Texas A&M University

NSF Awards · FY 2024 · 2024-10

This IUSE Level 2 Engaged Student Learning project aims to serve the national interest by improving curricula in construction science, thereby advancing the construction industry's competitiveness in the global market. Construction science curriculum reform can provide students with opportunities to build adaptive expertise on abstract and soft construction science principles and methods which involve critical thinking, problem solving, communication and collaboration skills. Adaptive expertise, which is defined as an ability to transfer existing knowledge and skills to new contexts is a highly desired ability that construction science students must possess upon graduation. This project aims to design and develop serious simulation game-driven educational modules for the construction science curriculum in three different modes including face-to-face, online, and virtual reality. The proposed research involves a series of experiments to be performed at Texas A&M University and Prairie View A&M University which offer two distinctively different student demographics. The core hypothesis of this research is that serious simulation game-based education modules can significantly improve students' learning outcomes including adaptive expertise which is a highly desirable competency for construction science graduates as the future workforce as it impacts students' interest and motivation. A rigorous experimental design and evaluation plan established in this research will potentially provide reliable data to build a coherent base of evidence that could demonstrate the effectiveness of new learning modules and inform improvements, which can enable the advancement of education theories. The planned development and testing of web-based and virtual reality modules will contribute to creating new knowledge on which design and implementation features are critically important for successful learning in virtual environments. This new knowledge can provide reliable guidance for future developers and educators. The intended research results and products will provide a clear blueprint for transforming the undergraduate construction science curriculum into an active learning-based curriculum using serious simulation games as a major transforming agent. The outreach activities for K-12 students and teachers can potentially increase the level of understanding of the construction science discipline and the construction industry, which may lead to higher enrollment in construction science and more students choosing a career path in the construction industry. 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 2024 · 2024-10

This IUSE Level 2 Engaged Student Learning project aims to serve the national interest by increasing opportunities for training and enhancing safety in the construction industry, The industry is gearing towards wearable robots as an ergonomic intervention that enhances human strength and performance while reducing muscle fatigue and stress. Consequently, it is crucial to equip construction engineering and management (CEM) students with the skills needed to implement this intervention effectively. This research aims to investigate an interactive virtual reality environment designed to develop CEM students' competencies in implementing wearable robot solutions for addressing ergonomic risks. The proposed research will potentially benefit the construction industry by providing the future workforce with the competencies to advance wearable robot solutions. This interdisciplinary research proposes to equip CEM students with relevant human-wearable robot interaction competencies through guided experiential learning in an interactive virtual reality environment called ViRLE. A first research goal is to identify the required skills, knowledge, and abilities for deploying wearable robot solutions by interviewing industry practitioners. Next, the project involves plans to integrate tools from wearable robots, virtual reality, tangible interactions, and sensing technologies to develop ViRLE. The project team intends to apply constructivism and cognitive apprenticeship theories to design activities and study how use of the technology impacts student learning. The research team then will attempt to identify the characteristics of ViRLE that facilitate interaction with wearable robots in reducing ergonomic exposures of construction tasks. Finally, planned implementation of ViRLE will occur in two institutions with diverse student populations to assess its potential to support the acquisition of these competencies. The research team envisages providing an interactive learning environment that is suitable for students of diverse demographics. 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 2024 · 2024-10

The predicting power of computational tools is of paramount importance in engineering and science. They offer insights into the behavior of complex systems, modeled by partial differential equations inside a region of interest. Boundary conditions expressing the influence of the surroundings must be provided to complete the mathematical models. However, there are many instances for which the boundary conditions are not available to practitioners: the understanding of the physical processes might be lacking, for instance when modeling the airflow around an airplane, or the boundary data is not accessible. This project aims to design numerical algorithms able to alleviate missing information on boundary conditions by incorporating physical measurements of the quantity of interest. The problems to be addressed fit under the strategic area of machine learning, and the potential scientific impact of this research is far-reaching. It includes improved meteorological forecasting, discovering biological pathways, and commercial design. In traditional numerical treatments of elliptic partial differential equations, the solution to be approximated is completely characterized by the given data. However, there are many instances for which the boundary conditions are not available. While not sufficient to pinpoint the solution, measurements of the solution are provided to attenuate the incomplete information. The aim of this research program is to exploit the structure provided by the PDE to design and analyze practical numerical algorithms able to construct the best simultaneous approximation of all functions satisfying the PDE and the measurements. This project embeds the design, analysis, and implementation of numerical methods for PDEs within an optimal recovery framework. It uncovers uncharted problematics requiring new mathematical 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 2024 · 2024-10

This project aims to create and study an Equitable and Interactive Mathematical Modeling (EIM2) program that positions students as decision makers in their own learning. Despite the value of connecting students’ life experiences with their mathematical learning, the practical implementation of this strategy has proven challenging in a classroom setting. EIM2 addresses this issue by supporting students to engage in equitable mathematical modeling, a process of using mathematics to analyze and quantify scenarios through a lens of equity. The EIM2 program involves collaborations with sixth and seventh grade students, a professional learning community series with their mathematics teachers, and the creation of the dynamic, online platform that hosts EIM2 modules. The EIM2 dynamic online platform allows students to easily select scenarios based on their interests; experience the scenarios with visuals and animations; and compare, synthesize, and refine their mathematical ideas. The program development will be guided by design principles and hypothesized learning processes that support students’ cultural competence, their evaluations of multiple mathematical solutions, and their mathematical identity development. Using multi-tier design-based research and a mixed-methods approach, the EIM2 program will be continuously evaluated and refined through multiple iterations to ensure usability and efficacy. Over the four-year project span, this project will (1) explore the nature and impact of the EIM2 program, assessing how it promotes a shared vision for the learning of all students, including racially and ethnically minoritized students, in classroom settings; (2) examine whether and how students’ engagement in EIM2 supports their achievement and identity development in mathematics; and (3) understand how teachers enact EIM2 and whether they change their attitudes toward modeling over time and across contexts. The proposed project fills a theoretical gap related to scalable design models for interactive mathematical modeling curricula that are culturally sustaining for students. Our model improves upon current practices of mathematical modeling by transforming existing curricula to reflect students’ lived experiences and to foster their active learning, leveraging the interactive nature of digital curricula. Our proposed work has the potential to be transformative for STEM education through the co-creation of asset-based instructional materials built on a deep understanding of students, which can be applicable for other STEM education fields of study. 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.  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

Reliable concentration measurements of chemical elements require careful quality control. Part of this quality control depends on the use of reference materials of known concentration to assess the accuracy of measurements and intercalibrate between laboratories. For trace element concentration analyses of seawater, the supply of reference materials has become exhausted in recent years. This project aims to collect large samples of water from the Pacific Ocean and Gulf of Mexico to be used as a new set of reference materials. Trace metal concentrations will be measured by several experts using different methods and conditions. The concentration values will be compared against one another, and a consensus value will be developed by these experts using state-of-the-art statistics. The remainder of these large-volume water sample will be stored for distribution to the scientific community in the future. These consensus materials are expected to improve the quality and accuracy of seawater trace metal concentration data over the next decade. Several students will participate in the three research cruises and receive training on trace metal sampling and intercalibration studies. The project also provides support for an early career scientist. The primary objective of this project is to improve the quality of trace metal data in the ocean over the next decade through optimized methodologies and well-studied consensus samples that can be used to assess accuracy. Samples will be collected from the surface and 1000 m at Station ALOHA, which brackets common ranges of open ocean dissolved metal concentrations. In addition, two surface stations in the Gulf of Mexico that have lower salinities and higher organic content due to the influence of the Mississippi River outflow will be used to test the boundaries of sample storage and analytical intercalibration. Samples will be collected and homogenized within large volume tanks and dispensed into 500mL bottles for archiving and distribution to the community. These samples will be analyzed initially by fifteen laboratories worldwide for a suite of trace metal concentrations, to address two goals. First, the data will be compared statistically, via a collaboration with an expert statistician from the National Institutes of Standards and Technology, to calculate consensus concentration values for each element. This consensus concentrations will be reported on the GEOTRACES website, and the statistical best practices will be published. Second, this network of collaborators will explore common intercalibration issues that have arisen over the last decade by utilizing a range of analytical methodologies and conditions. The primary scientific impact of this project will be a set of well-studied consensus samples that can be used to monitor the accuracy of trace metal analyses of seawater over the next decade. This project will support one early-career scientist, one PhD student from the University of Southern California, and 5-15 graduate and/or undergraduate students from Texas A&M University who will participate in the staging and collection of samples from these expeditions. 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

Responding to public interest in artificial intelligence (AI), governments, scientists, and industry actors are collaborating to better understand the implications of AI technology so that they can maximize its benefits and reduce its risks. The workshop will build on this ongoing research by convening a diverse group of public sector, industry, and other expert stakeholders to discuss how scientists and policymakers can improve protections against biological threats using public policy around emerging AI tools and biotechnology. The workshop aligns with NSF’s goals for broad public impact by creating bridges across siloes of expertise with the goal of sustained future collaboration. This workshop is responsive to recommendations by the National Academies, the National Science Advisory Board for Biosecurity, the U.S. Intelligence Community, the National Security Commission on Emerging Biotechnology, and others to the need for focused inquiry to make meaningful progress on key policy issues that inform the federal and international approach to emerging applications of artificial intelligence for biology. The project will consist of a full-day workshop with breakout sessions for focused discussion in Washington, DC. Workshop organizers will invite individuals from diverse backgrounds and affiliations to build new collaborative connections across attendees. The workshop will lay the groundwork for future technical and public policy research in AI and biotechnology, seed cross-sectoral collaborations on high-priority scientific questions, and prioritize promising solutions. The discussion will leverage the expertise of a geographically and professionally diverse set of expert participants across academia and industry. The organizers will create a written report to summarize recommendations from the workshop that aims to be informative for national and international science policy. The report will be publicly available online and shared by the organizing committee. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2024 · 2024-10

The spawning grounds of the Southern Bluefin Tuna (SBT) lie between the northwestern Australian coast and Indonesia. Waters from the Pacific flow through Island Southeast Asia as the Indonesian Through-Flow (ITF) and into the western Indian Ocean. Nutrient conditions in the ITF are insufficient to support optimum feeding and growth of SBT larvae, and numerous links between the nutrient cycles and food chain remain unknown. There are very few measurements of both major and trace nutrients like nitrogen, phosphorus, and iron made in the region. These nutrients support phytoplankton that form the base of the food chain and affect higher trophic levels, including SBT. This work will investigate the potential sources of major and minor nutrients to the region, including island inputs, Australian dust, monsoon rains, and sedimentary fluxes which will help develop a “nutrient budget” and reveal which sources contribute the greatest to the success of early SBT life stages. This project is a US contribution to the 2nd International Indian Ocean Expedition (IIOE-2) that will advance understanding of biogeochemical and ecological dynamics in the poorly studied eastern Indian Ocean. The project will support an early career postdoctoral researcher and outreach activities will include participation in Open House events and REU summer program at home institution. The ITF can undergo potential changes to the distributions of major and trace nutrients during passage through the straits of southeast island Asia. Coastal currents can resuspend shelf sediments containing nutrients like Si and Fe, while the atmospheric deposition of both natural and anthropogenically derived material from Australia and southeastern Asia can supply N, P, Fe, and other trace metals. Monsoon rains also supply nutrients via wet deposition of aerosols and runoff. These inputs introduce nutrients to the surface ocean of the ITF, where phytoplankton can convert inorganic nutrients to biomass and in turn fuel the entire food chain. In this research aerosols and water column trace metals will be investigated, as a complementary component to another NSF-funded BLOOFINZ project which seeks to understand the N cycling and food-web dynamics of the southern bluefin tuna spawning grounds. The proposed research will address three hypotheses: (H1) The ITF receives inputs of bioactive trace elements from wet and dry atmospheric deposition and resuspended sediments, and (H2) transports these nutrients across the Indian Ocean via the South Equatorial Current (SEC). (H3) Inputs of Fe from atmospheric deposition of mineral and anthropogenic aerosols or continental shelves could help alleviate N limitation in the ITF. To investigate these hypotheses and understand the inputs, removals, and cycles of trace metals in the ITF, the concentrations, elemental ratios, and fluxes of material in aerosols and marine dissolved and particulate phases will be quantified and compared. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NIH Research Projects · FY 2024 · 2024-09

PROJECT ABSTRACT Thousands of students yearly are drawn to biomedical engineering (BME) by the prospect of a career linking medicine and engineering. Unfortunately, students from historically underrepresented racial/ethnic minority (HU; e.g., Hispanic\Latinx) groups do not advance or integrate into the BME professional community at rates comparable to their majority peers. National statistics indicate that Hispanic, Black, and non-Hispanic racial/ethnic minority groups accounted for only 12% of bioengineering and BME graduates in 2021. Data collected at Texas A&M University, a Hispanic Serving Institution and ranked 5th nationally in bachelor’s degrees awarded in BME in 2022, show that students from HU groups (on average ~25% of BME majors, up to 33% in recent years) lag behind their peers from historically overrepresented (HO) groups on measures of academic success (e.g., time to graduation). Social Network and Mentoring Co-Regulation theories, and preliminary data suggest these disparities may be due to HU students holding less privileged positions within their peer network, having less access to mentors, and therefore having less access to social capital (i.e., information, support, and resources) and support for self-regulated learning (SRL; e.g., help-seeking). The long-term overall objectives of this work are to develop a generalizable model of how social networks facilitate students’ biomedical research career success and to diversify the BME research workforce. This study will (1) examine the impact of generalizable “Creating Birds of a Feather” (CBoaF) peer and mentor network interventions designed to promote diverse students’ “Hallmarks of Success” outcomes (e.g., well-being, GPA, degree conferral), and (2) assess the contributions of students’ SRL and social capital. The central hypothesis is that being well-connected within peer and mentor networks will promote access to social capital and support for effective SRL, which will promote success in BME. The project will implement a longitudinal, randomized experiment via a professional development workshop across three cohorts (2024-2026) of first-year BME majors to ensure a large and diverse sample of emerging biomedical engineers (N=360) from HU (n=108) and HO (n=252) groups. Aim 1 will test a peer network intervention by randomly assigning students to one of three workshop groups: (1A) CBoaF enhanced inclusive activity workshop at a Mixed Low/High table (containing students with low and high connections with peers), (1B) CBoaF enhanced inclusive activity workshop at a Same-level table (i.e., low-only, high-only), or (1C) Alternative workshop control group without CBoaF activities. Aim 2 will test a mentor network intervention by randomly assigning students to one of two mentor introduction groups: (2A) Introduction + CBoaF similarities highlighted or (2B) Introduction only. Exploratory Aim 3 will assess the degree to which intervention effects are stronger for students from HU groups compared to their HO group peers. This study will show how and why helping diverse BME students form stronger social networks can promote their well-being and BME career success.

NIH Research Projects · FY 2025 · 2024-09

Project Summary Bacteriophage, or phage, infect and kill bacterial cells. At a programmed time, phage use a tailored suite of proteins to release newly built virions by lysing the host cell. As the most common cell lysis event on the planet, phage-induced lysis drives both microbial biogeochemical nutrient cycling and population dynamics in cellular life. Host cell lysis in tailed phages is an active process carried out by phage proteins that specifically target each cell envelope layer. In Gram-negative bacteria, phage holin proteins compromise the inner membrane, endolysins degrade the peptidoglycan, and spanins disrupt the outer membrane. Molecular studies in a few classic systems, such as phages T4 and lambda, exposed common functional principles governing lysis. However, striking differences in their mechanisms of action also taught us about bacterial envelope layers and their regulation. In addition, bacteria often co-opt phage lysis proteins to accomplish diverse behaviors beneficial to a population such as seeding biofilm establishment and releasing toxins. Therefore, studying novel phage lysis proteins will advance our understanding of both phage and fundamental principles in their hosts. Further, we and others have demonstrated that identifying or predicting the function of novel lysis proteins based on sequence analysis alone is ineffective. Therefore, the studies proposed here focus on experimental characterization of novel phage lysis protein mechanisms. For example, in E. coli phage Mu, where bioinformatics predicted a canonical lysis pathway, we demonstrated that Mu lyses cells using a protein of unknown mechanism in place of a holin. In a second example, we showed that E. coli phage phiKT produces an antimicrobial peptide-like protein instead of spanins to disrupt the outer membrane, akin to eukaryotic antimicrobial peptides. Finally, we have identified for study lysis protein candidates that are significantly different from known proteins in phages infecting abundant and understudied human gut microbiota. Our goals for the next five years center on in-depth molecular characterization of two specific novel lysis proteins and diverse candidates from less-studied phages infecting gut microbiota. We will use genetic, biochemical, and microscopy approaches to elucidate mechanisms of individual proteins. Direct competition assays will probe the contributions of lysis proteins to phage persistence and fitness, characteristics that drive environmental microbial population composition. Overall, these projects are expected to uncover divergent mechanisms of action used by phage in E. coli model systems and other prevalent gut microbiota. Broadly, this foundation can be leveraged to study lysis protein types active across many different bacteria, which is critical to understanding global microbial population fitness and turnover. The new molecular strategies uncovered here may lead to more efficacious medical treatments since lysis is also the basis of successful phage therapy, which aspires to use phages as alternative treatments for antibiotic-resistant bacterial infections.

NSF Awards · FY 2024 · 2024-09

This project examines the sources of microplastic particles in rivers and waterways, focusing in particular on the effects of different human activities and land use patterns. Relying on sediment analysis and complementary analytical methods, the project characterizes the extent of spatial variation in the assemblage of microplastic particles in different regions and waterways. Furthermore, the research examines the impacts of human activities, as shown in the development of roadways and changes in land cover, on the geographical distribution of microplastic particles in rivers. This information helps to inform efforts to reduce microplastic emissions and presence in waterways. To complete the analysis, the researchers develop new software that environmental scientists can use to study analogous systems. To link the microplastic fingerprints of rivers with human sources, this project quantifies the types and amounts of microplastics in major watersheds. The research involves the analysis of sediments sampled from diverse watersheds and regions. The composition of hundreds of microplastic particles in each sample is determined using infrared spectrometry, image analysis, and convolutional neural networks that permit rapid identification of particles. Subsequently, established and novel statistical tools are used in combination to characterize the microplastic ‘fingerprint’ of each river and to investigate how mixing different rivers affects downstream propagation of microplastics. Aspects of land use within each catchment are assessed using remote sensing datasets and Geographic Information Systems (GIS) methods and compared against the microplastic emissions. This project supports a summer school to train undergraduates on separation and spectroscopy methods, a module for high school education separating microplastics from river samples, and methodological courses at annual meetings to disseminate new mixture modeling software. The findings have implications for identifying the sources of microplastic emissions and how policymakers can prioritize and reduce emissions into the environment. 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

Archaeology provides key information on the development of human adaptive capacities and how environmental changes affected societies over millennia. This project investigates multiple archaeological sites located at high altitude. A principal goal is to determine the degree of resilience of small-scale foraging groups before the advent of pastoralism, agriculture, and complex civilizations. In addition to contributing information on how small-scale societies can persist in challenging environments, the project builds research capacity through a formal collaboration between US and partner institutions. The senior team provides hands-on training in archaeological science methods in the field and laboratory for more than 10 graduate and undergraduate students. Students and senior team members share the project’s results with academic and public audiences through lectures. Among the project’s broader impacts is the production of educational materials aimed at schoolchildren and the public, including an informative plaque for the site, a display case for the school containing 3D printed replicas of artifacts, and an illustrated printed booklet on the local archaeological sites and their significance. Studying how humans affect and are affected by their environments is important for understanding the development of cultural and adaptive patterns as well as the modern configuration of inhabited landscapes. This project investigates early human ecology by targeting the earliest and longest-occupied archaeological sites known from one high altitude region. At each site the team constructs detailed and high-resolution radiocarbon records of occupation and compares these with local paleoenvironmental records. This makes it possible to determine when humans first settled the region, whether occupation was sustained or was discontinuous, whether specific environmental changes may have impacted occupation, and how people responded and ultimately persisted in this region over thousands of years. Addressing this last issue requires more than establishing presence vs absence of people in residential sites. Analysis of artifacts and plant and animal remains from these archaeological sites provide information on occupation intensity and behavioral patterns over time, including subsistence, mobility, technology, resource use, and social connections between distinct ecological zones. The team addresses whether changes in these behavioral patterns coincided with environmental shifts. Together, these approaches reveal how past environmental changes affected people and how foraging societies in high mountain landscapes have dealt with environmental challenges over long time spans. 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 ability to process large sets of data on research computing (RC) platforms has led to remarkable advances in science and engineering and has become an indispensable tool for researchers, industry professionals, and students. The importance of RC platforms and tools (and in particular those involving AI technology) has been recognized by the highest levels of the United States government, as evident by the National AI Initiative Act of 2020, which calls for the creation of a National Artificial Intelligence Research Resource (NAIRR). Unfortunately, for many individuals, RC use and adoption is hindered by the complex way in which these resources are accessed. While web browsers and smart devices have become the dominant access mechanism for remote consumer and enterprise computing services, the adoption of such mechanisms has lagged for many RC service providers, creating an accessibility gap that impedes further adoption. Open OnDemand is a mature, National Science Foundation (NSF) funded, open-source platform that enables remote web access to RC services, thereby simplifying use of those resources and facilitating collaboration. RC clients can manage files and jobs, create and share apps, run GUI applications, and utilize a traditional terminal all through a web browser from anywhere on any device because it requires no specialized client software and has a simple interface that is easy to learn and use. The Open OnDemand community has begun to expose platform limitations and request additional features and capabilities, many of which will require significant collaborative development efforts to implement. Further adoption of this powerful platform, and the resultant scientific advances, can be accelerated via a variety of innovations: (1) Catalog, providing capabilities to conceptualize, create, modify, share, and publicize apps; (2) Classroom, focused on Open OnDemand in educational environments; (3) Customization, involving innovation in client interfaces and new technologies; and (4) Community, facilitating communications among the community. A cross-cutting aspect of the GOODLUCK project will pilot and deliver integrated sets of tools, documentation, and training materials, which researchers can select from to create an environment tailored to their research. Initial beneficiaries of these efforts include: (1) biochemists utilizing cryogenic electron microscopy (Cryo-EM) to identify the underlying mechanisms of viral action; (2) materials scientists designing new alloys from first principles; (3) art design students creating novel works via the use of generative artificial intelligence; and (4) aerospace engineers improving the performance of drones via computational fluid dynamics. In summary, the GOODLUCK effort will provide tools and processes that allow researchers to more easily and extensively utilize Open OnDemand, as well as to analyze and share their computational results, regardless of their access to RC resources or position in the research lifecycle. 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

With support from the Improving Undergraduate STEM Education: Hispanic-Serving Institutions (HSI Program), this Track 2 project aims to serve the national interest by enhancing the effectiveness of undergraduate geoscience education at an HSI, Texas A&M University. The project aims to strengthen student academic experiences through the development and classroom implementation of a novel intelligent sketch-based tutoring system. SedimentSketch, a visual, personalized, and dual language tool, will combine new curricular materials and sketch recognition algorithms to improve student learning through sketching exercises and automatic, instantaneous feedback. Hispanic student performance indicators are markedly different from students of other ethnicities, with Hispanic students consistently having lower GPAs at graduation. The goal is to improve Hispanic students' GPAs and time to graduation. The proposed SedimentSketch tool can transform the higher-education geoscience curriculum for HSIs by enabling geoscience students to interact with the material and receive helpful feedback instantaneously outside of class and by cultivating a more inclusive learning environment. An outcome of the SedimentSketch application will be to help close the gap between Hispanic and non-Hispanic students' GPAs and improve retention in geoscience courses. SedimentSketch will be the first teaching tool in sedimentology that provides novel learning experiences to students as well as the flexibility of remote access to lab materials. The educational materials will be based on the PI’s sedimentary rock collections and materials from public sources, e.g., the International Ocean Discovery Program (IODP), and will include videos, games and interactive exercises. Overall, the project will improve career trajectories for Hispanic students by enhancing their learning and exposing them to an innovative tool for hands-on learning activities for real-life scientific and industry applications of sedimentology. Additionally, SedimentSketch will be a great tool outside of the HSI environment to accommodate students with special needs, improve learning and accessibility, and can be used for outreach. The HSI Program aims to enhance undergraduate STEM education and build capacity at HSIs. Projects supported by the HSI Program will also generate new knowledge on how to achieve these aims. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NIH Research Projects · FY 2025 · 2024-09

ABSTRACT Sepsis is a potentially life-threatening complication of an infection in critically ill patients and is characterized by severe tissue breakdown, leading to long-term muscle weakness, fatigue, and reduced physical activity. Early and targeted nutritional intervention is critical in enhancing rehabilitation from critical illness. The continuing high morbidity and mortality rate in sepsis illustrates the clinical and scientific need to dissect the underlying mechanisms by which sepsis induces accelerated catabolic response in muscle, and to assess the effectiveness of targeted nutritional approaches during rehabilitation from sepsis. The rationale is that the mechanistic insights generated will lay the foundation for the development of novel nutritional approaches for critically ill patients to enhance rehabilitation through improved muscle health and physical activity. The use of translational animal models is essential. Therapeutic nutritional support in pig models is viewed as highly translational to humans. Our pig sepsis rehabilitation model shows many characteristics of human sepsis rehabilitation like reduced muscle protein synthesis and increased protein breakdown, muscle weakness, reduced activity and lower muscle autophagy. The first aim of the proposed study is to test the hypothesis that a targeted, combined nutritional formulation of β-hydroxy β-methylbutyric acid (HMB anti-catabolic) and essential amino acids (EAA, anabolic) is superior to EAA alone or control in attenuating severe tissue breakdown during rehabilitation from sepsis. The hypothesis is that the EAA+HMB combination will simultaneously increase muscle protein synthesis, attenuate muscle catabolism and wasting, and improve the rehabilitation of muscle function, leading to enhanced physical activity. The second aim is to identify the metabolic and molecular mechanisms through which supplementation attenuates the dysregulated proteostasis that causes the severe protein breakdown. The proposed specific aims will be studied in our clinically relevant pig model of rehabilitation from an acute Pseudomonas aeruginosa sepsis. The nutritional intervention will be studied using a randomized, controlled, double-blind 3 arm design (EAA+HMB vs. EAA vs. control). The proposed study is innovative because a) the targeted novel approach of EAA+HMB nutritional supplementation to attenuate tissue breakdown and restore muscle function and functional outcome (strength, fatigue and physical activity) and b) mechanistic insights into sepsis-induced severe tissue breakdown and recovery. The use of an innovative stable tracer methodology with muscle and plasma sampling will enable the quantification of all metabolic fluxes and molecular endpoints. The results of the proposed study will have a positive impact by providing a mechanistic basis for the development of novel, cost-effective nutritional approaches for patients recovering from sepsis that will enhance their rehabilitation through improved muscle health and physical activity. Moreover, the obtained results will provide a strong justification for rapid translation into clinical application.

NSF Awards · FY 2024 · 2024-09

HSIs are substantially underfunded compared to other institutions of higher education due to relatively low endowment revenue and tuition fees. Chronic underfunding leads to infrastructural and technological disparities that disadvantage students as they pursue advanced educational and professional opportunities. This project creates an alliance between four Texas HSIs to improve institutional competitiveness through: 1) research focused on and directly relevant to creating opportunities across demographic and socio-economic groups; 2) the establishment of physical lab spaces with state-of-the-art computing resources and statistical analysis software; 3) the creation of a virtual lab across all four campuses to facilitate student and faculty mentorship and collaboration; and 4) preparation of students from HSIs to enter graduate programs. The project brings students and faculty together from R1, R2, and M1 classified HSIs to conduct research relevant to creating opportunities across all demographics and socio-economic groups through two interconnected studies. Applying multi-method comparative designs, these studies will advance a deeper understanding of attitudes, experiences, attitudes on immigration, and other thematic areas to address core questions on inequality and opportunity. The creation of physical and virtual lab spaces for these studies will foster equal participation in scientific innovation at each of the collaborating universities by allocating infrastructural resources in proportion to need. These labs will facilitate the development of research and the dissemination of important findings through yearly mini-conferences showcasing student and faculty work, academic publications targeting traditional disciplinary outlets, and white papers designed to make the research accessible to the general public. Collectively, the alliance of four universities will address four objectives: (1) the theoretical and methodological development of sociological research, (2) promoting research opportunities for students and faculty at under-resourced HSIs, (3) conducting meaningful research on critical sociological issues important to the Texas and national social context, and (4) creating a pipeline for students from HSIs into scientific training and doctoral graduate programs. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2024 · 2024-09

Modern quantum metrology, quantum communication, and quantum simulation currently depend on infrared or optical photons and their resonant interactions with atoms. However, higher frequency X-ray radiation could benefit these applications, due to lower noise levels, higher phase sensitivities, tighter focusing, broader bandwidths and, accordingly, higher temporal resolution and faster processing. Nuclear ensembles resonantly interacting with X-ray radiation also have some additional advantages. Their resonant transitions are less sensitive to perturbations by electric and magnetic fields, due to the tiny size of a nucleus compared to an atom. Additionally, in contrast to atomic transitions, spectrally narrow nuclear transitions are available even at room temperature and high solid-state densities. These benefits hold promise for building compact, room-temperature, solid-state nuclear clocks that could outperform atomic clocks both in terms of accuracy and stability. Such clocks could redefine the standards of time and other measurement units and be used in searches for new physics beyond the Standard Model, including searches for dark matter and potential time-dependence of fundamental constants. Applications for such clocks could also include navigation, chronometric geodesy, geology, seismology and climatology. Narrow nuclear resonances in solids could lead to the realization of compact and long-lived nuclear quantum memories, which would be useful for long-distance quantum communication and synchronization of quantum information networks. Postdocs and graduate students participating in this project will be trained in the highly interdisciplinary field of quantum nuclear X-ray optics, learning experimental, analytical, and numerical techniques. They will get an opportunity to participate in experiments at world-class synchrotron and X-ray free electron laser facilities, such as the Advanced Photon Source at Argonne National Laboratory, and the European Synchrotron Radiation Facility in Grenoble, France. Recent research by the PI’s team and their collaborators led to (i) pioneering resonant excitation of the Sc-45 nuclear isomer, establishing this isomer as one of the primary candidates for nuclear clocks and (ii) the first demonstration of a quantum nuclear memory for X-ray photons. The latter was based on the Doppler Nuclear Frequency Comb protocol suggested by the PI’s team. Building on that success, the current project aims at further breakthroughs in this field. It consists of two mutually related parts. 1. The first observation of coherent forward scattering in a Sc-45 sample, i.e., time-dependent collective coherent decay of the nuclear polariton resonantly excited by a train of X-ray pulses. This would allow a measurement of the linewidth (coherence time) of the nuclear transition. The linewidth is expected to be orders of magnitude narrower than any other nuclear transition explored so far. It could allow for a measurement of the gravitational red shift at record-small displacements. 2. The first demonstration of an on-demand quantum nuclear memory. Three different protocols will be explored for this purpose: (i) introducing a magnetic field gradient and switching its sign, (ii) switching the velocity directions in the Doppler Nuclear Frequency Comb memory, and (iii) switching the direction of the magnetic field in nuclear absorbers with multiple Zeeman sublevels. The team will also develop new techniques for shaping X-ray waveforms at the single photon level, which is required for achieving a high-fidelity and high-efficiency quantum memory. Interferometric phase measurements and photon statistics measurements of the original, and newly developed, sources of X-ray photons will also be performed. 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

With the increased sophistication of research workflows, it is essential for researchers to be well-versed in both domain-specific methods, and cross-cutting cyberinfrastructure (CI) technologies. While advanced CI holds tremendous potential to advance research, its success ultimately relies on the ability of researchers to effectively utilize these technologies to analyze data and gain new insights. Leveraging Texas A&M High Performance Research Computing’s recognized researcher-training programs and strengths in AI/ML- enabled research, PACES, Providing Accelerated Cybertraining for Emerging Scientists, will serve as a novel regional CI-centric researcher training avenue that focuses on researchers at emerging research institutions. PACES will offer researchers with training opportunities to (i) familiarize themselves with CI, and (ii) use CI to advance CI in their respective fields of science. With a focus on supporting researchers at Minority Serving Institutions (MSIs), PACES will help establish CI training programs at community colleges, emerging research institutions, and Tribal Colleges. PACES will offer asynchronous and live training for researchers at emerging institutions partnering with Texas A&M University. PACS will offer 2 levels of training to researchers who are keen to apply AI and data science approaches on advanced computing resources in their scientific fields. The first level is for researchers from all disciplines who are keen to develop skills and need to use advanced CI. The second level offers specialized training and consulting for researchers who are keen to address key challenges in the fields of natural hazards, chemical and materials design, genomics, geological sciences, and economics. Monthly virtual training sessions and asynchronous web-based training resources will support both tracks. Annual workshops along the introductory (2 days) and advanced track (5 days) will be offered. 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

Time is a fundamental resource in organizations, shaping how work is performed and influencing the overall efficiency and productivity of the organization. However, effective time management remains a significant challenge, particularly in contemporary workplaces featuring rapidity and flexibility. Ineffective time management in organizations is problematic, as it not only hinders project completion and impedes productivity but also leads to poor workload management and increases employee stress and burnout. This project investigates how leaders and followers can collaborate to initiate and coordinate their time management efforts within organizations. The research aims to enhance workplace practices related to time management and develop a workforce skilled in managing time effectively. By doing so, this project seeks to foster a more productive work environment and improve the overall well-being of the workforce. In this project, the research team focuses on the concept of temporal management, defined as the strategic use of time by individuals to affect work patterns, schedules, and productivity within an organization. Through three mixed-methods projects, this research aims to contribute significantly to understanding how leaders and followers collaboratively optimize time in organizational settings. The first project (Project 1) elucidates the conceptual nature and dimensionality of temporal management in leader-follower dyads. The second project (Project 2) advances and tests a formal theory of the dynamic occurrence and influence of leader and follower temporal management. The third project (Project 3) investigates the dyad-centric patterns of leader and follower temporal management and their impacts in the context of contemporary workplaces. 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

With this award supported by the Office of the Chief of Research Security, Strategy & Policy (OCRSSP), the U.S. National Science Foundation establishes the SECURE (Safeguarding the Entire Community of the U.S. Research Ecosystem) Analytics. In 2022, a new law directed NSF to address foreign threats to the security and integrity of the U.S. research enterprise. International collaboration is a vital part of the culture and success of the US research enterprise and is highly valued by researchers. The SECURE Center is the comprehensive organization funded by NSF to meet this need. SECURE Analytics is an affiliated center providing analytics expertise to the overall SECURE initiative. The SECURE Analytics team, headquartered at Texas A&M University with support from the Hoover Institution and Parallax, will conduct landscape analyses, risk modeling, and help the research community by sharing data and issuing timely reports regarding the research security landscape. SECURE Analytics will support the analytics needs of SECURE Center and the broader research community while working to protect the privacy of SECURE Center’s users. Specifically, SECURE Analytics will engage in four main activities. SECURE Analytics will serve as a clearinghouse for information to help enable the research community to understand the context of their research and identify improper efforts by foreign entities related to research results, knowhow, materials, and intellectual property. SECURE Analytics will share information concerning security threats and lessons learned from protection and response efforts with the research community. They will develop tools and technologies for standardized information gathering and data compilation, storage, and analysis related to the research security landscape and types of security incidents. They will provide training and support for relevant research faculty and staff in higher education institutions and national laboratories on topics related to security risks and responses. Through this work, SECURE Analytics will share their tools, code, and analytic methods to ensure transparency with the research community. They will also synchronize with the SECURE Center to support the development of risk assessment frameworks and best practices, to create reports on research security risks and situational awareness for the research and STEM education community, and identify patterns of risks to enhance the ability of the research community to respond to and mitigate 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.

NIH Research Projects · FY 2024 · 2024-09

Project Summary Repair of traumatic injuries relies upon glial cell line-derived neurotrophic factor (GDNF), and related extracellular cytokines collectively called GDNF family ligands (GFLs). GFLs interact with solubilized forms of the GDNF-family receptors (sGFRα1–4) forming complexes which then can bind and activate NCAM (nuclear cell adhesion molecule) and RET (REarranged on Transfection) receptors leading to intracellular signaling and a range of responses conducive to neuronal connectivity. GFLs have been tested in animals and in clinical trials. However, they have poor in vivo stabilities, unfavorable tissue permeation characteristics, and are expensive to prepare with batch-to-batch reproduciblity. Gene therapy approaches have also been attempted, but these are extremely risky because continued expression leads to uncontrollable growth post therapy. Few small molecule mimics of GFL•GFRα interface regions have been reported in the literature. This is surprising because appropriate small molecules could cause conformational changes in sGFRαs transforming them into NCAM/RET agonists which may communicate between cells (trans-signaling) to trigger valuable responses for repair of the peripheral nervous system after trauma. Preliminary studies feature design, synthesis, and testing of two mimics of the GDNF loop which is responsible for most if the GFL•GFRα interface interaction (ie the interface “hot loop”). These loop mimics bind GFRα1 (best so far Kd 240 nM), and are currently being tested in cellular models for repair of traumatic injuries to the peripheral nervous system (PNS). This application is to optimize these initial leads and test them more extensively. Year 1 will focus on on design, syntheses, and GFRα1-binding affinities for similar “cyclo-organopeptide hot loop mimics” by the PI (10 – 20 compounds). Free loop mimics with superior GFRα binding affinities, and samples of ones covalently anchored to hyaluronic acid supports (which mimic the media around synapses), will be selected for Aim 2. The PI is an expert on design and synthesis of growth factor hot loop mimics; he will oversee that part of the work closely. In year 2 the emphasis will shift to testing the best hot-loop mimics identified at that time in 2D and 3D-cellular models for PNS recovery from traumatic injury. Active compounds will also be assayed to test if they cause intracellular activation of NCAM and/or RET. That work will be overseen by Professor Sakiyama, the subcontractor on this application, who has extensive experience with GFLs and supported GFLs, particularly GDNF, tested 2D and 3D cellular assays for neurite outgrowth on sensory and motor neurons. She is an expert in neuronal repair.

NSF Awards · FY 2024 · 2024-09

Artificial Intelligence (AI) is fundamentally reconfiguring the engines of scientific discovery, technological innovation, and industrial manufacturing that fuel modern economies. Excitement for revolutionary advances in this domain is tempered, however, by unsettling changes to the nature of human work and fears of compounding uneven access to these innovations. Additionally, current AI is founded on ad hoc designs, sub-optimal links to foundational mathematics and computing theory, and poorly considered demands on energy sources. This National Science Foundation Research Traineeship (NRT) award to Texas A&M University will address these shortcomings and demands by training graduate master’s and doctoral students in the interdisciplinary fields of the mathematical, molecular, and materials foundations of AI. This effort anticipates training adaptive changemakers who will design AI to work with people and educate people to work with AI. The project anticipates fostering a culture of team science and community engagement by training sixty-seven (67) MS and PhD students, including nineteen (19) funded trainees, from graduate programs in statistics and mathematics, electrical and computer engineering, computer science, chemistry, materials science and engineering, mechanical engineering, and interdisciplinary education. Trainees will work at the intersection of foundational theory, AI algorithms, neuromorphic (human brain-inspired) materials discovery, and analog circuit design to close the AI for Materials & Circuits with the Materials & Circuits for AI loop. The traineeship will exponentially amplify the impact of AI by designing and creating new materials and computing architectures. Through close interactions within a rich ecosystem and communities, this project will help attain the full potential of AI to uplift communities, democratize scientific and technological discoveries, and build societal trust by emphasizing the theme of “better together,” i.e., humans and AI working in concert to realize human potential. Furthermore, the traineeship will serve as a test bed for a transformative doctoral education model with essential components for innovative graduate education that will be customized, iterated, refined, scaled, and sustained. The project will develop a new graduate professional certificate and will further devise a Skills and Experiences Pathway that will be expanded across graduate and professional programs. Ultimately, this project will advance new M.S. and Ph.D. training models steeped in a rich and diverse regional innovation ecosystem related to AI and Semiconductor Manufacturing that will serve as a blueprint for other institutions. The NSF Research Traineeship (NRT) Program is designed to encourage the development and implementation of bold, new potentially transformative models for STEM graduate education training. The program is dedicated to effective training of STEM graduate students in high priority interdisciplinary or convergent research areas through comprehensive traineeship models that are innovative, evidence-based, and aligned with changing workforce and research needs. 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 research team will study galaxies that just recently shut down their last burst of star formation, transforming from youthful active galaxies like our own Milky Way into sedate galaxies that age in place. While this process normally takes billions of years, the team has developed methods to find galaxies that experienced a rapid cutoff in star formation. The project will further develop these methods and use a wide set of data from telescopes around the world to understand why galaxies do (or don't) rapidly quench star formation. The program will support research experiences for undergraduate students at both Texas A&M University and the University of Colorado Boulder. Using novel modeling methods, the team has developed for large ground-based spectroscopic surveys, hundreds of massive galaxies that quenched their star formation within the last billion years have been discovered. The team will use an extensive multi-wavelength dataset for about 50 of these post-starburst galaxies to measure dust and cold gas masses, assess the incidence of active galactic nuclei, and measure obscured star formation. The team will also extend the selection methods to identify galaxies that quenched on slower timescales, which become increasingly prevalent at low redshifts. In addition, the team will expand existing research support programs for undergraduate students to include community building and skill-sharing exercises. 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

Advances in artificial intelligence (AI) have the potential to rapidly transform jobs, organizations, leisure, social life, health care, education, industry, domestic politics, and international relations. American colleges and universities are developing a variety of courses and modules to ensure that students gain not only the technical competencies needed to develop, understand, deploy, and use AI but also the ethical competencies needed to ensure that these advances are used wisely to contribute to a more productive workforce and a stronger, fairer, and more prosperous nation. Despite the rapid expansion of AI ethics education interventions across various institutions, there is a notable absence of empirical research systematically mapping or comparing these interventions. To address this gap, this project aims to conduct the first-of-its-kind national survey on the state of AI ethics education interventions and how faculty and administrators, as well as their institutions, approach AI ethics education. A key aspect of the research is the development of meaningful collaborations between the three R1 universities and regional institutional partners with diverse stakeholders. The research will be conducted through three regional networks, each anchored by an R1 institution that connects area higher education institutions (HEIs) such as (minority-serving institutions (MSIs), community colleges, and research-intensive universities) and actively engages them in the design, implementation, and dissemination of research. Using a variety of methods (e.g., quantitative surveys and qualitative interviews with faculty and administrators, as well as natural language processing analysis of survey and interview data), the project team will analyze the state of AI ethics interventions in diverse institutions across the United States by (a) mining existing interventions to produce a comprehensive overview of current and planned AI ethics education; (b) developing a framework for describing the ways in which the faculty perceive and conceptualize AI ethics education; (c) exploring the factors that affect the decision- making of instructors while proposing, designing, and offering various AI ethics-related interventions; and (d) identifying institutional capacity and needs to support effective AI ethics education. Overall, the research will allow STEM faculty and educational researchers to craft curricula and administrators to develop institutional initiatives that generate AI ethics competencies tailored to the needs of their students, their employers, and their communities. This project is jointly funded by the Directorate for Engineering, the Directorate for STEM Education, and the Directorate for Computer and Information Science and Engineering, and is managed by the Division of Engineering Education & Centers on behalf of the ER2 Program of the Directorate for Social, Behavioral and Economic Sciences. 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 examines how the history of biodiversity conservation has shaped and been shaped in the context of key American borderlands in the late twentieth and early twenty-first centuries, and how biodiversity conservation relates to local social and political issues. This project will foster relationships between stakeholders that span academia, community organizations, environmental non-profits and human rights organizations on both sides of the border in order to build more inclusive and equitable biodiversity science and conservation. This will also enhance public engagement with the environmental sciences in the borderlands and its effects on public policy. This research contributes to the fields of history of science and environmental history by investigating how border politics has shaped the history of scientific knowledge about endangered species. Using archival research and oral history interviews, the project seeks to answer the following research questions: How has the construction of the border wall between 1993 and 2021 shaped the scientific practices of conservation biologists researching endangered species in the borderlands? How has the politicization of environmental knowledge production in the borderlands influenced lines of scientific inquiry in conservation biology? How has scientific knowledge about endangered species been leveraged in debates surrounding the governance and politics of the borderlands? This research is significant because it casts light on the interlinkages between conservation biology, the national borders, and the nation state. 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 recent dynamic evolution of mobility, marked by the integration of self-driving and human-operated vehicles, demands a paradigm shift toward predictive planning driven by advancements in Generative Artificial Intelligence (GenAI). Given the complexity of mixed mobility systems, a convergence approach is necessary. This approach could revolutionize our understanding of adaptive transportation and technology while fostering a diverse, globally engaged workforce with top-notch skills. To address this timely imperative, the project team uses an innovative, three-year US-Korea mutual workforce development initiative, hosting over 13 graduate students from each country annually for 6.5 weeks. This initiative aims to catalyze advanced international research alliances in next-generation workforce development by focusing on the seamless convergence of GenAI and disruptive technologies into unified student learning outcomes. Built upon the success of a prior IRES initiative, which has inspired a parallel Reverse US-Korea IRES initiative by the National Research Foundation of Korea, the proposed program focuses on groundbreaking research that integrates large language models into adaptable and responsive mobility solutions. This IRES program propels the development of a diverse, globally engaged workforce with world-class skills, as it is designed to function with enhanced intensity and efficiency towards fostering the exchange of novel ideas among IRES fellows. Such a collaborative environment within the overarching theme of ‘Mobility Solutions of the Future with GenAI’ will enable IRES fellows to address impending grand challenges in diverse contexts in order for them to develop globally applicable, verifiable, and repeatable theories, use cases, and algorithms. Six foreign collaborators at KAIST in Daejeon, South Korea, will contribute to transformative action learning opportunities and mentorship for the IRES fellows during their stay in South Korea. The long-term goal of this project is to expedite the development of new knowledge and technologies in GenAI by cultivating a globally competitive and diverse research workforce. The main objectives of this IRES are twofold: first, to foster the growth of a skilled workforce in the emerging field of mobility-GenAI partnership, and second, to generate a new and effective mobility-GenAI knowledge use case database on an annual basis, which can then be integrated into a cohesive cyberlearning platform for sustainable workforce development. The research themes and their convergence in the project's final year will uniquely facilitate comparative analysis and predictive modeling of various management and operational scenarios. This acceleration can maximize societal benefits by fostering a highly safe, efficient, and sustainable future transportation system. Beyond transportation engineering, management, and operation, the interdisciplinary nature of this two-way US-Korea IRES research offers broader social benefits. It delves into understanding human-GenAI interactions concerning transportation policies and their consequential impacts on mobility, safety, and the environment. Collaboration with communities and industry practitioners equips IRES fellows with world-class skills to tackle the complexities and challenges of real-world problems. The proposed IRES workforce training activities are grounded in Experiential Learning Theory (ELT), which underscores the principle of ‘learning by doing.’ Following the ELT principles of knowledge creation and action learning, this proposal outlines a novel three-pronged pedagogical strategy: (1) the development of conceptualized use cases, (2) transitioning from traditional pedagogy to an entirely research-driven project-based action learning format, and (3) engaging use-infused research projects. This US-Korea mutual IRES is tailored to provide a robust foundation for theoretical and practical exploration, yielding groundbreaking insights into the interconnected impacts of mobility, safety, society, and global warming. 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.