University Of Texas At Austin

NSF Awards · FY 2025 · 2025-01

Texas communities face increasing challenges from weather-related disasters, population growth, and demographic shifts. Many rural and underserved areas lack the resources to effectively plan for and respond to these hazards. The Texas WEATHER program aims to address this critical need by fostering connections between hazard scientists and local practitioners for actionable knowledge at the science-policy-practice interface. The objective for this project, Texas WEATHER: Whole-community Efforts to Adapt, THrive, and Enhance Resilience, is to develop a community of practice through outreach and engagement, ultimately improving Texas communities' resilience to extreme weather events. We will foster collaboration between academic institutions and local organizations to identify and support 'resilience stewards' - community leaders who can champion holistic, locally tailored responses to climate hazards. By bridging connections between these stewards and academic resources, we aim to enhance resilience, particularly in underserved communities that are isolated due to geography, language, or economic barriers. The broader impacts of this research include increased capacity through new relationships, knowledge, and skills, and an executive education curriculum at the intersection of future severity of extreme weather exposure and actionable strategies to enhance resilience. Additionally, this project will benefit the field by improving understanding of how to connect rural and other isolated communities to research centers and science support, as well as identifying gaps in knowledge on effective risk and preparedness communication to diverse communities. Lastly, findings from this research will inform recommendations to improve federal, statewide, and regional support systems. The Texas WEATHER project will employ a co-production approach to develop research questions based on the reciprocal exchange of experiential and scientific knowledge between community partners, hazard scientists, and policy specialists. The project will unfold over two years through four sequential activities aimed at co-producing a research agenda with 2-3 place-based communities to develop actionable solutions for community-scale resilience. Methods include utilizing social vulnerability data and mapping tools to identify at-risk areas, forming community relationships based on reciprocity principles, and engaging communities through online workshops, semi-structured interviews, and surveys. The project will foster peer learning on weather challenges and equip community leaders with strategies for future extreme weather impacts. Key principles guiding the project include reciprocity, reflexive co-production, and asset-based community development, ensuring that community partners are valued for their local expertise. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NIH Research Projects · FY 2025 · 2025-01

PROJECT SUMMARY/ABSTRACT The overall goal of the LEADER Summer Undergraduate Research Program (LSURP) is to increase the diversity of talent in the applicant pool for graduate programs in the environmental health and biomedical sciences both at the University of Texas at Austin (UT Austin) and our peer institutions. Research has unequivocally shown that an inclusive and diverse workforce substantially advances scientific discoveries and that groups of heterogeneous perspectives and backgrounds outperform homogeneous groups in problem-solving and making more significant contributions to scientific impact. However, data indicates an underrepresentation of different populations pursuing graduate studies in biomedical and environmental health sciences, which does not reflect the current and growing demographics in the US. Compounding this problem, the most competitive graduate applicants at The UT Austin College of Pharmacy (UT COP) have prior research experience and familiarity with biomedical and environmental health sciences. Furthermore, candidates who can demonstrate resilience and grit in character when facing challenges are sought out due to the nature of graduate training and the scientific process. With this understanding, the LSURP aims to provide an immersive summer research experience for its participants from Texas' Historically Black Colleges and Universities and Hispanic Serving Institutions (HBCUs & HSIs) and broad exposure to environmental health and biomedical sciences careers. Finally, this program provides training in best practices for graduate school applications, focusing on developing a personal statement that clearly describes the student's background, attributes, and familiarity with environmental health sciences careers. Recruiting and training the next generation of the environmental health sciences workforce, particularly enhancing the workforce's pipeline and diversity, is in direct alignment with theme three: "Enhancing EHS Through Stewardship and Support" of the NIEHS strategic plan.

NSF Awards · FY 2025 · 2025-01

The rapid advancements in computer science (CS) and artificial intelligence (AI) necessitate high-quality computing education for all K-12 students to prepare U.S. citizens for an increasingly technology dependent workforce and society. The Expanding Computing Education Pathways (ECEP) Alliance of 30 U.S. states and territories supports expanding computing and AI education for all K-12 students via state-initiated pathways and practices. ECEP fosters collective impact by promoting cross-state collaboration, equipping state leaders to use data to implement evidence-based and sustainable strategies that promote universal computing and AI education. Non-ECEP states also benefit by participating in professional coaching and convenings, accessing ECEP-developed tools and resources, and working in partnership with ECEP states to expand our nation’s collective capacity around computing and AI education. ECEP's mission is to increase universal capacity for, access to, participation in, and experiences of computing and AI education for all K-12 students. Four primary goals guide ECEP’s work. ECEP seeks to: 1) Collaboratively increase K-12 student pathways to computing and AI-intensive degrees; 2) Strengthen state capacity for CS and AI strategy implementation; 3) Establish consistent, data-driven measurement of student access and participation in computing courses; and 4) Catalyze improved CS and AI student outcomes through targeted state team professional coaching and peer collaboration. The ECEP Alliance prepares students for a future workforce that will require continuous learning, engagement with AI and advanced technologies, and success in an evolving education landscape. ECEP services include research-based toolkits, co-sponsorships that provide investments for scaling initiatives that will support all students, monthly Alliance calls, national convenings with state leadership teams, and a comprehensive communication infrastructure that promotes sharing of best practices across states. The Common Metrics Project, ECEP’s signature data initiative, builds state capacity to leverage and visualize data that leads to improved learning opportunities and outcomes for all students. The Scaling Innovative Pedagogy (ScIP) course, ECEP Connect, and ECEP Deep Dives are other key professional development experiences that are open to CS/AI educators and leaders across the U.S. ECEP delivers a return on investment by building the capacity for high-quality, universal CS and AI education for 33 million K-12 students across ECEP states. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2025 · 2025-01

Among all antipsychotic medications, clozapine is not only the most effective but also has a well-established relationship between its blood concentration and its therapeutic effect. However, clozapine can cause serious side effects (such as seizures or neutropenia) when its blood concentration is high. Although there is a commercially available gold nanoparticle-based immunoassay to monitor clozapine concentration in patients’ blood, it suffers from the issues of poor limitation-of-detection, unstable readings and high assay cost. Here the team is developing new biosensing tools that are more sensitive, reliable and affordable than the colorimetric assay currently in the market. To extend the project's impact, the investigators will work with local K-12 students in several outreach programs, with the goal to train the students to develop novel tools based on the new understanding of aptamers and encourage them into a career path in science, technology, engineering and mathematics. Through the support of this project, the team of investigators will select aptamers that bind clozapine with high affinity and specificity and use them to establish highly reliable aptamer-based sensing tools for clozapine dose titration and routine clozapine’s blood concentration monitoring. Aptamers are DNA or RNA molecules that can bind specific targets strongly, including small-molecule therapeutics. Aptamers will be selected and optimized through the innovative SELEX-NGS platform, which effectively identifies sequences that bind rapidly and strongly with specific small-molecule targets, while the affinities against other structurally similar compound are low. Taking advantage of the selected clozapine aptamers, a personal glucose meter can be repurposed to detect clozapine concentration in blood. In addition, a differential sensor array that discriminates clozapine from its structurally similar compounds can be developed. Moreover, a nanopipette sensing platform for digital quantification of clozapine molecules will also be created. Taking advantage of novel DNA carrier designs, clozapine molecules can be counted one by one as they translocate through a nanopipette device. The acquired nanopipette readings can also be used to further improve the aptamer sensor designs. The proposed research involves both tool development and fundamental understanding of small molecule-aptamer interactions. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2025 · 2025-01

The Eocene (56-33.9 Ma) was a time of profound climatic variability as Earth transitioned from the hothouse, ice-free conditions of the early Eocene, to the warmhouse of the middle Eocene, to the coolhouse of the Oligocene, characterized by lower temperatures and the development of permanent ice sheets on Antarctica. Based on these large changes in Earth’s climate, it is reasonable to assume that the composition of the pelagic calcifier ecosystem in the Eocene changed in response to evolving patterns of ocean circulation, continental weathering, and cooling temperature, but there are few Eocene data available to test this assumption. The proposed research will reconstruct changes of the South Atlantic subtropical gyre ecosystem from the early Eocene to the early Oligocene, and it will determine how these changes impacted carbonate production at the surface and its preservation at the seafloor. Data will be generated using sediment cores collected below the oligotrophic waters of the South Atlantic subtropical gyre during the International Ocean Discovery Program Expeditions 390C and 393 (Sites U1557 and U1558). These data will improve the societal understanding of the evolution of the carbon cycle under different climatic regimes. This research will also generate the first complete Eocene record of pelagic carbonate communities and carbonate accumulation rates for the western South Atlantic Ocean, which remains a poorly studied region during a key interval of the Cenozoic. Broader impact activities include the support for two early career researchers, and an outreach program that will promote the participation of high school students from disadvantaged backgrounds in summer research experiences. Additionally, several undergraduate and graduate students will be involved in research activities. On geological time scales, the biologically-mediated production of calcium carbonate at the ocean surface and the burial of this calcium carbonate at the seafloor influence the marine chemistry and, indirectly, the CO2 concentration in the atmosphere. Thus, these processes are important components of the carbon cycle. However, their evolution through time is not well constrained, in particular with oligotrophic systems, which are less productive but cover vastly more area than upwelling regions. The goal of this project is to study how oligotrophic pelagic calcifier communities evolved during a time characterized by different temperatures and CO2 concentrations in the atmosphere (i.e., Eocene) and how these changes have affected carbonate burial at the seafloor. Specifically, the investigators will test the following hypotheses: 1) Surface carbonate productivity changed from the early Eocene to the early Oligocene (~56-32 Ma); 2) These changes were connected to changes in the composition of the pelagic calcifier ecosystem; 3) Changes in the pelagic calcifier ecosystems were driven by changes in surface currents; 4) Above the carbonate compensation depth, carbonate burial was not impacted by deep-ocean circulation. The investigators will evaluate project hypotheses by generating and interpreting mass accumulation rates of planktic foraminifera and calcareous nannoplankton (the main producers of carbonate found in deep sediments), planktic foraminiferal assemblages and percent fragmentation, and benthic foraminiferal accumulation rate from two new International Ocean Discovery Program sites drilled in the western south Atlantic, Sites U1557 and U1558. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2025 · 2025-01

This research will develop a fundamental understanding of the multi-functional response of a new class of engineered materials: three-dimensional woven architected nano-composites. Built by using leading-edge additive manufacturing techniques with sub-micron resolution, these woven architected materials will be highly deformable and will provide electrical responses that change based on the amount of deformation sustained by the materials. This coupled mechanical and electrical behavior endows these materials with deformation- and pressure-sensing capabilities, enabling their use as highly sensitive and tunable sensors. The fabrication, modeling, and experimental research will prepare this new class of materials for application in high-performance electronic skins - a key technology for next-generation soft robots, prostheses, and bio-electronics. This research will also develop fundamental theoretical and computational tools that will enable prediction of electrical and mechanical properties of these materials. The research efforts will be complemented by (i) an educational outreach effort that will introduce mechanics and materials concepts to a broad general audience, (ii) enhancements in undergraduate curricula with novel information and concepts on multi-functional materials, and (iii) a mentoring program focusing on broadening underrepresented-community involvement in the mechanics of materials academic field. This research will determine the material-structure-property relations of 3D-woven architected nano-composites to validate the hypothesis that printable nano-composites with tunable electrical properties such as resistivity or dielectric constant, combined with the complex nonlinear mechanical responses of 3D-woven architectures such as self-contact and entanglement, will provide a new paradigm for the design of high-performance electro-mechanical sensors. Combined experimental, computational, and analytical approaches include microscale 3D printing of functional nano-composites, in-situ microscale multi-axial mechanical testing, in-situ macroscale electromechanical characterization, nonlinear computational modeling and simulations, and a circuit-model-based analysis. These approaches will be used to connect the material and structure of the architected nano-composites to the electromechanical performance of stretchable 3D architected nano-composite sensors. 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 2026 · 2024-12

MODEL-DIRECTED DESIGN OF VAGINAL STENTS TO PREVENT POST-RADIATION STENOSIS Debilitating vaginal stenosis is an under-recognized, poorly understood sequela that occurs in up to 75% of patients who undergo pelvic radiation treatments. Patients with vaginal stenosis suffer from pain, inability to pass menstrual contents, difficulty with pelvic exams, inability to have intercourse, and may require corrective surgery. There is a critical need for a patient-forward vaginal stent that can apply constant pressure to maintain vaginal caliber post-radiation treatment. Our proposed design for a self-fitting vaginal stent utilizes a shape-memory polymer (SMP) foam that can assume a secondary, compressed shape for ease of deployment. Upon insertion, the change in temperature and hydration initiates foam expansion to shape fit to the individual patient and restore the lumen of the stent to allow egress of vaginal secretions. Our exciting prior work on the development of aSMP stent by this team demonstrated deployment with self-fitting shape recoveryand retention in both benchtop and pilot rabbit studies. In the proposed studies, we will add an antifouling coating to prevent bacterial attachment and a new geometry for ease of removal. Computational models that capture the complex geometry, material behavior, and boundary conditions will be developed to provide an in silico testing framework to accelerate design of gynecological devices. Upon completion, we will have developed aself-fittingvaginal stent that addresses current egress failures, developed new computational vaginal models and benchtop testing for gynecological device development, and elucidated key mechanisms in preventing/reversing post-radiation stenosis. The synergistic team undertaking the proposed studies has the requisite experience in clinical practice (Hakim), polymer engineering (Cosgriff-Hernandez), shape-memory polymer chemistry (Grunlan), and computational modeling (Rausch) to complete these studies. Although our immediate goal is to address the urgent clinical need of post-radiation patients, a self-fitting vaginal stent would have direct extension as a therapeutic modality for post-surgical vaginal stenosis.

NSF Awards · FY 2024 · 2024-12

Florida coral reefs are declining dramatically since 1980’s, but there are still some coral species that are doing surprisingly well, such as the great star coral Montastrea cavernosa. According to recent analysis, this species is composed of six genetically distinct but otherwise identical-looking lineages, termed the “cryptic species”. The investigators hypothesize that the remarkable resilience of M. cavernosa comes both from ecological differentiation and ecological redundancy among its constituent cryptic species. Cryptic species complexes are very common in corals, and our project is designed to elucidate fundamental aspects of their ecology and adaptation. Results from this project will help guide future coral reef management, conservation, and restoration. The data from this project are being used for training early-career marine biologists in ecological genomics, and support one graduate and two undergraduate students. The charismatic and fragile nature of reef-building corals makes the project an ideal vehicle to reach out to the general public about the challenges marine ecosystems are now experiencing. To test the hypothesis, transcriptomics are being used to estimate the relative importances of within-cryptic-species plasticity and between-cryptic-species ecological differentiation for survival of the M. cavernosa cryptic species complex as a whole under stress. The team is using samples from common garden and reciprocal transplantation experiments, and will also apply genomics to determine whether cryptic species share adaptive genetic variants. This project is supported by the Biological Oceanography Program and Evolutionary Processes Cluster. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2024 · 2024-12

The current clinical trial system is notoriously inefficient and resource intensive with only 10% of drugs that enter the system eventually being approved for patient use. This is due, at least partially, to how difficult it is to test all the possible interventions (for example, drugs or surgery) in large groups of patients to determine that they are safe and effective. Furthermore, it is not possible to vary the order, dose, and timing of any promising intervention as there are simply too many combinations and not enough resources (or patients) available to test all the options. Thus, the clinical trial system currently attempts to determining the utility of one intervention delivered in a very limited number of schedules. This project will use mathematical models built on the key characteristics of cancer to build virtual patient populations upon which a large range of interventions can be “tested” via simulation. The results of this project could be used to, for example, select the intervention strategy with the highest likelihood of significantly reducing patient mortality, enriching the patient population to those most likely to benefit from the intervention, or eliminate interventions that are unlikely to achieve FDA approval. Success in this project will result in a fundamental shift in the way clinical trials are currently designed and executed. There are three main technical objectives for this project. The first one is to the improve computational efficacy of organ-scale, biology-based mathematical models to enable high throughput screening of novel interventional strategies. Through the use of surrogate models, we will ensure that the timescales of computing the effect of a set of interventions on a virtual patient population are within acceptable timescales—all while maintaining the interpretability of the results. The second one is to develop rigorous mathematical techniques to generate a cohort of stochastic virtual patients with unique patient anatomies and physiologies with uncertainty (i.e., distributions of model parameters to capture inter- and intra-patient heterogeneity) by combining parameter distributions obtained from model calibration to the historical patient data. We will then validate this approach by reproducing the results of historical clinical trials. The third and final technical objective is to perform a virtual clinical trial that systematically tests an array of practical therapeutic interventions that vary the dose and timing of standard-of-care chemotherapies on a virtual breast cancer patient population to determine the safety and efficacy of novel therapeutic interventions. This will provide the method to perform computationally efficient virtual clinical trials at scale. By completing these technical objectives, we will provide the community with a methodology to dramatically improve the efficiency of in-person clinical trials or, even, eliminate them entirely by evaluating in silico a large range of interventions—in parallel—on representative virtual patient cohorts of the target disease. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2024 · 2024-12

2507652 (Locmelis). The United States considers 50 mineral commodities as ‘critical minerals’ that are essential to the national economy and safety. The United States relies heavily on imports for 43 out of the 50 critical minerals, which means strengthening domestic supply chains for these minerals is a pressing need. This requires a multidisciplinary approach including mineral exploration, material flow analysis, mining and mineral processing practices, metallurgy, policy making, supply chain economics, sustainability, reclamation, and workforce development. This award provides partial support for the North American Workshop on Critical Mineral Research, Development and Education to be held August 13-14, 2025 at the University of Texas at Austin. Participants will include stakeholders from academia, the private sector and government agencies. They will discuss conventional and unconventional resources of critical minerals from multidisciplinary perspectives, including mineral exploration, resource and reserve evaluation, mineral processing and recycling, policy making, supply chain economics, workforce development, and artificial intelligence and machine learning in critical mineral research and development. International leaders will give keynote presentations on the current state of knowledge, highlighting potential strategies to address critical mineral research, development, and education. Presentations by participants, poster sessions, and breakout sessions will facilitate discussions of additional concepts and perspectives. It is anticipated that the workshop will serve as a long-term platform for the budding critical mineral community in the USA and will nurture relationships among participants from academia, government agencies and the private sector – thus facilitating multi-disciplinary collaborations with the goal of strengthening U.S. critical mineral supply chains. The workshop will provide a truly multidisciplinary platform to discuss critical mineral research, development and education from different perspectives. Sessions will be structured around pressing research needs in several areas, including mineral resources, mineral processing and recycling technologies, policy making, supply chain economics, and pathways towards growing the U.S. critical mineral workforce. The workshop committee will prepare a comprehensive multidisciplinary report that that will summarize the workshop discussions and can be used to inform decision-making in pursuit of strengthening U.S. critical mineral supply chains. The hybrid in-person/virtual workshop will nurture the budding national critical mineral community by providing a multidisciplinary discussion platform for participants from academia, the private sector, and government agencies. Travel grants, primarily for students and early-career researchers, will help build a community that spans generations and ensure the meeting’s lasting impact. The development of a healthy and active critical mineral community is pivotal to the national safety and economy because the United States remains import-reliant for most commodities on the critical minerals list. The workshop’s breakout sessions are designed to foster networking between stakeholders from a wide spectrum of disciplines who otherwise rarely interact. To ensure an impact beyond the dates of the workshop, the workshop activities will be used to help start research and educational collaborations. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2024 · 2024-12

The broader impact/commercial potential of this I-Corps project is the development of a wearable technology for dysphagia monitoring. Dysphagia is a healthcare condition characterized by difficulty in swallowing food or liquid. Current dysphagia management involves invasive, uncomfortable, and costly procedures, reaching only a fraction of the 9.5 million affected individuals in the U.S. annually. The proposed technology is a knitted fabric sensing collar offering non-invasive, continuous monitoring with significantly increased diagnostic precision. Its comfort and usability make it suitable for continuous wear, enabling early detection and management of swallowing disorders. In addition, this technology facilitates early intervention and reduces clinical session times by 25%, and integrates into telehealth frameworks. The device may be extended to potential applications in other muscle monitoring needs, paving the way for broader diagnostic and therapeutic uses. The goal is to integrate advanced wearable technology into routine medical practice, enhancing patient quality of life and fostering more efficient healthcare delivery systems. This I-Corps project utilizes experiential learning coupled with first-hand investigation of the industry ecosystem to assess the translation potential of an advanced sensing knitted wearable technology designed for continuous, real-time monitoring and classification of swallowing actions. This device integrates three knitted strain sensors into a low-profile, ergonomic collar, enabling seamless wear with everyday attire. The sensors accurately measure laryngeal elevation, crucial for dysphagia management and rehabilitation. Bluetooth technology transmits data to a graphical user interface (GUI), providing immediate clinical feedback for remote monitoring and timely intervention. In addition, the device incorporates advanced signal processing and machine learning models, enhancing swallowing classification accuracy by 15-50% over traditional surface electromyography (sEMG). The proposed technology offers a non-invasive, efficient alternative to conventional dysphagia management with up to 12 hours of continuous monitoring per battery charge, reducing the frequency of clinical visits. This wearable sensing technology may offer a promising solution to enhance patient outcomes and facilitate proactive healthcare monitoring. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2024 · 2024-12

The broader impact/commercial potential of this I-Corps project is the development of an advanced surgical tool designed to improve patient safety and reduce complications during surgery. The project focuses on creating a device that integrates real-time monitoring and enhanced visualization to prevent tissue damage that causes Mastectomy Skin Flap Necrosis, a devastating complication for breast cancer patients. This technology can be applied across a wide range of surgical procedures beyond mastectomies, potentially reducing the incidence of complications, minimizing the need for additional surgeries, and lowering overall healthcare costs. By addressing a critical need for improved surgical precision, this project aims to enhance overall patient outcomes and reduce healthcare expenses, positioning the technology as a valuable asset in medical settings with broad implications for improving surgical practices and patient care. This I-Corps project utilizes experiential learning coupled with a first-hand investigation of the industry ecosystem to assess the translation potential of the technology. This solution is based on the development of a surgical retractor equipped with sensors that continuously monitor retraction levels and provide real-time feedback to surgeons. The technology integrates a miniature camera to enhance visualization, similar to an endoscope, and relies on data-driven algorithms to assess safe pressure ranges. Preliminary research has demonstrated the retractor's capability to identify retraction levels and alert surgeons before excessive pressure causes tissue damage. By integrating these features, the technology offers a novel approach to preventing complications and advancing surgical practices. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2024 · 2024-12

The differential development of economic systems between societies and the effect access to resources, distribution of goods, and subsequent wealth and power has on social systems are important discussions facing archaeology and the contemporary world. Throughout human history, the distribution and access to environmental resources has been an important stimulus for the development of different forms of economic systems, political institutions, and regional interaction. This doctoral dissertation project builds on and contributes to such discussions by developing a strategy for understanding the procurement and distribution of a key economic resource. The project develops a multimethod and reproducible approach for lithic sourcing to elucidate variations in procurement, production, and distribution of lithics through diverse economic networks. Being one of the most accessible and widely used lithic resources, chert products serve to provide a deeper understanding of the political and economic systems among all sectors of society. The methodological developments that this project provides opens the door to a wealth of new research projects involving lithic economies. This project produces a large and detailed computerized database of open-source geochemical and petrographic data. The project examines the application of petrographic, geochemical, and computational methods to chert sourcing with the goal of creating a methodology for identifying and distinguishing between individual raw material sources. The ability to determine where lithic material was being procured and what exchange spheres they were operating within is one of the most fundamental and productive paths for studying the organization of prehistoric economy. Provenance studies involving chert have largely been underutilized due to methodological challenges associated with sourcing siliceous material. The research combines micropaleontological, petrographic and Laser-Ablation Inductively-Coupled-Plasma Mass-Spectrometry data with Social Network Analysis to determine procurement and distribution patterns of chert resources. Through geological survey and laboratory analysis, this project builds a reference collection of primary and secondary raw material potentially utilized and explore the integration of chert materials into discussions on community-based production, local exchange and social and economic divisions. 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

Hurricane Helene made landfall in Florida on September 26, 2024, and proceeded to produce extreme damage and casualties from wind and flooding after moving inland. This Rapid Response Research (RAPID) study seeks to determine the role that land surface conditions may have played in the maintenance of Helene after landfall. The researchers intend to further investigate the theory that wet conditions prior to a landfall can slow the decay of hurricanes. The overall project will offer insights into how different meteorological, land-based, and public perception factors interact during hurricanes, contributing to improved prediction models and better disaster preparedness strategies. The research team intends to examine precipitation and land-atmosphere feedbacks during Hurricane Helene to further address the Brown Ocean Effect, which is a phenomenon where wet soil surfaces contribute to the maintenance of a tropical system after it makes landfall. The research team will pull together surface, radar, and satellite measurements of precipitation to produce a detailed view of key rainfall parameters. Satellite-based soil moisture data will be examined and numerical weather modeling will be used to simulate how soil moisture influenced the storm’s intensity post-landfall. The project will also compare precipitation forecasts from AI-based models with traditional weather models. Finally, the project has a social media component, where the researchers will analyze social media posts to understand how public perception evolves in response to hurricanes, which can be critical for future disaster communication strategies. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NIH Research Projects · FY 2026 · 2024-11

ABSTRACT The mental disorder of schizophrenia is often debilitating, affecting cognition, behavior, perception, and speech. Adults aging with the diagnosis of schizophrenia spectrum disorders are a vulnerable population, with unique, complex problems that include multiple physical and psychological co-morbidities. Factors associated with increased morbidity and mortality are frequently identified in this population, individuals who are often reported to live 20 years less than peers who are not diagnosed with their condition. Their early mortality and increased morbidity have been linked to social isolation and loneliness. In our pilot exploratory interviews with these individuals from May 2021 to May 2022 (N = 10 state hospital and 20 nursing home residents), many adult women aging with schizophrenia spectrum disorders stated that they felt alone and reported a need for increased social engagement and connectedness in their institutionalized settings (Walker, 2023; Walker & Harrison, 2023). We view them to be experts in their own health care, and we are further seeking their perceptions to better understand social connectedness from their viewpoint. In this project, we explore (a) the life course experiences of institutionalized adult women aging with a diagnosis of schizophrenia spectrum disorders with a focus on their experiences of social connectedness, (b) their perceptions of how social connectedness is approached in their institutionalized settings, and (c) their perceptions of how social connectedness could be improved in their institutionalized settings. Homogeneous purposive sampling and qualitative analysis of interviews will be used to determine factors related to participants’ perceptions. Our overall purpose is to inform interventions to decrease loneliness and social isolation and to improve social connectedness in adult women aging with the diagnosis of schizophrenia spectrum disorders in institutionalized settings. This small, self- contained research project represents a step in assisting healthcare professionals to improve health care outcomes among adult women aging with the diagnosis of schizophrenia spectrum disorders, promoting both their physical and mental health as part of a long-term project to improve their quality of life and provide solutions to the complex problems associated with their condition.

NIH Research Projects · FY 2026 · 2024-11

PROJECT SUMMARY Polyomavirus (PyV)-associated diseases arise in immunocompromised hosts and involve reactivation of viral replication from a persistent reservoir. However, little is known about the factors that control reactivation. There is an urgent need to fill in these gaps because developing a more thorough understanding of how viruses establish, maintain, and reactivate from persistent infection may yield information on effective treatment options for PyV disease and allow more patients to benefit from immunosuppressive therapies without the threat of developing PyV-associated diseases. Our long-term goal is to screen PyVs armed with genome-wide shRNA libraries. Our objective here is to optimize PyV shRNA expression and develop methods to conduct screens of shRNA-expressing PyVs. We hypothesize that PyV genomes can tolerate shRNA insertions and that these will be active during infection. The rationale for this proposed research is that the mechanism of PyV persistence/reactivation has been unresolved for decades, and that deciphering this is key to any true understanding of PyV biology. Further, such understanding may lead to new approaches for the prevention and treatment of PyV-associated disease as well as be informative more broadly to small DNA virus persistence. We plan to test our central hypothesis and complete the objectives outlined in this proposal via the following two specific aims: 1) Optimize PyV shRNA expression, and 2) Conduct proof-of-principle pilot screen on shRNA- armed PyVs. This application utilizes two recent advances including non-invasive assays of virus shedding over time and a proven combined shRNA molecular barcode strategy that monitors changes in the composition of a population of viruses. In Aim 1, varied shRNA backbone elements and genomic orientations are probed to generate the most active shRNA inserts with minimal impact on the PyV lifecycle. For the second aim, a pilot shRNA-armed PyV library targeting a subset of components of the type I interferon (IFN) pathway will be screened to assess altered acute and persistent shedding and persistent organ infection. Our contribution here is expected to be a proof-of-principle for shRNA library technologies for small DNA viruses, which will allow broader understanding of small DNA virus persistence and may lead to new approaches for preventing and treating PyV-associated diseases. This is expected to be significant because it will provide a foundation for future knowledge of translational importance regarding PyV-associated diseases while also helping to solve a long- standing mystery: How do small DNA viruses undergo long-term and in some cases life-long infections? The research proposed in this application is innovative because it focuses on arming individual viruses with shRNAs capable of dissecting the functional contribution of numerous host genes to acute and persistent infection in a single experiment format – a potentially powerful approach that has so far been absent in the study of small DNA viruses.

NSF Awards · FY 2024 · 2024-10

The recent revolution in artificial intelligence (AI), together with advances in telescopes and computing, has opened new frontiers for astronomy data processing and analysis. Meanwhile the rapidly increasing amount and complexity of information has created fresh barriers to access and explore data. The NSF-Simons AI Institute for Cosmic Origins (CosmicAI) will grow transformative AI advances with the overarching goal to increase accessibility of astronomy data and knowledge for researchers, students, and the general public. CosmicAI will leverage partnerships between academia, government, and industry to develop capabilities that enable astronomical researchers to conceptualize, define, and execute research projects via trustworthy, efficient, robust, and explainable AI methods. By building next-generation AI tools the Institute will accelerate discoveries related to the most basic human question: Where do we come from? To advance participation by the full astronomical community, CosmicAI will develop an extensive, multi-tiered AI educational program and launch an AI data platform to host data and support AI analysis tasks. The NSF-Simons AI Institute for Cosmic Origins (CosmicAI) will grow transformative AI advances, reform research workflows, and increase astronomy and AI accessibility through developments in four fundamental AI pillars: trustworthiness, robustness, explainability, and efficiency. CosmicAI will advance foundational generative AI and democratize data and knowledge access by developing an AI Copilot model built on astronomical data archives. In preparation for the prodigious data production of the next-generation observing facilities, CosmicAI will advance AI techniques for the analysis of large-scale, high-dimensional data in order to optimize the processing, delivery, and analysis of astronomical observations. CosmicAI will boost the transparency of AI methods by developing techniques for explainable inference and applying them to interrogate the nature of dark matter. Finally, CosmicAI will extend AI techniques to confront computational bottlenecks and accelerate current modeling capabilities, including tackling the formation of complex molecules, a process that underpins problems spanning galaxy formation to habitability. CosmicAI will serve as a nexus point to boost participation in AI advances: CosmicAI will launch the first AI-Astronomy online transcripted certificate program, distribute AI educational material in bootcamps, summer schools, and online education repositories, and launch an AI Data Platform to host astronomical data and support AI analysis tasks to enable the full community to engage with AI advancements. This institute is jointly funded by the NSF and The Simons Foundation. 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

Metals are critical components of modern society. Aside from recycling, metals are recovered from ore deposits such as magmatic sulfide systems that often formed close to the surface and thus provide relatively easy access to metals like nickel and copper. Because ore deposits are non-renewable, we must find (at least) one new deposit for each exhausted resource to guarantee a steady metal supply in the future. Currently, mineral exploration efforts focus mostly on the upper crust because this approach has worked well over the past century. However, a recent decline in new discoveries implies that most of the deposits near the surface have already been found. Consequently, the opening of search space deeper in the Earth is necessary to guarantee a steady metal supply. This project will address this issue by investigating the formation of a series of variably metasomatized magmatic sulfide deposits in the Ivrea-Verbano Zone in Italy. These deposits formed in the deep crust and were tectonically uplifted to mineable levels. Although the deposits were previously mined for nickel and copper, surprisingly little is known about how they (and similar deposits elsewhere) originally formed. This project will use the deposits in the Ivrea-Verbano Zone to better constrain how and where such deposits preferentially form, particularly focusing on the role of mantle metasomatism in deep crustal ore formation. The findings will provide an important step toward including such deposits in future exploration models. The research will be integrated with an educational component that will reach K-12, undergraduate and graduate students. A high school outreach program in collaboration with Project Lead The Way will use examples from this project to highlight geoscience career paths that K-12 students may initially not be aware of, particularly in rural Missouri. The PI will also incorporate aspects of the proposed research into a summer training program for undergraduate students to help students prepare for a post-baccalaureate geoscience career. Furthermore, two graduate students will be actively involved in the research component. The Ivrea-Verbano Zone is a well-preserved cross section of the subcontinental lithospheric mantle and lower continental crust that outcrops subvertically and therefore allows for comprehensive studies of deep lithospheric mass transfer. Previous studies on the Ivrea-Verbano Zone have significantly contributed to our understanding of the lower continental lithosphere. However, research that focuses on the source of metalliferous hydrous melts/fluids, and their role in the transport and local deposition of metals (i.e., ore deposits) is rare. To address this knowledge gap, this project will investigate different localities in the Ivrea-Verbano Zone that represent variable degrees of metasomatism and sulfide mineralization: (1) strongly metasomatized / highly mineralized pipes, (2) un-metasomatized / moderately mineralized sills/intrusions, and (3) a strongly metasomatized / sulfide-poor mantle peridotite. Field studies will be integrated with petrographic observations, bulk rock, mineral and fluid inclusion analyses, isotopic (Cu, Sm-Nd) and geochronological studies. The proposed research will provide new insight into (1) the physical and chemical processes that produce, transport and concentrate fluids and metals in the deep lithosphere; and (2) the spatial and temporal scales of such processes. The PI is actively recruiting K-12 teachers, undergraduate and graduate students to be involved in this project. The goal of the K-12 outreach program is to raise awareness for geoscience disciplines and to encourage students to consider geoscience or other STEM career paths. Undergraduate and graduate student projects will be individually tailored to prepare each student for a career path of their choosing in academia, federal/state agencies, or the private sector. 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

Spectrum sharing in mid-band offers unprecedented opportunity to harness desirable frequencies for commercial 5G operators and for unlicensed use, although higher priority incumbents need to be reliably detected. As an example, the requirement of detecting naval ship-borne radar signals by the environmental sensing capability (ESC) sensors in the 3.55-3.7GHz Citizens Broadband Radio Service (CBRS) band severely limits the transmission power for 5G operators. The objective of this project called MEDUSA: Mid-band Environmental sensing capability for Detecting incUmbents during Spectrum shAring is to detect the presence of static/mobile radar and anomalous transmissions within concurrent and comparatively higher power 5G and 4G-LTE signals. It achieves this through machine learning (reacting to existing interference) and receiver antenna design (proactively avoiding interference). MEDUSA will enable ESC sensors to work with different types of wireless signals, both for individual spectrum monitoring and via collaborative methods for enhanced incumbent detection accuracy. The project will result in open-source release of antenna design files, datasets and learning algorithms for the research community. It also includes several outreach and dissemination activities such as hosting recordings of interviews with spectrum experts on the project website, recruiting students from under-representative groups, and designing course projects that use CBRS-related datasets. The project has three goals for radar detection in the Citizens Broadband Radio Service (CBRS) band but is also generalizable for other frequencies. First, it proposes a deep learning framework to enhance the discriminative abilities of the environmental sensing capability (ESC) sensor while preserving privacy by using spectrogram inputs. These sensors will detect radar pulses within 5G and 4G-LTE signals with powers stronger than FCC-mandated levels by 5 dB. Furthermore, the PIs will develop transfer-learning methods for unseen conditions. Second, it will advance the science of collaborative inference, when multiple ESC sensors make (i) independent and (ii) joint decisions by fusing individual predictions using the algorithms developed as part of the first goal. It also proposes a method of fusion of spectrograms and raw in-phase and quadrature (IQ) samples. Third, when the geographically separated and arbitrarily spaced ESC sensors are time and phase synchronized, they form a massive virtual array for receive beamforming. The PIs will design real-time weight adaptation algorithms and horn antennas that can create nulls towards known 5G/4G-LTE base stations. Finally, the research goals will be validated in emulation as well as over experimental testbeds through the NSF Platform for Advanced Wireless Research (PAWR 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

Artificial Intelligence (AI) has led to groundbreaking progress in tasks such as image recognition, classification, speech, and natural language processing. However, the implementation of machine learning AI models is costly in terms of energy, storage, and computation, making them unsuitable for integration into resource-limited sensors. Weightless Neural Networks (WNNs) represent a distinct class of neural models inspired by the processing of input signals by biological neuron dendritic trees. They are small, fast, and energy-efficient. This project focuses on integrating WNN-based intelligence with cardiac and chemical sensors at the point of sensing. It leverages expertise in machine learning, circuit design, and sensors to develop integrated systems for health and chemical sensing, combining the investigators’ prior work on tiny machine learning networks, ultra-thin wearable health patches, flexible circuit manufacturing, molecular chemistry, molecular biology, electromagnetics, and micro and nanofabrication technology. Of particular interest are intelligent systems for cardiac health sensing and innovative chemistry applications. The integration of intelligence and sensing developed in this project is expected to benefit the common public via health monitoring advances. Integration of intelligence within ultra-thin, lightweight and multifunctional wearable patches which can conform to soft and curvilinear skin surfaces is important for cardiac and other health monitoring applications. Such health monitoring can lead to preventive health measures and personalized healthcare. Inexpensive solutions in this domain can make the use of such sensors pervasive, enhancing health equity for the masses. The chemical sensing platform developed under this project will serve as a tool to enable the promise of basic scientific discovery in chemistry and molecular biology. The project is also expected to train a large workforce in semiconductor technologies. The joint activity between the University of Texas at Austin and the University of Texas at San Antonio involves communities underrepresented in STEM, including women and minorities, as well as first-generation college 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 · 2024-10

This Faculty Early Career Development (CAREER) project aims to enhance the sustainability and resilience of transportation and power systems (TPSs) in response to rapid deployment of electric vehicles (EVs) and clean energy. Traditional, system-specific approaches are often inadequate or unable to address close couplings and decentralized decision-making scheme. This research meets this fundamental challenge by offering a novel mechanism design for system-level planning and operation. The methodologies developed have the potential to extend to other infrastructure systems, where heterogeneous stakeholders interact with each other over a large-scale network. Research findings will help inform future strategies for EV adoption and grid integration of intermittent clean energy sources. The integrated research and education activities are intended to facilitate knowledge transfer to students, practitioners, and the public, including K-12 and college students, utility companies, and transportation planning agencies. The scientific goal of this CAREER project is to advance the understanding of the mechanism design of decentralized TPSs. More specifically, the research efforts will advance the knowledge on (1) network modeling strategies to elucidate the spatiotemporal interactions among heterogeneous and decentralized stakeholders with incomplete information, (2) optimal information sensing and sharing strategies for decentralized TPSs, and (3) equity-aware market mechanism design to optimize TPSs leveraging EVs and clean energy. Meanwhile, it will integrate convexification, decomposition, and variational analysis theories to cope with the computational challenges brought by multi-agent interaction, multi-stage decision-making, and multi-dimensional scenarios for TPSs planning and operation. 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

Testing is the primary means of validating software correctness in practice, and developers often write tests at different levels of granularity. Specifically, unit tests validate individual functions, integration tests validate interactions among functions, and system tests validate end-to-end system behavior. This decades-old categorization of tests is valuable, evidenced by widespread tool and framework support, but it hinders developers from testing at finer granularity levels, such as statements within functions. Yet, many software bugs occur at such finer granularity levels and those software bugs often escape the forms of tests that are used today. This project aims to enable developers to perform fine-grained testing, thereby increasing software quality. Developers will be able to test hard-to-reach and hard-to-comprehend code fragments, complementing existing testing methodologies. The resulting higher-quality software is expected to contribute positively to the US economy. The research will be integrated into curriculum and training. The project's underlying research objective is to increase the efficiency and efficacy of software testing by removing decades-old artificial boundaries that exist between tests and code. To achieve this objective, this project will (1) develop a language and a framework for expressing and using fine-grained tests; (2) automatically generate fine-grained tests from code or existing tests, making it easier to retrofit them to existing code; (3) adapt fine-grained tests to software evolution and use fine-grained tests to improve current regression testing techniques; (4) use fine-grained tests to improve fuzzing and runtime verification; and (5) begin supporting fine-grained testing of non-functional properties, focused on specific security and performance bugs. Proposed techniques will be evaluated via experiments on open-source projects, to evaluate their ability to increase the coverage and bug-finding capability of existing test suites. 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

There has been tremendous growth in U.S. colleges and universities in the number of students with nonapparent disabilities including learning disabilities such as dyslexia and auditory processing disorder; ADHD; and psychiatric conditions. There is not much research about how professors can best support these students. In transitioning from high school to college, disabled students must become more proactive self-advocates to navigate the rules and systems for accommodation, which include talking with each instructor each semester about what they need. Misinformed STEM faculty have little empathy for students with nonapparent disabilities. STEM professors tend to be less willing to accommodate and less approachable than those in other disciplines, which discourages students. These instructors learn slowly and inefficiently, student by student, both the importance of and strategies for supporting students with disabilities. By studying what students with disabilities say works and doesn’t work, and then comparing that with key experiences that help professors improve their teaching of disabled students, this project can inform interventions to improve STEM undergraduate learning environments for students with nonapparent disabilities. This project will combine theoretical perspectives from faculty change and disability studies in education to build theory of STEM faculty readiness and commitment to supporting students with nonapparent disabilities. The PI will use journey mapping methodology to interview targeted STEM instructors about events and influences that increased their knowledge and ability to support students with nonapparent disabilities. Student research assistants will use a parallel journey mapping protocol to interview STEM undergraduates with nonapparent disabilities. The research will combine disparate bodies of knowledge on disability studies and undergraduate instructional change. It applies novel research approaches including journey mapping, longitudinal and change-focused studies of faculty, and combining student and faculty perspectives on accessible teaching. The PI will be mentored by an experienced disability researcher, and the project will develop and document strategies for mentoring disabled student coresearchers. Besides being published in archival journals, the results will also be integrated into future faculty coursework, keynote addresses, and faculty development workshops presented by the PI. The project is supported by NSF’s EDU Core Research Building Capacity in STEM Education Research (ECR: BCSER) program, which is designed to build investigators’ capacity to carry out high-quality STEM education 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 · 2024-10

Reinforcement learning has emerged as one of the predominant frameworks for real-time decision making and control. It has been the driving force behind several recent high-profile successes of artificial intelligence, enjoying success in areas as diverse as robotic control, wireless communications, and protein structure prediction. While reinforcement learning provides a powerful and flexible framework for learning, data efficiency is a fundamental challenge: this framework is known to require significant computational resources and vast amount of data. This challenge limits the applicability of reinforcement learning and keeps it from being applied in problems where training data and computational power are limited, including important applications such as wildfire monitoring and the search-and-rescue of lost people using unmanned aerial vehicles. This project addresses this challenge by developing new mathematical foundations of multi-task reinforcement learning and novel learning algorithms that require less data in the aggregate when multiple tasks are jointly learned. The project integrates the research findings with rigorous educational and outreach activities, course development, and student training. This project focuses on answering two fundamental questions: (1) Under what conditions does it take less data and computation to learn multiple tasks jointly than it would to learn each task individually? and (2) Can reinforcement learning algorithms learn something meaningful with only a limited amount of data and computation? Our approach to answering these questions draws on techniques from online learning, compressed sensing, and stochastic modeling. In particular, this project covers both offline settings, where the similarity structure between tasks is learned from a given data set, and online settings, where this learned structure is used to efficiently adapt to a new task “on the fly”. The project also addresses the fundamental problem of catastrophic forgetting in multi-task learning, where the learned policy loses the ability to perform a previous task after training for a new task. Over the course of this project, the proposed research activities will be evaluated systematically through a series of simulations of multi-robot navigation. 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

Massive Multiple Input Multiple Output (mMIMO) antenna systems will be an important technology for future mobile telecommunication networks to meet high data transfer rates, assured reliability, and reduced latency, all of which will enable many new and exciting applications. A key feature of mMIMO is the ability for directional transmissions by suitably coordinating the settings of large number of antenna elements, which requires careful alters the signal characteristics of phase and amplitude for each element. In collaboration with Torsten Braun at the University of Bern, Switzerland, this project aims to address the challenge of computing the parameters required to properly configure these massive antenna arrays in real time using the concepts of reinforcement learning and federated learning (FL). This project will forge new connections not only between the U.S. and Switzerland through collaborative research, bi-directional visits, and joint coursework development, but also between machine learning and wireless communities. The PIs will give also record short video tutorials on applied machine learning targeting wireless engineers, and release these on media-sharing platforms. Finally, all findings derived from the research activities, including position/vision papers, will be disseminated in top peer-reviewed conferences and journals in networking and communications. Beamforming for directional transmissions in mMIMO involves adjusting the phase and amplitude of the transmitted signals to direct the signal to the intended receiver and minimize interference with other users. However, the channel estimation process, a pre-requisite step for setting precoding data bits transmitted over a multi-antenna system, can be computationally intensive and time consuming. This project considers the challenges associated with beamforming in an mMIMO system, significantly increasing the computational overhead over classical MIMO. Even with rapid strides in computing technology, classical processing cannot keep up with the demands of configuring an mMIMO system in real-time, such that the entire process is completed within the channel coherence time. The project has two major scientific objectives to address this challenge. It aims to advance the state-of-the-art in resilient and personalized channel estimation using (i) distributed and federated learning and (ii) multi-modal sensor data. For objective (i), the research will address challenges of contamination of pilot signals due to interference and design of personalized FL for channel estimation based on shared knowledge among. For objective (ii), the research will leverage multimodal sensor data, transfer learning and attention-based transformer neural networks to minimize model training costs and delay. The concepts, approaches, and algorithms developed will be validated in real-world experiments and simulations based on realistic collected data on the NSF Colosseum and in over-the-air testbeds. The data sets, code developed for the models and algorithms will be available for independent validation and re-use. This proposal was awarded as part of the NSF-Swiss NSF Lead Agency Opportunity for unsolicited proposals (NSF 23-049). 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.