University Of Texas Rio Grande Valley

NSF Awards · FY 2026 · 2026-09

Every year, billions of birds undertake seasonal migrations, traveling vast distances across North America. These movements require physical adaptations and reflect fine-tuned responses to ecological signals such as resource availability and optimal timing for reproduction. Avian migration has substantial implications for human well-being, including ecosystem service provision, impacts on food security, and the potential to spread pathogens. However, despite the relevance of large-scale seasonal avian movement, relatively few studies have explicitly sought to understand how changes in migratory patterns emerge from ecological principles and these studies remain difficult due to several challenges. First, migration data across species are often sparse or inconsistent, and existing approaches frequently overlook behavioral plasticity and context-dependent responses. Second, these limitations stem in part from the fact that migration occurs across broad time and spatial scales, making direct field observations difficult to obtain. To overcome these challenges, this project integrates a trait-based framework with high-resolution, species-specific acoustic data to advance understanding of the mechanisms driving variation in migration timing across the United States. The project is designed to benefit students in southern Texas and will focus on providing training in avian ecology, biostatistics, and computer programming. Students will receive research training through a new Course-Based Undergraduate Research Experience on Signal Processing in Avian Ecology along with immersive workshops on bioacoustics. These courses are designed as an integral component of the research project, where students will directly contribute to both data collection and analysis. This research addresses a fundamental question in ecology: How do species’ functional traits determine responses to environmental variability and affect adaptation and survival? Avian migration, a large-scale, time-sensitive process, offers a suitable system for exploring this question. Investigators will examine how migration timing arises from species-specific physiological thresholds and morphological adaptations, and assess the ecological mechanisms underlying these patterns among species with different natural history traits. To achieve this, investigators will implement an artificial intelligence (AI) workflow using open-source programming languages to identify species-specific nocturnal flight calls from migratory birds. This approach leverages Deep Neural Network models to detect birds from nocturnal audio recordings and collect high-resolution migration data. This information will be integrated with life history traits derived from mark-recapture data to explain migratory temporal dynamics across avian species. Finally, investigators will evaluate how urban noise and artificial light at night influence migration behavior through its effects on acoustic communication and the use of social cues. Findings from this work will advance understanding of the mechanistic drivers of migration phenology across species and environmental conditions. This project also advances NSF’s priorities in 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.

NSF Awards · FY 2026 · 2026-02

Offshore structures support critical activities such as energy production, transportation, and environmental observation in coastal and ocean environments. These structures rest on seabed sediments that can be disturbed by strong currents. Erosion of the seabed near the foundations of the structures creates depressions that threaten structural stability, increase maintenance costs, and disrupt nearby marine habitats. This project will use computational fluid dynamics to better understand how ocean flows interact with vertical offshore foundations and seabed sediments. Results of the project will help improve the design, maintenance, and advanced manufacturing of marine energy and offshore platforms, including oil rigs and bridge pylons. The project will support workforce development in science and engineering through student training and outreach activities. The research will use large-eddy simulations to examine how tidal currents interact with vertical offshore foundations and sediment particles across a wide range of flow conditions. The project will quantify how near-bed flow structure and velocity gradients influence the forces acting on sediment and initiate erosion. In addition, the motion of sediment particles will be tracked to determine patterns of suspension, transport, and deposition around offshore structures. The results will be used to develop simplified relationships that link flow conditions and structural characteristics to seabed response, enabling improved prediction of erosion processes. This work advances fundamental knowledge in environmental and coastal fluid mechanics and provides tools that can be used to design more resilient offshore foundations. Students will be trained in computational modeling and environmental engineering, and high school students and educators in be engaged in hands-on learning activities. The project will support sustainable offshore energy development and protection of marine environments. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2026 · 2026-01

Lignocellulosic biomass offers a carbon-neutral alternative source of fuels and chemicals which are currently sourced from petroleum and other fossil fuels. Converting lignin, a largely underutilized yet chemically rich byproduct of the biorefining industry, to valuable aromatic platform chemicals is an energy-intensive process that has made this approach cost prohibitive. Recently, dye-sensitized photoelectrosynthetic cells (similar to photovoltaic cells) have emerged as a low-cost and environmentally friendly technology for converting solar energy into chemical fuels or electricity. These photoelectrochemical cells offer a means of using renewable solar energy to drive energy-intensive chemical conversions at ambient temperature and pressure. Here, the collaborative fundamental research project will study how a dye-sensitized photoanode can chemoselectively oxidize lignin with a suitable catalyst as a first step toward a complete light-driven lignin depolymerization process. This approach will expand on the use of heterogeneous catalysis for the oxidation of primary and secondary alcohols to produce carbonyl or carboxyl compounds for the fine chemical and pharmaceutical industries. This work represents a new application for dye-sensitized photoelectrosynthetic cells, and the research findings from the project will be disseminated to the public through research publications, conference presentations, and by organizing and hosting educational outreach programs for future professionals in the STEM field. The PIs will also actively recruit and support underrepresented minority students through the outreach program. Organic oxidation reactions are important in organic synthesis or lignocellulosic biomass processing. Chemoselective oxidation of the aliphatic and/or benzylic alcohol moieties in lignin is a good target for controlling the degradation of lignin to generate desired small molecular products. This project aims to elucidate a photoelectrosynthetic chemoselective oxidation of alcohol moieties in lignin by combining the use of aminoxyl mediators with a dye-sensitized photoanode (DSP) at room temperature. Essential to this approach is the use of a dye-sensitized electrode interface to activate a nitroxyl mediator via light-induced charge separation. This presents both a new approach for driving the photochemical oxidation of the secondary benzylic alcohol and the primary aliphatic alcohol functional groups found in lignin, as well as a new photocatalytic application for dye-sensitized photoelectrosynthetic cells, which have traditionally focused on solar water splitting. The approach will involve (1) the synthesis of photoactive polymeric catalysts and the elucidation of their underlying photochemical electron transfer properties for activating nitroxyl mediators, (2) the fabrication and evaluation of mesoporous semiconductor-based electrodes specifically designed for the chemoselective oxidation of lignin dimer model compounds with a series of nitroxyl mediators, and (3) the elucidation of mechanistic pathways for the light-driven oxidation of 2o benzylic and 1o aliphatic alcohols using a DSP and address practical challenges presented by the use of oligomer model compounds and technical lignin. This research is significant as a first test case of a DSP to carry out the selective oxidation of real lignin at room temperature as a first step toward light-driven biomass conversion to value-added chemicals. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2025 · 2025-12

The University of Texas Rio Grande Valley REU Program in Applied Mathematics and Computational and Data Science (AMCADS) will engage eight talented students in mathematics each summer for a nine-week immersive research experience. Students will work collaboratively in teams under the direction of the senior researchers on mathematical problems with real-life applications in biology, physics and health sciences. Students will learn how to use MATLAB and Python programs; understanding how to address each model computationally will have a broad impact on the students' ability to tackle other mathematical models and be competitive as graduate applicants as well as in industry. Students will be also enriched academically with workshops in scientific writing, presentation skills, and graduate school readiness. One of the project's main objectives is to encourage participants to consider graduate programs in mathematics and data sciences and to help them discover which area of research interests them most. The project will thus strengthen the U.S. scientific workforce. The students and the PIs will disseminate results through conferences, publishing papers, and by coding programs that will be made freely available. Specifically, the student researchers will study and analyze novel and engaging topics such as numerical simulations for fractional and stochastic partial differential equations (PDEs), modeling the spread of diseases, modeling the accumulation of toxins causing Alzheimer's disease, and modeling collective group behavior. As a second objective, solutions of the PDEs will be rendered as the data used for training and testing the neural networks. At the end, students will learn how to apply the produced neural networks to actual data. Students will also produce predictive models and compare their outcomes to simulations of the actual models of the dynamical processes. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2025 · 2025-10

Medical image segmentation is essential for clinical decision making and disease monitoring, yet current deep-learning approaches are limited by their reliance on imaging data alone and lack of contextual understanding. Vision-language models (VLMs) offer a promising alternative by generating textual annotations, but their dependence on manually crafted prompts and poor adaptation to segmentation tasks constrain their clinical utility. Moreover, these models struggle to generalize across imaging modalities and anatomical regions. This project develops an automated framework that generates segmentation-specific textual descriptions without human-created prompts, improving annotation efficiency and segmentation quality. It further integrates dynamic knowledge-graph reasoning to embed evolving medical expertise into the annotation process, enhancing adaptability across diverse imaging contexts. The approach aims to create robust and generalizable artificial intelligence (AI) tools for real-world clinical use. Broader-impact aspects of the project include the engagement of students through hands-on research, interdisciplinary collaboration, and open-access tools that advance science and education as well as clinical relevance that is ensured through close collaboration with medical experts, allowing the research to address real-world healthcare needs and support translational impact. The project introduces a multimodal framework that leverages the bidirectional relationship between images and text to refine segmentation. Key components include: (1) an auto-prompting mechanism driven by graph-based reasoning to produce task-specific textual descriptions; (2) a knowledge-graph module that encodes and updates domain expertise to improve generalization; (3) a multi-level feature-alignment strategy with asynchronous fusion and bidirectional encoding to enhance multimodal learning; and (4) a closed-loop learning paradigm wherein segmentation and annotation mutually refine each other. Together, these components establish a comprehensive system for automated and adaptive medical image segmentation with high clinical relevance. 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

NON-TECHNICAL SUMMARY: Many of the world’s most dangerous bacteria carry hidden warning labels on their surface—special types of molecules called lipopolysaccharides. Detecting this molecular badge is one of the most reliable ways to identify harmful microbes before they have a chance to spread. However, most current technologies for detecting lipopolysaccharides are too slow, too costly, or not sensitive enough to reliably protect food, water, and healthcare systems. This project tackles that challenge by creating innovative materials called nanoparticles, which are so small that thousands could fit across a single human hair. These gold and silver nanoparticles are carefully shaped and engineered to recognize and bind to lipopolysaccharide molecules, similar to how a key fits into a lock. By precisely controlling the size and surface features of these nanoparticles, the research aims to develop rapid, accurate, and affordable sensors for bacterial contamination. These tools have the potential to offer early warnings about invisible threats, improving public health responses before outbreaks occur. By advancing the science of material design and developing better ways to protect public systems, this work directly supports national priorities in health, security, and scientific innovation. The knowledge and technologies generated through this research could contribute to a safer, healthier future, and will train the future STEM workforce. Finally, this work includes development of an AI-based virtual lab which that be incorporated into outreach activities. TECHNICAL SUMMARY: This project focuses on the rational design and synthesis of curvature-engineered gold and silver nanoparticles with diverse morphologies, including spheres, rods, prisms, and concave structures, to create advanced biomaterials for the selective and ultra-sensitive detection of bacterial lipopolysaccharides. Precise control over nanoparticle morphology and surface curvature, combined with tailored chemical functionalization, is expected to significantly enhance molecular recognition at the nano–bio interface, resulting in improved binding affinity and selectivity toward lipopolysaccharide targets. This research will employ mechanochemical synthesis, seed-mediated growth, and template-assisted fabrication to produce nanoparticles with finely tuned dimensions and well-defined shapes. These nanomaterials will be further functionalized with biomimetic ligands, such as synthetic peptides and molecularly imprinted receptors, designed to mimic natural recognition processes. Advanced spectroscopic, microscopic, and analytical techniques will characterize nanoparticle geometry, surface chemistry, ligand presentation, and sensor performance. Quantitative affinity assays and computational modeling will elucidate how nanoparticle shape and curvature influence ligand density, accessibility, binding affinity, and selectivity toward lipopolysaccharides. The project aims to establish robust structure–function relationships, linking nanoparticle geometry and surface engineering to biosensor sensitivity, selectivity, and operational stability in complex biological and environmental samples. Conventional detection methods often require lengthy culturing steps or sophisticated laboratory infrastructure, limiting their effectiveness in time-sensitive scenarios. By contrast, these nanomaterial-based sensors offer real-time, point-of-need diagnostics, potentially transforming how we monitor pathogens in healthcare, environmental surveillance, and the food industry, ultimately reducing morbidity, mortality, and economic burden. Prototype sensors developed from these shape-controlled nanomaterials will enable rapid optical or electrochemical detection of bacterial contamination. This capability is crucial for addressing urgent global challenges related to microbial resistance, waterborne diseases, and food safety. This interdisciplinary effort will advance the fundamental understanding of biomaterials and biosensing, with broad implications for diagnostic technologies, environmental monitoring, and public health safeguarding. In addition, this work will train the future STEM workforce in science by including development of an AI-based virtual lab which that be incorporated into outreach activities. 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

NON-TECHNICAL SUMMARY This research project explores a novel approach to creating stronger, more heat- and stress-resistant metals for extreme environments, including space, nuclear reactors, and advanced energy systems. A traditional method for strengthening metals rely on mixing in tiny particles that often degrade under harsh conditions. This project is developing an innovative technique that forms these particles during 3D printing itself, using reactive gases such as nitrogen and oxygen. These gases are injected into a pool of molten metal and chemically react to form durable ceramic particles inside the metal as it solidifies. The project is utilizing a process called Directed Energy Deposition, a type of additive manufacturing, to conduct this gas-metal reaction while printing parts layer by layer. This eliminates the need for expensive and energy-intensive pre-processing steps, such as mechanical alloying. The result is a more efficient and scalable method for producing metal components that are stronger, more durable, and suitable for extreme applications. The work supports national priorities in energy, defense, and manufacturing by reducing production costs and enabling the development of new high-performance materials. It also contributes to building a skilled STEM workforce. Each year, the project is engaging college and high school students in hands-on research at the University of Texas Rio Grande Valley. Outreach programs include dual-credit courses, K-12 STEM workshops, and public events that highlight how advanced manufacturing connects to real-world challenges. This project is helping expand UTRGV’s research capacity and supports the goal of furthering scientific exploration and opportunity throughout the nation. TECHNICAL SUMMARY This research project investigates in-situ gas-phase alloying during Directed Energy Deposition (DED) as a scalable, energy-efficient method for synthesizing and dispersing nanoscale strengthening phases within metallic matrices such as stainless steel 316 and aluminum-silicon alloys. Instead of relying on conventional dispersion-strengthening methods, such as mechanical alloying, this approach introduces reactive gases, such as nitrogen and oxygen, directly into the laser-induced melt pool. These gases react under far-from-equilibrium solidification conditions to form ceramic nanoparticles that enhance the mechanical and thermal performance of the resulting materials. The project pursues three integrated research objectives: (1) model and validate gas-metal reactions and nanoparticle nucleation using thermodynamic, kinetic, and computational fluid dynamics tools; (2) characterize the dispersion, size, and morphology of nanoparticles using transmission electron microscopy, scanning transmission electron microscopy with energy-dispersive spectroscopy, and electron backscatter diffraction; and (3) correlate nanoparticle features with mechanical properties such as hardness and grain size using structure-property-process modeling and nanoindentation testing. A semi-empirical framework will relate gas partial pressure, thermal gradients, and melt flow dynamics, including Marangoni convection and turbulence, to nanoparticle formation and distribution. This research is is seeking to demonstrate an improvement of more than 20 percent in hardness, refined grain structures with diameters below 10 microns, and uniform nanoparticle dispersions. Educational impacts include integration into undergraduate and graduate coursework, dual-credit high school classes, K–12 workshops, and community seminars. The outcomes support NSF’s goals in advancing materials research and manufacturing technologies, while promoting STEM workforce development, and supporting UTRGV’s goal of contributing to cutting edge science in support of national interests. 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

Scientists have long been interested in the ways that populations organize themselves and change over time. Previous research demonstrates that people and groups make choices about systems of organization based on many variables, including availability of resources, environmental stability and fertility, established traditions, and newly emerging practices. This collaborative project conducts research to explore the impact of environment and resource use on long-term changes in social organization, including questions about if, when, how, and why groups developed stratified societies. The project findings advance knowledge and theory about what variables have predominant influence in the evolution of social organization. The study’s use of macro- and microbotanical analytical techniques (including phytolith analysis of soil samples, analyses of starch granules and phytoliths extracted from dental calculus, and integrative microbotanical and stable isotope analysis) advance administrative priorities for investments in understanding the adoption of biotechnological innovations in scientific research. The project also provides training for graduate students in these analytical and other archaeological methods. The team investigates how environmental conditions changed and influenced choices about social organization across thousands of years, examining whether different environments (i.e., cloud forest vs. dry grassland) may have promoted different choices and opportunities that led to different organizational structures and strategies. The team excavates key locations to collect evidence of site occupation and use, and to reconstruct the environmental legacy of a river valley through specialized analyses of human, animal, and plant remains and remote sensing of the valley. Through these methods, the team tests if the environment afforded more flexibility and options in the choices that people could make about resource acquisition and use, especially when compared with other sites from the same period. 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

Metal additive manufacturing has revolutionized various industries by providing sustainable and unparalleled customization in part production. However, to become compatible with mainstream manufacturing processes like casting and forging, the material deposition rate needs to be significantly enhanced. Currently, a substantial amount of thermal energy is concentrated from a single source to melt a large volume of metal powders, which often results in defects and undesirable material properties in the printed components. This Faculty Early Career Development (CAREER) award supports research that aims to address this challenge by reducing the thermal gradient in the laser energy input and integrating efficient and cost-effective energy sources, such as induction heat and ultrasonic vibration. By achieving a balanced thermal environment within the process zone, a defect-free high deposition additive manufacturing process becomes possible. If successful, research enabled by this award may transform large-scale manufacturing industries. By providing hands-on exploration and igniting intrinsic motivation, the award is expected to support the workforce development for the nation’s sustainable future in advanced manufacturing. This CAREER project aims to investigate and control the metallurgical transformation of deposited material during high-deposition rate additive manufacturing processes. Thermal energies from multiple sources, such as laser with beam shaping, induction heating, and ultrasonic vibration, are simultaneously employed onto the melt pool. However, current knowledge in integrating multifaceted thermal inputs to metal additive manufacturing is limited. A critical challenge of additive manufacturing lies in energy delivery to the processing zone, which powers thermodynamic forces, drives metallurgical transformation, and governs the formation of microstructures. These microstructures, in turn, determine the quality of printed parts. The research tasks include: (1) Establishing dynamic control of energy inputs to achieve favorable thermal distribution at the process zone by integrating laser (with beam shaping), induction heating, and ultrasound. (2) Increasing material deposition by preheating the substrate and on-the-fly powders using induction heating. (3) Minimizing residual stress in the part through in-situ induction heat treatment. (4) Developing an AI-based prediction model for microstructure and defect control. The outcomes may facilitate the establishment of a convergent research and education platform on additive manufacturing research and innovation. 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 supports research in the field of mathematics, focusing on a problem known as the d-bar-Neumann problem. This problem is central to understanding how shapes and structures behave in several complex variables, an area of math with important connections to physics and engineering. The project will develop new tools and methods to solve long-standing questions and will also strengthen research and education at the University of Texas Rio Grande Valley (UTRGV), a Hispanic-Serving Institution. Through workshops, courses, and community engagement, the work will support the training of the next generation of researchers. The research will explore the relationship between the d-bar-Neumann problem and the geometry of complex domains. One of the main goals is to find geometric conditions that ensure solutions to the d-bar-Neumann problem are smooth up to the boundary, which has been a major open question in the field. The investigator will use advanced mathematical tools from homological algebra and microlocal analysis to study this question. The work builds on recent progress made by the principal investigator and will seek to clarify the role of boundary geometry in solving this problem. The results are expected to improve understanding in complex analysis and geometry and may lead to progress on other major problems involving regularity and geometric structure. 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

Tremors are involuntary, rhythmic movements that make daily activities like eating, writing, or dressing very difficult for millions of people, especially those with Parkinson's disease or essential tremor. Current treatments such as medications and surgeries are often not effective because how tremors originate and spread through the body is not completely understood. This project will pinpoint primary tremor-generating muscles and expose how upper limb tremors start and move through muscles and joints of the arm and hand by developing computer models of the human-arm musculoskeletal system. This critical knowledge will inform the design of more effective tremor-reducing devices, such as wearable orthoses or muscle stimulators, ultimately improving quality of life for those affected. The project share findings with the public, develop educational modules for undergraduate courses, and train students to expand a future STEM workforce. These efforts will raise awareness and foster the next generation of scientists and innovators to accelerate new solutions for individuals living with movement disorders. Leveraging musculoskeletal modeling (MSM) and system dynamics approaches, this project will advance the understanding of upper limb tremor propagation mechanisms and identify the key muscles contributing to tremor genesis. Existing models are limited by linear and simplified assumptions, which fail to accurately capture the complex, nonlinear interactions between neural drive, muscle activity, joint displacements, and fatigue. To address these limitations, this research will enrich a publicly available upper limb MSM with muscle-fatigue components that will enable simulations of tremor transmission across the upper limb’s joints and muscles. The work focuses on developing dynamic models of tremorogenic muscles and computer simulations for tremor suppression devices, including wearable orthoses and functional electrical stimulation systems. The research postulates that the interplay between neural signals, muscle activation patterns, and joint mechanics significantly influences tremor propagation and that insights from simulations can guide the design of more targeted, effective intervention devices. This project will employ OpenSim simulations and numerical analysis to identify the principal tremorogenic muscles, elucidate the tremor-fatigue relationship, and develop strategies for tremor reduction. The outcomes will contribute substantially to the field by providing a more accurate, scalable MSM, advancing the mechanistic understanding of tremor disorders, and informing the next generation of neuromuscular therapeutic interventions. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2025 · 2025-09

Many natural and engineered systems from weather patterns and ocean currents to biological processes, are governed by dynamics that are inherently uncertain or randomly influenced. Understanding these systems requires accurate simulation of complex equations that combine deterministic laws with random effects. Stochastic partial differential equations (SPDEs) provide the mathematical foundation for modeling such systems under uncertainty. One particularly important example is the stochastic Navier–Stokes equation, a probabilistic counterpart of the classical equation that underpins our understanding of fluid turbulence and remains an unsolved Millennium Prize Problem. This project develops rigorous and reliable numerical methods to approximate solutions of such SPDEs, addressing a key national need: predictive simulation tools that can operate robustly in uncertain, noisy, or data-limited environments. By improving the reliability of simulations in fields such as weather science, energy systems, and aerospace engineering, this work supports the NSF mission to advance science, promote national prosperity, and prepare a skilled STEM workforce. Technically, this project focuses on the development and analysis of finite element methods for nonlinear SPDEs with provable strong convergence. In particular, the research establishes error estimates in strong norms and investigates how solution regularity, noise structure, and discretization interact to determine convergence rates. The primary goal is to build mathematically rigorous tools for computing individual sample paths and quantities of stochastic interests, such as the expectation of solutions with quantifiable accuracy. Key applications include the stochastic Navier–Stokes equation in fluid turbulence, stochastic Keller–Segel systems modeling chemotaxis in biological systems, and stochastically forced nonlinear wave equations. By combining techniques from numerical analysis, stochastic calculus, and computational science, the project will contribute both foundational mathematical results and practical tools for uncertainty-aware simulations across scientific and engineering disciplines. 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

NON-TECHNICAL SUMMARY: The biological growth of filamentous fungi cells (hyphae) and their bonding with substrate particles leads to highly sustainable composite materials called fungal composites. Practical approaches to improve fungal composite mechanical properties are necessary to realize this potential fully. A promising approach to improving fungal composite mechanical properties is reinforcing the substrate with rigid fillers, as commonly used for synthetic polymer composites. The fundamental challenge underlying this approach is that hyphae do not bind naturally to synthetic particles to form the composite due to a lack of nutrient elements. This project will support research on understanding mechanisms to promote bonding between fungal hyphae and synthetic particles to obtain improved mechanical properties in fungal composites that rival traditional composites. The findings from this study will support the development of advanced fungal materials that will advance national technological leadership in biological materials and conserve national non-renewable resources, which will result in significant societal and economic benefits. Additionally, the project will contribute to STEM workforce development on biological materials through outreach activities for K-12 students and research opportunities for undergraduate and graduate students. TECHNICAL SUMMARY: This project will support fundamental materials research on understanding the mechanisms that govern the interfacial bonding of fungal hyphae with synthetic particles to develop novel fungal composites. These composites use glass beads as reinforcing fillers through a rational design of the interface between hyphal matrix and glass beads. Specifically, the research will aim to design a hydrogel-based interfacial layer supplemented with nutritional elements for glass particles that will promote hyphae-particle interfacial bonding. Hydrogel composition and material properties will be tailored to support invasive hyphal penetration into the hydrogel layer through the exertion of mechanical force. A systematic study will be performed to understand the complex interactions between hydrogel material properties, nutrition content, fungal species, and particle-hyphae interface structure. The effectiveness of hydrogel-based interfacial engineering will be evaluated through systematic measurements of hyphae-particle interfacial bonding strength and macro-scale mechanical properties of glass-filled fungal composites. A novel particle-filled fiber network model will be implemented to understand microstructural deformation and failure mechanisms not accessible experimentally. The model will be validated with respect to mechanical testing data and used to establish structure-property relations. Together, effective ways will be identified to control hyphae-glass particle bonding strength and concomitantly achieve dramatic composite reinforcement through rigid glass particles. Additionally, the project will broaden participation and contribute to STEM workforce development on biological materials through outreach activities for K-12 students and research opportunities for undergraduate and graduate students. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NIH Research Projects · FY 2025 · 2025-09

PROJECT SUMMARY Statistics is the staple of all biomedical research. Yet, most existing statistical methods and software packages do not trivially extend to the analyses of datasets of the unprecedented size and level of detail as those being collected and made publicly available by the BRAIN Initiative and by the NIH Blueprint for Neuroscience Research. These datasets include the Adolescent Brain and Cognitive Development Study (ABCD) and the Human Connectome Project (HCP), among others. Dense phenotyping in these studies creates many opportunities for generation and testing of hypotheses about brain structure and function in health and disease. However, the lack of availability of flexible statistical methods that accommodate large-scale inferences, and the lack of suitable implementations even when such methods exist, hinders our ability to integrate data across different domains, scales, and representations used for study. This project is intended to modify the free and open-source analysis tool PALM (Permutation Analysis of Linear Models), augmenting its abilities to allow researchers to explore voluminous amounts of data using optimal and valid statistical methods, with minimal assumptions, and with a fast implementation that makes rigorous permutation tests computationally accessible. PALM already offers several important approaches and features not available in any other software, and has become a popular tool in brain imaging. The goals of this project are: (1) develop and implement a general approach to conduct analyses of repeated measures (longitudinal) and of genetic (familial) data using permutation tests, with an emphasis on BRAIN Initiative data; (2) develop and implement novel test statistics that are sensitive to spatial and temporal processes across the brain, even for data measured in different domains, scales, and representations; (3) expand PALM’s multivariate capabilities, to uncover statistically independent latent factors from multiple dimensions (e.g., cellular, behavioral, genetic, or imaging) that can span multiple disease categories; (4) disseminate existing and novel statistical methods with fast and efficient software implementation that makes use of parallelization when multithreading, graphics processing units (GPUs) and/or high performance computing systems (HPCs) are available, (5) ensure that PALM is compatible with recently developed data models and standards used by the BRAIN Initiative, and (6) ensure that PALM can be integrated with cloud repositories that host large datasets. The development of novel theory and algorithms will consider computational efficiency, standardized data models, and the hardware architectures available.

NIH Research Projects · FY 2025 · 2025-08

PROJECT SUMMARY High variability in blood pressure (BP) over 24-hour (24-h) is associated with target organ damage and increases the risk of most common age-related diseases including glaucoma, stroke, Alzheimer’s disease-related disorders, and cardiovascular complications. While the exact mechanisms remain unclear, evidence suggests that organs may experience periods of unstable blood flow and supply when BP excessively varies. This mechanism has been studied by estimating averaged BP variability or categorizing nocturnal BP dipping. However, these metrics do not fully capture hypotensive episodes and rather simplify BP circadian rhythms. As a result, the contribution of 24-h BP variability for the risk-stratification of outcomes remains poor, hampered by limited reproducibility and inconsistent significant associations. We urgently need innovative metrics and methods to change the paradigm of studying BP variability as absolute values. 24-h ambulatory BP monitoring (ABPM) provides a set of time-series BP points from which specific BP could be generated. However, we are not aware of studies examining 24-h BP dysregulation beyond mean absolute values and current evidence lacks whether diseases correlate with specific 24-h BP patterns. Our overall hypothesis is that 24-h BP dysregulation is a syndrome characterized by 1) hypotensive states and 2) abnormal patterns in BP that correlate with the presence and development of health outcomes. By leveraging and analyzing 24-h BP data from three large population-based studies (~4000 participants aged ≥40y), this project aims 1) to examine the relationship of 24-h hypotensive states with age-related ophthalmic (glaucoma- related damage), neurological (Alzheimer’s disease-related disorders), and cardiovascular outcomes; 2) to study whether these outcomes exhibit specific 24-h BP patterns; and 3) to correlate 24-h BP patterns with hypotensive states and conventional metrics of BP. In Aim 1, we will evaluate 24-h hypotensive states by providing a new conceptual framework that considers magnitude, frequency, and time. Ho: drastic, sporadic, and prolonged drops in BP over 24-h are associated with worse outcomes (burden and progression). In Aim 2, we will construct BP signals into a three-dimensional space and cluster patterns based on the BP signals’ distance. Ho: patterns of 24-h BP are specific to age-related complications. In Aim 3, we will correlate metrics derived from Aim 1 as well as 24-h BP level and variability with the generated patterns of 24-h BP waveforms. We will additionally use longitudinal ABPM to test whether patterns of 24-h BP change over time and whether greater BP variability and more 24-h hypotensive states accompany such longitudinal changes. The paradigm presented here proposes to study 24-h BP dysregulation as a syndrome characterized by 24-h hypotensive states and abnormal patterns associated with specific diseases. The approach we propose would address unmet needs for biomarkers of abnormal BP circadian rhythms in clinical and research settings.

NSF Awards · FY 2025 · 2025-08

This research examines how living in a border region shapes how residents alter their behavior due to increasing environmental variability such as shifts in temperature and precipitation patterns. Results can inform community-based organizations in border regions on how to best support vulnerable communities when challenged by environmental change and its consequences. Empirical analyses could also provide regional, state, and federal institutions with high-resolution data that could improve policymaking at multiple scales. Faculty leaders will mentor three graduate students, two of whom will be trained at University of Texas Rio Grande Valley (UTRGV), the second largest Hispanic-Serving Institution in the US, with 91% of students identifying as Hispanic and more than half being first-generation. Utilizing ethnographic research including document review, participant observation, semi-structured interviews, and surveys along with community collaboration and geo-spatial analyses over a period of 36 months of fieldwork, this study aims to address this need by investigating the overarching research question: How does living within a border region shape experiences of everyday adaptations to environmental change? This study seeks to answer this question through the following objectives: (1) Investigating how border specific attributes (e.g., surface flooding and its effects in the region, increased surveillance and security, informality) influence how environmental change is unfolding in the research site; (2) Documenting the ways people use everyday adaptations to adapt to environmental change in the region; and (3) Analyzing how adaptation and border policies respond to and reflect social and environmental changes and (Obj1) everyday adaptations (Obj2). This research is a collaborative effort from scholars in anthropology, environmental social science, sociology, and geoscience at the University of Texas Rio Grande Valley and the University of Delaware. Findings will expand theoretical and empirical understanding of the ways that border environments constrain adaptation to environmental change. 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

NSF's LIGO is the world's premier detector of gravitational waves, observing the Universe through gravity rather than light, and allowing us to witness "dark" phenomena (such as black holes) that are undetectable by other astronomical observatories. These observations are revolutionizing our understanding of astrophysics and cosmology. To look farther and more precisely at the Universe with gravitational waves, advances in instrumentation and calibration must be pursued, but extending the scientific reach of LIGO becomes more challenging with every round of improvement. This award supports research to increase the precision of the advanced LIGO detectors. Additionally, this award supports research into Newtonian Noise (local gravitational interference) to improve the sensitivity of the next generation of gravitational wave detectors. The team will train students in STEM areas of research. With the next major, but incremental upgrade, LIGO A#, the mass of the interferometer end mirrors will increase by a factor of 2.5. This will severely limit the capability of the current photon calibrators unless their laser power is scaled up accordingly. This project will design and test a photon calibrator power upgrade and investigate calibration improvements in time for LIGO A#. Improvements will be related to absolute power calibration and low-latency calibration. On the Newtonian Noise side, efforts are concentrated on characterization and mitigation. This project will develop a 3D numerical model to generate realistic predictions of atmospheric Newtonian Noise effects, based on on-site temperature and airflow measurements. Newtonian Noise is expected to be a dominant low-frequency noise source for 3rd generation detectors. 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

Alloys composed of very high melting point elements in equal or near-equal atomic ratios, called refractory medium/high entropy alloys, have tremendous potential for high-performance materials innovation due to their unique mechanical, thermal, and chemical properties, surpassing the traditional superalloys. Fabrication of parts with these alloys employing conventional methods presents significant challenges. Laser-based metal additive manufacturing (AM) process is a promising route to build complex parts made of refractory alloys and metal matrix composites with minimal processing steps and dependence on supply chain. This Engineering Research Initiation (ERI) project aims to achieve high melting-temperature medium entropy alloys with thermal stability, high strength-ductility balance, and high wear resistance by direct alloying the constituent elements with laser-based AM processes. This project has the potential to transform the fabrication of alloys for extreme environments and operating conditions. The advanced manufacturing education and research opportunities associated with this project will facilitate creating a pipeline to graduate education through experiential learning. The student traineeship will contribute to the steady growth of the nation’s trained workforce and leadership for next-generation manufacturing and bring prosperity in the region. The outreach efforts will spread awareness of science, engineering and technology education in students and the community. The overarching goal of this research is to advance the understanding of material-process-structure-property relationships for additively manufactured new high-temperature ceramic reinforced metal matrix composites (MMC) of refractory medium entropy alloys (RMEA). There is a substantial knowledge gap in understanding how the AM process can influence the solid solution formation, element diffusion, phase evolution, and the resulting microstructural and mechanical properties of additively manufactured refractory medium/high entropy alloys. This research aims to investigate a single-step alloying process through the following main research tasks: 1) Perform alloying with laser powder-bed fusion AM using RMEA pure element powders and ceramic particles; and 2) Characterize the microstructure and mechanical properties of 3D printed RMEA and RMEA-MMC specimens. Upon successful execution, this project will enable the rapid discovery of novel high-performance alloys, leading to a transformative additive alloying approach. This advancement will enhance the fabrication of near-net-shape parts with next-generation high-temperature alloy composites for supersonic, nuclear, automotive, and defense 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 · 2025-05

This project provides domestic travel support for US-based Ph.D. students to attend the Society for Industrial and Applied Mathematics, (SIAM), International Conference on Data Mining (SDM 2025) and for the Doctoral Student Forum, which will be held in Alexandria Virginia, U.S in May 1–3, 2025. The award will be advertised and announced on the SDM 2025 website as well as various data mining mailing lists. SDM is an annual conference that presents the world’s premier research in data mining, bringing together researchers and practitioners from academia, industry, and government. The conference includes keynote talks from invited speakers, technical programs, specialized workshops, tutorials, Blue Sky Idea papers and Doctoral Student Forum. Participation in conferences is an important aspect of the educational experience of graduate students. Through participation in SDM 2025, students interested in pursuing research in data mining can interact with peers who share similar interests from other universities, as well as hundreds of leading re-searchers in data mining from around the world. Its proceedings are published both in archival form and on the SIAM website. Participation in premier research conferences in data mining is an integral component of the training of Ph.D. students in data mining. The SDM 2025 Doctoral Student Forum aims to provide an opportunity for Ph.D. students to present their work and receive constructive feedback and mentoring from established researchers in data mining. Such feedback and mentoring are expected to improve the quality of the student’s thesis research. The Doctoral Forum participants will be able to interact with their peers from other universities as well as hundreds of leading researchers in data mining from around the world. In addition, they will attend the technical sessions, plenary talks, panels, tutorials and workshops of their choice at the conference. Similarly, student recipients of the travel award will be able to attend the conference technical programs and interact with peers and senior researchers. All doctoral students will be invited to attend a mentoring panel to discuss a variety of career-related issues. This experience altogether will be extremely formative and fruitful towards the shaping of their future research endeavor 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 · 2025-03

A fundamental trait of cells is the fact they are encapsulated by a membrane, whose assembly was most likely one of the early steps during life’s emergence on the early Earth. Membrane assembly is a spontaneous event driven by the simple fact that oil and water do not mix. This project seeks to understand how membrane encapsulated vesicles (protocells) might have formed and reproduced in the absence of any genetic information. The project investigates how molecules that are amphiphilic (simultaneously polar and non-polar) assemble into vesicle compartments, and how these compartments might drive the formation of additional membrane building blocks. This project tackles whether information can be carried over across generations of vesicles, with offspring adapting to their environment over time. If true, this will demonstrate the feasibility of a form of Darwinian evolution on a system that does not (yet) contain genes. A Course-Based Undergraduate Research Experience Astrobiology course where students design a space exploration mission will be expanded. The course is designed to develop a teamwork ethos, critical thinking, data analysis, and public presentation skills. The novelty of the type of laboratory work involved would provide students the opportunity to develop a unique skill set in microfluidics. This project has three main scientific goals: (i) use catalytic minerals to promote the synthesis and polymerization of organosulfur species to yield amphiphilic molecules. (ii) Probe the capability of such amphiphiles to self-assemble into vesicle compartments and develop a robust methodology for studying their individual and population-wide composition. And (iii) assess if such vesicles display generational heredity – allowing for a primitive form of evolution by natural selection. Selection pressures, such as changes in temperature and pH, will be applied to determine if the naturally-selected composition of older generations biases that of future ones. Custom-made microfluidic setups will be incorporated to enable the control and manipulation of fluids constrained to the microliter scale, where surface forces prevail over volumetric ones, and allow for the simulation of the out-of-equilibrium conditions naturally found in energy-rich geochemical settings. This interdisciplinary project is at the crossroads between chemical biology, biophysics, and evolutionary biology and is poised to tackle the critical challenge of how the first cells were built. 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

Large Language Models (LLMs) have become an emerging tool for complex reasoning tasks in data modalities such as image and video, by efficiently harnessing rich unlabeled data. Yet, few researchers have focused on time series data, which is widely used in critical applications with limited amount of annotation. Time series data poses three key unresolved challenges: 1) there is a lack of high-quality text information for help with identification that aligns with time series data; 2) deep learning models to reason with both time series and text data are under-researched; and 3) novel explainable artificial intelligence (AI) tools to ensure the trustworthiness of such models and give users confidence in model predictions are lacking. This project aims to address these challenges and develop a time series text-based cross-modality Question Answering (QA) system. The project will also promote close collaboration between UTRGV and Yale University to encourage Hispanic undergraduate students to pursue higher education and build UTRGV's capacity to conduct research on advanced AI topics including LLM, time series, foundational model, and trustworthy AI at UTRGV. This project aims to develop a time series text-based QA system that can accelerate a wide range of research by providing expert-level explanations. The technical aims of this research project can be divided into three key components: 1) Develop an automated high-quality time series annotation pipeline by designing a multi-view prompt-based QA generation framework to build training data and labels. 2) Develop a novel text and time series cross-modality pre-trained model to better enable knowledge extraction from time series data and fuse information across two modalities. 3) Enhance transparency and interpretability of the built model by developing a combination of time series-oriented explanation and text-oriented explanation. Collaborating with Idaho National Lab, the system will be evaluated by analyzing and forecasting extreme weather that can cause energy infrastructure damage, using the associated time series and text information. 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

Rapid advances in artificial intelligence (AI) and cyber-physical systems (CPS) are transforming critical engineered infrastructure systems, such as transportation, promising connected and autonomous transportation services, networks, and systems. Despite their potential economic and societal benefits by addressing persistent traffic safety, congestion, and accessibility issues, AI-powered transportation CPS can also be a double-edged sword as they can cause intentional and/or unintentional harm to transportation system users, ultimately breaching public trust in and hindering mass adoption and derived benefits of transportation CPS. Alarmingly important examples are the unintentionally unreliable decisions of AI under complex uncertain situations arising in safety-critical autonomous driving, AI vulnerability to intentional adversarial attacks against transportation CPS elements, and unintentional discrimination of AI decisions against certain transportation CPS user groups. This project aims to develop novel and comprehensive trustworthy AI tools providing the umbrella addressing technical (specifically, AI safety and AI security) and social (specifically, AI fairness) trust issues arising in AI systems embedded in transportation CPS. The research activities are closely integrated with education and outreach objectives, including 1) training an underrepresented workforce that is equipped with the knowledge and skill sets to address AI trustworthiness in transportation CPS; 2) teaching undergraduate and graduate students about trustworthy AI in transportation systems; 3) motivating K-12 and minority students to pursue AI and engineering careers; and 4) building a broader research-education community. This project consists of three multidisciplinary research thrusts at the nexus of AI safety, AI security, and AI fairness in the context of transportation CPS. Collectively, this three-pronged framework aims to fill knowledge gaps in 1) addressing trustworthiness of a wide spectrum of learning-based AI methods, including graph neural networks, large language models, reinforcement learning, federated learning, and tensor completion; 2) integration of multiple, non-mutually exclusive aspects of AI trustworthiness, such as the AI safety-fairness nexus, which is crucial given that these and other aspects of AI trustworthiness should ideally co-exist to ensure the integrity of transportation CPS; 3) adoption of a holistic, systematic approach to tackle trustworthiness issues arising in the data, modeling, and deployment stages throughout the AI system lifecycle; and 4) addressing AI trustworthiness in transportation CPS at the microscopic (e.g., connected autonomous vehicle control), mesoscopic (e.g., adaptive autonomous traffic signal control), and macroscopic (e.g., autonomous traffic network flow analytics) scales. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2024 · 2024-12

Through support from the Improving Undergraduate STEM Education: Hispanic-Serving Institutions (HSI Program), this project will revitalize core STEM courses by integrating interdisciplinary high-performance computing applications. The resources made available by the Educational Instrumentation program will strengthen undergraduate learning in computational mathematics, statistics, data sciences, and computer sciences at The University of Texas Rio Grande Valley. The project will secure cluster computing resources allowing students to perform high-throughput computations that will be integrated into the curriculum in courses including Linear Algebra, Probability and Statistics, Introduction to Deep Learning, Reinforcement Learning, Statistical Computing and Data Management, and Special Topics in Statistics. An estimated 1000 students and 10 faculty will utilize the project-funded equipment each year. In addition to providing improved experience in mathematics and data science courses, the new equipment will also be used in undergraduate research and capstone projects. The goals of this project are to enrich the learning and experiences of undergraduate students by providing critical computing resources in mathematics, statistics, data sciences, and computer science. The project will result in the development of new modules and practical exercises in the targeted courses leveraging HPC resources. The courses will utilize the GPU cluster for hands-on student projects and research. The impact of the project will be assessed using analytic software to track statistics of usage in active users on the cluster. Student and faculty experience will be gauged with various instruments. This project will result in student-faculty research output published in peer-reviewed journals. The updated course content will be disseminated and made available for other institutions to incorporate into their core courses. Additionally, the program will feature deliverables presented at conferences and other venues. The HSI Program aims to enhance undergraduate STEM education and increase capacity to engage in the development and implementation of innovations to improve STEM learning and teaching at HSIs. Projects supported by the HSI Program will also generate new knowledge on how to achieve these aims. 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 2024 · 2024-11

The CHAMPIONS (Developing Champions of Diversity with Appreciative Inquiry and Computational Simulation) project aims to address the persistent lack of diversity in the geosciences field by taking a unique and inclusive approach. By embracing the principles of belonging, accessibility, justice, equity, diversity, and inclusion (BAJEDI), this project seeks to empower geosciences faculty to become catalysts for change within their departments. The significance of this project lies in its bottom-up approach, which emphasizes compassion, empathy, and the lived experiences of individual faculty members. By creating a positive and inclusive environment that values diverse viewpoints and experiences, the project aims to foster a sense of empowerment for underrepresented minority (URM) faculty, enabling them to lead from within and drive sustained progress in broadening participation outcomes. The CHAMPIONS project will be implemented through a three-step process. 1) Convene an appreciative inquiry summit that engages geosciences faculty in identifying challenges and desired BAJEDI outcomes within their departments. This qualitative approach allows for a comprehensive understanding of the specific obstacles and opportunities that exist in each unique context. 2) Develop a computational simulation model that will be informed by the data collected from the summit. The model will simulate the long-term impacts of bottom-up interventions on BAJEDI outcomes in the geosciences. 3) Deploy the model to serve as a decision-making tool, training faculty in systemic and long-term thinking when addressing BAJEDI issues. By enabling faculty to visualize the potential effects of their interventions, the project aims to foster intentional and effective strategies. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2024 · 2024-10

This project aims to serve the national interest by improving undergraduate education through educator professional development and implementation of course-based undergraduate research experiences (CUREs) to promote participation in STEM for individuals who have been historically marginalized from such opportunity. Extensive evidence demonstrates that CUREs improve student learning and interest in STEM. In addition, CUREs are an opportunity to provide more students with research experiences. The first objective of this project is to provide professional development to STEM faculty (Biology, Biomedical Science, and Engineering) from The University of Texas Rio Grande Valley (UTRGV) and South Texas Community College (STC) to support their development and implementation of CURE curricula for undergraduate students. The second objective is to investigate faculty’s self-efficacy toward designing and implementing CUREs and their perceptions of undergraduate students’ abilities to engage in research. The third objective of this study proposes to investigate the extent to which participation in a CURE laboratory affects student outcomes, including learning, academic achievement, and attitudes/interest in STEM, compared to learners experiencing a traditional laboratory course. The fourth objective is strengthening the two- to four-year college pathway for STEM majors between UTRGV and STC. Research demonstrates that CUREs improve students’ achievement, retention, and self-perceptions about learning, as well as scientific thinking and practices. Driven by UTRGV and STC’s unique environment, with an approximate ninety percent Hispanic student enrollment, the study focuses on undergraduates historically underrepresented in STEM. This project plans to generate empirical evidence to advance our understanding of improving undergraduate engagement, interest, and learning in STEM through CUREs and to strengthen pathways for students in STEM education and careers. The project team aims to support STEM faculty’s development of CUREs through a year-long professional development program that includes course design, implementation, and evaluation. The team intends to study the effects of professional development and CURE implementation on STEM faculty’s self-efficacy toward CUREs and perceptions toward their students. The project also plans to investigate the impact of CURE curricula on students’ learning, attitudes, and interest in STEM compared to traditional laboratory courses. This study draws on experiential learning theories and empirical findings in the literature on CUREs. The study plans to utilize a mixed methods approach, including quantitative (i.e., surveys) and qualitative (i.e., curricula, observations, artifacts) data and a quasi-experimental design. Completion of this project endeavors to have broad impacts; in the short term, the project investigates ways to address the learning needs of undergraduate students in STEM courses through faculty professional development. The project intends to provide predominantly Hispanic students with course-based undergraduate research experiences, leading to improved STEM engagement, interest, and learning. However, the ultimate impact is much broader, as the research team plans to disseminate results nationally through STEM education conferences and journal publications. As a result, the project could help broaden Hispanic students’ participation in STEM, leveraging UTRGV’s unique role in serving a regional community that is more than ninety percent Hispanic. The NSF IUSE: EDU Program supports research and development projects to improve the effectiveness of STEM education for all students. Through its 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.