University Of Nevada Las Vegas

NSF Awards · FY 2026 · 2026-10

This Research Experiences for Undergraduates Site at the University of Nevada, Las Vegas supports 10 students each year in a 10-week summer research program on the security of federated learning, a way for many devices or organizations to train a shared artificial intelligence model without exchanging their raw data. This approach can help protect privacy, but it also creates new security risks because attackers may try to corrupt the training process, steal information from the model, or reduce system reliability. The project’s novelties are the integration of hands-on attack-and-defense research across the full federated learning process and the use of Red Team versus Blue Team exercises to study these problems in realistic settings. The project's broader significance and importance are that it advances safer privacy-preserving artificial intelligence, expands access to advanced undergraduate research opportunities, and helps prepare the future artificial intelligence and cybersecurity workforce. The project contributes to a stronger national capacity for building trustworthy data-driven systems. The research project focuses on threats and defenses in the data collection, training, and inference stages of federated learning. Students and mentors investigate representative attacks including botnet-style disruption, poisoning, backdoor insertion, privacy leakage, membership inference, and data reconstruction, and they evaluate defenses such as robust aggregation, anomaly detection, and differential privacy. The work uses a dedicated federated learning cybersecurity range, realistic datasets from computer vision, language, and network traffic applications, and distributed computing resources for controlled experimentation. Through iterative Red Team and Blue Team studies, the project produces software, tutorials, datasets, and empirical results that improve understanding of secure and privacy-preserving distributed learning. The anticipated outcome is stronger technical foundations for trustworthy artificial intelligence and a broader pipeline of students prepared for research and professional practice in cybersecurity and artificial intelligence. 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.

NIH Research Projects · FY 2026 · 2026-05

Cullin-RING ligases (CRLs), representing one-half of all ubiquitin ligases in humans, are notable for their roles in biology and in the induced-proximity drug discovery platform targeted protein degradation. CRLs collaborate with ubiquitin-carrying enzymes to promote protein ubiquitylation, the forging of poly-ubiquitin chains onto substrates, causing their degradation by the proteasome. Substrates are selected by CRL subunits that recognize amino acid sequence motifs as well as post-translational modifications that distinguish bona fide substrates from the thousands of proteins contained in cells. Recently, we uncovered the unexpected finding that ubiquitin-carrying enzymes, the catalytic component for CRL-dependent substrate ubiquitylation, also appear to play important roles during substrate selection. Here it was found that the carrying enzyme physically contacts the CRL substrate receptor subunit, helping to place the substrate Lysine residue that is modified by ubiquitin into the active site. We hypothesize the existence of a CRL-ubiquitin carrying enzyme code, where subsets of CRLs recognize one or a small subset of the CRL-dedicated ubiquitin-carrying enzymes. In this grant cycle, we seek to further uncover the CRL code using kinetics, structural biology, proteomics, single- molecule microscopy and cell biology. CRL dynamics, in particular the RBX1/2 subunit that recognizes ubiquitin-carrying enzymes, are critical to the function of the code and will be explored using single-molecule fluorescence resonance energy transfer between dyes appended to CRL subunits or ubiquitin-carrying enzymes. Structural biology, particularly cryo-electron microscopy, will be employed to study how CRL- ubiquitin carrying enzyme complex formation is stabilized and how the CRL code manifests through direct interaction between ubiquitin-carrying enzymes and CRL substrate receptors. High resolution pre-steady state kinetics will be performed on in vitro reconstituted ubiquitylation reactions to estimate the rates of ubiquitin transfer from ubiquitin-carrying enzymes to CRL-bound substrates, testing the hypothesis that the CRL code imparts efficient substrate ubiquitylation only for physiologically optimized CRL-carrying-enzyme partners. Substrate discovery proteomic screens will lead to new investigations into biologically interesting yet uncharacterized CRLs, including for natural biological substrates as well as the neo-substrates of targeted protein degradation. In combination, these studies will expand the CRL-ubiquitin carrying enzyme code, further explain how CRL dynamics enable physical bridging between the CRL and the carrying enzyme and define the structural underpinnings of the code.

NSF Awards · FY 2025 · 2025-10

This project will contribute to the national need for well-educated scientists, mathematicians, engineers, and technicians by supporting the retention and graduation of high-achieving, low-income students with demonstrated financial need at the University of Nevada, Las Vegas. A total of 21 scholars pursuing Bachelor of Science in Engineering degrees in Electrical and Computer Engineering will receive scholarships averaging $14,300 for up to five years. Scholars will receive faculty and peer mentoring, and the project will build strong scholar cohorts through cohort-building activities, a Welcome Meeting and Summer Orientation, family engagement, workshops, tutoring, summer bridge programs, and summer research and internship opportunities. Additional program features for scholars include a dedicated cohort space on campus and cohort scheduling. The overall goal of this Track 2 Scholarships in STEM project is to increase STEM degree completion of academically talented, low-income undergraduate students with demonstrated financial need. There is a significant national need to grow the STEM workforce and nurture key talent that will ensure economic competitiveness and provide domestic leadership across critical sectors. This project directly speaks to this need by supporting STEM student success, which will strengthen the workforce in chip design, power systems design, artificial intelligence, communications, security, and other key areas of need. The project will be assessed by an experienced evaluator who will monitor progress, assess outcomes, and establish key checkpoints to ensure program effectiveness, and the data generated will contribute to the knowledge base regarding effective strategies to support talented, low-income students in STEM. This project is funded by NSF's Scholarships in Science, Technology, Engineering, and Mathematics program, which seeks to increase the number of academically talented, low-income students with demonstrated financial need who earn degrees in STEM fields. It also aims to improve the education of future STEM workers, and to generate knowledge about academic success, retention, transfer, graduation, and academic/career pathways of low-income 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 2025 · 2025-10

This project aims to serve the national interest by improving undergraduate computing education through structured integration of generative artificial intelligence (GenAI) in courses, enhancing student preparation for AI-driven careers. It addresses the growing challenge of students relying on GenAI tools without guidance, which may limit critical thinking and team-based problem-solving. By embedding scaffolded GenAI practices into a Senior Design course at University of Nevada Las Vegas (UNLV), a large public research university, the project aligns student learning with modern software development workflows. A distinctive element is the use of a platform that analyzes thousands of real-world GenAI-assisted software patches to inform curriculum design and provide authentic case studies. Broader impacts include creating scalable, open teaching resources and replication guides to support adoption across institutions, strengthening the pipeline for a competitive software engineering workforce. This Level 1 Engaged Student Learning project seeks to advance understanding of how structured GenAI integration can improve student adaptability and contributions in collaborative development contexts. The project plans to pilot scaffolded GenAI workflows in UNLV's Senior Design course, emphasizing prompt design, debugging practices, peer review, and transparent documentation aligned with industry standards. A quasi-experimental design with treatment and control groups will allow rigorous comparison of structured GenAI instruction against independent exploration. The evaluation will use a convergent mixed-methods approach, combining repository analyses, surveys, reflections, and rubrics informed by professional software workflows to assess impacts on teamwork, adaptability, and documentation quality. Through longitudinal tracking and broad dissemination, including peer-reviewed publications, conference presentations, and shared tools, the project will generate insights into conditions under which structured GenAI use enhances learning. The NSF IUSE:EDU Program supports research and development projects to improve the effectiveness of STEM education for all students. Through the Engaged Student Learning track, the program supports the creation, exploration, and implementation of promising practices and tools. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2025 · 2025-10

This project aims to serve the national interest by implementing and establishing practices to improve student motivation in organic chemistry; a critical STEM discipline often fraught with challenges. By focusing on the concept of perceived costs — sacrifices and losses students associate with academic tasks — the study endeavors to develop targeted interventions to empower students and enhance their engagement with the subject matter. Through innovative approaches integrating Situated Expectancy-Value Theory (SEVT) and Self-Determination Theory (SDT), this research seeks to deepen understanding of student motivation and learning processes within the realm of organic chemistry education. This initiative aligns seamlessly with NSF's mission to support transformative projects that advance knowledge and foster educational equity. Moreover, by addressing the multifaceted challenges students encounter in organic chemistry, this project has the potential to yield significant breakthroughs in STEM education, contributing to NSF's broader goal of enhancing diversity and inclusion within the scientific community. The primary goals of this research project are to investigate the impact of perceived costs on student achievement and retention in organic chemistry courses, particularly focusing on historically marginalized student populations. The study will employ a mixed-methods approach, combining qualitative and quantitative analyses to explore the relationship between perceived costs, student motivation, and course outcomes. Additionally, targeted interventions, such as Writing-to-Learn activities, will be implemented and evaluated to assess their effectiveness in mitigating the negative effects of perceived costs and enhancing student engagement and achievement. By leveraging SEVT and SDT frameworks, the research aims to provide a comprehensive understanding of the mechanisms underlying student motivation and learning in organic chemistry education. The findings from this study are expected to inform evidence-based strategies for supporting student success in high-stakes STEM courses and may pave the way for similar interventions in other disciplines. The NSF IUSE: EDU Program supports research and development projects to improve the effectiveness of STEM education for all students. Through the Engaged Student Learning track, the program supports the creation, exploration, and implementation of promising practices and tools. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NIH Research Projects · FY 2025 · 2025-09

Binge drinking and high-intensity drinking are major contributors to alcohol-related harms in college communities, with evolving trends influenced by shifting social norms and generational attitudes. One such novel trend is BORG (Blackout Rage Gallon) drinking, a form of high-intensity alcohol use popularized through social media. BORG drinks are typically made with distilled spirits, water, caffeinated flavor enhancers, and powdered electrolytes in a gallon container. Other forms of novel high-intensity alcohol use include extreme drinking games, consuming alcohol mixed with energy drinks, and pre-gaming. Despite the popularity of alcohol use in college settings, there is no published scientific research on the epidemiology, motivations, expectancies, and consequences associated with novel high-intensity alcohol use behaviors, such as BORG drinking. This proposed study will apply the Integrated Behavioral Model (IBM), derived from the Theory of Planned Behavior, to understand the drivers of novel high-intensity alcohol use behaviors among college students. The central hypotheses are that college students engage in risky drinking practices they believe are safer or more socially beneficial, acceptable in their peer groups, and achievable, promoting risky drinking practices. The project will employ a two-phase rapid cross-sectional assessment. In phase 1 (Aim 1), we will survey a convenience sample of 300 students from three United States institutions of higher education (California State University, Dominguez Hills; Texas State University; and the University of Nevada, Las Vegas) to assess the prevalence and theoretical drivers of various risky drinking behaviors, including demographic subgroup differences in BORG drinking. In phase 2 (Aim 2), we will survey 1000 college alcohol users from a web-based web panel to validate and generalize the findings from phase 1, focusing on the beliefs, expectations, and social contexts influencing BORG drinking and other risky behaviors. This exploratory project is significant and innovative as it addresses a critical gap in understanding the scope, severity, and impacts of novel drinking behaviors among college students. The findings will inform the development of screening and intervention best practices and provide data on risk and protective factors for risky drinking, guiding future interventions.

NIH Research Projects · FY 2025 · 2025-09

Project Summary Emerging evidence indicates that many non-histone proteins can be lysine methylated and our studies have shown that addition of a methyl group to a specific lysine residue in non-histone proteins leads to the rapid proteolysis of the modified proteins by ubiquitin dependent proteolysis. We propose to investigate the mechanism by which the methylated proteins are proteolyzed by ubiquitin dependent proteolysis. Two classes of methyltransferases, SETD7 and KMT2/MLL family members, can methylate non-histone proteins and the methylated lysine residues in target proteins serve as the signals for proteolysis. We have identified multiple methyl lysine reader proteins and two independent ubiquitin ligase complexes for the proteolysis of methylated protein substrates. The substrate proteins regulate a wide variety of important biological processes including the control of various stem cells/progenitor cells including embryonic stem cells, neural stem cells, and hematopoietic stem cells, epigenetic regulation of histone and DNA methylation, and ATP dependent chromatin remodeling during embryonic and adult development. Alterations of these important biological processes lead to cancers and other disorders. Since lysine methylation dependent proteolysis is a new research area, we propose to systematically investigate the mechanism by which target proteins are modified and proteolyzed by lysine methylation to establish a new paradigm that controls the self-renewal and differentiation of different types of stem cells, development, cancers and other disorders. Our specific aims are: 1) To investigate the dynamic methylation/demethylation mechanism for proteolysis; 2) To examine how specific ubiquitin ligase complexes cooperate the methyl reader proteins to interact with the methylated substrates for proteolysis; 3) To develop animal models to investigate the significance of methylation dependent proteolysis. Elucidation of the proposed research will help understand how the dose dependent protein regulation and cell fate determination are regulated in various stem/progenitor and related cells during development and will help to understand pathological alterations in the methylation dependent proteolysis pathways that should shed new lights for the development of novel therapeutic strategies for various human diseases such as cancers and other developmental disorders.

NSF Awards · FY 2025 · 2025-09

This award is jointly supported by the Major Research Instrumentation (MRI) Program, the Division of Chemistry Research Instrumentation program, and the Mathematical and Physical Sciences Directorate Office of Strategic Initiatives. The University of Nevada Las Vegas (UNLV) is acquiring a 500 MHz nuclear magnetic resonance (NMR) spectrometer equipped with a liquid nitrogen cooled broadband probe to support the research of Professor Dong-Chan Lee, along with colleagues Pradip Bhowmik, Ernesto Abel-Santos, Jun Yong Kang, and Chandrabali Bhattacharya. This instrument facilitates research in the areas of organic materials, polymer chemistry, synthetic chemistry methodology, battery, and biomedical chemistry. In general, NMR spectroscopy is one of the most powerful tools available to chemists for the elucidation of the structure of molecules. It is used to identify unknown substances, to characterize specific arrangements of atoms within molecules, and to study the dynamics of interactions between molecules in solution or in the solid state. Access to state-of-the-art NMR spectrometers is essential to chemists who are carrying out frontier research. This instrument has significantly enhanced sensitivity owing to the cutting-edge cryoprobe technology compared to a standard room-temperature probe, which not only ensures the timely success of the ongoing projects but fosters opportunities to explore new research projects. This instrument enhances the educational, research and research training of students at all levels including K-12 outreach and students at the University of Nevada Las Vegas and the neighboring Nevada System of Higher Education (NSHE) Institutions. This state-of-the-art NMR spectrometer strengthens the infrastructure of UNLV which is in an EPSCoR state. In addition, more collaboration opportunities are envisioned within NSHE institutions, especially with Nevada State University and College of Southern Nevada, both undergraduate serving institutions in close proximity. Research enabled by this instrument is focused on 1) the characterization of materials with low solubility (polyaromatic nanoribbons, high molecular weight ionic polymers, and polymeric C-O) and complex molecules with minute quantity; 2) the elucidation of reaction mechanisms via in-situ real-time reaction monitoring; 3) studying small molecule-protein binding dynamics using ligand-observed NMR techniques for disease diagnosis and therapy; 4) investigating ionic transport properties in ionic liquids using Pulsed Field Gradient NMR that are important for battery applications; 5) understanding molecular self-assembly by identifying leading intermolecular interactions. The upgraded variable temperature capability significantly improves characterizing highly viscous polymers and controlling reaction rates for real-time reaction monitoring. In response to global helium supply challenges, the acquisition of this instrument includes a superconducting magnet with a low loss cryostat reducing the institution’s consumption of helium. 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.

NIH Research Projects · FY 2025 · 2025-09

Abstract Fluorescence immunoassay has been a mainstay in protein detection. Although advanced techniques like multi-photon excitation and time-resolved microscopy have been routinely performed to increase fluorescence intensity, unfortunately, these incremental changes alone cannot bring the fluorescence intensity level high enough for meeting the growing biomedical research needs for ultralow and ultrasensitive detection crucial for early diagnosis. A paradigm shift that fundamentally transforms existing fluorescence technology relying on linear fluorescence emission is necessary. One such transformative technique explores localized surface plasmon resonance (LSPR) in a nanoantenna to trigger the nonlinear fluorescence emission. This nonlinear optical process can enhance fluorescence intensity by several orders of magnitude, holding the promise to support high- resolution biosensing. However, the nanoantenna technique is still far from being routinely implemented in biological and biomedical fields due to a major obstacle not from plasmonics but from the mass transport: Most of nanoantennas typically rely on diffusion to capture interested molecules. The diffusion limit makes the nanoantenna-enhanced fluorescence only theoretically possible. This critical technology gap will be bridged by the technology proposed here. This project proposes a new technology by the fusion of the nanoantenna and super-hydrophobic surface to break the diffusion limit. Droplets over super-hydrophobic surfaces maintain quasi- spheres during evaporation and do not wet the surface. Now the droplet evaporation replaces the diffusion and concentrates molecules onto the sensitive regions of the nanoantenna, becoming the dominant mechanism of mass transfer. The combination of plasmonic nanostructures and super-hydrophobic surfaces offers a unique solution to the key challenge in the nanoantenna-based detection: difficulty in practically bringing molecules to nanostructures. The specific aims of this application are to test that the integration of nanoantenna with the super-hydrophobic surface is capable of enhancing (1) the fluorescence intensity; and (2) the fluorescence immunoassay's detection sensitivity. This research will contribute to the paradigm shift of fluorescence technology. Since chips with deposited nanoantenna can be viewed through a regular fluorescence microscope with much improved capability, the devices produced by this research can be readily implemented in existing analytical platforms with little additional cost, which will have a major impact on the biomedical field.

NIH Research Projects · FY 2025 · 2025-08

PROJECT SUMMARY Pyrethroid (PY) insecticides were the dominant chemical class used on insecticide-treated nets (ITNs), with an estimated 2.13 billion PY ITNs delivered in sub-Saharan Africa between 2004-2022. While the global malaria mortality rate halved between 2000 and 2015, progress has stalled and even started to reverse, coinciding with growing PY resistance. In recent years, ITNs containing the synergist piperonyl butoxide (PBO) and the pyrrole chlorfenapyr (CFP) have proven to be superior to PY ITNs in locations with high intensity PY resistance. With greater ITN choice, National Malaria Control/Elimination Programs (NMC/EPs) face critical vector control decisions which are complicated by the increased cost of new ITNs, regional variation in transmission intensity, mosquito species and highly dynamic insecticide resistance mechanisms. In response to the threat of PY resistance most countries in sub-Saharan Africa have pragmatically adopted a regional geographic mosaic of PY, PY-PBO and PY-CFP ITNs. Decision making is often based on insecticide susceptibility and ITN cone bioassay data, but the latter do not capture the efficacy of current, non-neurotoxic insecticides. The gold standard entomological method to assess ITN performance is experimental hut trials, but there are expansive geographical gaps with no existing infrastructure. Construction of new experimental hut sites is prohibitively expensive (estimated $150-300k per hut site) and inflexible due to their fixed location. In this study we will validate a novel portable experimental hut tent (PEHT) as a low-cost system for global vector control evaluation. PEHT design is based on World Health Organization (WHO) west African experimental hut dimensions, using seven feet high cabin style tents and 3D printer technology to prepare reproducible mosquito entry points. Specific Aim 1 is to optimize the PEHT design using existing mosquito spheres for release-recapture experiments; followed by validation alongside conventional concrete huts in an established field station in Tamale, Ghana. Comparability in trends will be assessed for all primary (mosquito mortality) and secondary (blood-feeding inhibition, deterrence and induced exophily) endpoints, to ensure that the PEHT can reliably capture all entomological measures necessary to predict the epidemiological benefit of the ITN under evaluation. Specific Aim 2 is to utilize PEHTs to determine the performance of PY, PY-PBO and PY-CFP nets, including aged community nets sampled from high malaria burden communities and tested against local, wild PY resistant mosquitoes in rural south-west Ghana. This will be followed by input of primary and secondary PEHT data into the Imperial College London MINT online model to support Ghana’s NMEP vector control decision making. PEHTs will have global utility for evaluation of ITNs to support regional, data driven vector control decision making. This innovative system would be particularly useful in areas of Nigeria and DR Congo, two countries with the greatest number of malaria cases and virtually no experimental hut infrastructure.

NIH Research Projects · FY 2025 · 2025-08

SUMMARY Sexual and gender minorities (SGM), defined here as individuals who identify as lesbian, gay, bisexual, transgender, queer, intersex, asexual, and/or another identity, continue to be underrepresented in Alzheimer’s disease and related dementia (AD/ADRD) clinical trials. Over 350,000 SGM older adults in the U.S. are currently living with AD/ADRD, and expected to increase to over a million by 2050. Several studies have found higher rates of subjective and objective cognitive impairment/decline and diagnosis of AD/ADRD among SGM communities compared with non-SGM communities. SGM subgroups, such as Black/African American, Hispanic/Latino/a/e/x, gender minorities, and lower socioeconomic backgrounds, have been found to experience increased rates of AD/ADRD and risk factors. Ensuring inclusion and participation in AD/ADRD clinical trials is crucial for advancing equitable clinical care and research for diverse aging populations. We will use community-engaged mixed methods research to identify specific barriers and facilitators affecting trial participation and co-develop a tailored AD/ADRD clinical trial readiness educational program to improve future trial engagement. This community-engaged and mixed-methods research proposal has three specific aims. In Aim 1, we will identify and explore domains of explore barriers and facilitators to AD/ADRD clinical trial participation through 8 to 10 focus groups (n=60). These findings will help to inform Aim 2 and the identification of intersectional correlates. In Aim 2, we will examine intersectional correlates of willingness to participate in AD/ADRD clinical trials among SGM older adults aged 50 and above (n=400). This survey will target four underrepresented groups—Black/African Americans, Hispanic/Latino/a/e/x, gender minorities, and low socioeconomic status individuals. Next, working with our community advisory board (CAB), we will co-design and evaluate the acceptability and usability of a community-tailored AD/ADRD clinical trial readiness educational program (Aim 3) for diverse SGM older adults aged 50+ (n=80). This study will enhance understanding of the barriers and facilitators of diverse SGM communities’ participation in AD/ADRD clinical trials; develop a community-tailored educational program and tools to ensure greater knowledge and promote opportunities for the community to participate AD/ADRD clinical trials; and disseminate lessons learned back to diverse SGM communities and AD/ADRD researchers and clinical trialists. Future studies will test the efficacy of our educational program, aiming to diversify enrollment and increase participation in AD/ADRD trials among SGM populations, with promise for advancing clinical care for diverse aging populations.

NSF Awards · FY 2025 · 2025-08

This project will develop methods to discover and design small molecules that control harmful microbes, prevent microbial-induced infrastructure deterioration, and detoxify fungal toxins in crops. It will draw on biology, mathematics, statistics, and computer science by leveraging artificial intelligence (AI) and machine learning (ML) tools to accelerate the identification of bioactive compounds. These efforts will lay the groundwork for future advances in human and animal health, the prevention of microbial corrosion in infrastructure, and enhanced food safety. Spanning the EPSCoR jurisdictions of South Dakota and Nevada, the collaborative effort will strengthen regional economies and national well-being. South Dakota State University (SDSU), as the lead institution, will partner with South Dakota Mines (SDM) and the University of Nevada, Las Vegas (UNLV) to broaden STEM access and involve students across a broad spectrum of experiences. This use-inspired project will develop a generative AI/ML platform for the rapid screening and optimization of small molecules targeting key microbial proteins. It will pursue three thrusts: (1) activators inducing bacterial cell death; (2) inhibitors of conserved biofilm maintenance proteins to mitigate biofouling; and (3) neutralizers of fungal toxins. The methodology integrates computational molecular modeling, high-throughput virtual screening, and experimental validation in bacterial and fungal assays. Infrastructure enhancements include an AI/ML-driven screening platform across all three universities to accelerate the discovery of new antimicrobial agents, the development and distribution of affordable teaching modules and lab kits for K-12 and undergraduate instruction, and the expansion of biofilm and aflatoxin assay capabilities at SDSU, SDM, and UNLV. The project's workforce development comprises interdisciplinary training for students, mentorship of early career faculty, and professional workshops for rural K-12 teachers, thereby strengthening regional research capacity and sustainability. This framework establishes foundational capacity for next-generation antimicrobial discovery and biosafety solutions. This project is supported by the EPSCoR Research Infrastructure Improvement Program: Focused EPSCoR Collaborations Program (FEC), which supports interjurisdictional teams of EPSCoR investigators to perform research in topics that align with NSF priorities, with the goals of driving discovery and building sustainable STEM capacity. 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.

NIH Research Projects · FY 2025 · 2025-08

Project Summary A critical challenge in pandemic preparedness is the rapid identification of viral outbreak sources and tracking mutations that lead to new variants. Current public health surveillance methods, relying on resource-intensive laboratory testing of patient specimens, often yield incomplete data due to underreporting. The recent mpox outbreak in the Democratic Republic of the Congo—with over 22,000 suspected cases since January 2023 and the emergence of a new strain (clade 1b)—underscores the need for more effective surveillance tools. To address these limitations, we and others have developed wastewater approaches to screen municipal sewage for viral levels and variants. This method capitalizes on the shedding of pathogens like SARS-CoV-2 and mpox into sewer systems through bodily fluids, providing a comprehensive, real-time snapshot of community infection levels and viral evolution. Over the last five years, our team has built and implemented a comprehensive wastewater COVID-19 surveillance program that includes a community engagement responsive element and serves 2.4 million residents and 50 million annual tourists in Southern Nevada. In Summer 2022, we adapted this program to pilot a study tracking the clade IIb mpox outbreak in Las Vegas. Building on these achievements and developing novel reagents for clades I and II, we have a time-sensitive opportunity to test our central hypothesis: that enhanced wastewater surveillance, coupled with new computational tools, can enable rapid detection of mpox variants from both clades, facilitate assessment of antiviral drug efficacy, and inform strategic prioritization of vaccination sites. This high-risk, high-reward proposal extends our previous successful approaches with SARS-CoV-2, influenza, and drug use in wastewater, potentially breaking new ground in mpox research. Our proposal directly responds to the 2022 and 2024 mpox public health emergency of international concern declarations and aligns with NIAID's 2024 mpox research agenda. The identification of even a single mpox outbreak or treatment-resistant strain through our wastewater studies would significantly advance innovative research in genomic epidemiology and public health surveillance, potentially transforming our approach to managing emerging infectious diseases.

NIH Research Projects · FY 2025 · 2025-08

Project Summary The long-term goal of my independent research program is to develop robust LNP platforms for treating genetic diseases and protein replacement therapies. Lipid nanoparticles (LNP) have emerged as a promising technology for mRNA delivery without the drawbacks associated with viral delivery. Despite this progress, a major hurdle remains the lack of efficient and selective delivery vehicles, as most mRNA targets are difficult to reach and primarily accumulate in the liver. Despite progress in this field, these fundamental questions remain unanswered - 1) How do LNPs interact with endogenous systems and get trafficked, and can we leverage these interactions to redesign and improve delivery systems? 2) How does the incorporation of endogenous ligands change key LNP properties such as encapsulation efficiency, endosomal escape, protein expression, and immunomodulation responses? Addressing these critical questions is essential for developing more effective next generation LNP systems that can function as advanced therapeutics and create a streamlined approach to move these innovations from lab to clinical application. Over the next five years, my research program will seek to bridge the knowledge gap regarding how surface decoration with endogenous ligands interacts with plasma proteins and influences the physicochemical and biochemical properties of LNPs. To accomplish this, we will explore three distinct avenues that will work together synergistically to improve the performance of LNPs in vivo. First, we will develop LNP platforms with enhanced targeting properties by incorporating endogenous small molecules and neutral lipids as a fifth component to modify LNP surface properties. We will assess how these modifications impact biodistribution in vivo, with a primary focus on targeting the pancreas and immune system. Next, we will gain a mechanistic understanding of the interaction between targeting LNPs and protein corona. We will investigate how the physicochemical characteristics of nanoparticles influence protein corona formation and interact with endogenous receptors that regulate trafficking to the target site. Identified proteins will be further validated using their agonists as the fifth component. Finally, we aim to optimize LNP formulations by incorporating endogenous small molecules as the fifth component that can stabilize ribosomal assembly, and boost ribosome production, thereby increasing protein expression. We will examine how the physicochemical properties of nanoparticles affect endosomal trafficking and contribute to enhanced protein expression.

NSF Awards · FY 2025 · 2025-08

Semiconductor education for high school students holds immense importance in meeting the increasing demands of workforce in America's expanding semiconductor ecosystem in the years ahead. There have been noteworthy efforts both in the U.S. and abroad to bring semiconductor education to high school students. However, without a fundamental knowledge in circuits and electronics and prior hands-on experiences, it is very difficult for students to comprehend the concepts they are exposed to and connect what they learned in the classroom with their future careers. Another significant challenge is how to recruit, train, and retain skilled workforce in the US semiconductor sector. This challenge is particularly prominent for local schools in Las Vegas area. To address the national and local needs, this project aims to develop an AI-driven Career Inspiring Experiential Program for Semiconductor Education (ACIES) through a partnership among the University of Nevada Las Vegas (UNLV), local high schools, and industries. A total of 96 college-bound students will be trained through this program. The major objectives of this project include: 1) Enhancing high school students' knowledge, skills, and interests in microelectronics and semiconductor fields; 2) Enhancing students' self-efficacy in learning electrical and computer engineering as well as physical sciences; 3) Bridging the gap between education and professional development in related career pathways; 4) Investigating factors (awareness, interests and others) that impact high school students' career choices in science, technology, engineering, and mathematics (STEM) and information and communication technology (ICT) fields. The activities include: 1) Developing the ACIES curriculum in microelectronics and semiconductor tailored specifically for high school students; 2) Engaging 24 high school students annually in immersive learning experience through a top-to-down approach spanning three phases of workshops: System-Level Design, Circuit-Level Design, Semiconductor Manufacturing in each spring and summer; 3) Connecting these college-bound students with their future career pathways through field trip visits, interviews with professionals, and apprenticeship/internship at local industries; 4) Testing the curriculum by integrating it into local high schools' robotics/engineering design classes. The project will collaborate with the SEMI Foundation and local industries in providing career exploration support, field trips, and apprenticeship/internship opportunities. Four research questions addressing the project objectives will be studied. The mixed-methods approach will be used to effectively evaluate the project's approach in improving learning outcomes by incorporating students' feedback on what helps foster transformative pathways for learning microelectronics and semiconductors. The collaboration among a major university, the local school district and local industries represents a unique opportunity to spark innovation and drive economic development in Southern Nevada. This project is funded by the Innovative Technology Experiences for Students and Teachers (ITEST) program, which supports projects that build understandings of practices, program elements, contexts and processes contributing to increasing students' knowledge and interest in science, technology, engineering, and mathematics (STEM) and information and communication technology (ICT) careers. 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.

NIH Research Projects · FY 2025 · 2025-07

Project Abstract EZH2 is the catalytic subunit of the PRC2 (Polycomb repressor complex 2) that mediates the trimethylation of histone H3 lysine 27 (H3K27me3), a critical histone modification associated with gene silencing during cell fate specification and development. Overexpression or activating mutations in EZH2 are associated with various types of human cancers, including blood cancer. But the mechanisms by which the overexpressed or overactivated EZH2 causes various cancers or promotes their progression remains largely unclear. We have recently found that EZH2 is lysine-methylated and such methylation regulates EZH2 protein stability. EZH2 therefore joins to a group of proteins that include DNA (cytosine-5)-methyltransferase 1 (DNMT1), SOX2, and HIF1α which undergo methylation-regulated and ubiquitin-dependent proteolysis. Our group has identified a methyl lysine reader, L3MBTL3, that recognizes the methylated K20 in EZH2 to promote its proteolysis by the CRL4DCAF5 ubiquitin ligase complex. Using the CRISPR-Cas9 gene editing, we have generated a mouse knock-in mutant strain that converts the K20 codon to arginine (K20R) in Ezh2 to produce the Ezh2K20R mutant protein that is resistant to the methylation-dependent proteolysis. We found that the homozygous Ezh2K20R mice produced an expansion of hematopoietic stem/progenitor cell (HSPC) compartments in bone marrow with hepatosplenomegaly. With the Ezh2K20R mutant mice as a model, we hope to test the hypothesis that a gain-of-function mutant of Ezh2 may alter the balance of self-renewal and differentiation of stem cells/progenitor cells, a mechanism potentially linked to an overactivated EZH2 in promoting cancer development. We propose to characterize the Ezh2K20R knock-in mice to determine if the Ezh2K20R mutant protein affects the expansion or differentiation, or both, of the hematopoietic progenitor cells and to identify novel partners for Ezh2 during these processes. Our Specific Aim 1 is to characterize if a specific stage in the erythroid lineage progression is affected by the Ezh2K20R mutation. Our Specific Aim 2 is to investigate if Gfi-1b, a protein that is strongly induced by the Ezh2K20R protein in a hematopoietic organ, is a component of Ezh2-PRC2 complex. Our Specific Aim 3 is to use CRISPR-Cas9-mediated gene editing to generate S21A and K20R/S21A knockin mutations. As our earlier studies showing that S21-phosphorylation and K20-methylation are mutually exclusive, we would predict that S21A mutation being a hypomorphic mutation while the K20R/S21A double mutation being a stronger gain-of-function mutation than K20R alone. Through these studies, we hope to understand how Ezh2 orchestrate a developmental program of self-renewal and differentiation to regulate stem/progenitor cell during hematopoiesis.

NSF Awards · FY 2025 · 2025-07

This award will partially fund attendance for ten students attending the doctoral consortium for the 2025 IEEE International Symposium on Mixed and Augmented Reality (ISMAR). ISMAR is a well-known venue for research on the underlying technologies for virtual, mixed, and augmented reality. ISMAR also includes research on effective methods for interaction with, in, and through these systems and on applications of these technologies to socially important areas, including education, training, communication, and entertainment. The doctoral consortium will allow student attendees to present their research interests, plans, and results to a panel of researchers in related fields and receive specific and constructive feedback. At the conference's poster session, students will get in-depth feedback through one-on-one meetings with their mentors, and broader perspectives from expert and student peers from a wide variety of disciplinary, topical, and institutional backgrounds. The agenda of the doctoral consortium was designed specifically to help student participants to "think big" and develop the strategic insight and long-range vision that will help guide them toward continued important contributions to science and engineering research beyond graduate school. Beyond its impact on the research itself, the doctoral consortium will have a broader impact on both student attendees and the research community. Through interactions at the doctoral consortium and the conference itself, students will be able to develop their professional networks in the ISMAR research community as well as their awareness of and ability to navigate future career paths. Students are selected based on the quality of their applications, financial need, first time attendee status, and their ability to contribute a breadth of perspectives to the doctoral consortium that will enhance the experience for all attendees. 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 · 2025-07

The National Science Foundation (NSF) EPSCoR Graduate Fellowship Program (EGFP) supports EGFP designated institutions/programs in EPSCoR jurisdictions by providing funding for graduate fellowships for new or continuing EGFP-eligible applicants. EPSCoR Graduate Fellowships support a total of three years of stipend and associated cost-of-education (COE) allowance for each NSF EPSCoR Graduate Fellow. This award to the University of Nevada Las Vegas (UNLV) will support 15 EPSCoR Graduate Fellows, which will build capacity in research and graduate education at UNLV. Fellows’ research will utilize Nevada’s world-class geologic outcrops, mineral resources and water resource infrastructure to create new knowledge in topics relevant to water resource management, natural resources and mining, and will consequently build workforce capacity in these important sectors. This award will provide support for Fellows conducting research on specific topics or areas that align with the unique goals and programs supported by the Directorate for Geosciences (GEO). 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 · 2025-07

Origami is the art of folding paper into intricate forms. Structures composed of origami patterns have been used for decades in the space industry as they are very compact when folded and can unfold into intricate shapes. More recently, Origami structure have been used to produce inexpensive mechanical metamaterials. Mechanical metamaterials are novel materials that present mechanical properties that are not common to usual materials. However, the design possibilities offered by origami structures remain presently mostly unexplored. This project will develop models and numerical methods to compute new origami patterns and study their deformation. The tools developed in this project will enable engineers to design new origami patterns with new properties and therefore create new metamaterials and foldable structures. Possible applications include designing structures that unfold into a target shape or designing micro-structures to obtain a desired macroscopic property. This project will contribute to the study of the direct and inverse problems of designing origami structures. In the direct problem, one chooses a given periodic folding pattern and derives Partial Differential Equations (PDEs) describing the kinematics and energy of the limit surface. One then wants to study and approximate the solutions of PDE constrained optimization problems where the PDEs are nonlinear and can change type (between elliptic and hyperbolic) and degenerate. This project will use careful regularizations and nonconforming finite element discretizations in order to approximate the solutions of these difficult problems. The inverse problem consists in determining a crease pattern that will allow to fold from a flat state into a given target surface. Determining if a given pattern is flat foldable is known to be NP-hard. This project proposes to represent possible fold lines by damage in an elastic sheet and then to adapt the method of Ambrosio and Tortorelli to approximate minimizers of the Mumford--Shah functional. This will produce folding patterns on an initially flat surface which will be able to fold into the target surface. As paper deforms isometrically, this project intends to explore the approximation of nonzero Gauss curvature target surfaces to determine if notable properties emerge. 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 · 2025-05

With support from the Chemical Structure and Dynamics (CSD) program in the Division of Chemistry, Professor Zhou of the University of Nevada, Las Vegas is developing a state-of-the-art experimental platform for ion-neutral collision studies, focusing on molecules prevalent in the interstellar medium (ISM). This platform aims to replicate ISM conditions in a laboratory setting, covering a broad temperature range (10-5000 K) while ensuring high resolution, sensitivity, and minimal background. Accurately reproducing astrochemical reactions in the lab requires precise control over reactants, a challenge that is particularly complex for molecules containing carbon (C), hydrogen (H), oxygen (O), and nitrogen (N). To overcome this challenge, Professor Zhou and his students will demonstrate precise manipulation and broad tunability of collision energy using a merged beam configuration, where the neutral beam originates from a cryogenic buffer gas cell, and the ion beam is generated within a ring-shaped ion trap. Their work could lead to the establishment of a forefront astrochemistry facility, advancing our understanding of the molecular mechanisms shaping the universe. As the first experimental platform at the Nevada Center for Astrophysics (NCfA), it will expand NCfA’s mission into laboratory astrophysics, drive cutting-edge research, enhance STEM education, and attract top talent, further elevating the institution’s research profile. The proposed method centers on the ability to transport ions at the same velocity as neutral species, enabling low-energy collisions in a moving frame. Unlike pulsed beam experiments, this approach utilizes a ring-shaped RF trap to confine the ion beam, making it conceptually closer to static hybrid trap setups, where ion traps overlap with magneto-optical traps. In this configuration, the axial secular frequency of the trapping potential is significantly higher than the circular frequency of ion motion. This key feature distinguishes it from high-energy ion beams in storage rings. As a result, this method offers precise control over ion velocities, ranging from stationary to several thousand meters per second, enabling the study of collisions across a wide energy spectrum with high resolution. The system’s fine control over ion velocity makes it particularly well-suited for interfacing with supersonic or buffer gas-cooled molecular beams, allowing for precise manipulation of collision dynamics. Furthermore, the proposed approach enables extended interrogation periods lasting over a second. This prolonged interaction time is critical for studying slow ion-neutral reactions, allowing for the observation of detailed reaction dynamics that would otherwise be missed in shorter timescales. Enhanced duration not only provides greater control and precision but also significantly improves resolution in analyzing reaction mechanisms. 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 · 2025-05

Groundwater is a critical resource that sustains communities, ecosystems, and agriculture worldwide, but it is being depleted at alarming rates in many regions. One region particularly affected by this crisis is California’s Central Valley, one of the most agriculturally productive regions of the United States. To mitigate this depletion, water districts throughout the Central Valley have rapidly expanded managed aquifer recharge (MAR) programs, where excess surface water is used to replenish depleted aquifers. This project seeks to improve our understanding of MAR processes and their long-term impacts on water availability in the Central Valley. Leveraging airborne geophysical surveys and advanced computer modeling, this research will provide detailed insights into how water moves through the regional aquifer system and how MAR expansion will impact future water availability across the region. The knowledge generated by this research will equip water managers with tools to implement recharge more effectively and address the long-term challenges of growing water demands in a changing climate. Additionally, the project supports STEM education for students from underrepresented groups and engages stakeholders to ensure results are translated into actionable solutions. This research employs a hybrid modeling framework that integrates process-based hydrologic models with machine learning surrogates to investigate the impacts of managed aquifer recharge on the Central Valley aquifer system. Specifically, this project tests three key hypotheses: (1) recharge rates are highest along large paleochannels that host interconnected blocks of coarse-grained sediments; (2) tension-driven flow within the unsaturated zone limits recharge efficiency at sites with sharp contrasts in sediment texture; and (3) MAR will initially enhance groundwater storage in the northern Central Valley, with long-term benefits extending to southern regions through increased water transfers. Using data from a large-scale airborne electromagnetic survey, the study will perform site-scale recharge simulations using a process-based hydrologic code (ParFlow-CLM). These process-based simulations will then be used to train machine learning surrogates that estimate recharge rates across thousands of sites. Site-scale recharge rates will inform long-term, basin-scale simulations that assess the effects of MAR expansion on future water availability. This work advances our understanding of coupled human-natural systems and develops innovative modeling tools to guide sustainable groundwater management in one of the nation’s most vital agricultural regions. 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.

NIH Research Projects · FY 2026 · 2025-01

Project Summary. Outcomes of heart repair with unmodified mesenchymal stem cells (MSCs) have been suboptimal. While developmental studies indicate a role of Wnt11 in cardiogenesis, Wnt11 signaling in apoptosis, angiogenesis, inflammation, and cardiac biology in adult organisms remains largely unexplored. We hypothesize that Wnt11-treated MSCs will induce superior infarct repair through greater MSC retention and survival, and manifold salubrious molecular effects on the myocardium. This hypothesis will be tested in cultured MSCs in vitro and a mouse model of reperfused myocardial infarction (MI) in vivo. To enhance the translational relevance of findings, human bone marrow MSCs and CD34+ humanized mice will also be used for key experiments. Aim 1 will examine whether recombinant Wnt11 (rWnt11) will render MSCs resistant to apoptosis and washout. Murine bone marrow MSCs will be cultured in medium alone (control) or with rWnt11, and expression of adhesion molecules, susceptibility to apoptosis, and the underlying molecular changes will be examined. The impact of rWnt11 on MSC retention will be tested in vivo following intramyocardial injection of EGFP+ MSCs after MI. Aim 2 will elucidate molecular mechanisms underlying the reparative benefits induced by rWnt11-treated MSCs. Angiogenic potential will be assessed by morphology, transcription factors and structural proteins. The role of Wnt/Planar Cell Polarity and Wnt/Ca2+ pathways will be interrogated using specific inhibitors. The impact of rWnt11 treatment on the expression of inflammation modulating molecules will be tested. The impact of rWnt11 on MSC secretome and the miRNA cargo in MSC extracellular vesicles will be analyzed. Aim 3 will establish whether transplantation of rWnt11-treated MSCs will induce superior infarct repair in vivo, and further identify the mechanistic basis in a definitive fashion. MSCs cultured in medium alone or with rWnt11 will be injected into the infarct borderzone 2 d after a reperfused MI. Serial echo and a terminal hemodynamic study will be performed to assess global and regional LV function and structure. LV anatomy, infarct size, myocyte hypertrophy, and fibrosis will be assessed by morphometry in myocardial sections. The effects of rWnt11-treated MSCs on myocyte apoptosis, angiogenesis, myocyte proliferation, calcium handling and gap junction proteins, myocardial inflammation, macrophage populations, and oxidative stress will be determined quantitatively at both early and late time-points. Focused proteomic analysis will identify novel myocardial protein modifications. In a comprehensive and thoroughly mechanistic fashion, these studies will establish whether rWnt11 treatment can improve MSC-induced infarct repair. The impact will be two-fold: (i) the generation of an effective cellular product for heart repair that can be produced easily in a GMP facility will have major therapeutic potential for patients with ischemic heart disease; and (ii) extensive molecular studies will yield novel biological insights about Wnt11 signaling in adult cells, and may also discover additional therapeutic targets to further enhance Wnt11-based cellular reparative strategies.

NSF Awards · FY 2025 · 2025-01

When astrophysical objects are born, they accrete mass from the surrounding circumstellar disks. If the central objects are magnetized (including neutron stars, young stars, and young planets), the disk material will be lifted out of the disk and accrete to the central object following the stellar magnetic field lines. This process is called magnetospheric accretion. Magnetospheric accretion onto young stars produces strong ultraviolet (UV) emission at hot spots, which is crucial for measuring the disk accretion rate and understanding disk evolution. UV photons play a significant role in driving disk photo-evaporation and photo-chemistry, which are currently being studied by JWST. The accretion significantly impacts the inner disk, where most exoplanets have been discovered. Given its importance, the Hubble Space Telescope has just completed a new (the largest so far) Director's Discretionary program. Furthermore, a Small Explorers (SMEX) mission program called ESPEX (Early Star and Planet Evolution Explorer) is under investigation at JPL. The PI and the supported graduate student will collaborate with scientists at JPL to generate synthetic observations from PI's simulation data. These observations will be used to make testable predictions for the NASA ESPEX mission and to assist in its design. The proposed study leverages state-of-the-art numerical simulations at UNLV and space mission expertise at JPL to bridge the gap between theory and observations for the ESPEX mission. We will directly analyze the distributions of hot spots on the stellar surface in our simulations, examining both their spatial and temporal evolution to provide a comprehensive overview. We will generate synthetic light curves for the ESPEX mission. Initially, we will calculate the temperature distribution on the stellar surface from our simulations. Then, at a given viewing angle, we will use ray tracing methods to calculate the flux across various wavelength bands at different times. These calculations will assist ESPEX in optimizing its observational strategies. Finally, we will calculate the line profiles from our simulations, which encode both accretion and outflow signatures. The synthetic line profiles will inform our proposals for simultaneous follow-up observations on the ground. We will introduce concepts of observation and NASA mission design to the general public and students at UNLV. 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 · 2025-01

In long-lived trees, sugars and starches (carbohydrates) can be stored and used to support trees’ metabolism many decades later. Old carbohydrates are critical to helping trees recover from wildfires, hurricanes, drought, frost, and other extreme climate events. The age of these old carbohydrates can be estimated by measuring radiocarbon (a byproduct of nuclear bomb testing in the 1950’s and 1960’s). However because these measurements of the age of carbohydrates are uncommon, old carbohydrates are poorly represented in global models of vegetation and climate. This proposal will support a fellowship to build new equipment and technical capacity at the University of Nevada, Las Vegas (UNLV) to measure the age of carbohydrates in long-lived trees using radiocarbon. The PI, along with a predoctoral graduate student, will visit Northern Arizona University, during which the team will build the preparatory infrastructure to prepare samples for radiocarbon measurement. Carbohydrate age will be measured in two widely distributed and important tree species, ponderosa pine, and trembling aspen. Equipment will be transferred to UNLV to initiate a new facility to support research into carbohydrate ages, as well as additional research in southern Nevada using radiocarbon methods. This project will train undergraduates, graduate students, and the PI to initiate a new carbohydrate research facility in southern Nevada and improve our understanding of long-lived trees and their role in the global carbon cycle. Nonstructural carbon reserves in trees supply stored energy for later metabolism (e.g., sugars), and can be decades old. However such “old” reserves are typically absent from terrestrial vegetation models, perhaps because observational data at sufficient spatial and temporal scope are lacking. Towards quantifying the age and distribution of old carbon reserves across western forests, the PI seeks to radically enhance capacity for radiocarbon measurements at the University of Nevada, Las Vegas (UNLV). This fellowship will thus support a visit to Northern Arizona University for mentorship to design and build the infrastructure for a radiocarbon preparatory facility. During the visit, an accompanying PhD student will gain essential training to fast-track future research. Pilot samples of two of the most widely distributed North American tree species (Pinus ponderosa and Populus tremuloides) will be collected across an elevation gradient during the fellowship period to test the new system and generate pilot data towards estimating landscape-scale carbohydrate ages in western forests. After the visit, the system will be partially deconstructed and transported to UNLV. This proposal will thus initiate a new core facility for UNLV and the broader research community in Nevada, only 70 miles from the Nevada Test Site, where nuclear tests in the 1950’s and 60’s labeled the global biosphere with radiocarbon. The fellowship will be transformative for the PI given the research infrastructure generated. A new core facility at UNLV will enhance institutional research and training capacity by enabling cost-effective 14C measurements. The facility will leverage broadening participation programs such the Louis Stokes Alliance program SNNA-LSAMP to train underrepresented undergraduate researchers. Training will also directly benefit a predoctoral graduate student. Finally, students and the public will be fascinated by learning the inner workings of long-lived trees through outreach that leverages science education groups across the Southwest, including Las Vegas. 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 · 2025-01

Sustainable energy, such as solar and wind power, due to its commitment to economic, environmental, and social sustainability, has become the preferred energy paradigm for researchers and practitioners in the energy sector, both in the U.S. and globally. However, one of the key concerns in sustainable energy networks is the reliability of performance, which is often affected by unstable power generation and unpredictable anomalies. To address these issues, the reliability of sustainable energy networks must be improved by understanding new energy characteristics (e.g., solar and wind), designing new frameworks (e.g., decentralized structures), and embracing new technologies (e.g., artificial intelligence (AI)). This project aims to tackle these challenges through a systematic solution: an explainable AI-supported performance monitoring system in distributed sustainable energy networks, which will ensure reliable performance and sustainability. To achieve this, the PI and a graduate student will visit the host site at the University of Southern California to utilize its cyber-infrastructure, cutting-edge technologies, extensive research experience, and abundant domain expertise. Successful completion of this project will result in the development of an innovative system to improve the reliability of distributed sustainable energy networks, an educational module to advance the teaching and training of UNLV students in the AI/energy field, and enhanced sustainability in energy for the state of Nevada and the U.S. This project will thereby strengthen the PI’s scientific research, educational capacity, and societal contributions. The goal of this project is to design a systematic solution for detecting and classifying anomalies in distributed sustainable energy networks using an explainable AI-based method. The project will systematically and experimentally investigate several critical issues in distributed systems, multi-modal learning, and AI explainability. During the investigation, the project will contribute in the following ways: (1) building a hierarchical learning framework capable of processing different local conditions and heterogeneous cluster features for anomaly detection in distributed sustainable energy equipment, (2) proposing a multi-modal learning method that utilizes various factors of sustainable energy, aimed at improving the reliability of anomaly detection and classification with limited labeled data, and (3) developing an explainable AI (XAI) module to reduce the "black box" impact of AI models and facilitate human decision-making in operations. The project outcomes will advance knowledge and understanding in multi-modal learning and XAI for distributed energy systems and will guide further AI-driven applications to address crucial challenges. The expected results will enrich educational materials and support curriculum development in areas such as distributed systems, multi-modal learning, anomaly detection, and XAI. Research outcomes will be widely disseminated online, shared at research seminars, and seamlessly integrated with K-12 education and outreach activities, encouraging active participation from underrepresented student groups. This project will enable the PI to develop a long-term collaboration with a nationally prominent institution. Consequently, this project will significantly enhance the PI’s competitiveness as a researcher and educator, improve the research capacity of the PI’s home institution, and contribute to developing a diverse workforce in the PI’s jurisdiction. 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.