University of Texas at San Antonio

NSF Awards · FY 2026 · 2026-07

Artificial intelligence (AI), large-scale data, connected devices, cloud and high-performance computing systems, that together form the nation's cyberinfrastructure, are central to national competitiveness, yet most computing students encounter them only in advanced elective courses. This project infuses aspects of AI, Big Data, and Parallel and Distributed Computing concepts and practices into three foundational computing courses. The first two form the usual introductory programming sequence, and third is the computer systems course that is often taken shortly after them. Thus, all computing majors, not only those pursuing upper-level elective courses, will develop critical skills for understanding and contributing to the modern computational ecosystem. The project addresses a persistent barrier to such curriculum modernization: many instructors need focused preparation, classroom-tested examples, and adaptable teaching materials before they can confidently introduce these topics in early courses. To overcome this bottleneck, the project develops courses and materials to train about 200 current and future instructors through three intensive in-person summer workshops, an additional six hybrid tutorials at major conferences, and complementary online workshops. Summer trainees adapt and implement the course exemplars at their own institutions and contribute evaluation data, classroom-tested refinement and local adaptation, enabling broader adoption. With the potential to impact about 250,000 students over 5-10 years, the project serves NSF's mission by strengthening computing education, expanding access to AI and advanced cyberinfrastructure skills, and building the nation's long-term technological and research workforce capacity. The project advances knowledge in computing education by producing rigorously classroom-tested exemplars infused with Artificial Intelligence (AI), Big Data (BD), and Parallel & Distributed Computing (PDC) for Computer Science 1 (CS1), Computer Science 2 (CS2), and Computer Systems courses. Implementation across 60 diverse institutions will generate evidence-based models that can be widely adopted, thereby transforming early computing education at scale. The project investigates how AI-enabled learning tools and pedagogy can modernize core curricula by enabling students to construct, explore, and reason about modern computing systems earlier and in more depth than was previously possible. Evaluation data from trainees' implementations - including student learning, retention, and institutional adoptability - will contribute to generalizable knowledge on the design and scaling of Cyberinfrastructure-centric curriculum innovations. The project incorporates aspects of AI, BD, and PDC concepts and practices into the foundational computing courses ensuring that all computing majors, not only those pursuing upper-level electives, develop critical Cyberinfrastructure-ready skills. The project's three core course exemplars will be nationally adoptable, with trainees providing local adaptations to build a community-driven ecosystem of shared materials. Two key innovations in this project are: (i) harnessing AI both for pedagogy and for enabling course modernization: AI tools will make it possible for introductory students to develop, experiment with, and understand software, data, and other artifacts that were previously too complex to explore meaningfully at scale; and (ii) explicit integration of how BD and PDC power AI, giving computing students insight into the Cyberinfrastructure ecosystems underlying AI-driven discovery 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 2026 · 2026-07

This Faculty Early Career Development Program (CAREER) award supports a research and education program that improves the understanding of how head impacts and traumatic events cause brain injury in humans and animals. Traumatic brain injury is a major cause of long-term disability and economic burden in the United States. However, brain injuries often occur at microscopic scales that cannot be seen in living humans, limiting efforts to understand and prevent them. This project will develop detailed digital models of human and animal brains to examine how features such as brain shape, folding patterns, and nerve fiber pathways influence where injuries occur and how they spread through the brain. The project will advance scientific understanding of brain injury and help inform the development of more effective protective strategies for the general population. It will also support the design of animal studies that better reflect human injury, reducing the need for new animal testing in alignment with current efforts by United States science agencies and regulators. The project includes educational activities that use brain models, public exhibits, and teacher training to engage learners, support workforce development in science and engineering, and promote broad access to scientific knowledge. The goal of this project is to determine how structural differences in brains across species, influence mechanical responses and injury thresholds under head loading. The research advances fundamental biomechanics and mechanobiology by explicitly linking brain structure to tissue-level deformation and injury mechanisms. The project will develop high-resolution, species-specific computational brain models that capture regional anatomy, cortical folding, and white-matter fiber architecture derived from medical imaging data. These models will be validated and used to simulate head impacts from controlled animal experiments as well as reconstructed or recorded human head impacts. The simulations will resolve brain deformation patterns, enabling direct comparison of mechanical responses across species. By relating predicted tissue deformation metrics to observed injury patterns, the research will establish mechanically grounded injury criteria comparable across species. The project will develop a predictive framework that integrates machine learning with biomechanical modeling to map head motion to brain deformation and injury risk, supporting translational studies while contributing to fundamental advances in biomechanics and mechanobiology. 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

Tropical freshwater fishes are popular aquarium pets throughout the United States and often become invasive when aquarists release unwanted pets into lakes, streams, and wetlands where they consequently impact native species and the freshwater resources upon which humanity depends. Most of these tropical fishes are intolerant of cold winters and subsequently establish invasive populations only within subtropical regions like Florida and southern portions of California, Arizona, and Texas. However, environmental change and the growth of cities that absorb solar heat are causing freshwater environments to warm, and this warming may facilitate the northward expansion of invasive tropical fishes. This research will use laboratory experiments to measure the cold tolerance and growth potential of three tropical freshwater fishes in order to understand their ability to expand their invasive range northward. A subset of these experiments will be performed by students enrolled in course-based undergraduate research at the University of Texas at San Antonio. At the same time, research teams composed of undergraduate students and faculty from three universities in south, central, and north Texas will monitor winter water temperatures in streams to understand baseline temperatures in their respective regions as well as the urban heat island effects that increase temperatures beyond the baseline and therefore potentially facilitate the expansion of the three tropical invasive fishes. Lastly, the laboratory physiology and field temperature data will be combined with climate models to build forecast maps of future invasion risks for the three focal species. This research will test two key hypotheses. First, tropical species differ from temperate species in thermal physiology. Second, urban heat islands in temperate climates create thermally suitable conditions in the winter for tropical invasive species, which facilitate population persistence and spread. Conversely, urban heat islands create thermally stressful conditions in the summer for temperate native species, which cause population extirpations and range contractions. The first hypothesis will be tested using a species comparative framework, specifically exploiting three tropical-temperate species pairs from three different phylogenetic lineages. Researchers will characterize the thermal niche dimensions of these three tropical and three temperate species by measuring acute cold and heat tolerance, acclimation potential, and organismal energy budgets. To test the second hypothesis, researchers will use the aforementioned thermal niche dimensions to build process-based species distribution models and simulate geographic range shifts under alternative scenarios of climate change and urbanization. These simulations will be validated using field-based population monitoring and will be carried out for three urban centers arrayed along a latitudinal climate gradient in Texas, thus providing a space-for-time substitution to understand drivers of range limits. The broad aim of this research is to identify the mechanistic underpinnings of species range edges to advance biogeographic knowledge of ecologically- and phylogenetically-diverse fishes and to forecast range shifts caused by global 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 2026 · 2026-01

This Level 1 IUSE ESL project from the University of Texas at San Antonio aims to serve the national interest by developing technology to increase student learning and success in early college mathematics courses. This project expands on the existing Adaptive Learning for Interdisciplinary Collaborative Environments (ALICE) platform to develop an AI-assisted adaptive learning platform for students in College Algebra, Precalculus, Calculus I, and Calculus II. These courses remain critical steps on the pathway to nearly all STEM degrees. The project will use detailed student learning outcomes to ensure that the ALICE platform personalizes learning pathways, addresses gaps in understanding, and enables data-informed instructional adjustments. Faculty will use student data from the platform to support both individual students and class-wide teaching practices. The dual goals of this project are to deploy an AI-assisted adaptive learning framework and equip faculty with the skills and knowledge to effectively utilize it. Faculty will participate in professional development, including an 8-week summer workshop, to gain new skills and insights into how to use ALICE and other AI-assisted tools relevant to their pedagogy. The project will conduct three classroom pilots and use the insights gained to iteratively improve project outcomes. Data from student surveys and academic outcomes will be used to study the effectiveness of ALICE on various aspects of the learning experience. Project evaluation will be conducted by an independent evaluator who will track project implementation and progress towards stated goals and deliverables. 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 · 2025-12

This three-year REU Site: Shaping the Future Workforce for Artificial Intelligence, Robotics, and Semiconductor Manufacturing is hosted by the University of Texas at San Antonio. The program features research projects that the integrate artificial intelligence (AI) into robotics and semiconductor manufacturing and holds transformative potential for enhancing the capacity and efficiency of chip production in the United States. Ten undergraduate students will be recruited from colleges across the nation. There is a shortage of students entering the workforce or graduate programs in these fields. REU students will receive direct mentorship from faculty and industry professionals, fostering a supportive and collaborative learning environment. The REU program also integrates research and education by offering training sessions, practical lab experiences, and opportunities for students to contribute to professional communities, such as judging robotic competitions and demonstrating research findings to peers and high school students. REU participants will also volunteer at professional conferences and host an annual REU conference. The program encourages participants to pursue graduate degrees in STEM disciplines, an essential step to strengthening the competitiveness of the future workforce. The goal of this REU Site is to educate and train undergraduates, equipping them with a robust foundation and diverse skill sets in AI, robotics, and semiconductor manufacturing. The REU projects are designed to tackle frontier challenges through practical applications, such as assembling robotic systems, collecting and analyzing data using sensors in manufacturing lines, applying AI algorithms for data processing, and developing monitoring frameworks for semiconductor production. These activities provide novice researchers with valuable, hands-on experience. To further enhance student engagement and development, the program incorporates a learn-practice-service cycle into the research activities, while also fostering teamwork and building self-confidence through recognition and rewards. These elements enable participants to gain essential technical and professional skills, preparing them to make meaningful contributions to the field and pursue graduate studies and STEM careers. This Site is supported in part by funds provided to the National Science Foundation by the Semiconductor Research Corporation. 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 hallmark of many debilitating diseases, such as Alzeimer’s disease (AD) and type II diabetes (T2D), is the presence of abnormal masses/aggregates of proteins termed “amyloids”. These amyloids, in which composition is disease dependent, are generally considered to be ideal markers for disease diagnosis and therapeutic intervention. Unfortunately, existing probes are limited in that they are only able to detect the presence of a single targeted amyloid protein. This project will develop a new class of generic, multiple-mode, multi-target amyloid probes that will detect a wide variety of proteins associated with different amyloid diseases. Design principles for the multimodal probes can be transformed to numerous molecular-recognition applications for targeted drug therapy, biomarker detection, and disease diagnostics (e.g., cancers and COVID-19). The proposed multi-disciplinary research activities will provide diverse training for students at all levels, especially from underrepresented and low-income families. The students will develop knowledge and skills in data mining, molecular simulations, neuroscience, and lab-on-chip techniques in close relation to public health problems. Finally, the integrated educational and research activities will enrich the curriculum of the Corrosion Engineering program at the University of Akron. The overall objectives of this project are to (1) fully explore, identify, and engineer – with both data-driven simulations and experiments – a new family of AIE@βPs (an aggregation-induced emission (AIE) molecule conjugated with small β-sheet-forming peptides (βPs)) probes capable of early and enhanced detection of multiple pathological aggregates and co-aggregates formed by the same and different amyloid proteins, which co-exist in human body fluids across different amyloid diseases and (2) conduct fundamental sequence-structure-recognition studies on these multi-mode, multiple-target AIE@βPs probes. The AIE molecule targets the aggregated amyloids and avoids the aggregation-induced quenching, while βPs target the β-structures of amyloid aggregates via specific β-sheet interactions. The project’s objectives will be achieved via three tasks: (1) develop a machine-learning model, combined with molecular simulations and biophysical experiments, to screen, identify, and validate a library of βPs capable of self-assembling into β-sheet structures and cross-interacting with both Aβ (associated with AD) and hIAPP (associated with T2D); (2) design and synthesize a series of AIE@βPs probes to detect Aβ, hIAPP, and hybrid Aβ-hIAPP species at different aggregation states for demonstrating “conformational-specific, sequence-independent” mechanisms via synergetic AIE- and βPs-induced binding modes; and (3) transform AIE@βPs probes into different amyloid sensors via surface immobilization by controlling their packing structures, densities, and patterns of AIE@βPs. In parallel, multiscale molecular simulations will be conducted to study the structures, dynamics, and interactions of βPs and AIE@βPs with amyloid aggregates in solution and on surfaces, which will be correlated with amyloid recognition mechanisms of AIE@βPs by experiments. If successful, this work will provide new design principles and sensor systems for early amyloid detection beyond few available today. 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: Icing is a natural phenomenon that plays a crucial role in sustaining life on Earth, but unwanted icing can cause severe economic, environmental, and life-threatening consequences. Conventional antifreezing materials, such as icephobic water-free organics or hydrophilic hydrogels containing antifreezing additives, often suffer from weak mechanical properties under subzero temperatures, which limits their practical applications. To address this issue, this research will explore a design strategy for developing a new class of fully polymeric hydrogels that possess inherent antifreezing properties and enhanced mechanical strength without requiring antifreeze additives. A successful project could pave the way for a new family of antifreezing hydrogels with diverse structures and other built-in functions for different applications under subzero temperatures, including flexible supercapacitors, soft robotics, electronic skin, and wearable devices. The research is multi-disciplinary and will provide a valuable learning experience for undergraduate/graduate students and high-school teachers in the areas of polymer chemistry/physics, molecular simulations, and engineering design. Additionally, the PI will also introduce experimental and computational components to the curriculum to enhance student learning of engineered materials and promote the field of hydrogel-based materials by organizing international conferences, special journal issues, and STEM student activities. TECHNICAL SUMMARY: The overarching goals of this research are twofold and aim to (1) develop and engineer a new family of fully polymeric hydrogels with intrinsic antifreezing and enhanced mechanical properties and (2) gain a fundamental understanding of antifreezing/toughening mechanisms of these hydrogels at different spatial and time scales ranging from atomic to macroscopic levels by using a combination of polymer chemistry and molecular simulations. The design strategy for these antifreezing hydrogels is to integrate strong water-binding polymers with tightly crosslinked and highly interpenetrating double-network structures, allowing to enhance polymer-water interactions for competitively inhibiting ice nucleation and growth, as well as to activate multiple energy-dissipation pathways for improving hydrogel mechanical properties. Parallel to experimental works, multiscale molecular simulations with new polymerization algorithms will be developed to study water structures, dynamics, and interactions around polymers confined in networks at both resting and stretching states, as well as at different subzero temperatures. Computational study allows to reveal different but correlated antifreezing and toughening mechanisms at atomic levels. Finally, experimental and computational data from the benchmarking hydrogel systems will be compared and correlated to better understand the complex composition/structure-dependent antifreezing and mechanical performance of such hydrogels. This will lead toward an optimal design of antifreezing hydrogels in an iterative way by investigating changes in polymer chemistries, pendant groups/crosslinkers, network structures, and water behavior. Overall, the development of new antifreezing hydrogels with enhanced properties could impact areas such as improved energy efficiency, environmental protection, biomedical treatments, and industrial 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 · 2025-10

This Noyce Track 1 project aims to serve the national need of preparing highly-qualified STEM teachers to address educational disparities in San Antonio, Texas. Additionally, this project will support 15 scholars pursuing degrees in mathematics and science by providing scholarships and targeted support including action research, community-engaged learning, and evidenced-based student-centered pedagogy training. The proposed project components will enable high-achieving prospective teachers to become secondary STEM teachers with extensive expertise in student-centered and culturally responsive instruction. This project at The University of Texas at San Antonio (UTSA) includes partnerships with high-need school districts in the San Antonio area. Project goals include producing 15 new STEM teachers over five years, specializing in mathematics and science education, and preparing them to teach in high-need schools. The theoretical foundation of the project is grounded in evidence-based student-centered learning and action research, which has demonstrated effectiveness in narrowing achievement gaps and fostering deeper engagement in STEM classrooms. This project will be iteratively evaluated. Evaluation of the project will be guided by the following evaluation questions: (a) To what extent are project components implemented with fidelity and quality, and (b) What is the impact of participation in the Noyce program on teacher preparedness, efficacy, retention, and student outcomes? Multiple forms of data will be collected including document analysis, interviews, observations, and surveys to assess implementation fidelity and program impact. Fidelity metrics include adherence, dosage, quality, and engagement, while outcome metrics include recruitment and placement, project completion, teacher retention, CRP implementation, and development of a community of practice. The results of this project will be disseminated to help enhance the field. This Track 1: Scholarships and Stipends project is supported through the Robert Noyce Teacher Scholarship Program (Noyce). The Noyce program supports talented STEM undergraduate majors and professionals to become effective K-12 STEM teachers and experienced, exemplary K-12 teachers to become STEM master teachers in high-need school districts. It also supports research on the effectiveness and retention of K-12 STEM teachers in high-need school districts. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2025 · 2025-10

Artificial Intelligence (AI) is poised to dramatically alter our world; however, one of the biggest challenges to progress in AI is the high energy consumption of the hardware and the lack of flexibility for adapting to new environments or problems. By contrast, the human nervous system has evolved to become flexible, nimble, and energy-efficient. This award will support the development of biotechnology to grow cellular brain organoids capable of replicating the network and activity observed in the brain. These organoids will be integrated into an engineered system and, using insights from cognitive science, programmed to solve increasingly complex associative problems. The research team will evaluate how this bioengineering technology compares to state-of-the-art computer chips that mimic brain function. The team will also study the social and ethical implications of using biological tissue to address AI challenges. This project aims to foster a new field at the interface of computer science, neuroscience, and neuroengineering. There is increasing interest in engineering systems that incorporate organoids - self-organized 3D cellular structures in vitro that resemble organs. In the context of the societal need to develop machine learning (ML) and AI for increasingly complex tasks under low-energy consumption demands, brain organoids offer considerable promise. Brain organoids may recapitulate fundamental computational learning and memory functions not achievable with silicon-based approaches. Research supported through this award will use cellular reprogramming tools to convert blood cells into human induced pluripotent stem cells (hiPSCs), with subsequent differentiation into neurons. These will be used to build brain organoids that integrate excitatory and inhibitory neurons in a configuration similar to that found in the neocortex. The motivating hypothesis is that the self-organization of these cortical-like brain organoids can be harnessed to perform computational, learning and memory tasks. However, for brain organoids to be viable for ML/AI applications, it is essential to demonstrate that they can be reliably produced and programed using biological mechanisms of learning and memory. The proposed research will establish a framework to characterize, validate, and ‘program’ brain organoids. Ethical considerations will be embedded throughout the project to enable in-depth analyses of emerging societal and ethical questions surrounding the use of brain organoids in ML/AI. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2025 · 2025-09

This project is inspired by the challenges and opportunities that generative AI has created for K- 12 education. Students have unprecedented access to AI tools, which can streamline tasks but also shortcut authentic learning. Educators are eager to explore the potential for AI to enhance instruction and deepen student engagement. Beyond just using AI, understanding how AI works is recognized as a new form of computational thinking which is essential for everyone to learn. With guidance from the AI4K12 framework, teachers have a foundation for recognizing which ideas need to be introduced to their students, but there remains a gap in teacher preparedness to introduce these ideas effectively to all students and in authentic middle school contexts. This project will address these gaps by exploring how to embed the use of AI and understanding of AI into core middle school subjects, including science, language arts, and social studies. Results from the project will provide guidance that is applicable nationwide on effective ways to support teachers in effective use of AI and instruction on how AI works that they can bring to their students. Two school districts (one in Texas and one in New York) will work with researchers from three universities to carry out the project. University personnel will work directly with 60 teachers and academic technology specialists and instructional technology coaches across seven middle schools in the two partner districts over the two project years. This will include designing and piloting browser-based software tools and instructional materials that integrate AI concepts across the curriculum areas while developing professional learning communities among school leaders and teachers. The project will investigate three research questions: (1) What are the current understandings, policies, and practices of using and teaching AI literacy in the partner districts? (2) What are the key AI literacies every student should know in alignment with the district curricular goals? (3) How can understanding of AI be integrated into the existing curriculum to deepen learning? The student populations of the partner districts broadly represent the demographics of the United States as a whole, ensuring broad applicability of project findings to all Americans. The goal of the project is to build district capacity to provide all students with the opportunity to participate in AI and associated computational thinking education in their schools. This project is funded through the Computer Science for All: Research and RPPs program. 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

A Digital Twin (DT) is a representation of a real-world system that continuously exchanges data between digital models and their physical counterparts, allowing them to simulate, monitor, and predict the behavior of real-world systems in real-time. As such, DTs hold transformative potential across critical sectors including manufacturing, infrastructure, energy, and defense. However, existing methods for updating the digital models with real-world data are often too slow for real-time use. To overcome this barrier, this research introduces a novel mathematical and computational framework to dramatically accelerate digital model calibration, enabling faster and more accurate digital twin applications. The potential benefits of this work are far-reaching, advancing capabilities in predictive maintenance, process optimization, and risk mitigation, directly supporting the US economic productivity, public safety, technological innovation, and competitiveness. The project also fosters the next generation of scientists and engineers through interdisciplinary training and hands-on research experiences for graduate and undergraduate students. Together, these contributions lay the groundwork for a new generation of scalable, real-time Digital Twin systems with wide-reaching impact across science, industry, and education. Digital Twins require continuous two-way communication between physical systems and high-fidelity digital models. However, the cost in time and resources to update the digital models with real-world data is often prohibitive. To address this technical challenge, this project explores a fundamentally new approach for DT model updating centered on the efficient computation and exploitation of high-order derivatives obtained via a new class of hypercomplex algebras. These derivatives will serve as the foundation for a new derivative-informed Bayesian updating method that dramatically reduces the number of required model evaluations while preserving accuracy. The project is structured around three interconnected aims. Aim 1 develops hypercomplex algebras specifically formulated to compute arbitrary-order derivatives efficiently and accurately, even in high-dimensional settings. Aim 2 computes and applies the new hypercomplex algebras to accurately and efficiently obtain sensitivities of high-fidelity digital models. Aim 3 develops a derivative-informed Bayesian updating strategy that utilizes the derivatives to reduce the cost of model updating while maintaining high accuracy. The anticipated outcomes include faster and more accurate model calibration, improved uncertainty quantification, and reduced operational costs, enabling scalable, real-time DT systems across high-impact domains such as aerospace, defense, infrastructure, and healthcare. 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

Human-animal interactions can lead to positive or negative effects in health, fitness, and social network dynamics for both humans and non-human species. This doctoral dissertation project analyzes how social networks are affected in human-animal interactions, focusing on a social non-human primate species that shares space with human communities. The study investigates how costs and benefits in both species impact their respective social network dynamics. To attain this goal the study analyzes the: (1) dyadic ties in the human social network, (2) individual position of humans and non-human primates in the multispecies network, and (3) costs and benefits of these dynamics. This study is innovative in its use of social network analysis to a multispecies network. The study provides training and learning experiences for students. To assess the costs and benefits of multispecies social network dynamics, the study integrates data collected using behavioral ecology and ethnographic analyses. Baseline location, activity, focal and ad-libitum data, as well as group scans, are collected in the non-human primate species. Plant phenology, presence/absence of food phenophases, and food abundance are documented. Dominance ranks are assessed using submissive interactions with PERC packages in R. Undirected weighted networks are created based on social interaction data. Spatial data is used to assess spatial overlap and food encounters. Mixed methods are used to collect quantitative and qualitative ethnographic data. Multi-species interaction matrices are analyzed for correlation, and proximity levels are assessed. With these varied data, the study evaluates the correlation between individual traits, temporal and spatial variation in perceived costs and benefits of accessing the niche created by another species, as well as social network metrics. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2025 · 2025-09

The shift to online and hybrid learning has exposed critical limitations in existing virtual STEM laboratories, particularly their inability to replicate the hands-on, collaborative experiences essential for deep science learning. Traditional labs provide embodied and communicative interactions that are often absent in current online tools – especially in experiments involving delicate, dangerous, or spatially complex procedures. This project will address that gap by developing immersive virtual reality (VR) laboratory environments that feature realistic avatars and tactile feedback, enabling collaborative learning that mirrors the cognitive and social dynamics of physical labs. By advancing access to high-quality science education, the project supports national goals to enhance health, prosperity, and workforce development. Approximately 500 students and 50 teachers will be directly impacted through outreach and implementation efforts with the aim of equipping learners with the skills necessary for future scientific and technical careers and to increase the overall STEM workforce. The project will investigate how immersive VR laboratories, enhanced with high-fidelity avatars and vibrotactile interfaces, support collaborative science learning. A key research objective will be to examine how variations in avatar visual realism and tactile interactivity influence conceptual understanding, procedural competence, emotional engagement, and collaboration. The central hypothesis is that increased embodiment and realism will improve both individual and team-based learning outcomes. Technological development will focus on reconstructing avatars with full-body fidelity and embedding sensory feedback systems that simulate real-world lab interactions. The research will include controlled user studies in chemistry, comparing learning and collaboration outcomes in VR labs versus traditional in-person labs. Data collection will include quantitative measures such as pre/post-tests and interaction performance metrics, alongside qualitative observations, interviews, and surveys. Analysis will involve both statistical and thematic techniques to capture the cognitive, affective, and collaborative impacts of immersive learning. The project will culminate in the release of an open-source VR lab platform and accompanying dataset, enabling broader access and seeding future innovations in collaborative online STEM education. This project is funded by the Research on Innovative Technologies for Enhanced Learning (RITEL) program that supports early-stage exploratory research in emerging technologies for teaching and learning. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2025 · 2025-09

The project will focus on understanding and modeling career decisions of engineering undergraduate students and engineering faculty at different points in their careers and considering their income backgrounds. Using insights from scarcity theory, a concept from behavioral economics, the study will examine how limited resources affect career decision-making in engineering fields. It will look at how financial stress influences what students prioritize when making critical career choices in engineering. From the findings, educational tools will be developed to help train educators and advisors on ways to support economically disadvantaged students to succeed in engineering careers. The study will include innovative research and education to develop and build capacity for a resilient technical engineering workforce. The project will employ a longitudinal mixed methods design to investigate how engineering students from different income levels make critical engineering career decisions, to what extent these decisions are impacted by a scarcity mindset, and how engineering students’ decision-making changes over time. Multidisciplinary research using scarcity theory from behavioral economics and social cognitive career theory from vocational psychology will be integrated to understand engineering students’ career decision-making. The research project will be divided into three phases. The research questions that will be addressed include: (Phase 1) What factors contribute to undergraduate engineering students’ decisions to persist in an engineering degree? (Phase 2) Despite economic disadvantage, how do undergraduate engineering students make career decisions as they persist in an engineering degree, and how do these decisions change, if at all, as they progress in their studies? (Phase 3) What strategies do engineering faculty use to consider economic disadvantages of undergraduate engineering students in their courses? In Phase I, a survey instrument will be developed and tested by adapting questions from Social Cognitive Career Theory and Scarcity Theory. The survey will be distributed widely to engineering colleges. Data from the survey aims to identify the ways scarcity theory factors influence critical career decisions among engineering students and faculty. Phase 2 will employ qualitative methods to track the evolution of critical career decisions of low-income engineering students over time. Data from Phase 1 and Phase 2 will be corroborated to contribute to existing career development models and gain a deeper insight into career decision-making strategies of economically disadvantaged engineering students. Phase 3 will investigate how educators consider low-income students in course design and delivery. The research findings from all three phases will be translated into educational interventions for engineering faculty, advisors, and teaching assistants. Professional development workshops will be designed and implemented for graduate student TAs in engineering education courses and for faculty in professional development workshops. To facilitate active learning in the workshops, interactive decision trees and illustrated novelas will be designed using the research findings. Expected outcomes include educational lesson plans for interactive early career faculty workshops and TA trainings, interactive web-based decision trees for engineering counselors and advisors, and a brief with recommendations for adjustments to institutional support resources and course structures for engineering college administrators and leaders. The educational tools will be available for public use and research findings will be shared widely through journal publications and conference presentations. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2025 · 2025-09

The objective of this Civic Innovation Challenge (CIVIC) project is to support research on developing and piloting 3D digital models of homes for simulating how extreme heat behaves indoors and outdoors. Built out of collaboration with local households, these models look to help identify areas most affected by heat and allow testing of different solutions, such as shading, ventilation, or material upgrades. Extreme heat is one of the deadliest natural hazards in the United States, and its impacts are intensifying due to urban expansion. In neighborhoods like Westside in San Antonio, Texas, many homes -- especially those built before modern energy codes -- lack proper insulation and cooling systems, making them vulnerable during prolonged periods of high temperatures. This project looks to create a shared platform for residents and city partners to explore retrofit options, assess energy use, and make informed decisions about home improvements. By connecting scientific tools with real-world decision-making, it aims to demonstrate how translational research reduces disaster risks and supports long-term planning in housing and infrastructure. It seeks to serve as a model that other neighborhoods can adapt to similar challenges. This pilot project implements and evaluates co-creation of digital twins for enrolled homes in San Antonio’s Westside neighborhood. Digital twins are 3D, data-enriched models created using low-cost LiDAR sensors via smartphones and validated with professional-grade scanners. Each home is equipped with indoor and outdoor temperature sensors to capture real-time heat data. These data then drives high-resolution thermal simulations using SimScale for indoor airflow and comfort and ENVI-met for outdoor microclimate modeling. Two key hypotheses are tested, namely that, (1) digital twins support better co-design of heat-resilient retrofits, and (2) coordinated outreach improves access to existing home repair and energy-saving programs. Three project phases include digital modeling, collaborative retrofit planning via a new digital platform, and implementation of targeted upgrades. Surveys, interviews, and sensor data are analyzed to evaluate effectiveness. Deliverables include a simulation platform, a home retrofit toolkit, and guidance materials that can be shared and scaled to other cities and housing markets. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2025 · 2025-09

The construction industry--which increasingly relies on rapidly evolving technologies related to Artificial Intelligence and building information modeling--remains vital to economic development across the United States. Although this industry accounts for a significant proportion of the Gross Domestic Product, there are few empirically-based approaches that foster pathways to high-demand, lucrative construction careers among K-12 learners. To address this issue, this conference will bring together construction industry members, K-12 educators, educational researchers, extension specialists, and instructional designers to identify promising approaches and trajectories for STEM (Science, Technology, Engineering, and Mathematics) learning that builds youths' awareness of, interest in, and competencies related to construction careers of the future. This conference will focus on rural regions, given the need for continued infrastructural and economic development in these regions, and given the unique educational circumstances faced by many rural schools. Conference participants will use cutting-edge technologies while engaging in STEM practices on construction sites, in addition to participating in interactive and vision-setting conference sessions. They will discuss innovative and effective strategies for supporting K-12 youth in using emerging and evolving technologies, as embedded within STEM practices, in preparation for construction careers. Syntheses from the conference discussions will be shared with relevant networks of practitioners and researchers, such as 4-H Extension Networks. In this collaborative project, several institutions will host a two-day in-person conference, with virtual elements, on advancing practice and research related to K-12 STEM learning for the construction industries of the future. K-12 educators, who currently teach innovative construction practices in the context of STEM learning, will partner with construction industry members to generate and share ideas related to promising educational practices, particularly in rural regions across the United States. A mixed-method modified Delphi study will be used to ascertain and synthesize expert opinions on technology-infused K-12 learning that can foster US global leadership in the construction industries of the future. Results will be disseminated through high-traffic, peer-reviewed curricular repositories used by K-12 educators; through state, regional, and national professional organizations that serve practitioners in rural settings; and through national networks of STEM educators and educational researchers. 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.

NSF Awards · FY 2025 · 2025-07

Mauna Loa, on the Big Island of Hawai’i, is one of the largest and most active volcanoes on Earth. In the last few hundred years many flows emitted from either the summit or rift zones have reached the ocean. If that were to occur today, lava would cut the main highway around the island and cross the densely inhabited coastal strip. Lava flows erupted from the SW rift zone in the last 200 years reached the coast in as little as 3 hours. Future eruptions could potentially travel even faster. Lava flow hazard assessment requires accurate data on the viscosity of lava, which controls how fast it flows. These data must include the temperature range, crystallinity, and bubble content during eruptions. The UTSA HAMsTER lab recently developed a technique to measure the viscosity of erupting lava, including bubbles and crystals. However, the range of bubble and crystal contents that can be measured with this technique needs to be investigated. Lava will be collected from 4 different eruptions of Mauna Loa, and then used as starting materials for the experiments. The data will lead to better modeling of volcanic processes and lava flow hazards. These improvements will also apply to other volcanoes in the US and elsewhere. The project will support an Air Force veteran for the first two years of his PhD. Lab members will explain and demonstrate volcanoes to K-12 schools in San Antonio. The goal of the project is to explore the capabilities of a new technique for measuring the rheology of three-phase basaltic lava (liquid + crystals + bubbles). This technique uses a high-temperature rotational concentric cylinder rheometer, with short heating and measurement times that enable retention of original crystal textures including olivine phenocrysts, and pyroxene and plagioclase microlites. The mineralogy and CSD's of previous rheology techniques applied to basaltic lava have never adequately reproduced those found in field samples. Preliminary experiments using the new technique on lava from Kīlauea have crystal size and shape distributions that are indistinguishable from samples collected in the field. These samples had much lower viscosities (by up to an order of magnitude!) than would be predicted using current rheology models that are used in lava flow modeling. Starting materials for new experiments will be lavas from Mauna Loa, including very high high crystal and bubble contents. This project will specifically (i) test what bubble contents can be retained during measurement and (ii) test current models for the rheology of three-phase suspensions. The expected contributions are (i) experimental measurements of direct relevance to future eruption hazard modeling at Mauna Loa and similar volcanoes, (ii) a unique rheological dataset for testing suspension rheology models, and (iii) validation of the range of conditions accessible to the new experimental method. 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 · 2025-06

This award will to provide partial support for the 14th US National Combustion Meeting of the Combustion Institute that will be held at Boston, MA on March 16 - 19, 2025. This conference is a gathering of combustion researchers in academia, industry, and government across the United States. It provides a forum for graduate students, postdoctoral students, faculty and other researchers to network and share progress in combustion research. The conference's technical program consists of a series of plenary lectures, contributed oral presentations, and information gathering sessions. Many of the papers will be presented by students, and the meeting is an opportunity for students to network with their peers and faculty from other institutions. A goal of the meeting is to expose students to the latest research and offer opportunity for career development. The conference brings together leading experts to give plenary talks in several areas. Sessions will include the topic areas of Computationally and Data Intensive Research, Detonations and Supersonic Combustion, Diagnostics, Fire Research, Heterogeneous Combustion/Spray and Droplets, IC Engines, Gas Turbines, and Rockets, Laminar Flames, Novel Energy Conversion Techniques, Reaction Kinetics and Fuels, Soot and Nanomaterials, Turbulent Flames. A focus of this conference is an opportunity for graduate students to present and get feedback on their research and thereby enhance their presentation and technical writing skills. The award will support travel grants to non-local students and registration support for local students and mentoring sessions to promote interaction between students and faculty across the US in an informal setting. 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 · 2025-06

Battery-less sensors that harvest energy from ambient sources are revolutionizing the possibilities for IoT applications, particularly in remote or challenging environments where sustainable, maintenance-free operation is crucial. When combined with tiny machine learning technologies, these devices are further enabled with in-situ data processing and real-time decision-making capabilities. However, integrating tiny machine learning algorithms into battery-less IoT devices presents significant challenges involving data acquisition, model training, and model deployment, all under stringent constraints of power, cost, memory, and computation. Operational intermittence due to frequent power failures, further complicates functionality. Once deployed, tiny machine learning models typically operate under fixed sensor sampling rates and are only able to make timely decisions when there is sufficient power. Moreover, the extended operational life of these systems introduces a novel challenge: the obsolescence of AI algorithms or programs as sensory inputs and/or environmental conditions evolve. There is a need for a holistic framework that offers energy efficient solutions capable of responding to sensory input, adapting to environmental changes, and maintaining code currency to ensure continued accuracy and responsiveness. This project seeks to address these challenges by developing innovative methods for adaptive tiny machine learning integration in battery-less IoT systems, ensuring long-term, reliable operation in dynamic, energy-variable environments. This research will advance battery-less systems through three coherent advancements: 1) fundamental redesign and optimization of tiny machine learning models for self-powered IoT devices to enable timely data analysis and decision-making; 2) development of a holistic framework that supports adaptive data collection, decision-making, and communication strategies attuned to the dynamics of varying ambient environments; and 3) design of reliable and efficient code update mechanism to maintain program currency under frequent power interruptions. The proposed co-design techniques will lay a foundation for designing intelligent self-powered IoT devices and applications. The outcomes of this research will include novel cross-layer co-design techniques, tiny machine learning model design methods, software packages, and end-to-end deployment solutions. In addition, this project will encourage the participation of all groups including underrepresented groups and K-12 students in STEM, enrich the current curriculum, and prepare future engineers in machine learning, embedded systems, and IoT applications. All designs and outcomes will be made publicly available. 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 · 2025-04

This I-Corps project focuses on a sophisticated data analytics platform designed to enhance event recognition capabilities within security systems. With retail theft increasing in the United States, the economic ramifications are profound, affecting employment and forcing closures of retail establishments. Traditional security measures such as perimeter breach detection, facial recognition, and reliance on human oversight are increasingly inadequate due to high rates of false positives and the limited operational attention spans by human operators. By integrating advanced artificial intelligence technologies, this project aims to improve the accuracy and efficiency of behavioral pattern analysis in security applications, thereby addressing significant economic losses and enhancing public safety across various sectors. This I-Corps project utilizes experiential learning coupled with a first-hand investigation of the industry ecosystem to assess the translation potential of the technology. This solution is based on the development of algorithms capable of interpreting complex human behaviors using new and emerging artificial intelligence technologies coupled with foundational machine learning techniques. These scientific advancements allow for a more nuanced understanding of security threats and a significant reduction in false positives. By accurately mapping human activities and interactions within monitored environments, this technology promises substantial improvements in security response times and decision-making processes, providing significant benefits to commercial entities by safeguarding assets and ensuring public safety. 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 · 2025-04

With the support of the Chemical Catalysis program in the Division of Chemistry, Professors Oleg Larionov and Michael Doyle of the University of Texas at San Antonio are studying new ways to construct common chemical building blocks used to make drugs, agrochemicals, and advanced materials. New catalytic methods will be developed that will facilitate construction of these building blocks from simple precursors by using sustainable and earth-abundant catalysts. The project involves an international collaboration with French Professors Nicolas Blanchard (University of Upper-Alsace Mulhouse) and Jean-François Brière (University of Rouen Normandy) and funding provided by the French National Research Agency (ANR). Professors Blanchard and Brière are experts in chemical reactivity essential to the project and complimentary to the expertise provided by the two US researchers. These research activities will provide opportunities to train students and postdoctoral scholars in modern chemical methods and prepare them for careers in the chemical sciences and positions in academia and industry. With the support of the Chemical Catalysis program in the Division of Chemistry, Professors Oleg Larionov and Michael Doyle of the University of Texas at San Antonio in collaboration with Professors Nicolas Blanchard and Jean-François Brière from France are studying new catalytic methods of construction of heterocycles based on catalytic cycloaddition reactions of azines and azine N-oxides. The research will facilitate chemo- and stereoselective access to important and synthetically challenging classes of heterocycles using earth-abundant metal and organic catalysts. Furthermore, it will also provide important insights into the underlying catalytic mechanisms through a synergistic combination of experimental and predictive modeling techniques and will inspire the development of new and more sustainable catalytic cycloaddition reactions. 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 · 2025-02

Augmented Reality (AR) is an emerging technology that overlays digital content onto a user's view of the real world in real time, creating interactive and immersive experiences. AR applications are expanding across various industries, including smart manufacturing, healthcare, navigation, education, and entertainment. Since users may rely on AR applications to directly understand and interact with the physical world, failures and errors in these systems can lead to severe consequences, including safety risks. For instance, a flawed AR-based navigation application could cause accidents or damage the surrounding physical environment. Such real-world risks underscore the critical need for testing and quality assurance practices in AR application development. Despite the demand for high-quality AR applications, their testing support remains in its early stages. The challenge of testing AR applications stems from the difficulty of handling real-world inputs and understanding their outputs blended with real-world scenes. Since real-world test environments are costly to build and difficult to control, alternative environments such as videos and Virtual Reality (VR) test scenes are adopted in practice. This project aims to develop innovative techniques to automate the testing of AR applications for higher efficiency and comprehensiveness and investigate the bug-detection effectiveness of VR test scenes. The project includes plans to engage with students from underrepresented groups in computing and to enrich the software engineering curriculum. Specifically, this project will develop an infrastructure that allows existing automatic Graphics User Interface (GUI) testing techniques to be applied to AR apps. The infrastructure will (1) automate the test scene construction by loading playback videos and configuring them at runtime, (2) automatically identify interactive areas on the screen by excluding non-interactive objects using dynamic filtering, and (3) automate GUI event triggering by inferring possible interactions of interactive areas through analysis of their event-handling functions. The project will also develop techniques to automate test oracle in AR application testing. The techniques will check inconsistencies between AR rendering and code execution, and predict the correctness of virtual object placement using models trained with labeled screenshots, video frames, and application logs. Additionally, a study will be performed to assess whether VR-based test scenes can accurately simulate real-world scenes and reveal bugs in AR apps. Pairs of real-world scenes and VR scenes will be constructed and test executions of AR apps on them will be compared based on various metrics such as code coverage, mutation scores, and user-perceived rendering difference. The project will further study the automatic revision of VR testing scenes with the guidance of code coverage. 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

This doctoral dissertation research project studies the professional networks that impact global wildlife conservation efforts. The research contributes to the scientific understanding of how global scientific organizations function and how their impact can be enhanced to achieve the goals of global conservation communities. In addition to providing scientific training for a graduate student in anthropology and interdisciplinary conservation science, research findings will be disseminated via workshops, podcasts, and career development programs for graduate and undergraduate students, global stakeholders in conservation science, and the public. The investigators specifically test for the effectiveness of collaborative conservation efforts across several international institutions working towards tackling the challenges of global wildlife conservation. To expand understandings of the relationships between professional organizational networks and collaborative conservation science, the research utilizes a multi-sited, grounded theory approach to data collection. Data will be collected through qualitative techniques including semi-structured interviews, behavioral observations, and archival research. The research contributes to environmental anthropology, conservation science, and the science of scientific expertise and transnational scientific networks. This research is supported by the Cultural Anthropology and Science of Science: Discovery, Communication and Impact programs. 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

Autistic individuals often face traumatic encounters with first responders due to a lack of understanding and training on autism-specific behaviors. Research shows that a significant percentage of autistic youth have negative interactions with police, leading to fears among autistic individuals and their caregivers. Current de-escalation protocols based on behavioral science are effective but not widely accessible or integrated into first responder training. This project aims to address these gaps by developing a coordinated, IoT-enabled generative AI platform that enhances real-time response to emergency behavioral events (EBEs) involving autistic individuals. The broader significance of this project lies in its potential to transform emergency response systems by providing first responders with the tools and knowledge needed to handle EBEs effectively. The outcome will improve safety and well-being for autistic individuals and their families in challenging scenarios that may require first responder engagement. The project will conduct a feasibility and pilot study to develop a smart, connected community for coordinated EBE response using IoT and generative AI technologies. The research will involve collaboration with community stakeholders, including autistic individuals, caregivers, emergency responders, and community initiatives, to identify current gaps and test trustworthy solutions. The project will detail the data needed for effective EBE response, current solutions used by stakeholders, barriers to integration, and the potential of AI and IoT technologies in enhancing response efforts. Three main research aims are being pursued: 1. Social Science Study: Identify gaps in current de-escalation approaches and barriers to coordinated response through community-based participatory research (CBPR) involving surveys and focus groups. 2. Identification of IoT and AI Solutions: Survey and evaluate AI models and IoT technologies that support personalized de-escalation strategies and seamless data sharing among first responders. 3. Pilot Study: Test the feasibility and effectiveness of the developed IoT-AI platform in a controlled setting, gathering feedback from participants to refine the system for real-world application. Preliminary work by the team has demonstrated the effectiveness of augmented reality for training first responders and highlighted the need for integrated, real-time support during EBEs. This project builds on these findings to develop a comprehensive solution that addresses the unique needs of autistic individuals and enhances community safety through advanced technology and stakeholder collaboration. 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

The objective of this Civic Innovation Challenge (CIVIC) project is to support research focused on designing and piloting an Ethical Urban Digital Twin (EUDT) tailored for San Antonio's Westside neighborhood — a historically disadvantaged Hispanic community. Collaboration between the University of Texas at San Antonio, Texas A&M University, the Historic Westside Residents Association, and the City of San Antonio seeks to provide holistic solutions to mitigate heat-related problems. With rising temperatures, measures like enhanced insulation, air conditioning, and natural ventilation are crucial to individuals’ health. However, past studies often investigate indoor or outdoor spaces separately, overlooking the inherent connection between the two and limiting the offering of integrated solutions. While digital twin holds promise to bridge the gap between indoor and outdoor spaces for thermal comfort evaluation, they generates ethical concerns related to privacy, transparency, and fairness that might lead to undue burden on disadvantaged communities. The broader significance of this research project lies in its potential to serve as a scalable model for aiding other communities facing similar socioeconomic and climate challenges. In Stage 1, the research project centers on building an Ethical Urban Digital Twin for disadvantaged communities to mitigate heat risk. Academic partners will use privacy-preserving sensors and algorithms to prototype digital twin models of indoor and outdoor environments for homes, offices, and businesses. Low-cost PurpleAir sensors will be installed to gather real-time microclimate data, leveraging past work and existing assets in the Westside neighborhood. Community engagement will involve workshops and interactive sessions with residents and stakeholders to co-design the EUDT and address ethical concerns. The integrated models intend to enable simulation of various environmental scenarios to assess thermal comfort and identify effective mitigation strategies. The anticipated outcomes of this research include: (1) an integrated indoor and outdoor digital twin prototype powered by real-time environmental sensors; (2) community feedback on the ethics of digital twin prototype and the feasibility of risk mitigation solutions; (3) a Stage 2 work plan ready for immediate implementation. This project is in response to the Civic Innovation Challenge program’s Track A. Climate and Environmental Instability - Building Resilient Communities through Co-Design, Adaption, and Mitigation and is a collaboration between NSF, the Department of Homeland Security, and the Department of Energy. 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.