data sources, as well as advances on ecological theories of animals interacting with topographic complexity while responding to novel environmental 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 · 2026-07

This project will support to help students attend the 4th EAI International Conference on Security and Privacy in Cyber-Physical Systems and Smart Vehicles (SmartSP2026), to be held in Detroit, Michigan, on November 5–6, 2026. The funding will help cover student travel, lodging, and registration costs. SmartSP 2026 focuses on cyber-physical systems, smart vehicles, intelligent transportation systems, and related security and privacy challenges. Student participants will be selected based on academic performance, research experience, and demonstrated interest in CPS, smart vehicles, and security. This support will provide students with opportunities to attend technical sessions, present their work, receive feedback, and interact with researchers in the field. The project will support workforce development in Cyber-Physical System and smart vehicle security. 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 a study of the fundamental properties of warm dense matter (WDM), a poorly understood plasma state of matter made of electrons and ions at high density and relatively low temperature. The focus of the study will be on WDM formed under the influence of unusually energetic “non-thermal” electrons. These electrons travel at nearly the speed of light, penetrate deep into dense plasma, and change how energy is transported and how matter is heated under extreme conditions. A better understanding of these processes is important for both natural and laboratory systems, including solar and stellar plasmas and the efforts to develop sources of fusion energy here on Earth. Progress has been limited because WDM is difficult to model and to investigate experimentally as the material enters a poorly understood state between an ideal plasma and condensed matter. Existing theories do not yet fully describe this regime, and precise experimental data remain limited. To address these limitations, non-thermal-electron-driven WDM will be created with a high-power, short-pulse laser and probed using ultrashort hard X-ray pulses from X-ray free-electron lasers (XFELs). The project will provide training opportunities for graduate and undergraduate students at cutting-edge XFEL facilities in the United States, Japan, and Germany. The goals of this project are to determine the transient plasma conditions in non-thermal-electron-driven warm dense matter through spatially and temporally resolved XFEL-based measurements. A solid-density foil will be isochorically heated by laser-driven non-thermal electrons generated by a relativistic-intensity, femtosecond laser. The plasma conditions of the heated foil will be diagnosed using X-ray Thomson scattering (XRTS) and X-ray transmission imaging. To achieve these goals, the project will develop and deploy a radiation-hardened, high-collection-efficiency X-ray spectrometer for single-shot XRTS and establish near-K-edge X-ray transmission imaging as a complementary diagnostic for dense plasmas. These high-precision data will be used to build and benchmark reliable physics models describing how nonthermal electrons partition energy during warm dense matter formation and to enable direct comparisons with particle-in-cell and quantum molecular dynamics simulations. 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 projects seeks to develop advanced materials that are able to pre-concentrate oxidized mercury compounds (HgII) to enable improved measurement of their concentrations and characterization of their chemical behavior. Better understanding the abundance and chemistry of oxidized mercury is critical for assessing mercury exposure risks in both environmental and industrial settings and for informing effective management and policy decisions. The objectives of the project are to: (1) model chemical and physical interactions between simple HgII compounds and crystalline oxide substrates, and (2) correlate HgII capture with oxide surface properties under laboratory and field conditions. This is a novel interdisciplinary project that combines surface chemistry, glass science, and atmospheric mercury research, and will support the training of students and early-career researchers in interdisciplinary environmental science. 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

Airborne particles released by wildfires, ranging from fine smoke to larger ash and firebrands, are increasingly recognized as a significant yet understudied factor affecting freshwater ecosystems. These particles span a broad range of sizes (from micrometers to centimeters) and exhibit diverse chemical compositions, including both nutrients and harmful substances. As they are transported through the atmosphere and deposited across landscapes, they can have a variety of ecological impacts on aquatic ecosystems, including altering thermal and physical dynamics, contributing excess nutrients, and introducing toxic metals. Despite their potential significance, the chemical composition, spatial patterns, and ecological consequences of particles released by wildfires are still not well understood. Additionally, existing national monitoring programs tend to concentrate on smaller particle sizes (e.g., smoke), often overlooking the larger, and possibly more ecologically impactful, particles. This FIRE-NET initiative aims to bridge this gap by bringing together scientists from various disciplines to study how wildfire particles move through the atmosphere and impact freshwater ecosystems. This effort will enhance public knowledge and support informed environmental decision-making to protect water resources in a world increasingly shaped by fire. It will also support the training of early-career scientists, the development of accessible tools and protocols, and public engagement through open data sharing and outreach events. Through a series of virtual and in-person meetings, this FIRE-NET network will bring together experts in atmospheric science, aquatic ecology, biogeochemistry, fire modeling, and soil science. The group will collaboratively 1) Discuss and recommend methods for better quantifying how wildfire airborne particles spread across the atmosphere and deposit on the landscape; 2) Assess the chemical composition of wildfire airborne particles as they are transported from the source of ignition and deposited in the landscape; 3) Create a more detailed conceptual framework to refine our understanding on how wildfire airborne particles influence lake ecosystems; and 4) Propose protocols to improve the monitoring of wildfire airborne particles during active fires so we can quantify their potential influences and identify the mechanisms leading to changes in lakes. These activities will combine empirical field data, fire and atmospheric modeling, machine learning, and interdisciplinary collaboration to understand particle dynamics and ecological outcomes. Focusing on fire-affected sites in the western U.S. and leveraging existing data networks, the project will develop tools and frameworks that enable more accurate prediction and management of wildfire impacts on aquatic ecosystems, thereby helping to build environmental resilience in the face of increasing wildfire frequency and severity. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2026 · 2026-01

Wildfire is a key process for many types of ecosystems. Over the past century, however, fire activity has changed dramatically. One important driver of this shift has been the accumulation of flammable materials, such as fallen leaves and dead wood, that provide fuel for wildfires. Despite its importance, fuel accumulation remains difficult to model because it involves processes unfolding on very different timescales: the slow buildup and decay of fuels and their rapid combustion during wildfire. This project investigates how fuel accumulation and fire activity are changing over time in a range of landscapes in western North America. This includes places where fire is limited by fuel availability as well as those where fire activity is driven more by flammability. The project uses these insights to improve models that predict future fire regimes and works closely with land managers to co-develop model improvements and scenarios that support decision-making. This project: (1) examines, quantifies, and reduces uncertainty in models of the slow but steady process of fuel accumulation, and (2) uses improved models to investigate how changes will influence rates of energy conversion at both slow and fast time scales and across watersheds. The project first investigates the process of decomposition, which reduces a critical source of uncertainty in projecting future fuel accumulation. The project also uses long-term empirical datasets of litter decomposition to perform uncertainty analysis and multi-objective assessment of several decomposition models. In parallel, the research team engages with management partners to co-produce model development and to design management-relevant scenarios. Then, the team uses those scenarios across several watersheds in western North America to project how combined scenarios of environmental variability and management alter wildfire regimes. Coproduction efforts align the team’s modeling efforts with management needs and provide managers access to critical technical infrastructure. The project engages the next generation of scientists in the theory and practice of fire ecology, biogeochemistry, ecohydrology, and fire regime modeling. The project also delivers a workshop to train modelers at the graduate student and early-career researcher level. This convergence research- spanning biology, geosciences, and mathematical and physical sciences- advances theories of ecosystem energetics in the context of natural and managed fire regimes. 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

Water that evaporates from ground surfaces cannot contribute to plant growth, streamflow or groundwater. Understanding the factors that control evaporation is therefore critical for predicting water availability and related natural resources. Plant litter—the leaves and twigs that accumulate on forest floors—plays a complex and poorly understood role. Some research shows plant litter acts like mulch in a garden bed, covering and shading soils to reduce evaporation. However, prior research also shows that plant litter can capture and store rainfall, causing much of it to evaporate before reaching soils. This study will quantify the relative influences of these competing effects: the mulching effect that reduces soil evaporation versus the interception effect that enhances evaporation. This research will combine observations in field sites across diverse United States (US) ecosystems, controlled laboratory and field experiments with collected samples, and development of a predictive simulation model. This integrated approach will advance understanding and capabilities to predict how litter properties, climate conditions, and ecosystem characteristics interact to influence evaporation across US landscapes. The project will train graduate students at University of Nevada Reno and Cleveland State University, develop new experiential learning modules, create openly available datasets, and support engagement with various audiences including land managers and other stakeholders. This project will pursue four key research tasks to advance understanding of water fluxes at the forest floor and their variation with weather, climate, and ecosystem traits. Litter-specific storage capacities and drainage parameters will be quantified using an in-lab precipitation generator and litter collected from 32 sites of the National Ecological Observatory Network (NEON). Soil and litter traits as well as surface evaporation rates will be measured in-situ at a subset of NEON sites. A replicated field experiment using soil plots with litter layers collected from NEON sites will test litter effects on soil water storages and fluxes. Evaporation modeling parameters will be developed from the collected laboratory and field data and used in a soil water-balance model to test how evaporation is affected by the interplay of meteorology and litter traits across a range of realistic climate scenarios. By using a systematic macro-scale approach and multiple prongs of investigation, this study will enhance mechanistic understanding of how litter enhances precipitation interception versus resisting soil evaporation, and reveal site-specific variations to develop generalizable conclusions that can ultimately support future 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 · 2026-01

Large earthquakes occur infrequently, often separated from each other by decades or centuries. This makes it difficult for scientists to predict and understand large earthquakes because they have only a few historical examples to base their models on. To get around this limitation, scientists use computer simulations to create “synthetic” earthquakes that they can study. Unfortunately, these synthetic earthquakes currently require thousands of hours to compute. In this project, machine learning techniques will be developed to generate realistic, synthetic ground motions in minutes, enabling geologists to efficiently study how large earthquakes work and assess the hazards they may pose to the people of California and Nevada. The software developed will be publicly available for other researchers to use, and educational resources will be created to train future scientists and increase public awareness about large earthquakes. Large earthquakes occur on time scales of centuries or more. Earthquake prediction has proven elusive and recordings of large and damaging earthquakes are rare. Wave propagation simulations currently provide the only option for overcoming the lack of observational data, however, sufficiently realistic physics-based models are extremely computationally expensive. To address this, we will develop a physics-based machine learning model order reduction technique, Operator Inference (OpInf), to create time-dependent parametric surrogate models of seismic ground motions, fusing simulated ground motion wavefields with previously observed earthquake records. The OpInf model will be dramatically faster than traditional physics-based methods and more generalizable. The OpInf models will be applied to diverse faulting regimes in California and Nevada to quantify differences in the source and path influences on the ground motion. All software developed will be open-source and publicly available. We will train the future scientists in earthquake science and advanced model order reduction techniques. Our research will appear in events in California and Nevada to enhance public awareness of earthquake safety procedures. We will collaborate with both the U.S. Geological Survey and Statewide California Earthquake Center to assess how our research could enhance existing hazard assessment and earthquake early warning. 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 Major Research Instrumentation (MRI) grant will enable the acquisition of a high-rate Atomic Layer Deposition (ALD) system for research, education, and semiconductor workforce development in the Northern Nevada region. ALD is a critical technology used to deposit thin film oxides and nitrides required for leading-edge devices in fields as diverse as semiconductors, quantum technology, mechanical engineering, chemistry, and biology. The ALD will be located in the new Davidson Foundation Cleanroom, which is a facility shared by researchers and students throughout Northern Nevada. It will be the first ALD at the University of Nevada, Reno (UNR) and a plasma-enhanced ALD system at any Nevada university. This would significantly advance UNR's multidisciplinary research programs, which span electrical and biomedical engineering, energy, and functional materials to nanostructured materials and semiconductor devices. This system will dramatically enhance UNR’s research and educational capabilities and foster critical industrial collaborations, further boosting Nevada and the Nation’s economic competitiveness and student employment opportunities. Atomic layer deposition (ALD) enables conformal deposition of complex, tunable metal oxide and nitride thin films with approximately 1-100 nm thicknesses. ALD is currently used in the semiconductor industry and is finding more and more applications for the deposition of conformal thin oxide and nitride films with engineered properties. A site-specific, cutting-edge ALD is needed to pursue research in several key areas: (1) nano- and micro-structured materials and devices; (2) materials interfaces; and (3) nano-mechanical devices for the atomic-scale control required for the next generation of sensors, catalysts, quantum devices, and more. The high-rate plasma-enhanced ALD system acquired through this program offers deposition rates 5~10x higher than conventional ALD systems, enabling the system to serve a larger number of researchers and students for conventional thicknesses, and research into the applications of thicker ALD films (10~100 nm) in areas such as quantum technology. The system is also capable of isotropic atomic layer etch (ALE), a complementary atomic-scale processing technique. The system is configured for maximum flexibility to meet the needs of Nevada’s rapidly growing research community. It will enable novel research in areas ranging from semiconductor, photonic, and quantum devices to biomedical engineering, energy, and functional materials. 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

Robert Noyce Teacher Scholarship Program Track 1 Project at University of Nevada, Reno (UNR) aims to address the national need of preparing highly qualified STEM teachers, with a particular focus on rural Nevada. Nearly 90% of Nevada's school districts serve rural communities. These districts struggle to attract highly qualified STEM teachers. To help address the critical shortage of highly qualified STEM teachers in Nevada, this project supports the recruitment of 10 Noyce scholars across science and mathematics content areas to be prepared as secondary STEM teachers to teach in rural and high-need school districts. The program plans to provide first-year and sophomore students with financial support, paid internships, and mentorship to introduce them to K-12 STEM teaching. It further supports 10 Noyce scholars with scholarships, testing and licensing fee assistance, mentorship from UNR, Great Basin College (GBC), and Elko County School District (ECSD), apprenticeships, cultural immersion, and community outreach, while also retaining graduates in high-need rural schools through induction activities and salary support. Noyce Scholars are projected to earn a Bachelor of Science in Secondary Education degree alongside their Bachelor of Science in a STEM content area and be enabled to be highly effective science and mathematics teachers with extensive expertise in culturally responsive teaching in rural environments. This project at the University of Nevada, Reno includes partnerships with Great Basin College and Elko County School District, which is a rural high-need school district in Nevada. Project goals include the recruitment, preparation, and retention of 10 undergraduates across seven program areas (Biology, Microbiology and Immunology, Mathematics, Atmospheric Sciences, Chemistry, Geology, and Physics) over the five years of the project. If successful, these efforts will result in the development of a pipeline of highly qualified, STEM educators who serve Nevada's rural communities and provide a model to address the nationwide shortage of STEM teachers prepared to teach and live in rural communities. The project will be iteratively evaluated. The following elements are proposed to guide the evaluation approach: (a) whether the project succeeded in meeting its stated goals (outcomes), (b) aspects of the project that were integral in facilitating and hindering success (lessons learned), (c) intended and unintended outcomes of the project (accomplishments), and (d) aspects of the project that are sustainable beyond the funding cycle of the grant. The results of this project will be disseminated to help enhance the field. This Track 1: Scholarships and Stipends project is supported through the Robert Noyce Teacher Scholarship Program (Noyce). The Noyce program supports talented STEM undergraduate majors and professionals to become effective K-12 STEM teachers and experienced, exemplary K-12 teachers to become STEM master teachers in high-need school districts. It also supports research on the effectiveness and retention of K-12 STEM teachers in high-need school districts. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2025 · 2025-10

This program aims to create semiconductor amplifiers with outstanding noise performance in the microwave spectrum. Researchers from the California Institute of Technology (CalTech) and the University of Nevada-Reno (UNR) will accomplish this goal by leveraging a new nanofabrication method, atomic layer etching, which permits semiconductor device manufacturing with atomic-scale precision. If successful, these new amplifiers will be 30% more sensitive to radio emissions from space, meaning that less time will be needed for radio telescope measurements than is currently needed, potentially enabling new scientific discoveries. Based in part on this research, the team will develop a new college course at the UNR on nanofabrication, which will bring new technical training opportunities to students at UNR and in Northern Nevada. High electron mobility transistors (HEMTs) are used ubiquitously throughout radio astronomy observatories. Despite their impressive noise performance, their noise temperature has plateaued in recent years, in part due to challenges in scaling their dimensions even smaller. This project aims to overcome these limits by leveraging a new nanofabrication method, atomic layer etching, into the fabrication of low-noise HEMTs for the first time. The microwave noise performance has potential to improve by 30%, thus enabling significantly improved observational efficiency. These new HEMTs will be directly deployable to radio telescope systems as a drop-in replacement for the low-noise amplifier, meaning that an immediate improvement in sensitivity can be achieved without requiring any other upgrades. To maximize the broader impact of this project, the team will develop a new course on nanofabrication at the University of Nevada-Reno as well as incorporate graduate research activities into their new cleanroom facility. 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 Research Experience for Teachers (RET) Site project at the University of Nevada, Reno, will provide STEM educators (grades 6-14) with immersive research experiences in the cutting-edge field of Edge Artificial Intelligence (AI). Edge AI, which involves processing data directly on local devices rather than in centralized cloud systems, is a transformative technology critical for future advancements in areas such as autonomous systems, smart cities, and advanced manufacturing. By equipping teachers with first-hand knowledge and research skills in Edge AI, this project aims to address the urgent national need for a technologically skilled workforce and to enhance the quality of STEM education. The project will empower educators to inspire their students by bringing authentic, state-of-the-art computing and engineering concepts into their classrooms, thereby promoting scientific progress and contributing to the nation's economic prosperity and technological leadership. This initiative will directly benefit society by fostering a deeper understanding of AI among future generations and strengthening the pipeline of talent prepared for the challenges and opportunities in rapidly evolving high-tech fields. The primary goal of this RET Site is to provide Nevada teachers with a strong foundation in Edge AI principles and engage them in hands-on research using SmartEdge, a custom-designed, transferable Edge AI testbed. The research will focus on areas including machine learning, Internet-of-Things (IoT), robotics, and cybersecurity, with projects tailored to explore real-world applications such as smart IoT devices, robotics, and autonomous vehicles. Participating teachers will work alongside faculty mentors and graduate students to develop research projects and subsequently translate these experiences into novel K-14 curriculum modules aligned with state educational standards. The project scope includes intensive six-week summer research programs, year-round support for curriculum implementation, and activities designed to build a collaborative community of STEM educators. This approach will not only provide teachers with valuable research experience but will also generate new educational materials and pedagogical strategies, contributing to the broader understanding and integration of Edge AI concepts in K-14 STEM education. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2025 · 2025-10

This project investigates the principles and methodologies for Artificial Intelligence of Things (AIoT) devices to make decisions quickly and accurately, to meet the requirements for safety, accuracy, and latency even with limited communication and computing power. By developing new machine learning techniques that work well in the presence of communication resource limitations, this project will spur a new line of thinking for a variety of AIoT applications that face the challenges of communication-constrained machine learning, such as smart health, connected cars, augmented reality, and smart city, benefiting society at large. This project will also contribute to skilled workforce development in this area of national needs, by integrating the research findings into undergraduate- and graduate-level education and organizing various summer programs on data science, AI and machine learning to broaden the participation of K-12 students. This project aims to develop innovative Communication-constrained Adaptive Machine Learning (CAML) methods that adapt to varying communication bandwidth, computational power, and data size of edge devices, thereby enabling heterogeneous devices to work in concert. Specifically, the project will investigate CAML for two popular edge network settings: edge networks with a central coordinator and fully decentralized edge networks. For edge networks with a central server, this project studies distributed fine-tuning for on-device machine learning model adaptation through dynamic tier-based low-rank model adaptation, allowing each device to train a local adapter of a suitable rank. This approach ensures synchronized model updates across devices with varying capabilities, improving the training efficiency of large machine learning models. Cross-layer optimization schemes will be developed to speed up the learning process while ensuring accuracy. To address the scenarios with extreme communication resource limitations, the project introduces seed-aided machine learning model tuning algorithms that enable meaningful model updates with minimal data exchanges. For decentralized edge networks, the project develops communication-efficient workload balancing algorithms to reduce synchronization delays. The innovations in this project promise to advance federated and collaborative learning across diverse edge environments, enhancing scalability, efficiency, and robustness in real-world AIoT applications. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2025 · 2025-09

Modeling is a key scientific practice emphasized in the Next Generation Science Standards (NGSS), but students often need significant support to engage in the practice meaningfully. This project aims to develop an augmented reality (AR) learning environment integrated with a large language model (LLM)-powered pedagogical agent to guide middle school students' modeling practice. It will bring together computer scientists, human-computer interaction researchers, science education researchers, and K-12 teachers to co-design the learning environment. The AR environment will allow students to interact with simulated objects and phenomena as they develop their models. The LLM-powered agent will provide timely assessments of students' diagrammatic models, offer personalized feedback, and share insights with teachers to inform instruction. This project has the potential to transform the way students engage in modeling practice in K-12 science classrooms. Over three years, it will directly impact approximately 10 middle school teachers and 400 middle school students. The project is guided by three core objectives: 1) to build AR simulations for modeling tasks, 2) to develop an on-device multitask LLM to assess diagrammatic models, and 3) to investigate student modeling practice within the AR-LLM learning environment. A key innovation of the project is the closed-loop feedback system: students first receive suggestions for model revisions from the LLM and revisit AR components, and the LLM agent then provides feedback on students' performance to teachers to inform instructional decision-making. The project will investigate students' perceptions of their modeling experiences and how the AR elements and LLM-generated feedback support their model development respectively. Primary data sources will include classroom video recordings of student consensus model building, screen recordings of individual modeling activities, and semi-structured focus student interviews. A multiple-case study approach will be used to investigate student learning. Research products will be widely shared with practitioners, teacher educators, and researchers through publications, conference presentations, teacher workshops, and publicly accessible teacher resources. This project is funded by the Research on Innovative Technologies for Enhanced Learning (RITEL) program that supports early-stage exploratory research in emerging technologies for teaching and learning. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2025 · 2025-09

The human gut microbiome is made up of trillions of microbes that influence digestion, immunity, and overall health. Scientists have found that people vary widely in the communities of microbes they host, but the reasons for this variation remain unclear. While studies suggest that genetics plays a role, it is not yet understood how specific genetic differences shape the microbiome. This project uses the Mexican tetra, a small fish with surface-dwelling and cave-dwelling forms, to explore how genes influence gut microbes. These fish have evolved in drastically different environments and display unique metabolic traits and microbiome profiles, even when raised under identical lab conditions. Their natural diversity makes them an ideal model for identifying the genes that shape gut microbes and for testing how those microbes affect traits like fat storage and blood sugar levels. In addition to advancing scientific knowledge, the project promotes STEM education and public engagement. A new course module for graduate students will provide training in modern genetic techniques. Undergraduate students will gain paid, hands-on laboratory research experience through a structured mentorship program. To support early science learning, the team will partner with the UNR Museum of Natural History to create an interactive lab activity for visiting elementary school groups using live fish. The museum will also host a public exhibit featuring the Mexican tetra to highlight how animals adapt to extreme environments. Through research, teaching, and outreach, this project will help train future scientists and increase public understanding of genetics, evolution, and the microbiome. Vertebrate gastrointestinal (GI) microbiomes are critical to host metabolism, physiology, and fitness, yet the genetic basis for host-driven variation in microbiota composition remains poorly understood. This research program will reveal genetic pathways that shape vertebrate GI microbiota composition and underlie natural diversity in host-microbiota interactions using the Mexican tetra, Astyanax mexicanus, as a model system. The project includes three integrated objectives. First, metagenomic sequencing and transcriptomic analysis will be used to compare GI microbiota composition, microbial function, and host gene expression across gut regions and life stages in multiple surface fish and cavefish ecotypes. Second, quantitative trait loci (QTL) mapping in surface/cave F2 hybrids will identify host genetic loci associated with microbiota composition and host traits. Third, CRISPR/Cas9 genome editing will be used to functionally test the effects of specific candidate variants by performing allele-swapping experiments between ecotypes. In parallel, microbial manipulation experiments that include microbiota transplants and metabolite supplementation will determine the extent to which microbial communities drive host phenotypic differences, such as fat accumulation and starvation resistance. This approach uniquely combines natural evolutionary replicates, high-resolution genetic mapping, and functional testing of both host genes and microbial contributors to investigate host-microbiota interactions. The results will reveal the molecular and physiological mechanisms linking host genetics and microbiota composition and will clarify the bidirectional relationship between microbiomes and vertebrate metabolic traits. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2025 · 2025-09

This project establishes state-of-the-art graphical processing unit (GPU)-based High-Performance Computing Infrastructure (HPCI) at the University of Nevada, Reno (UNR) to advance research in efficient and scalable multi-modal artificial intelligence (AI). As AI systems increasingly integrate and analyze diverse data sources—including images, video, audio, text, and real-time sensor streams—there is a critical national need for infrastructure that supports the generation, compression, and high-performance processing of large-scale, multi-modal datasets. The HPCI resources empower researchers to develop next-generation AI models and data workflows, enabling rapid innovation in areas such as intelligent data fusion, privacy-preserving analytics, and distributed computation. The project fosters broad academic, industry, and community collaborations, and expands participation in AI and STEM fields through outreach and educational activities open to all. By promoting advances in data-centric and efficient AI, this initiative accelerates scientific progress and supports national interests in technology, health, and public welfare. The project deploys a cutting-edge GPU cluster to support five integrated research thrusts: (1) construction of diverse and representative multi-modal datasets for robust model development; (2) creation of data-efficient distillation and compression techniques to reduce storage and computational costs without sacrificing fidelity; (3) design and optimization of high-performance multi-modal large language models for scalable analysis and real-time applications; (4) implementation of federated and privacy-preserving learning paradigms for secure, decentralized data collaboration; and (5) integration of real-time IoT and sensor data streams to enable dynamic analytics in distributed environments. These efforts address foundational challenges in scalable AI, data-centric computing, and privacy-aware analytics. The resulting infrastructure and research outputs support a wide range of scientific, industrial, and societal applications, empowering impactful research on in next-generation, efficient, and trustworthy multi-modal AI. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2025 · 2025-09

With the support of the Chemistry of Life Processes program in the Division of Chemistry, Professor Yftah Tan-Gan from the University of Nevada, Reno and Professor Michael Meijler from Ben-Gurion University of the Negev are investigating the chemical interactions that bacteria utilize to communicate between each other. Bacterial species employ a variety of chemical signals to assess their surroundings and modify their behavior in response to environmental changes. This process is called quorum sensing. Since many bacterial species occupy the same environmental niches, including inside the human host, identifying the chemical crosstalk between different bacterial species and between bacteria and the host is pivotal. This study identifies the chemical signals and the target proteins involved in communication among bacterial species that colonize humans, providing critical insight as to how bacteria coordinate colonization and other population-wide processes and laying the foundation to control and alter bacterial behavior. Undergraduate and graduate students involved in this project acquire specialized training in both chemistry and microbiology, from synthetic chemistry and chemical proteomics to microbiology. This project involves an international collaboration that brings together the PI’s expertise in developing peptide-based signal modulators with the collaborator’s expertise in bacterial communication and chemical proteomics to allow cross-training of students in different chemical biology techniques. This interdisciplinary project is aimed at identifying the chemical interactions between Enterococcus faecalis, Staphylococcus aureus, Pseudomonas aeruginosa, and Streptococcus oligofermentans. These species are prevalent members of the microbiota that often encounter each other inside the human host. To this end, traditional microbiological methods will be integrated with chemical biology techniques involving synthetic peptides, photoactivatable probes, and chemical proteomics to pursue the following objectives: 1) determine the effects of signaling molecules from E. faecalis, S. aureus, P. aeruginosa and S. oligofermentans on the behavior of one another; 2) identify receptors for these signals among the affected bacteria; and 3) examine the roles of these receptors to unravel how each of these species reacts and adjusts their behavior to the presence of the other bacteria. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2025 · 2025-09

This project investigates how plants use calcium signaling mechanisms to respond to changes in temperature, salt stress, and wounding. The long-term goal is to understand how plant cells sense and respond to stressful environments, and to use that knowledge to develop crop plants that are more productive. Projections indicate that worldwide food production must increase by more than 70% by the year 2050 to feed the expected increase in the human population. To accomplish this, there is a need to develop crops that are more tolerant to suboptimal agricultural lands and chaotic weather events. This research project seeks to better understand how plants use calcium as an intracellular signaling molecule, and may lead to new ways to improve high-temperature tolerance in plants. Calcium signals are initiated when cell membrane ion channels open and allow calcium to enter the cytoplasm of a cell. The transient signal ends after calcium efflux transporter molecules remove the calcium and restore normal resting calcium levels. The focus of the research is to understand how cells regulate normal resting levels of calcium, and whether long-term changes in those resting levels provide a mechanism to reprogram cells to adapt to different stresses or developmental situations. The project incorporates genetic tools to manipulate the resting levels of calcium and test for their importance during plant development and responses to the environment. Additionally, the project will provide an opportunity for teams of undergraduate students to gain a true research experience to help develop critical thinking skills relevant to science and technology oriented careers. It will also provide research training for graduate students at both institutions. Calcium ion signaling is a fundamental feature of eukaryotic cells, and is critical for plants and animals to respond to biotic and abiotic stress conditions. While most research in plants has focused on stimuli that trigger calcium ion influx into the cytoplasm at the start of a calcium signal, relatively little is known about how calcium efflux systems reset basal levels of cytosolic calcium, and what happens if those basal levels are reset to different starting points. The focus of this project is on the role of ATP-dependent calcium efflux pumps referred to as Autoinhibited Calcium ATPases (ACAs). The project uses genetic knockouts of different calcium efflux pumps in Arabidopsis to increase or decrease basal cytosolic calcium concentrations when mutant plants are grown on media with varying concentrations of external calcium. The central hypothesis being tested is that calcium pumps play a critical role in defining basal levels of cytosolic calcium concentration, and that stable changes in basal calcium concentration can manifest as global changes in cellular physiology, gene expression, and an organism’s response to developmental or environmental stimuli. The first aim is to identify changes in calcium dynamics resulting from increasing or decreasing the activity of calcium pumps. The second aim is to identify environmental conditions that reset basal calcium concentrations. To provide undergraduates with an authentic research experience, a CURE (Course-Based Research Experience) will be developed in which students may identify molecules that trigger calcium signals in plant roots. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2025 · 2025-09

Our Milky Way Galaxy is home to hundreds of millions of black holes with masses of about 10 times that of the Sun, and the vast majority of these stellar mass black holes are expected to be found in binary systems with a companion star. The black hole often accretes matter from the companion star, releasing an enormous amount of energy, mostly in the form of X-rays; such systems are known as black hole X-ray binaries (BHXBs). Some BHXBs undergo outbursts that change the structure of the black hole accretion disk and produce relativistic jets of material. Researchers at the University of Nevada, Reno (UNR) through an interdisciplinary collaboration between astrophysics and computer science will analyze radio observations of 20 BHXB systems pre- and post-outburst to reveal the physical drivers for how jets respond to accretion flows. They will build a catalog of the systems that will be accessible through an interactive website. The survey will place stringent constraints on the Galactic BHXB population and thus our understanding of stellar populations. The project will contribute to the training of the next generation of scientists by including two graduate students in the research, as well as expanding an existing outreach program that will provide astronomy lessons to 1000 middle school students across Nevada. This project focuses on the hard X-ray state of BHXBs that occurs during the initial outburst triggered by thermal-viscous instabilities in their disks and during the return to quiescence at the end of the outburst cycle. As more BHXB outbursts have been monitored, more systems have been found to deviate from an expected radio/X-ray luminosity correlation, the slope of which reflects how jets respond to mass accretion. The physics driving this behavior is still debated, and one major limitation to the physical interpretation is that the current sample of hard state BHXBs is of heterogeneous quality with unaccounted systematics. The UNR researchers will address this limitation by producing a publicly available catalog that will include well defined quality control metrics, uniform procedures to calculate luminosities, and a rich set of features (e.g., spectral properties, orbital parameters, and outburst histories) to allow multi-dimensional analysis. They will then apply global statistical analyses to the ensembled dataset of all hard state/quiescent BHXBs to tease out the physical properties that most strongly correlate with a system’s behavior during an outburst. Finally, the researchers will use what is learned about the radio/X-ray luminosity correlation to perform an archival large-sky survey to discover new quiescent BHXB candidates in the Milky Way. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2025 · 2025-09

Recent breakthroughs in laser excitation of a unique low-energy nuclear transition in thorium-229 embedded in solid-state hosts have enabled a new generation of highly accurate, portable timekeeping devices. Compact nuclear clocks based on this transition hold an important promise to revolutionize global navigation, telecommunications, precision timing, and fundamental physics applications by offering unparalleled stability and robustness under varying environmental conditions. While advancing emerging quantum technologies, this research supports the development of a skilled quantum workforce. Materials science, quantum chemistry, condensed-matter physics, nuclear physics, and atomic, molecular, and optical physics are combined in an interdisciplinary approach. A theoretical formalism for evaluating isomer shifts, internal conversion rates, and electron-bridge decay rates is developed to inform nuclear-clock design and performance. Relativistic atomic theory is bridged with state-of-the-art quantum-chemistry and materials-science computational techniques by this framework. Couplings between electronic degrees of freedom in condensed-matter environments and thorium-229 nuclear states are of particular interest and are subjected to ab initio relativistic treatments. Quantum electrodynamics is applied in thin-film geometries to calculate Purcell enhancement factors and to establish foundational elements of nuclear quantum optics. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2025 · 2025-09

This award supports the Institute on Collaborative Language Research (CoLang), which has taken place biennially since 2008, hosted at various locations in the United States. The program brings together experts in language documentation, linguistic analysis, computational approaches to the study of language, and practical application of academic findings. These experts provide training for undergraduate students, graduate students, and independent scholars who are interested in acquiring technical and linguistic skills for studying language and communicating their findings to the broader public. The institute’s focus is on language in general, with the potential to expand our knowledge about how language functions. We attract facilitators with a broad range of experience and capacities as well as students who are motivated to further develop their interests and career goals in language and related fields such as language databases and artificial intelligence. The institute consists of two sessions, each one lasting for two weeks. During the first session of CoLang, students choose eight workshops to attend. Depending on their interests and career goals, students can choose to learn about cutting-edge technology, computational methods in language documentation, basic concepts in linguistics, how to assess successful language programs, and other related topics. Given that artificial intelligence and translational research are increasingly represented as research areas among linguists, practical workshops on these topics are also offered. During the second session of CoLang, students apply the skills and knowledge learned in the workshops by participating in a more intensive, hands-on practicum that focuses on specific languages. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2025 · 2025-09

Advancing the frontiers of knowledge in plasma and atomic physics is vital for understanding the Universe, where plasma is the most common state of observable matter, and for advancing new technologies including fusion energy. Over the last decade, observations of short-lived extreme states of matter such as ''hollow ions'' -- ions missing their innermost electrons while retaining outer ones -- have been made possible by very active research conducted on x-ray free-electron lasers and by substantial increases in the laser power. It was recently shown that these unique states of matter can also be created using pulsed power plasmas. Based on a deeper knowledge of how hollow ions are formed and behave, powerful new tools for examining other complex, high-energy-density plasma environments can be developed. This award will contribute both to advancing knowledge in dense plasma and atomic physics, and to the development of plasma science and technology workforce, which is in high demand. The goals of this project are defined as the achievement of better understanding of the nature of hollow ions, regarded as exotic states of matter in pulsed power plasmas, and the development of their unique applications for moderate- and high-density plasmas. This will be accomplished through a continued search for new spectral features from hollow ions specific to pulsed power plasmas, along with the analysis and modeling of relevant experimental data. Additionally, a possible connection between the generation of nonthermal electrons and hollow ions will be investigated, and plasma non-uniformity and charge-exchange processes will be studied. The comprehensive modeling and analysis of X-ray emissions from these ions is anticipated to provide fundamental insights into their formation pathways, potentially enabling their use for new advanced diagnostics for hot and warm dense matter. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2025 · 2025-09

Studying the history of deformation on ancient faults and shear zones can advance understanding of modern earthquakes, past motions of tectonic plates, mountain building processes, and the evolution of the Earth’s crust. This project will develop and validate a new method for directly dating shear zones. The method involves isotopic dating combined with characterization of deformation within grains of the mineral apatite which is found in many shear zones. This project will focus on shear zones within two case-study locations in the western United States, which expose large extensional shear zones that experienced multiple deformational events over tens of millions of years. Research at these sites will be used to refine the dating technique, learn about crystal-scale deformation, and recover a detailed history of shear-zone activity. The goal of this project is to produce a validated method to directly date rock deformation within shear zones. The new tool will enable Earth Scientists to link the timing of deformation and with information on temperature and rock strength to derive an integrated history of shear zone deformation. The project supports national and societal interests by training two PhD students, advancing the research programs of early- and mid-career faculty, and building a pipeline from community colleges and four-year institutions to engage undergraduate students in STEM through field and laboratory research. This work will test and validate a novel analytical technique of integrating crystallographic vorticity axis (CVA) analyses using electron backscatter diffraction (EBSD) with apatite U-Pb petrochronology to directly date deformation. The method will be developed and validated using samples from the Albion-Raft River-Grouse Creek (UT) and Chemehuevi (CA) Mountains, which expose major extensional shear zones that exhume mid-crustal rocks. Deformation in distinct shear zones at different structural levels is anticipated to yield predictably differing ages and deformation patterns that can be uniquely related to temporally and tectonically disconnected deformational events. Comparing these deformation ages to previous interpretations, low temperature thermochronology, and basin sedimentation records will confirm that the obtained chronology aligns with expectations, thus validating the method. Combining thermometry with this approach will allow the reconstruction of integrated temperature, time, and rheologic history of shear zone deformation. This new method will have broad applications to a wide range of geologic questions. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2025 · 2025-08

Embedded 3D bioprinting has been extensively utilized to fabricate human organ and tissue analogs; however, creating human tissue constructs with large scale and high cell viability remains a significant challenge. The research goal of this EArly-concept Grant for Exploratory Research (EAGER) project is to establish a new embedded section-by-section bioprinting process aimed at achieving a high cell viability during the fabrication of large-scale human tissue constructs. In this new process, a human-relevant sized construct will be segmented into multiple smaller sections and sequentially printed within a support bath using a short nozzle. Between sections, a special material will be added to the support bath, and its viscosity and flow behavior will be adjusted by changing the temperature. The expected outcomes of this project are 1) identifying cell damage mechanisms in this 3D bioprinting process and 2) elucidating cell damage during the addition of support bath materials. This new 3D bioprinting strategy will facilitate the reconstruction of full-scale human organs and tissues for diverse biomedical applications. The education goal is to enhance the biomanufacturing training through outreach activities, such as bioprinting demonstrations to K-12 students, bioprinting-related curricula for undergraduates and graduates, biomanufacturing-themed activities to attract talented students. This project aims to advance and surpass the state-of-the-art embedded 3D bioprinting processes by ensuring a high initial cell viability of over 80 percent when producing human tissue constructs with dimensions exceeding 20 mm along the printing orientation. Three integrated research objectives will be pursued, including 1) establish a theoretical platform through mechanics modeling and experimentation to identify the cell damage mechanisms at the cellular level and during bioextrusion; 2) elucidate thermal damage to living cells via numerical simulation, modeling, and experiments to optimize key conditions when adding support bath materials; and 3) evaluate the capability of the proposed embedded section-by-section bioprinting process to print large-scale cellular human tissues. The knowledge gained from this project will jump-start the development of advanced support bath materials and innovative 3D bioprinting processes for tissue engineering in the future. 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.