University of Texas at Arlington

NSF Awards · FY 2026 · 2026-07

Next Generation (NextG) wireless networks are critical for supporting advanced applications such as autonomous driving and smart factories. However, current communication systems are reactive and can only act on observed network information. This prevents them from anticipating the immediate needs of safety critical applications. This project addresses this limitation by developing a framework that allows the wireless network to become proactive rather than reactive. By integrating data from environmental sensors like cameras and lidars, the network can predict the location and future resource needs of a user before those needs are explicitly required. This capability will make autonomous transportation and smart factories safer and more efficient. To prepare the future workforce for these advanced wireless technologies, the project supports science and engineering education through the development of experiential robotics games for high school students. The research team will also engage the public through interactive museum demonstrations to promote widespread understanding of the next generation of wireless technologies. This project develops the Artificial Intelligence (AI) driven Integrated Sensing, Computing, and Communication framework to enable proactive network resource management. First, the project creates deep learning models using a Multi-Headed Transformer Architecture to translate raw sensor data into a predictive state representing the physical context and intent of the user. Second, the investigator will design a Hierarchical Graph Neural Network to model interactions among multiple users and resolve conflicting resource demands directly at the network edge. Third, the framework employs Multi-Agent Reinforcement Learning to map these user states into optimal resource allocation policies across different network layers. To ensure these complex AI models can operate on physical hardware, the research team will formulate a decentralized computing algorithm. This algorithm partitions the neural networks across edge devices by simultaneously optimizing for communication latency, hardware capabilities, and feature importance. Finally, the theoretical models will be validated through emulation on an open-source orchestrator and physical deployments on both indoor and massive outdoor testbeds. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2026 · 2026-06

Many everyday materials change their behavior as they are mixed, prepared, or processed. These include materials such as inks, gels, pastes, slurries, emulsions, and suspensions, which appear in activities ranging from crafting and food preparation to advanced manufacturing. Small differences in preparation can determine whether a material spreads smoothly, holds its shape, or fails during use. People who work with these materials often rely on trial and error to adjust mixtures, timing, and preparation steps. Important practical knowledge about these processes is rarely documented, which makes it difficult for others to reproduce successful results or learn from failed attempts. This project develops tools that help people observe, measure, and share how these materials behave during preparation and use. By making these processes easier to study and communicate, the project supports learning, experimentation, and collaboration in hands on making. The work expands opportunities for students, educators, and community makers to engage with science and engineering through practical experimentation with materials, strengthening training in advanced manufacturing, creative technologies, and material development. This project develops a sensing and data platform for studying viscous materials used in fabrication and experimental making practices. The platform centers on a low-cost rheometer that measures the resistance of materials as a small sample is drawn into and expelled from a tube while a pressure sensor records the resulting signals. These measurements provide information about how a material resists flow. Computational analysis of these signals allows materials to be compared, grouped, and tracked as they change during preparation processes such as mixing, setting, or curing. The project will develop tools that organize these measurements into interactive maps of material behavior. A browser extension will connect these measurements to existing online recipe repositories, allowing users to annotate preparation steps with sensor data and document both successful and failed experiments. The project will also support the creation of a shared reference collection of materials. Laboratory studies and deployments in maker communities will examine how these tools help users diagnose material problems, refine preparation methods, and reproduce experimental recipes. Educational activities will integrate the sensing tools and platform into university courses, maker workshops, and community 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 2026 · 2026-06

High-performance computing (HPC) enables scientists to simulate and study complex natural phenomena that are difficult or impossible to observe directly, including climate change, astrophysical evolution, fluid motion, and biological processes. These simulations generate enormous volumes of data, often reaching petabyte or even exabyte scales, posing severe challenges for storage, data transfer, and I/O performance in HPC systems. While scientific data compression has become essential for enabling efficient use of computing infrastructure and for helping researchers analyze and share simulation results more effectively, a widening gap exists between the intensive computational workloads of contemporary data compression and the insufficient support of existing general-purpose hardware. This project addresses this gap by developing an innovative algorithm-hardware cross-layer framework that synergistically integrates AI and neural learning methods across multiple compression stages, significantly enhancing the reliability, quality, and overall performance of data compression. The completion of this research will improve the usability of large-scale simulations and strengthen the computing infrastructure supporting research across various scientific domains. This project will develop an algorithm-hardware co-design for high-performance AI- and neural learning-integrated scientific data compression. The research has three closely connected components. First, it will establish algorithmic principles for novel multi-mode neural compression by integrating AI and neural models with classical scientific compression to improve compression quality, reliability, and computational efficiency for diverse scientific data requirements. Second, it will design specialized hardware support for the distinctive computation and dataflow patterns of AI-fused neural compression, including new architectural primitives and microarchitectural optimizations to improve throughput, energy efficiency, and system utilization. Third, it will develop automated methods for jointly designing, mapping, and evaluating compression algorithms and hardware under diverse application requirements, architectural constraints, and I/O budgets. The software and hardware techniques of this project will be implemented via FPGAs and evaluated for real-world scientific simulations on HPC systems. The research outcomes will advance multiple fields, including HPC, AI, machine learning, computing architecture, and data-intensive scientific research. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2026 · 2026-05

One of the central goals of biology is to explain how and why species evolve. In this research, the researchers study waterfowl to understand how adaptations related to diet have contributed to the evolution of this group of birds. Many waterfowl have independently evolved similar characteristics to support specific feeding strategies (e.g., filter feeding, underwater diving, grazing). These repeated evolutionary changes provide an ideal opportunity to study how organisms evolve, to illuminate the function of specific traits, to understand how genomic change connects to trait change, and to test theory about how species respond to ecological change. This project also highlights the importance of museum collections for supporting research and provides a tailored educational opportunity for college students to learn about the many ways museum collections can support cutting-edge science and biotechnology. This project combines analyses of body shape, genetics, and environmental conditions to understand how adaptive traits evolve and influence diversification over long evolutionary timescales. By studying species that independently evolved similar traits, the researchers will use statistical comparative methods to examine whether shifts into new feeding strategies allow lineages to expand into new ecological roles leading to the formation of new species; or whether dietary shifts lead to increased specialization making species more vulnerable to extinction under changing environmental conditions. This project will also determine if there are consistent patterns in how the skeleton adapts to support shifts in dietary ecology. Specifically, the researchers will combine evolutionary analyses with detailed measurements of bone shape variation to test if waterfowl species that adopt similar feeding strategies also evolve similar combinations of skeletal features. Since feeding behaviors often require multiple parts of the body to coordinate function, natural selection may favor integration among skeletal components. Therefore, the researcher will test if different bones evolve in concerted ways to support specific feeding strategies. The researchers will jointly analyze this skeletal shape data with comparative genomic data to investigate the genetic basis of these adaptations. The study will use methods that link evolutionary changes in traits to changes in DNA across species to test whether similar feeding-related traits that evolved independently in different waterfowl species are associated with similar genetic changes (e.g., mutations in protein-coding or regulatory DNA). Finally, the study will evaluate how environmental conditions ultimately shape the observed variation in traits and genomes. This project will combine evolutionary analyses, comparative genomic data and ecological niche models to test how environmental variables (e.g., temperature, habitat type, productivity) may drive adaptation. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2026 · 2026-05

Freshwater supplies are under pressure due to scarcity and increasing energy production. Oil and gas extraction and related industries produce large amounts of oily wastewater. This water is often treated as waste instead of being cleaned and reused. Membrane filtration technologies, such as ultrafiltration (UF), offer a promising way to recover this water. However, oil fouling limits the effectiveness of these technologies. Oil droplets accumulate on the membrane surface and block water flow, which lowers treatment efficiency. Chemical demulsifiers are commonly used to treat oil–water mixtures, but their behavior during membrane filtration is not well understood. The mechanisms of demulsification at the membrane surface and how it affects oil fouling are not well understood. This CAREER project will study how chemical demulsifiers can be used in a strategic way to reduce fouling and improve membrane performance. By explaining how membrane demulsification works, the project will support development of more reliable and affordable methods to treat oily wastewater. The project will also broadens participation in environmental engineering through the Students for Environmental Engineering Development (SEED) program. The SEED program will engage K–12, undergraduate, and graduate students in hands-on learning about water sustainability and resource recovery. This CAREER project will elucidate the governing mechanisms of membrane demulsification and oil fouling mitigation in ultrafiltration (UF) systems for treating oily wastewater. The central hypothesis is that chemical demulsifiers, used either as membrane surface modifiers or as feedwater additives, regulate oil fouling by altering the kinetics of oil droplet coalescence and the interfacial interactions between oil droplets and membrane surfaces. The research will pursues three objectives: (1) to identify the mechanistic roles of oil droplet coalescence thermodynamics and kinetics and crossflow shear in governing membrane demulsification during UF processing; (2) to define structure–property–performance relationships for polymeric demulsifiers grafted onto membrane surfaces; and (3) to develop combination strategies of using reverse emulsion breakers as feed additives to further enhance membrane demulsification and mitigate oil fouling. A combination of crossflow UF experiments, Direct Observation Through the Membrane imaging, membrane fouling modeling, and extended DLVO theory will reveal oil droplet–membrane surface interactions and identify dominant fouling mechanisms. The project will incorporate data-driven modeling approaches (i.e., artificial intelligence (AI) tools) to identify relationships among operating conditions and membrane performance metrics. Quantum-based simulations using density functional theory (DFT) will be used to inform molecular-level interactions between demulsifier functional groups and model oil compounds, linking electronic structure to macroscopic fouling behavior. This project will generate new fundamental knowledge to advance membrane manufacturing and more effective strategies to control and mitigate membrane oil fouling in UF. The project team will develop lectures and hands-on experiments for high school, undergraduate, and graduate students to learn about the science and engineering of challenges around water sustainability in environmental engineering, including the treatment and reuse of oily wastewater. They will also include public engagement activities to further disseminate project findings with industry and utility stakeholders via the Industry Partnership Program of UTA’s Civil Engineering. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2026 · 2026-05

Drones are increasingly integrated with Internet-of-Things (IoT) systems to support mission-critical applications such as infrastructure monitoring, emergency response, and smart transportation. However, this integration also introduces significant security and safety risks. Malicious or hijacked drones can be exploited for conducting surveillance, bringing disruptions to wireless communications, or carrying out physical attacks on critical infrastructure, all posing a major threat to public safety, economic stability, and national security. The project's novelties are the use of millimeter-wave (mmWave) sensing as a unified, scalable, and cost-effective foundation to detect, authenticate, and assess drones, as well as to secure their communications within IoT eco-systems. The project's broader significance and importance are its potential to strengthen the resilience of critical infrastructure, contribute to national defense, and advance trustworthy deployment of emerging drone technologies. In addition, the project supports education and workforce development by engaging a diverse group of students, integrating research outcomes into curriculum development, and releasing open resources that benefit the broader research community and society. The investigator is designing, prototyping, and evaluating mmWave-powered security mechanisms for drone-integrated IoT systems using commodity and deployable radar platforms. The research is organized into four integrated thrusts. Thrust 1 develops mmDrone-Surv, a cutting-edge mmWave framework for extended-range drone detection and tracking. Thrust 2 designs mmDrone-Auth, an advanced drone authentication system utilizing hardware fingerprinting. Thrust 3 seeks to build mmDrone-Scale, a novel solution for accurate remote measurement of drone payloads. Thrust 4 proposes mmDrone-Key, an innovative scheme that uses mmWave sensing dynamics to establish secure keys between legitimate drones and IoT devices. The project also includes a comprehensive prototyping, validation, and evaluation plan to evaluate and ensure the effectiveness of the proposed solutions. The outcomes will advance the state of the art in wireless sensing-based security and provide foundational tools and insights for securing next-generation drone-integrated IoT systems. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2026 · 2026-04

The 2026 IEEE International Conference on Network Protocols (IEEE ICNP) will be held in Tempe, Arizona on October 5–8, 2026. It will bring together leading researchers and practitioners from academia and industry to present and discuss the latest advances in network protocols. Network protocols are fundamental to modern computing and communication systems. They enable reliable data exchange across diverse networks (e.g., Internet, cloud, and quantum) and directly affect the performance and resilience of critical infrastructure. ICNP 2026 will cover a wide range of crucial research topics, including applying AI/ML to improve networks, network protocols for improving AI/ML, wireless networks, including cellular, satellite, and sensor networks, quantum networking, and security, privacy, and applied cryptography for supporting network functions. As a premier venue in the field, ICNP provides a unique opportunity for graduate students to engage with cutting-edge research, interact with senior researchers, and become part of an active international research community. Participation in ICNP is a critical component of graduate training. It enables students to present their work, receive valuable feedback, and build professional networks. This project provides travel support for approximately 15 U.S.-based graduate students to attend ICNP 2026 and its associated workshops. Funded students will actively participate in the conference by attending technical sessions, keynotes, and workshops, as well as engaging in poster/demo sessions and networking events. Students will have the opportunity to showcase their research, receive feedback from leading experts, and gain exposure to cutting-edge developments in network protocols. These activities will not only enhance students’ technical knowledge and professional skills but also accelerate the translation of research into real-world networking systems. By supporting student participation, this project helps cultivate a skilled workforce capable of advancing networking technologies that underpin critical societal services, such as healthcare, energy systems, transportation, emergency response, education, and national security. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2026 · 2026-04

High-latitude and polar regions present a unique set of challenges for continuous observations because of their remoteness and extreme and harsh environments. This project seeks to develop the next generation of small, low-power, autonomous, multi-instrument adaptive, ground-based geospace observation arrays, named AUtonomous Remote Geospace Observation and Research Array (AURORA). It is designed to fill large gaps in the currently existing ground-based instrument arrays in the high-latitude and polar regions. With advanced technologies in solar panels, batteries, bidirectional satellite communication, low-power sensors (fluxgate and searchcoil magnetometers, radio receivers, etc.), and high-performance single-board computers, AURORA will enable year-round observations with cost-effective, multiple instruments in these remote logistically challenging locations. This will significantly improve the ability to study (1) interhemispheric asymmetries from the viewpoint of geomagnetic and ionospheric variability, (2) the mesoscale of solar-wind - magnetosphere - ionosphere coupling in high-latitude and polar regions, as well as other space weather phenomena. Deep-field autonomous observatories have the potential to co-locate instruments across disciplines in polar science and facilitate international collaborations. This project will also contribute to training the future workforce. It will support two early-career researchers including a female early-career scientist. Graduate and undergraduate students will participate in the research, assisting with instrumentation design and testing. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2026 · 2026-03

This project will contribute to the national need for well-educated scientists, mathematicians, engineers, and technicians by supporting the retention and graduation of high-achieving, low-income students with demonstrated financial need at the University of Texas at Arlington. A total of 40 scholars pursuing undergraduate degrees in the mathematical sciences will receive scholarships averaging $10,021 for up to five years. Scholars will receive faculty and peer mentoring, and the project will build strong scholar cohorts through biweekly meetings, participation in mathematics contests, community service, and the department’s large and active student chapter of the Mathematical Association of America. Additional activities for scholars include career mentoring from working professionals, targeted support in key courses, and specific training to build key job skills. The overall goal of this Track 2 Scholarships in STEM project is to increase STEM degree completion of academically talented, low-income undergraduates with demonstrated financial need. There is a significant national need to grow the STEM workforce and nurture key talent that will ensure economic competitiveness and provide domestic leadership across critical sectors. This project directly speaks to this need by supporting STEM student success, which will strengthen the workforce in mathematics, statistics, and other key areas of need. The project will be assessed by an experienced evaluator and the data generated will contribute to the knowledge base regarding effective strategies to support talented, low-income students in STEM. This project is funded by NSF’s Scholarships in Science, Technology, Engineering, and Mathematics program, which seeks to increase the number of academically talented, low-income students with demonstrated financial need who earn degrees in STEM fields. It also aims to improve the education of future STEM workers, and to generate knowledge about academic success, retention, transfer, graduation, and academic/career pathways of low-income students. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2025 · 2025-10

This project aims to serve the national interest by developing personalized and immersive learning solutions for teaching Federal Aviation Administration (FAA) regulations (part 107) for operating Unmanned Aircraft Systems (UAS) in combination with basic UAS operation to support current and next-generation STEM students. The goal of this IUSE:EDU Level 1 Engaged Student Learning project includes developing an advanced Extended Reality (XR) training system that combines FAA Part 107 regulations and UAS operation, powered by Generative AI (GenAI) assistance. The system aims to improve regulatory compliance, safety, and operational proficiency in drone operations by leveraging GenAI's capabilities to generate multimodal content and real-time guidance. The project plans to integrate FAA Part 107 regulations into a GenAI-assisted XR-based UAS training system to enhance the realism and effectiveness of pilot education. The overarching goal of the project is to enhance interactivity, personalization, and support for trainees, creating a dynamic learning environment that continuously adapts to user needs. This project seeks to develop SKYGEN-XR (Smart Knowledge to Fly with GenAI and XR), an advanced training system which will integrate XR and GenAI to enhance drone operation education in the Architecture, Engineering, and Construction (AEC) industry. As unmanned aircraft systems (UAS) become vital tools in AEC, their use in complex environments brings safety risks and requires compliance with FAA Part 107 regulations. Traditional training methods often fall short of preparing operators for these challenges; SKYGEN-XR plans to address this gap by introducing an immersive, AI-assisted platform that combines regulatory instruction with realistic, scenario-based simulations. The project aims to improve safety, regulatory compliance, and operational proficiency by providing innovative interactive and personalized learning experiences. The project’s goal is to contribute to research in AI-driven compliance training, XR learning, and educational technology design. The project plans to explore new methodologies for regulation-focused instruction and promote active learning to increase student comprehension and retention. Once developed, the SKYGEN-XR will be implemented at the University of Texas at Arlington and K-12 partner institutions. The NSF IUSE: EDU Program supports research and development projects to improve the effectiveness of STEM education for all students. Through the Engaged Student Learning track, the program supports the creation, exploration, and implementation of promising practices and tools. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2025 · 2025-10

This project aims to serve the national interest by improving curricula in undergraduate computing education. The project will implement an innovative, interactive education tool, Model-By-Numbers, that will introduce and develop in students the skills needed to model their software for verification. Software models can be used to guarantee that software systems are correct by defining expected behavior purely in mathematical logic terms and testing against that. However, due to the rigorous abstraction involved there is a steep learning curve for software modeling languages, and as a result, these languages are typically not included in the undergraduate computer science curriculum. This Level 1 Engaged Student Learning project will help cultivate a strong software modeling foundation in undergraduate students, enabling them to go on to learn advanced modeling languages more easily and enter the workforce with the knowledge needed to develop higher quality software. Model-By-Numbers will introduce students to the Alloy modeling language, providing three different types of exercises designed to gradually get students comfortable expressing system properties as abstract Alloy formulas. Model-By-Numbers will leverage the structure of the Alloy language itself to generate high quality contextual feedback in real time and automatically generate practice exercises at three levels of difficulty. The project will study the effectiveness of Model-By-Numbers on skill development and the impact Model-By-Numbers has on the perception of learning modeling languages. The NSF IUSE: EDU Program supports research and development projects that improve the effectiveness of STEM education for all students. Through the Engaged Student Learning track, the program supports the creation, exploration, and implementation of promising practices and tools. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2025 · 2025-10

Multimodal sensing refers to the integration of multiple sensor types to collect various environmental data. By leveraging the strengths of different modalities, it enables more accurate and comprehensive understanding of complex scenarios. As a result, multimodal sensing is integral to mission-critical Internet-of-Things (IoT) systems such as smart cities, intelligent transportation, and defense applications. Robust cybersecurity is essential to their reliable operation, ensuring core objectives like encryption, authentication, and access control. However, while IoT systems increasingly rely on multimodal sensing, cybersecurity approaches that exploit this capability remain largely unexplored. This project addresses this gap by developing multimodal sensing-based security mechanisms to enhance the protection of mission-critical IoT systems. It also lays the foundation for broader applications of multimodal sensing in security. In parallel, the research advances scientific knowledge at the intersection of wireless networking, mobile sensing, and AI. Educational materials will be made publicly available, and the research will be integrated into curriculum development, undergraduate research, and increased participation in computing. This project pursues a challenging research agenda, termed MSS, focused on developing, prototyping, and evaluating innovative Multimodal Sensing-Based Security (MSS) mechanisms for mission-critical IoT systems with inherently heterogeneous sensing capabilities. The research is organized into three integrated thrusts. Thrust 1 develops MSS-Key, a novel framework for establishing ad hoc secure keys between IoT devices by leveraging indirectly correlated multimodal sensing data shaped by unforgeable physical-domain randomness. Thrust 2 focuses on MSS-Val, a framework designed to protect multimodal sensing-native IoT systems against manipulated sensor inputs. Thrust 3 introduces MSS-Aug, a framework that streamlines and automates the integration of new sensing modalities into existing MSS systems via autonomous labeling and cross-domain generative learning. To support and evaluate the proposed techniques, the project will also develop a dedicated MSS testbed equipped with a wide range of sensor modalities. This testbed not only enables rigorous experimental validation but also serves as a hands-on educational platform, engaging students and promoting broader public awareness of cybersecurity challenges in IoT systems. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2025 · 2025-10

This project will serve the national interest by strengthening undergraduate STEM education through a game theory (GT)-based learning experience that fosters critical thinking, interdisciplinary reasoning, and multi-criteria decision-making about water resources management (MCDM-WRM), an issue of growing national and global importance. This IUSE Engaged Student Learning Level 1 project will introduce an evidence-based and student-centered approach to help undergraduate students analyze stakeholder competition and cooperation in water resources management. This project seeks to understand how GT–based learning experiences can enhance students' abilities to make informed decisions about stakeholder dynamics in water-resource management. GT offers a structured way to explore how individual decisions influence collective outcomes, helping students evaluate trade-offs, anticipate stakeholder actions, and cultivate actionable consensus. Expected outcomes include enhanced student decision-making, engaging learning opportunities for all students, and improved water literacy. The goals of the project are to: 1) design and implement GT-based learning resources to support undergraduate students' understanding of the strategic interactions between stakeholders and, thereby, enhance their MCDM-WRM, and 2) conduct discipline-based education research (DBER) to evaluate factors that influence undergraduate students' understanding of the strategic interactions between stakeholders and their MCDM-WRM within the context of GT-based learning. The project plans to include the design, implementation, and iterative refinement of the instructional module, embedded in a general education natural resources course. The research component of the project will use pre/post assessments, student artifacts, and interviews, and investigate: (1) how undergraduate students engage in MCDM-WRM involving multiple stakeholders; (2) which course- and student-level factors most influence this engagement; and (3) how instructional design can best support students in developing more effective MCDM-WRM skills. The NSF IUSE: EDU Program supports research and development projects to improve the effectiveness of STEM education for all students. Through the Engaged Student Learning track, the program supports the creation, exploration, and implementation of promising practices and tools. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2025 · 2025-10

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.

NSF Awards · FY 2025 · 2025-10

The aerospace industry is vital to the U.S. economy and national security, driving innovation, high-skilled employment, global competitiveness, and technological leadership. The Bureau of Labor Statistics expects the aerospace industry to have an annual employment growth of 6% from 2023 to 2033. However, the aerospace workforce faces challenges including: 1) an aging workforce and knowledge drain, 2) skill shortages in emerging technologies, 3) education and training gaps, 4) limited pathways for community college students, and 5) high turnover rate and workforce retention issues. Acknowledging these challenges, this ExLENT Beginning project aims to develop a Community-College-University-Industry workforce pipeline strategically located at the Dallas-Fort Worth (DFW) area, one of the nation’s hubs for the aerospace industry. This project aims to build and strengthen an aerospace workforce pipeline connecting Tarrant County College (TCC), the University of Texas at Arlington (UTA), the UTA Research Institute (UTARI), and RECARO Aircraft Seating. All the sectors are local to the DFW area. The project is expected to 1) effectively align educational outcomes with workforce needs, 2) expand career access, 3) strengthen industry-academic partnerships, and 4) enhance aerospace literacy and outreach. The developed workforce model can be scaled nationwide for the aerospace industry, ultimately advancing national prosperity and welfare, and securing national defense. This project seeks to develop the aerospace workforce pipeline through a biannual 10-day cohort-based training program and a 2.5-month summer internship program. The trainees include community college students, university undergraduate students, and veteran students. All the trainees will be advised by academic and industry mentors from UTA, UTARI, TCC, RECARO, Lockheed Martin, and ANSYS. The training and internship programs will leverage 1) AR/VR modules, 2) hands-on training, 3) project-based collaborative learning, and 4) real-world industry-driven immersive learning to equip the trainees with critical industry-tailored skills in aerospace composite manufacturing, non-destructive evaluation, testing, digital twinning, and AI-enabled aerospace technologies. Furthermore, this project will host open-house days to boost the public's aerospace literacy and STEM interest. This ExLENT project will foster cohorts of students, veterans, researchers, and industry engineers to develop the aerospace engineering community collectively, reinforcing the U.S.’s leading position in aerospace and defense technologies. The ExLENT Program, supported by the NSF TIP and EDU Directorates, seeks to support experiential learning opportunities for individuals to increase their interest in and access to career pathways in emerging technology fields. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2025 · 2025-10

This NSF CAREER project aims to enhance power system stability and safety in the presence of large-scale inverter-based resources (IBRs) by leveraging their emerging grid-forming control (GFM) mode. The project will bring transformative change to power system stability assessment and control by analyzing grid stability boundaries under current and prospective operating conditions to provide emergency control protocols under disturbances. This will be achieved by developing a set-theoretic analysis framework with sparsity formulations to increase scalability and sample guidance to reduce conservatism. The intellectual merits of the project include building a reduced-order model of IBRs in GFM mode that retains engineering insights, a unified framework for analyzing stability and safety, a methodology that can integrate samples into analytical formulations, and a sparsity formulation and associated distributed computing and control paradigm for large-scale applications. The broader impacts of the project include enhancing modeling and analytical tools for the grid, advancing nonlinear system stability analysis using scalable theory-driven computing paradigm, strengthening the integration between power systems and power electronics, and broadening the participation of students in power systems. Grid sustainability will rely on GFM IBRs as a type of critical asset to resolve control challenges like weak grids and low inertia. The direct methods can provide benefits in monitoring and control to enhance transient and frequency stability of IBR-rich systems. However, due to the inherent limitation in scalability, most of the works are IBR-centric, where interactions between IBRs and grid dynamics remain uncaptured. The proposal will unify the stability certificate (for rotor angle response) and safety certificate (for frequency response) into a consistent backward reachability computation problem. Specifically, the project will (1) exploit sparsity patterns in system states and problem formulations, leading to distributed optimization and parallel computing; (2) rigorously integrate samples into the analytical framework through a constraint generation strategy to reduce conservatism; (3) propose a distributed and scalable control protocol enhanced by the phase angle control capability of GFM-IBRs for large-scale power grids. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2025 · 2025-10

With support from the Improving Undergraduate STEM Education: Hispanic-Serving Institutions (HSI Program), this Level 1 implementation and evaluation project aims to test two ways of helping first-generation undergraduates at a Hispanic-Serving Institution build creative problem-solving skills and a stronger sense of belonging in science, technology, engineering, and mathematics. Many first-generation students reach college with little prior exposure to hands-on innovation or role models in STEM careers, so they can struggle to picture themselves as scientists or engineers and to persist when coursework becomes difficult. The project will compare a semester-long faculty-mentoring pathway with a creativity-skills curriculum that teaches proven techniques for generating original solutions to real STEM challenges. By learning which pathway best boosts students’ confidence, perseverance, and inventive thinking, the work will guide colleges in designing programs that expand the nation’s STEM talent pool and open rewarding careers to students who are the first in their families to attend college. The research team will recruit sixty undergraduate participants from an existing campus support program and randomly assign approximately equal numbers of first-generation and continuing- generation students to one of three groups: a social mentoring condition, a cognitive creativity- training condition, or a control group. Each intervention will span fifteen weeks, with matched seminar time, structured homework, and modest stipends. Outcomes—including self-efficacy, outcome expectations, creative performance on a judged research-proposal video, and pre-career behaviors such as STEM interests, goals, and actions—will be measured before and after the intervention with validated surveys, rubric-based ratings, and academic records. Guided by social cognitive career theory, the study will test hypotheses about how mentoring and creativity instruction influence motivational mechanisms and will examine whether effects differ by student first-generation status. Findings will be disseminated through peer-reviewed publications, conference presentations, and free online access to all training materials and evaluation tools, enabling other campuses to replicate successful practices. This project is funded by the HSI Program, which aims to enhance undergraduate STEM education and increase capacity to engage in the development and implementation of innovations to improve STEM teaching and learning at HSIs. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2025 · 2025-10

The 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 · 2025-10

Machine learning (ML) is increasingly used to combat cyberthreats. ML enables tools known as security classifiers to identify potential cyberthreats, e.g., to detect malicious software ("malware") or a network intrusion. Such classifiers are typically developed by collecting data on threats (e.g., malware samples) and benign entities (e.g., legitimate software), then building an ML model that learns patterns in the gathered training data that suggest the presence of threats. The model is then used in real systems to help identify new undetected threats. However, for many security problems, good training data is hard to find. Threats may be relatively rare, or not shared by people and companies that experience them. This leads to unbalanced datasets that contain mostly benign cases, which ML models often struggle with. Threats also change over time, as malicious software is constantly evolving, and models may quickly go out of date. This project will develop ways to address these data challenges by developing methods for Generative Artificial Intelligence (GenAI) tools to create synthetic but useful data for network and application security tasks. Through this, the project will advance knowledge of both GenAI systems and more practical, effective defenses against cyberthreats. The project team will also create novel educational resources on AI and security topics and provide educational opportunities for pre-college teachers and students and research opportunities for undergraduate students. The project's goal is to boost and maintain the performance of a security task by addressing training data challenges. The work is structured around three research thrusts. The first thrust focuses on conducting an in-depth study to evaluate the effectiveness of existing GenAI schemes in addressing data challenges in ML-based network and application security tasks, highlighting cases where they fall short and where there are opportunities for improvement. The second thrust is to develop a novel GenAI framework called Aura, which will be purpose-built for the security domain to generate high-quality synthetic data, even when training data are limited, biased, or have noisy labels. The third thrust will extend Aura to support security operations after deployment by designing novel techniques to mitigate concept drift and by enabling continual learning against evolving security threats. Aura will also provide novel model interpretation schemes to attribute predictions to synthetic data in the training set. Beyond the contributions to the specific problem of generating useful synthetic data, the project will also provide a case study of the larger goal of leveraging AI-based techniques to support security and privacy, an area of high interest to the research community. 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

While generative artificial intelligence (AI) offers tremendous benefits in reshaping software engineering, they raise pressing ethical and legal concerns, particularly around the widespread use of large-scale training datasets, often assembled through web scraping, that may contain copyrighted software code, or proprietary content without proper consent. The opaque data usage challenges existing intellectual property laws and complicates questions of ownership, attribution, and accountability. As generative AI becomes increasingly integrated into software development practices, this accountability gap undermines legal compliance, erodes trust in AI-driven tools, and hampers broader adoption of responsible AI. This project is motivated by the need to bridge this gap by developing new tools, legal insights, and accountability models to ensure that generative AI can advance responsibly while respecting copyright, licensing norms, and user rights. This project will contribute a comprehensive framework for responsible generative AI development. The proposed research focuses on (1) analyzing licensing inconsistencies and defining AI-relevant copyright interpretations, (2) uncovering memorized copyrighted code using novel prompt engineering techniques, (3) designing watermarking-based tools for identifying and mitigating unauthorized AI-generated code, and (4) developing a practical measure of accountability for generative AI in software development. In addition, the project will propose institutional frameworks including licensing, consent, and revenue-sharing strategies. Together, these efforts will guide legal, institutional, and technological interventions that promote ethical AI practices while supporting innovation. The outcomes will benefit policymakers, developers, and creators alike, ensuring that generative AI evolves in a way that respects legal boundaries and societal values. 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

As artificial intelligence (AI) becomes increasingly embedded in the technologies used by both students and teachers, it is essential for them to understand how to be safe while using AI. Furthermore, AI and cybersecurity technology together are better at detecting malicious activities than conventional security systems. The need to blend the two disciplines into a single, integrated curriculum for K-8 education is highlighted by the interconnectedness of AI and cybersecurity as complementary systems. This project will "plant the seeds" of these literacies by spiraling content on topics from computer programming, internet fundamentals, and introduction to data and AI along with cybersecurity topics in small doses throughout students' K-8 education. This project will lay the foundation for the students to eventually develop a comprehensive understanding of how different technologies work and interact. This project aims to investigate how teachers from rural schools approach the integration of computer science (CS), and in particular, AI and cybersecurity components, into their existing curricula. This project will directly impact at least 82 rural teachers from South Carolina through multi-year participation in professional development and coaching and approximately 8,100 students indirectly in their classrooms. Resources, including lesson plans, developed through the project will be made available as open access on the project website, through partnerships, and via conference presentations and publications. The overarching goal of this project is to improve STEM teaching and learning by examining successful teacher professional development (PD) models in response to a need for preparing students with both STEM literacy skills and other technical competencies needed to be informed citizens and contributors to society. The project will blend AI and cybersecurity into a unified curricula, supported by the following objectives: (1) analyze how K-8 educators incorporate AI and cybersecurity topics into disciplinary teaching components; (2) explore the learning supports that K-8 educators need to effectively incorporate AI and cybersecurity topics into current curricula to assist students in meeting content-focused learning objectives; and (3) compare the integration opportunities available to K-8 educators in rural areas of South Carolina. In the end, hypothesizing that this research can form the basis for the creation of more scalable PD programs for K-8 teachers as well as continuous assistance with CS integration. The research will follow a mixed-methods design, combining survey data, classroom observations, and interviews to examine how educators make sense of and apply these concepts over time. Statistical analyses will identify significant patterns across teacher implementation contexts. Qualitative data--drawn from semi-structured interviews, teaching artifacts, video documentation, and field notes--will be coded to surface pedagogical strategies, challenges, and evolving teacher understandings. In addition, an app will be created to allow teachers to document real-time classroom integration of AI and cybersecurity, providing a robust, longitudinal dataset. The findings will inform scalable, context-sensitive professional development models and contribute to a broader understanding of how to support interdisciplinary CS learning in K-8 settings. All data collection will be designed to ensure methodological rigor and trustworthiness through triangulation and ongoing member checks with participating educators. This project is co-funded by NSF's DRK-12 and ITEST programs. The Discovery Research preK-12 program (DRK-12) seeks to significantly enhance the learning and teaching of science, technology, engineering, and mathematics (STEM) by preK-12 students and teachers, through research and development of innovative resources, models, and tools. Projects in the DRK-12 program build on fundamental research in STEM education and prior research and development efforts that provide theoretical and empirical justification for proposed projects. The Innovative Technology Experiences for Students and Teachers (ITEST) program 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 · 2025-09

The Texas Gulf Coast continues to face severe flooding, habitat loss, and rising emissions, posing increasing risks to human safety, economic stability, and environmental health. This project brings a novel, nature-inspired solution – the Living Engineered Reef System, designed to simultaneously protect shorelines, store carbon dioxide, and restore marine habitats vital for local economies and biodiversity. To overcome existing technological barriers, the project adopts an interdisciplinary, application-ready approach that integrates earth sciences, materials engineering, biological sciences, and data-driven tools such as artificial intelligence and remote sensing. By co-developing this solution with a broad network of community members and collaboratively assessing implementation challenges and economic feasibility, the project lays the groundwork for its immediate large-scale adoption. As the project advances, it will bring new income-generating opportunities, specialized training in science and technology, and above all greater technological competence against coastal resilience challenges. This R2I2 project – the Living Engineered Reef System, offering a unique integration of three-fold benefits, namely, reduced coastal flood risks, oyster habitat (re)growth, and carbon storage, which the traditional reef restoration efforts are currently failing to deliver. Using an interdisciplinary approach grounded in coastal hydrology, materials engineering, and biological sciences, and harnessing advanced technologies such as additive manufacturing, artificial intelligence, and remote sensing, the project aims to evaluate the scalability and cost-effectiveness of this living reef system through a focused pilot study and stakeholder engagement. Specifically, in phase I, the project will identify practical implementation barriers, perform detailed techno-economic assessments, expand stakeholder engagement, and establish a robust co-design framework through community-driven workshops. The anticipated outcomes include technical guidelines for optimized design pathways and a comprehensive understanding of the reef system’s efficacy, setting the stage for immediate and sustainable large-scale implementation in phase II. These objectives directly support NSF's mission to advance national health, prosperity, and welfare and secure national defense through enhanced coastal resilience. 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 eyewall in mature hurricanes contains small vortices, which have been seen in satellite and weather radar data. While the presence of these vortices is well-known, their impact on the hurricane structure and intensity is still uncertain. In this award, the research team will use a recently derived mathematical framework to analyze hurricane vortices with a goal of better explaining how the small-scale vortices relate to the large-scale hurricane structure. The primary societal benefit of this project would be through the potential for improved forecasting of hurricanes. A graduate student and postdoctoral researcher would be involved in the project, allowing for the training of the next generation of scientists. Liutex is a recent methodological advancement in the field of mathematics and fluid dynamics that intends to better represent vortices in fluids, like the atmosphere. Liutex is a vector whose direction is aligned with the rotational axis and whose magnitude is twice the angular speed. It separates the pure rigid rotation from shear. Small-scale vortices within hurricanes provide a strong test case for Liutex. The research team plans to apply Liutex to observations and numerical modeling of three-dimensional winds in hurricanes to quantify the relationship between vortex structure and storm intensity and to identify mechanisms of vortex formation and mergers. The overarching goal of the project is to gain a deeper understanding both theoretically and numerically of how the eye-eyewall meso- and miso- vortices form and provide a sound method to identify them and quantify their structures. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2025 · 2025-09

Recent severe hurricanes have brought attention to the need for home mitigation approaches to reduce loss of life and property. Consideration of the impact on homes from simultaneous wind, wave, and surge hazards during hurricanes is critical for designing appropriate home structural retrofit strategies. This project will support research that looks to advance the understanding of the effectiveness of structural mitigation approaches at reducing damage to coastal households from wind-wave-surge hazards. This project will examine homeowners’ preferences and biases in the selection of retrofit strategies based on their risk perceptions and tolerance, leading to more precise targeting of education and funding aimed at increasing mitigation adoption. This is critically important due to the nation’s aging home infrastructure, increased hurricane intensity and frequency, and widespread hurricane damage in recent years with extensive economic and societal impacts. A user-friendly web application will be developed based on the research findings to allow users to compare retrofit options and make informed decisions. This award contributes to the National Science Foundation (NSF) role in the National Windstorm Impact Reduction Program. Data generated from this project will be archived and made publicly available in the NSF-supported Natural Hazards Engineering Research Infrastructure (NHERI) DesignSafe Date Depot (https://www.DesignSafe-ci.org). This project is a collaborative effort involving researchers at the University of Texas at Arlington and Chapman University. It seeks to develop and evaluate a multi-level interdisciplinary mitigation behavior model that will simulate household-level home retrofit decisions in response to hurricane hazard events. The model will be based on an occupant’s hurricane wind-wave-surge exposure and vulnerability indicators and will simulate how household and community-level vulnerability changes over time due to adoption of (or failure to adopt) structural mitigation. The benefit/cost of mitigation options and reductions in building fragility will also be explored. The approach will be applied to the Houston-Galveston and Coastal Bend regions of Texas, which have experienced severe wind and surge damage during recent hurricane events. The web application seeks to allow convenient selection of logical mitigation packages, based on the findings. An extensive dissemination, training, and outreach campaign will be conducted in coastal communities through workshops and the dedicated web site, with the goal of increasing awareness of home mitigation options among hurricane-affected populations. 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

Advanced materials such as composites, metamaterials, soft materials, and architected materials are inherently heterogeneous and multiscale in nature. Currently, multiscale modeling serves as the most effective approach for analyzing and designing these materials. However, the growing complexity of microstructural features and macroscopic structural configurations presents significant challenges to achieving both computational efficiency and predictive accuracy. While emerging machine learning (ML) models offer a cost-effective alternative, their effectiveness is often limited by the lack of high-quality training data in many real-world engineering applications. Moreover, advanced ML techniques are still not routinely incorporated into traditional mechanics or materials engineering curricula. To address these challenges and support both research and education in multiscale material and structural modeling, this project supports research that develops a cloud-based cyberinfrastructure that integrates new multiscale modeling theory with multi-fidelity ML models. This platform seeks to provide open-access tools, curated datasets, and comprehensive training resources to advance materials science, enable efficient structural analysis and design, and support workforce development in ML-assisted material and structural modeling. The goal of this project is to develop a cloud-based, open-source multiscale modeling software called OpenMSG, providing an ultra-efficient prediction toolkit for mechanical and multiphysics behaviors of highly heterogeneous materials and structures. To achieve the goal, this project first develops new multiscale models based on mechanics of structure genome (MSG), which can discretize analysis domains using efficient beam and shell elements while still considering strong material heterogeneity and anisotropy. The new models will provide an unprecedented combination of computational efficiency and accuracy and generate highly correlated multi-fidelity data. Building on these multiscale models, the project then develops a framework using multi-fidelity neural networks (NNs) for ML-assisted multiscale modeling. This hybrid approach further reduces the computational burden while preserving model accuracy across design spaces. The models and framework are demonstrated using additively manufactured functionally graded materials and composite blade designs, which also showcase OpenMSG’s capabilities in advancing the fundamental understanding of heterogeneous material behavior and facilitating the efficient design of complex engineering structures. OpenMSG will be developed, tested, and maintained on the widely recognized Composites Design and Manufacturing HUB (cdmHUB) with the support of CI experts from HUBzero. Leveraging established user bases and infrastructure on cdmHUB, this project delivers not only a cutting-edge multiscale modeling tool but also fosters a sustainable global user community dedicated to data-driven multiscale materials and structural modeling. This award by the Office of Advanced Cyberinfrastructure is jointly supported by the Division of Civil Mechanical and Manufacturing Innovation within the Directorate for Engineering. 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.