University of Texas at Arlington

NSF Awards · FY 2025 · 2025-07

As automobiles become more connected and intelligent, ensuring their cybersecurity is essential to maintaining safety and trust in future transportation systems. Self-driving cars are expected to play a significant role in future transportation. However, their advanced features—such as sensors, cameras, wireless networks, computer vision and machine learning—create new ways for attackers to interfere with vehicle operations. This project aims to protect autonomous vehicles by developing methods that secure both high-level software that makes driving decisions and low-level hardware that controls physical actions like steering and braking. The intellectual merit of the project lies in addressing critical gaps in understanding security vulnerabilities resulting from the complex interactions between self-driving software and vehicle control systems. The project will generate new insights into how information flows between these levels, particularly in adversarial environments, and investigate defense mechanisms against cyber-physical threats. The broader impacts of the project include improving the security of self-driving cars and, with them, the safety of shared civil transportation infrastructure. The outcomes will extend to other cyber-physical systems that integrate computer vision, automation, and real-time networks, such as drones, smart agriculture, and manufacturing systems. The project will also create educational programs to train future cybersecurity professionals at both undergraduate and graduate levels, building a skilled workforce to address cybersecurity challenges in intelligent systems. This project advances the understanding of security-relevant interactions between self-driving autonomy functions—such as perception systems and machine learning algorithms—and the vehicular control systems’ automaticity functions, including electronic control units and in-vehicle networks. These functions operate within sensor-actuator control loops, exchanging cyber-physical state information that introduces opportunities for emerging attacks with potentially catastrophic consequences. This research investigates the nuanced interactions across these interfaces, particularly in adversarial environments, and investigates hybrid attacks targeting sensor-to-controller and controller-to-actuator channels to compromise vehicle operation. The project explores real-time security mechanisms, graceful degradation of system performance under attack, intrusion tolerance, and safe recovery of compromised systems. By addressing these integration points, the project aims to enhance the understanding of cyber-physical threats in automotive systems and contribute to broader advancements in detecting, mitigating, and recovering from attacks. 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 support about 12 U.S.-based student researchers participating in the Doctoral Consortium to be held in conjunction with the 2025 International Conference on Pervasive Technologies Related to Assistive Environments (PETRA 2025). The PETRA mission is to promote interdisciplinary research on ways to use pervasive ambient intelligent environments to improve quality of life and enhance human performance with greater capabilities. The conference covers an array of topics in computational science, psychology, nursing, rehabilitation, artificial intelligence, computer vision, robotics, computer engineering, user interfaces, neuroscience, and many other areas. Through attending the doctoral consortium, promising young scholars will receive thoughtful mentoring from, and connect to, more senior members of the community, with benefits to both student attendees and the community as a whole. The Doctoral Consortium program will afford student authors of papers accepted for presentation at the conference a unique opportunity to gain additional exposure for their innovative ideas and connect to an academic network both among themselves and with senior colleagues. The program consists of special sessions at the beginning, during, and at the end of the conference that involve close interactions with mentors around developing and publishing their work, as well as a chance to present posters to the wider conference. Students will be selected based on financial need, quality and relevance of the students' work to PETRA research topics, and creating a diverse consortium in terms of personal, disciplinary, and institutional backgrounds. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2025 · 2025-05

Metamaterials are engineered materials with structures that can exhibit mechanical and other properties not found in natural materials. They are being increasingly considered for diverse industrial applications, such as in aerospace and healthcare. Achieving the desired properties of a metamaterial hinges largely on the precise fabrication of its complex geometry, and Additive manufacturing (AM) is well-suited for metamaterial fabrication. However, geometric imperfections introduced during the AM-fabrication of a metamaterial compromises its properties. This Faculty Early Career Development (CAREER) project supports research that aims to integrate advanced sensing and artificial intelligence (AI) for characterizing geometry-property relationship of metamaterials in the presence of certain geometric imperfections. This project seeks to enable high-quality and scalable fabrication of metamaterials, potentially reducing manufacturing costs and enhancing product performance, which in turn will broaden applications of metamaterials and strengthen the competitiveness of the US manufacturing sector. This project will also integrate research outcomes into educational and outreach activities, preparing the next generation of AI-savvy manufacturing professionals and engaging small and medium-sized manufacturers to drive lasting societal and economic impact. The goal of this research is to establish a computational framework for property-centric monitoring and optimization of AM to achieve property-as-desired metamaterials. This framework will be built by exploring crucial interdependencies among AM process parameters, as-built metamaterial geometries, and the resulting metamaterial properties. To achieve this, the research is structured around four key objectives: The first, in-process characterization of geometric imperfections aims to efficiently represent high-resolution point cloud data, collected layer by layer, as a concise yet comprehensive profile that captures as-built geometry with imperfections. Second, linking as-built geometry to properties based on the obtained profile, aims to extract a geometric imperfection-pertinent probability distribution and integrate it into predictive models linking between as-built geometries and properties of the metamaterial. Third, property-centric monitoring and control that leverages the geometry-property relationship to develop a property-centric monitoring scheme for identifying deviations that substantially impact the desired properties. These insights will be fed back to optimize and control the AM process. Fourth, cross-geometry generalization aims to enable researched models to generalize across metamaterial geometries with minimal re-training, ensuring broad applicability in diverse AM-produced metamaterials. Findings from this research seek to enhance manufacturing decision-making across broader advanced manufacturing domains, particularly in addressing the impact of process uncertainties on product properties. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2025 · 2025-05

This CAREER project addresses a major challenge in understanding the near-Earth space environment. Studies of the region of space surrounding Earth often focus on phenomena that are either very large (several times the Earth's diameter or larger) or very small (down to kilometers or meters) phenomena. However, what is still unknown is how the small phenomena affect things on a larger scale. This study addresses this challenge by studying small (meters in size) structures known as "Time Domain Structures" (TDS), which are thought to be associated with plasma waves and strong electric currents in space. The main objective is to map out where TDS is likely to occur in near-Earth space and under what conditions, allowing us to understand their relation to larger phenomena in space. This work gives us a better understanding of space weather, which can lead to disruption of radio and cellular communications, GPS navigation, satellites, and the electric power grid. The PI will develop a cross-disciplinary, research-focused intermediate-level undergraduate course on the space environment. A public outreach project will convert the solar and space data into sounds to be shown in a local planetarium, an opportunity to attract more participation in space physics. The project aims to determine the distribution of time domain structures (TDS) in the magnetosphere as a function of solar wind and geomagnetic conditions, magnetospheric current systems and boundaries, as well as their characteristic changes associated with local mesoscale conditions. The three scientific objectives are: 1) Determine how the distribution of time domain structures in the magnetosphere changes with changing solar wind and geomagnetic conditions. 2) Determine how the distribution of time domain structures compares with magnetospheric current systems and boundaries. 3) Determine how the characteristics of time domain structures change by region and how the local mesoscale conditions change the behavior. The team will address these by generating statistical maps of SWD measurements and comparing them with maps of other plasma parameters. These maps will be binned by solar wind conditions and geophysical activity indices. Additionally, detailed studies of events observed in different regions of the Earth's magnetosphere will be performed to determine the mesoscale context for the SWD observations. 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 RAPID project focuses on water quality dynamics after the 2025 wildfires in Los Angeles (LA). Large fires often trigger landslides and debris flows. Compounding this issue is LA’s history of intense winter rainfalls called atmospheric rivers. Together they present a significant risk of fire-flood-landslides “triple-disaster”. The project will implement a new turbidity monitoring network to record post-fire turbidity levels in surface streams to identify the direct impact of fire on water quality. The team will also estimate char cover and soil burn severity from real-time satellite images obtained during the fire events. The data will be used in AI-driven models to establish the first baseline for future post-fire risk assessments and mitigation strategies. The methodology and scientific evidence resulting from this project can be applied broadly to mitigate downstream impacts of wildfires in other fire-affected regions. It advances NSF’s mission to safeguard the nation’s water through innovative data and AI solutions. This RAPID funding will support a research team to collect critical data to understand the wildfire-induced water quality dynamics in Los Angeles. The research will fulfill three objectives: (1) conduct a 3-month turbidity data collection campaign in LA at strategically selected sites aligned with burn areas and high debris hazard probability to establish the initial post-fire baseline, followed by a 9-month data collection campaign to track turbidity under varying dry and wet conditions, (2) estimate perishable fire data, including char cover and soil burn severity, across the entire fire-affected region in LA, by analyzing high-resolution real-time remote sensing observations collected during and immediately after the fire events, and finally (3) quantify how streamflow and turbidity evolve during potential fire–flood sequences using an AI model and therefore identify drivers that may be controlling the magnitudes and recovery timescale of turbidity toward initial post-fire baseline. These detailed spatiotemporal analyses will not only secure short-lived signatures of post-fire water quality but also pave the way to the first integrated framework for mapping the triggers and timelines of cascading fire-water hazards, which is currently absent for the LA region. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2025 · 2025-03

This Faculty Early Career Development (CAREER) project supports research on understanding how compound flood mechanisms influence decision making about shoreline adaptation in multi-jurisdictional governance systems and how feedbacks from those decisions modify spatiotemporal patterns of flooding risk. Compound flooding from coastal and terrestrial sources presents a serious threat to coastal communities and is predicted to worsen in the future. Coastal managers, elected officials, and policy makers at local and regional levels are grappling with a host of issues related to reducing the impacts of flooding on people and built environment. Their decisions about how to adapt have crucial implications, and, in some cases, may lead to new patterns of hazard, exposure, and vulnerability. Despite inherent connectivity between coastal-terrestrial processes and human decision making in defining flood hazards, the feedbacks between these processes and their influence on future risk in coastal communities are not well understood. This project develops an integrated modeling framework to examine the emergent behavior that arises due to the interactions among local and regional decision makers. It also investigates the role of regional actors in forming effective flood adaptation approaches. Specific research activities include (i) a survey of stakeholders’ adaptation preferences given information about flood interdependencies, (ii) development of an integrated modeling framework that couples compound flood assessment with multi-agent simulation of decision making, and (iii) evaluation of the role of multi-level actors in regional adaptation planning. The project designs and implements new educational opportunities for undergraduate students to engage in interdisciplinary coursework and research experiences related to the modeling of coupled human and natural systems. Findings from the research will increase awareness of interdependencies between flood hazards and adaptive responses among coastal managers and inform planning efforts on reducing regional flood risk. 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

Proper coordination of gene regulation is essential to the viability of all organisms. RNA interference (RNAi) and chromatin modifying pathways are dynamic networks critical to regulating the genome and executing cellular programs in all metazoans, including humans. While we understand much about the basic molecular mechanisms of RNAi and chromatin modifying pathways, we know very little about how these interdependent pathways are controlled or interact temporally or spatially. This project’s overarching goal is to uncover mechanistic details of how the overlapping networks of RNAi and chromatin modifying pathways maintain appropriate gene regulation during stress. Successful completion of the proposed work will advance our understanding of principles of gene regulation and provide key insights into how perturbations to these networks during stress trigger the onset of diseases, such as cancer and infertility. This project will engage undergraduate and graduate researchers by providing paid early career research opportunities, interdisciplinary training, and establishing a regional scientific seminar series and conference that will provide networking and presentation opportunities for trainees while also exposing them to research outside of their institution. It is evident RNAi and chromatin modifying pathways collaborate. Prior work has identified chromatin modifying factors required for deposition of repressive chromatin marks at RNAi-targeted loci across eukaryotes. However, our understanding of the intricate mechanisms governing complex interactions between these two essential gene regulatory pathways remains rudimentary. Using the C. elegans germline as a model system and interdisciplinary approaches (genetics, physiological assays, multi-‘omics approaches, and bioinformatics), we will test the hypothesis that feedback motifs between RNAi and chromatin modifying pathways ensure balance between the pathways to maintain appropriate regulation of a distinct set of genes and protect germ cell identity during heat stress. This project will advance our understanding of (1) the epigenetic and transcriptional landscape of C. elegans germ cells during normal conditions; (2) how RNAi and chromatin modifying pathways collaborate to maintain this landscape during environmental stress; (3) how genetic and environmental perturbations affect gene regulatory pathways; and (4) the molecular mechanisms that maintain balance between these pathways to ensure homeostatic gene regulation. Moreover, the development of a tool to computationally identify putative feedback motifs using multi-‘omics datasets in any species for any pathway will systematically improve our ability to identify and examine higher-order regulatory motifs that maintain transcriptome homeostasis. 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

Although the adoption of the American with Disabilities Act (ADA) shifted the paradigm for physical accessibility, many aspects of the built environment are still not easy or satisfying for people who use wheelchairs. This is mainly due to the practice of the building industry, i.e., the compliance of those guidelines mainly focuses on design and construction requirements whereas the actual experience of wheelchair users is not fully investigated after the construction phase. One viable solution is to acquire and utilize the data from the community with disabilities on how they use wheelchairs to navigate and experience buildings. In this project, three main activities will be conducted. First, a community meeting will be prepared and executed to understand the environmental experience and stressors that wheelchair users experience in their daily environment. Based on the foundational knowledge of their environmental stressors, the research team will further develop WheelComV2 to acquire thermal stress of wheelchair users via non-intrusive sensing of chair monitoring. Finally, we will pilot our technological approach together with the community of local citizens who use wheelchairs. Ultimately, the proposed approach will enable them to be citizen scientists, leading to the disability-centric evaluation process to improve the physical accessibility of the built environment. The proposed research will establish the new field of disability-centric building accessibility evaluation, which aims at discovering the fundamental knowledge of human (with disability) and building interaction. In the field of human-building interaction, building scientists have used the concept of indoor environmental quality (IEQ) to quantify occupants’ environmental comfort (or more often stress) in buildings. However, none of these building IEQ guidelines are tailored for occupants with disabilities (e.g., people who use wheelchairs). Our disability-led research activities will define the environmental stress of wheelchair users, which we often naively assume from ambulatory occupants. Furthermore, our development of WheelComV2 will test the research hypothesis, i.e., the detection of irregular patterns in temporal data stream of wheelchair movement monitoring is indicative of wheelchair user’s thermal discomfort. The newly gained insight on our experiment will provide a foundational technological basis to develop non-intrusive sensing approach to better understand the experience of people who use wheelchairs in buildings. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2024 · 2024-11

A vortex is a rotational or swirling motion in fluids. Vortices are ubiquitous in nature, and they are viewed as key components in fluid dynamics, especially turbulent flow. However, there is no precise mathematical definition of a vortex, which complicates research on vortical flows and turbulence. This award will support a conference to be held December 11 – 13, 2025 at the University of Texas – Arlington. The conference will promote exchanges among international scientists and engineers directed toward reaching a consensus on vortex definition and prediction. Vexing problems in a variety of fields such as oceanography, meteorology, astronomy and physiological fluid dynamics make achieving this goal especially urgent. New ideas, methods, and applications related to vortex will be presented and discussed at this forum, and the results will be made widely available through publication of the conference proceedings. The first definition of a vortex was given in 1858 by Helmholtz, who considered a vortex as a vorticity tube. Since then, a variety of vortex identification criteria have been used by fluid dynamics researchers. This conference builds on a series of workshops and short courses aimed at vortex identification in engineering applications. Topics addressed at the conference will include vortex definition and identification methods as well as applications in aerodynamics, hydrodynamics, biological flows, space science, and other disciplines. The conference will be supplemented with a short course titled, “Liutex and Liutex-based subgrid models,” to be held immediately after the conference. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2024 · 2024-11

Pain affects over 15 million hospitalized babies annually. Early-life pain is associated with abnormal structural and functional brain development and results in adverse consequences, including cognitive impairments, altered emotional functioning, psychopathologies, and global pain sensitivity. Using facial expressions associated with brain-based evidence of pain, nurses only agree to the presence of babies’ pain 67-87% of the time. Thus, the inability to self-report pain makes babies vulnerable to under- and over-treatment of pain. The investigators created and pediatric nurses validated, a preliminary artificial intelligence (AI)-empowered pain classification model based on facial actions from a video dataset of newborn pain. This model provides 94% accuracy, 93% precision, and 95% recall in analyses of a small sample of babies. This model is not robust enough to be deployed for continuous pain assessment until it can be fully developed with a large sample of diverse babies. This project is being integrated into educational activities offered by the investigators, including the first massive open online course based on federated learning (FL) concepts and algorithms. The goal of this program of research is to advance the creation of an automated Pain Recognition AI-empowered Monitoring System (PRAMS) grounded by biological evidence of pain and supervised by nurses-in-the loop. A novel hybrid FL approach is being tested by using a diverse pain assessment dataset that is being created from time-series facial action video, physiological and clinical data of more than 200 babies before and after surgery in eight patient care units; thus, simulating inter-hospital distributed learning. Mathematical proof that this novel hybrid FL approach has advantageous convergence characteristics in convex learning problems is being provided to establish in the future similar convergence bounds for non-convex optimization. This project has great potential to advance the development of machine learning algorithms across heterogeneous datasets in a privacy-preserving FL approach that could leverage the statistical power of multi-site data to learn clinically meaningful features of even rare conditions. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2024 · 2024-10

This project aims to serve the national interest by promoting evidence-based science teaching practices in a K-6 bilingual teacher preparation program. The project has potential to yield evidence for ways to prepare future elementary teachers to engage in classroom instruction grounded in science practices that address inequities in science education for emerging bilingual students. Early learners from under-resourced communities with diverse cultural and linguistic backgrounds are underserved in elementary science education. This project addresses a significant need for certified teachers prepared to support STEM learning of elementary students by creating research-based resources that will help prepare undergraduate students to teach science in linguistically diverse K-6 classrooms by connecting their understanding of science content, pedagogy, and the linguistic abilities of bilingual students. Project goals and activities are in direct alignment with the IUSE Engaged Student Learning (ESL) track's focus on "the development, testing, and use of teaching practices and curricular innovations that will engage students and improve learning, persistence, and retention in STEM" that involves "creation, exploration, or implementation of tools, resources, and models" through a project that is both "evidence-based and knowledge-generating." Through a mixed-methods design, this project will investigate pre-service teachers' K-6 science content and pedagogical content knowledge while learning to leverage the linguistic abilities of emergent bilingual students for elementary science instruction. The goals of the project are to: (1) Better prepare bilingual teacher candidates to teach science content in bilingual K-6 classrooms through instructional design by improving science pedagogical content knowledge (PCK), cross-linguistic awareness of science-related concepts and topics, and improving pass rates on the science subtest of the Core Subjects and Spanish proficiency teaching certification exams; (2) Conduct education research to better understand bilingual teacher candidates' preparation to teach science content in bilingual K-6 classrooms through instructional design. The project focuses on a 1-year sequence within an undergraduate K-6 bilingual teacher preparation program. Year 1 of the project will focus on restructuring an existing bilingual science teaching methods course, developing online learning modules focused on Earth Science concepts aligned with K-6 science standards, and developing a summer bilingual science teaching internship program in partnership with community science education organizations. Years 2 and 3 of the project will focus on implementing this 1-year program and conducting iterative cycles of education research. The project includes efforts to disseminate the results of this work to the broader science and bilingual education community. The NSF IUSE Program in the Directorate of STEM Education, 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. Additionally, this project is funded in part by the HSI Program, which aims to enhance undergraduate STEM education, broaden participation in STEM, and build capacity 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 2024 · 2024-10

Ensuring the security and privacy of high-performance computing (HPC) infrastructures is of utmost importance due to their handling of sensitive data and critical scientific computations. HPC infrastructures commonly employ containers, which provide lightweight and isolated environments for running applications. Nevertheless, containers in HPC infrastructures encounter security challenges, including insecure container images and vulnerabilities related to isolation. Existing container image scanners face a major challenge of low coverage, while current container runtimes struggle to ensure both security and performance for HPC workloads simultaneously. This project addresses these challenges by developing secure containers specifically tailored for HPC infrastructures. The project introduces innovative solutions, including the development of an efficient image vulnerability scanner and a secure container runtime. These systems incorporate various customized optimizations for security and performance targeting HPC workloads. Additionally, educational efforts are made to integrate the research findings into graduate and undergraduate curriculum development. Outreach activities are conducted to encourage participation from underrepresented groups and promote cybersecurity awareness and HPC expertise in the states of Texas and Delaware. The project consists of two primary tasks. The first task focuses on designing an efficient image vulnerability scanner using innovative and feasible techniques. The research team designs a novel method for container image vulnerability detection based on cross-language code similarity detection. This approach combines graph neural networks with a language-agnostic code representation that leverages natural language processing techniques. Furthermore, it designs an efficient and scalable online search solution. The second task involves developing a secure and high-performance container runtime by utilizing a lightweight virtual machine hypervisor. Additionally, the runtime is optimized based on the characteristics of HPC workloads with the goal of improving both security and performance. 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

Real-world applications, such as software modeling, digital circuit design, manufacturing control, and status modeling of smart devices and smart systems, often require efficient techniques to model their behaviors and changes over time. Based on their specific requirements, different algorithms (including machine learning) are needed, such as reachability computation, pathfinding, and state prediction. For example, the graph neural network (GNN) algorithm can help to learn the compact vector representations of the states and transitions to capture the complex patterns and dependencies. However, existing computation architectures for such techniques are not very efficient for two major reasons: (i) the algorithms are not computationally efficient, and (ii) the data size is very large. This research pioneers the development of an accelerated computation architecture for system modeling techniques and applying them to critical smart environment applications. This project will address the growing national need for professionals in accelerated computation architecture, algorithms, and machine learning. The research will produce an accelerated computation architecture that serves as a foundational tool for fellow science and engineering practitioners in academia and industry. Educational initiatives integrate the research findings into graduate and undergraduate curriculum development. Additionally, outreach and educational activities are conducted to promote participation from K-12 and undergraduate students from populations underrepresented in computing. The overarching goal of this project is to design an accelerated computation architecture for state modeling techniques and to apply them to important smart environment applications. Towards that, this project includes three synergistic research thrusts. Specifically, Thrust 1 designs efficient computation techniques to accelerate the reachability computation in a state transition representation, which can be used to detect if any undesired (e.g., unsafe) state is reachable. Thrust 2 accelerates the computation of graph machine learning algorithms by adaptively reducing the overhead of instant updates and maintaining high-quality communities. Thrust 3 applies the techniques in Thrusts 1 and 2 to an important application domain of smart environments. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2024 · 2024-10

This project serves the national interest by providing undergraduate and graduate students in the mathematical sciences with professional development, mentoring, and deeper exposure to research opportunities. Specifically, this project will support the third offering of the Mathposium, a two-day student-organized conference at the University of Texas at Arlington, an Hispanic Serving Institution (HSI). Building from two prior, local offerings the 2024 conference will include the following activities for student participants: (1) student poster presentations; (2) lightning talks; (3) panels on careers and student research opportunities; and (4) vertical mentoring experiences and a networking lunch. Marketing and outreach activities will focus on institutions in Texas, Louisiana, and Oklahoma, although participants from any institution will be welcome to participate. A particular emphasis will be placed on encouraging participation of students from community colleges, HSIs, and HBCUs. This conference looks to provide mentoring and networking opportunities to promote access to opportunities for all students, including first-generation college students and members of populations that have traditionally been underrepresented in mathematics degree programs. The main goals of the conference are as follows: (1) expose undergraduates to mathematics research and current topics in the discipline; engage undergraduates and graduate students in activities designed to provide opportunities for mentoring; (3) create a friendly and inclusive environment around mathematics; and (4) allow student participants to share their research through posters and lightning talks. The conference will also include a faculty workshop on mentoring students for non-academic careers. This project is funded by the HSI Program, which aims to enhance undergraduate STEM education, broaden participation in STEM, and increase capacity to engage in the development and implementation of innovations to improve STEM 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 2024 · 2024-10

As social and political tensions become increasingly acerbic, the need for support-based interventions against interpersonal prejudice, stigmatization, and discrimination is becoming abundantly clear. Despite a relatively long history of research identifying strategies for reducing interpersonal negativity (e.g., “diversity training”), reports of continued negativity dominate the current headlines. This project will design and validate a theoretically-developed and empirically supported diversity intervention focused on training employees specific skills to be allies for one another and for marginalized others. Specifically, this project will extend current knowledge and implementation by (a) assessing the effectiveness of this type of training longitudinally to assess long-term impact and (b) developing an online version of the training for broad dissemination. This project will also include the development of a diversity research group, a summer diversity research institute for undergraduate scholars, and a workshop designed to educate organizational scientists about allyship and diversity research. The training content is informed by theories related to social identity, bystander intervention processes, social learning, and planned behavior. The training design is informed by theories of learning and contemporary best practices related to learning, retention, information processing, and knowledge transfer. Training modules include lecture-based presentations, video-based role modeling, perspective-taking empathy exercises, and interpersonal skills practice. The training will address knowledge and skill acquisition, deep levels of cognitive and emotional mental processing, and concrete skill-building to maximize transfer and implementation of the skills learned. This project can transform our understanding of diversity training initiatives by focusing on skill-building rather than on discrimination reduction and by employing systematic longitudinal and multilevel empirical validation. 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

Visual impairment (blind or low vision) affects over seven million people in the United States. Their daily activities are more challenging. They also have a harder time finding and keeping jobs. One major challenge for people who are blind or have low vision is getting around in unfamiliar places. Such places can change often and be unpredictable. Employers worry about hiring people with visual impairments because of potential legal issues. New developments in robotics could help people with visual impairments. This project will figure out if people with visual impairments can use robots at work. Telerobots have a human user who drives the robot, while the robot supplies sensor data. The research team will do surveys, interviews, and studies with individuals who have visual impairments. This will help to identify problems with training people with visual impairments to use telerobots. The goal is to help blind and low vision users to be independent and hold technology jobs. The project has several aims. The research team is partnering with Austin Lighthouse (ALH). ALH employs hundreds of legally blind and low vision warehouse workers. Surveys will clarify the challenges people with visual impairments have getting or keeping jobs. We will interview employees at ALH to understand their needs and if they want to use the technology. Another aim is to design and do pilot studies that simulate telerobotic tasks. This will help us learn how to train blind and low vision individuals to use telerobots. It will also help make the system easier and more intuitive to use. The team will test different user interfaces to develop a telerobotic training prototype. The team will confirm the survey and interview results through telerobot training sessions. These results could give insights into how blind and low vision people do physical tasks. 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 · 2024-09

Hazardous weather early warning systems disseminate timely and meaningful information about flash floods, tornadoes, and other weather hazards so that individuals, communities and organizations can prepare for and protect themselves against harm or loss. Early warning systems involve sensors, model forecasts, federal and local public safety organizations, private companies, and communications technologies that disseminate warnings to the public. Hazardous weather warning messages are general rather than tailored to the risks faced by the people who receive them. The same warning message goes out to everyone in an affected region, regardless of individual circumstances. Each person is responsible for ensuring they receive and understand the warning, figuring out if they or loved ones are at risk, and then deciding if they have the capability or interest in taking protective actions. While warning systems have been effective for segments of the population, there is great potential to improve individual-level decision-making and community/societal outcomes, especially in the face of rapidly intensifying weather events. This planning grant takes a human-centered approach to hazardous weather warning to: 1) develop a deeper understanding of how individuals assess their risk and take action as weather hazards evolve, and 2) apply this expanded knowledge to new ways of tailoring warnings to individual or group circumstances. In this planning grant, a multidisciplinary group of researchers and practitioners address how multiple factors – rain intensity, quality of the stormwater infrastructure, individual daily routines of travel, advanced preparation, risk perception, warnings, social and environmental cues, and socioeconomic vulnerability – interact to influence people’s perception and response to floods. The team establish a common knowledge base and language through sharing research, methods, and datasets. A focus group is held with residents of vulnerable communities in collaboration with a local nonprofit to investigate how different individuals process information from early warning systems. The planning project includes exploratory projects that contribute to an innovative plan for convergent human-centered research. This work identifies new relationships among risk perception, mobility, weather, and built infrastructure that can point to new directions for convergent warning research. In addition, the planning grant allows early work on developing the concept of personalized warnings. Broader impacts include outreach to vulnerable populations to learn about this group’s perceptions and actions during floods. 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 · 2024-09

This EArly-concept Grant for Exploratory Research (EAGER) supports research that advances the understanding of attention deficit disorders and cognitive assessment through innovative extended reality technology. Attention deficit disorders affect millions of children and adults, impacting academic performance, social functioning, and overall quality of life. Traditional diagnostic methods often lack real-world applicability and engagement, leading to potential misdiagnosis or delayed intervention. This award funds the development of a novel extended reality system that leverages principles of embodied cognition to assess attention deficit and other cognitive functions in immersive, interactive environments. By creating standardized yet adaptable virtual scenarios, the system aims to provide more accurate and comprehensive cognitive profiles to support traditional assessments. The system's potential to improve access to cognitive evaluations could benefit underserved populations and remote areas lacking specialized expertise. Furthermore, the project's interdisciplinary approach, combining elements of cognitive science, computer engineering, and human-computer interaction, promotes collaboration across diverse fields and encourages broader participation in research. Ultimately, this work seeks to enhance early detection and targeted intervention for attention deficit disorders, potentially improving educational outcomes, workplace performance, and overall societal well-being. This grant supports research that utilizes extended reality technology, which includes virtual reality and mixed reality, to create immersive environments where users engage in cognitively demanding tasks designed to assess attention, executive function, and other cognitive abilities. Central to the system is a virtual avatar, which guides users through tasks and adapts its behavior based on real-time performance data. The research team will develop machine learning algorithms to analyze multimodal sensorimotor interactions, enabling dynamic task adaptation and personalized cognitive profiling. The project aims to validate the system through user studies comparing its performance to traditional assessment methods. By investigating the relationship between physical movements and cognitive processes in virtual environments, this research contributes to the emerging field of embodied cognition in extended reality contexts. The findings from this study have the potential to improve cognitive assessment methodologies and provide new insights into the intricate connections between mind, body, and machine interactions in cognitive tasks. 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 · 2024-09

Liposomes are nano- and microscopic lipid bubbles that can be filled with DNA and injected into the human body to fix a defective gene. Liposome-mediated gene therapy offers promises for greatly improving therapeutic efficacy, but this promise depends on the quality of liposome DNA loading uniformity. Current methods for quantifying DNA loading uniformity in liposomes are cumbersome, time-consuming, and expensive. This work proposes a nanosensor technology that could quantify DNA loading efficiency on a single-liposome basis, providing improved accuracy in DNA dosage. Successful completion of this work will enable future gene therapy clinical trials to be more effective. The highly interdisciplinary nature of this research will generate excitement among students across a broad spectrum of STEM interests throughout the academic training and outreach programs. The Investigators propose to design a bimodal optical-electrical nanosensor that utilizes nanopores to rupture liposomes and analyze their contained genetic contents. The proposed nanosensor, suitable for clinical settings, aims to quickly and accurately determine the encapsulation efficiency of circular DNA (cDNA)-loaded liposomes for gene therapy. The nanosensor features a double-nanopore architecture, with each nanopore serving a specific purpose: (1) An applied voltage bias will electrophoretically drive a liposome to the first silica (SiO2) nanopore, which has a smaller diameter than the liposome. The walls of the nanopore will exert a shear force, rupturing the liposome. (2) The released cDNA will then translocate across the second amorphous silicon nitride (SixNy) nanopore, verifying the presence of genetic material. Above the SixNy nanopore is a 100 nm gold layer with a double nanohole (DNH) architecture. A laser focused onto the DNH will optically trap the cDNA molecule immediately after translocating through the SixNy nanopore. While cDNA is held in the optical trap, the voltage bias will be reversed, removing the liposome fragments from the nanosensor. The optical trap will then be turned off, and a recapturing protocol will be applied to repeatedly translocate the cDNA molecule across the SixNy nanopore to improve the signal-to-noise ratio (SNR) of optical-electrical measurements used to verify cDNA loop integrity. These measurements will quantify the fraction of liposomes that are empty, loaded with a single, intact cDNA, or with fragmented or multiple cDNAs. Importantly, the nanosensor technology has the potential to serve as an analytical tool for other soft nanoparticles, such as viruses and exosomes. Successful completion of this work will enable future clinical trials in gene therapy to be more effective, while also offering highly interdisciplinary training to undergraduate and graduate students. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2024 · 2024-09

Recent years have seen a remarkable surge in interest and use of unmanned aerial systems (UAS), also known as drones, leading to rapid advancements in UAS technologies. Despite this progress, much remains to be explored to fully harness the potential of UAS. This underscores the critical demand for skilled researchers specializing in UAS. However, shaping the future UAS research workforce encounters multiple challenges, including system complexity, lack of open platforms, and shortage of training materials. To tackle these challenges, this project develops a new training program that will equip students with the essential skills needed for conducting and potentially transforming foundational UAS research. This project involves a team of educators with complementary expertise in various aspects of UAS. The training program includes four modules, each focusing on a fundamental aspect of UAS: control, communication and networking, computing, and artificial intelligence (AI) applications. This modular design ensures scalability and facilitates integration into existing curricula. Additionally, by leveraging an open UAS Cyber Infrastructure (UAS-CI) developed by the project team, each training module includes numerous hands-on projects to equip trainees with practical skills in operating and advancing UAS-CI. Implemented through a month-long summer program and tutorials at relevant international conferences, this project develops at least 50 skilled UAS professionals annually, who are expected to become the future UAS-CI research workforce and drive transformative advancements in foundational UAS 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 2024 · 2024-09

This project aims to develop and evaluate innovative training approaches to enhance ethical decision-making skills among early-career researchers in the social sciences. Recognizing the limitations of current computer-based ethics training programs, the research team proposes to create hybrid approaches that combine the beneficial aspects of traditional face-to-face instruction with the scalability of digital platforms. The project seeks to improve the ability of researchers to navigate ethical dilemmas on key topics such as data management, how to conduct a research study, and professional and business practices. Project outcomes have the potential to enhance the integrity and responsibility of research not only in the social sciences, but also to serve as a model for innovative ethics instruction practices across disciplines, ultimately benefiting society through more professionally sound scientific practices. Additionally, by making the most effective training resources freely available, this project will foster ethical research cultures within academic and professional communities. The primary project goals are to develop new training protocols for ethical decision-making in research contexts, compare the effectiveness of different computer-based training procedures, and share the most successful approaches for widespread use. The project team will create text and video-based ethical dilemmas relevant to social science research, incorporating real-time expert feedback to enhance the ethical reasoning skills of trainees. Graduate students and advanced undergraduates will be recruited to participate in a training efficacy study and will be randomly assigned to one of five experimental conditions, including a control group. The team will evaluate multiple training formats, including innovative approaches drawing on situational judgment tests and critical incident-based methods. This project will provide empirical evidence on the efficacy of computer-based ethics training in improving complex competencies like ethical reasoning. This project is jointly funded through the ER2 program by the Directorate for Social, Behavioral and Economic Sciences and the Directorate for STEM Education. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2024 · 2024-09

The University of Texas at Arlington (UTA), located in the heart of the economically thriving, ethnically diverse Dallas-Fort Worth-Arlington metroplex and with an enrollment of more than 40,000 students, is the second largest in the University of Texas System. The goals of the Partnership in Research and Education in Materials (PREM) for Functional Materials between UTA and the Northwestern University (NU) Materials Research Science and Engineering Center (MRSEC) are to establish: (i) an interdisciplinary collaborative program at the cutting edge of materials research; (ii) an educational pathway for students by providing unique research and educational opportunities, comprehensive student mentoring, and professional development programs. The partnership also includes the participation of Grambling State University, and involves reciprocal faculty visits and exposure of students to the world-class research and facilities at NU. The pathway begins at the undergraduate level and progresses through graduate school, ultimately culminating in postdoc opportunities and placement into materials science and engineering careers. The PREM in Functional Materials will focus on two research thrusts: (i) Functional Mixed-Dimensional Heterostructures and (ii) Bioinspired Materials with Adaptive Functionality. Research Thrust 1 addresses electron transport across mixed-dimensional heterostructures, in which the heterogeneities in dimensions (e.g., 0D, 2D, 3D) and materials properties are used to precisely control charge transport, allowing only electrons with a specific energy and momentum to participate in tunneling. This energy/momentum filtering lowers the effective electron temperature to extreme values (e.g., less than 1 K), allowing the study of ultra-cold electrons across mixed-dimensional heterojunctions at room temperature. Under this thrust, new generations of compound semiconductors, alloys, and epitaxial heterostructures will also be investigated. In addition, the role of chemical composition and material architecture (e.g., epitaxy) in influencing properties (e.g., electron affinity, dielectric constant, and band alignment) will be quantified. These results will have impact on practical applications including energy-efficient computing, neuromorphic devices, and biological and molecular sensing. In parallel, Research Thrust 2 will employ a fundamental materials science approach to study stimuli-responsive smart biomaterials and cell-free bioprogrammable materials, filling a void in the bioinspired materials field. Combining experimental investigations with computer simulations, three phase transition phenomena under different stimuli will be probed including: (a) pH-triggered conformational change in polydiacetylene-peptide; (b) glutathione-stimulated morphological change in polyurethane polymer nanoparticles; and (c) AC magnetic field-activated phase transition, from hydrophobic to hydrophilic, in polymeric nanocomposites containing superparamagnetic iron oxide nanoparticles. Shape-morphing 3D materials with programmable morphologies and motions will also be synthesized, mimicking living tissues. This research will have far-reaching implications for the development of novel polymeric biomaterials and bioinspired materials that can be used for drug delivery, tissue repair and regeneration, and related biomedical 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 2024 · 2024-09

Graphs provide simple and yet powerful mathematical structures to describe pairwise connections among different parties while providing a natural way to develop a deep understanding for real-world environments. There are many situations, however, where graph connections are not readily apparent or are completely hidden. For example, hidden within mountainous microarray data from breast cancer are tree-structure graphs that can delineate breast cancer progressions from one stage to another and thereby are extremely helpful for doctors to devise the best treatment plan for a particular breast cancer survivor. Because they are hidden, the underlying graphical characteristics are not obvious to see and must be learned with intelligent learning models. In this project, the investigators plan to develop and analyze novel graph structure learning models that can uncover latent representations hidden within big data applications. Students will be trained as part of this project, working on the development of mathematical models, numerical algorithms, and software packages for public distribution. This project involves the development and analysis of advanced models and efficient algorithms for latent representation learning via self-supervised graph structure learning. Departing from existing methods, the proposed research tackles task-agnostic graph structure learning so as to not only broaden learning on various types of data, e.g., non-graph data or graph data with unreliable graphs, but also be generalizable, transferrable and robust to different learning tasks. Specifically, new self-supervised probabilistic graph structure learning models, including novel deep graph learning architecture extensions for single and multi-view data, will be formulated to increase the expressive power of the learners, and efficient algorithms to boost task-agnostic graph-based learning from shallow and deep perspectives will be developed to go with the new advanced mathematical models. The research results will appear as a combination of scientific publications and open-source and freely downloadable packages that can be used by researchers in diverse disciplines. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2024 · 2024-08

Solar flares, as the most intense eruptions of solar radiation, can cause enhanced ionization in the upper atmosphere on a global scale. This enhancement can impact aviation, maritime, and military communication systems and global navigation systems. However, our understanding of the flare-to-flare variations in the upper atmosphere is still illusive and thus prohibits forecasting capabilities. The results of this project will significantly deepen our understanding of flare-to-flare variations in the upper atmosphere and improve our predictability of the global upper atmosphere during solar flares. As Solar Cycle 25 is approaching its maximum accompanied by more frequent and intense solar flares, it is timely to carry out the research that will investigate the solar flares’ impact on the upper atmosphere in detail. This project will be valuable in mitigating the impact of solar flares on aviation, maritime, and military communication systems and global navigation systems, which are critical for both everyday life and national security. The overarching science goal is to investigate the evolutions of the global ionospheric total electron contents (TEC) during solar flares and to improve our ability to predict the responses of the global TEC to solar flares. Specific science questions that this project aims to address include: 1. What is the temporal evolution of the global TEC during solar flares? 2. How do the different phases of solar flares (e.g., coronal dimming and EUV late phase) impact the responses of the global TEC? 3. How can we predict the response of global TEC to solar flares using machine learning (ML) models? To address these SQs, the research team plans to utilize TEC data from the worldwide GNSS receivers, perform detailed analysis with an aim to investigate its responses to solar flares. The solar spectral irradiance during such events will be from empirical models (e.g., FISM2) or observations (e.g., SDO EVE). The investigation will use state-of-the-art physics-based numerical models (e.g., GITM) to investigate the responses of the TEC during different phases of solar flares, especially the EUV late phase and coronal dimming. The project plans to develop ML models to predict the global TEC responses to solar flares. To carry out the tasks, a team including experts in ground-based observations, numerical modeling, and machine learning would be involved in the research. The outcome of this project can help mitigate the impact of solar flares on our technological society. The project will support an early career researcher and promote integration of research and education. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2024 · 2024-06

This project conducts scoping and planning activities that inform the transitioning from an academic research artifact to a sustainable and robust open-source ecosystem. The research artifact is machine learning software that identifies statements worthy of attention in textual sources, allowing users to prioritize those texts for further research. The proposed open-source ecosystem offers several key benefits to continued development of the software. It expands the software's contributor base, bringing in people with a variety of expertise and enriching the project with diverse perspectives. It also broadens the software's user base and helps discover new use cases and application domains. The open-source ecosystem's managing organization advances the project in several ways: it facilitates the recruitment and retention of both users and contributors; it ensures the software product's quality, thereby enhancing security and transparency while reducing bias through the collective intelligence of its community and well-documented processes; and it helps sustain the software product through mechanisms such as fundraising. This project involves an array of scoping and planning activities for the open-source ecosystem. The team will employ methods for assessing the demands and pain-points of potential users of the software and determining necessary features for ecosystem development, and for developing strategies to recruit and engage potential users. Additionally, the project recruits stakeholders to assist in preparing a comprehensive plan for organizing and governing the open-source ecosystem, including the evaluation of organizational and governance models, strategies for continuous development, content auditing, and ensuring sustainability. The project fosters community growth by carrying out activities for identifying essential research and development capabilities within potential contributor communities and effective mechanisms for engaging such contributors. 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.