University Of Texas At Austin

NSF Awards · FY 2025 · 2025-08

Hydrogen is a promising low- to zero-carbon resource and could pave the way to a clean energy economy across multiple sectors, including chemical manufacturing, agriculture, and transportation. Electrocatalysis is at the forefront of technologies that could help realize a hydrogen economy at scale. Earth-abundant, low-cost electrocatalysts that help chemically split water into hydrogen and oxygen are a promising platform for hydrogen generation. A major challenge is that electrocatalysts often undergo complex transformations during their operation, and these transformations are poorly understood. This project addresses this knowledge gap by combining experimental and computational techniques to probe these atomic level changes and understand how local structural changes impact electrocatalytic activity. The project will provide a rich ecosystem for training students in state-of-the-art computational and experimental techniques. The research results will be integrated into mentorship of undergraduate researchers and educational outreach programming incorporating computational and experimental aspects of research in electrochemistry. Disordered electrocatalysts have been shown to outperform crystalline ones. In many systems, electrochemical cycling leads to the formation of (oxy)hydroxide phases on the surface, which serve as the active electrocatalytic layer. While transition metal (oxy)hydroxides are among the most active and prominent alkaline oxygen evolution reaction (OER) electrocatalysts, the specific structural features responsible for their performance, particularly in their disordered forms, remain poorly defined. This project will establish structure-activity relationships for the (oxy)hydroxides as a model system, focusing on three sources of disorder for modulating the active site: iron incorporation, variation in proton decoration, and local structural distortions of the octahedra. The project will address several fundamental questions: What are the relevant types of disorder present in (oxy)hydroxides? How are these types of disorder related to the activity? Do these types of disorder lead to new active sites and/or more active sites? The project will employ a combined theoretical and experimental approach to achieve the research objectives. Electronic structure calculations based on density functional theory (DFT) will be used to evaluate OER free-energies. Measured vibrational, X-ray diffraction, and X-ray absorption spectra will be combined with simulated counterparts, alongside electrochemical characterization. These complementary tools will be used in a feedback loop to validate atomistic models against spectroscopic signatures and trends in electrocatalytic activity. The outcome, which will draw explicit connections between structural disorder and OER activity, will provide the necessary foundation for understanding how (oxy)hydroxide electrocatalysts evolve during electrochemical cycling. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2025 · 2025-08

This project supports the documentation, analysis, and revitalization of a language that has undergone significant grammatical change. Grounded in theories of language contact, creole formation, and second language acquisition, the study contributes to our understanding of how languages change and adapt over time. Additionally, all the linguistically annotated texts derived from language documentation projects such as this project create machine-readable infrastructure that is vital for training AI models. The project brings together academic researchers and language stakeholders to document and analyze natural language use, creating educational resources and digital archives, and engages youth in research through training in language documentation and linguistics. This research investigates the structure and evolution of a contact language that has undergone significant grammatical change due to generational shifts and external influences. The project documents forty hours of natural speech through sociolinguistic interviews and linguistic elicitation, which are transcribed, annotated, and analyzed using computational tools. The resulting data feed into an open-access digital corpus. The project advances translational research by transforming theoretical inquiry through collaborative-based research that also supports linguistic vitality. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2025 · 2025-08

This project develops synthetic cells that mimic the essential functions of biological platelets – shape transformation, biochemical secretion, and mechanical adhesion – to promote blood clotting in a programmable and externally controllable manner. Platelets are among the simplest cells in the human body, yet they perform a complex and highly coordinated sequence of actions in response to vascular injury. The proposed synthetic cells emulate these sophisticated behaviors using light-activated control systems and biochemical recognition, enabling them to work in concert with natural platelets to initiate and reinforce clot formation. This work addresses a critical biomedical need by offering a potential solution to the persistent shortage of donor platelets used in trauma care, surgery, and cancer treatment. More broadly, the project demonstrates how synthetic cells can be engineered to interact functionally with living systems. The investigators are also implementing a robust educational outreach program that engages undergraduates, graduate students, K–12 teachers and students in hands-on research and classroom-based learning. By integrating cutting-edge research with multi-level education and outreach, this project trains the next generation of scientists and engineers. Synthetic cell research has made tremendous progress over several decades but now sits at a crossroads, where integrating multiple functionalities into a single synthetic cell remains elusive. This project aims to construct platelet-like synthetic cells with three coordinated, externally controllable capabilities. First, the project is developing light-responsive protein condensates that drive actin polymerization and cytoskeletal remodeling, enabling synthetic cells to undergo shape transformations that mimic platelet activation. Second, the team is engineering connexin nanopores that can be gated by near-infrared light to release dense granule components into the extracellular environment, thereby activating nearby natural platelets. Third, the project chemically reconstructs integrin complexes across synthetic cell membranes, forming transmembrane mechanical linkages that connect extracellular fibrin binding to intracellular actin networks. These three modules are developed independently and then integrated into a single synthetic cell platform capable of responding to external stimuli with precise spatiotemporal control. The resulting synthetic cells are evaluated in vitro for their ability to promote clot formation and activate natural platelets under both static and flow conditions. This project significantly advances the state of the art in synthetic cell engineering by demonstrating the coordinated function of multiple synthetic subsystems within a membrane-encapsulated platform. 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 · 2025-08

Project Summary Cystic fibrosis (CF) patients with nonsense mutations of the disease-causing CF transmembrane conductance regulator gene (CFTR) cannot benefit from existing small molecule therapies and need gene therapy of the CFTR mutation to restore any function and ultimately, for a cure. Since 95% of CF-associated comorbidities affect the respiratory tract, pulmonary administration of gene therapies would be advantageous, as it would increase the therapeutic index by significantly increasing dosing 10- to 20-fold without incurring the off-tissue toxicity seen with systemic administration. For inhalation gene therapy, the gene delivery systems must possess the physicochemical properties to overcome the transport barriers, such as mucus and reach the target cells in the airways at therapeutic concentrations. However, current approaches in the clinic would need repeated dosing since they do not target the basal cells, or the airway stem cells, for one-time, permanent correction. Also, gene therapy approaches currently in clinical trials do not fix the mutation but provide a corrected CFTR copy, whereby the therapeutic benefit is temporary. To address these challenges, it would be desirable to develop nucleic acid delivery systems that can reach the airway basal cells and fix the CFTR mutation towards permanent correction of lung CF disease. Here, we will develop peptide surface-functionalized lipid nanoparticles (pepLNPs) for pulmonary delivery that can reach the basal cells in vivo and deliver base editors in relevant cell culture and animal models to precisely edit and correct specific G542X and G553X nonsense mutations present in CF. Previously, we have identified peptide ligands that can penetrate through the mucus barrier present in CF and in preliminary evidence, demonstrate that these pepLNPs, can reach and achieve editing in the basal cells. Leveraging our platform technology, we propose to make pepLNP formulations that can stably encapsulate, protect, and deliver mRNA encoding base editing components, to reach, and edit basal cells in vitro and in vivo. Importantly, we will elucidate how these pepLNPs penetrate through the mucus barrier and are taken up by basal cells. Finally, we will validate that upon delivery to the basal cells, pepLNPs will deliver adenine base editors to correct the genotype and restore functional phenotype of cells that had possessed the previously undruggable nonsense CFTR mutations. This proposed work can have a transformative impact, as it aims to achieve one- time, “permanent” correction for durable gene therapy of CF by targeting the basal cells and specifically fixing the inherent CFTR mutation. While this strategy focuses on CF, in the long-term, this strategy can transform treatment of many genetic diseases needing permanent correction.

NSF Awards · FY 2025 · 2025-08

Sustainability of water supplies in the Andes Region in South America is put at risk by a warming climate, which is causing glacier retreat and contributing to the decline of lakes and reservoirs. This research project will analyze sediment cores from Lake Titicaca—the highest navigable freshwater lake in the world—and use these data to help uncover how natural Earth system processes have influenced water supply, glacier coverage, and ecosystems in the high Andes over the past 370,000 years. The findings will help explain when and why major droughts occurred in the past—and what these events can reveal about future water-related risks. Findings will be shared through a bilingual website, public presentations and outreach. All data and models will be freely available to researchers, policy makers, and local partners. By linking ancient climate events with present-day challenges, this work will help regional planners and communities understand and address future risks to water resources. This research project will develop a new record of precipitation, isotopes, temperature, and lake level spanning the past ~370,000 years using legacy drill cores from Lake Titicaca. The project will use these data to explore how the surrounding environment has changed, with a focus on understanding long-term water resource availability in the Andes. The analysis of geochemical and isotopic signals preserved in the sediments will be used to reconstruct how the regional environment responded to environmental shifts over glacial–interglacial timescales. To deepen understanding of these changes, the sediment-based environmental reconstructions will be paired with computer model simulations and AI-assisted methods for refining large-scale data to the local level. New time-slice experiments with isotope-enabled climate models spanning the last glacial cycle will help to better understand the drivers of environmental changes, including the relative roles of local and remote forcings on the South American Monsoon System, and will be a resource for future research by the paleoclimate community. This award is co-funded by the Division of Earth Sciences (EAR) and the Division of Atmospheric and Geospace Sciences (AGS). 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 · 2025-08

Project Summary The ideal replacement heart valve (RHV) should have sufficient durability, resistance to thrombosis, and excellent hemodynamics that lasts the remaining patient lifetime, which does not yet exist. The majority of current RHV are ‘bioprosthetic’ heart valves (BHV), with leaflets fabricated from chemically treated pericardium, as originally developed in 1971. While providing for an initially effective therapy, all BHV continue to suffer from limited durability resultant from mineralization and mechanical fatigue. The growing use of minimally invasive transcutaneous designs that utilize thinner leaflets results in even greater leaflet mechanical demands and may suffer from more limited durability. In addition, all pericardial biomaterials have intrinsic structural variability that greatly limits their ability to be further improved for extended durability. This present lack of improved RHV biomaterials continues to limit our ability to adequately address RHV limited durability. We have developed a class of hydrogel-coated electrospun (HES) biomaterials for cardiovascular applications. HES biomaterials have been successfully utilized as vascular grafts, in which a luminal hydrogel provided thromboresistance and an electrospun mesh sleeve provided mechanical reinforcement. HES biomaterials can be fabricated over a wide range of mechanical behaviors that encompass RHV design requirements, which we have shown can be accurately modeled. When combined with our high speed RHV simulation methods, it is possible to identify optimal biomaterial and leaflet geometry characteristics, so that we can develop RHV with greater durability. To facilitate both HES biomaterial development and leaflet functional performance, we will extend a novel non- contacting optical method to sensitively detect fiber structures with pixel-level resolution and exploit our significant in vitro and large animal model evaluations. We thus hypothesize that the rational development of HES biomaterial-based RHVs will lead to a new generation of durable replacement heart valves.

NSF Awards · FY 2025 · 2025-08

Hydrogen is becoming an important source of energy in the United States, and underground salt caverns are being considered for hydrogen storage. However, hydrogen is a highly reactive gas that can interact with natural microorganisms and minerals in the environment, potentially creating harmful chemicals like hydrogen sulfide or methane. These chemicals could contaminate drinking water supplies, damage wells, or escape into the atmosphere. This project will use experiments and mathematical modeling to advance scientific understanding of underground reactions of hydrogen in salt storage caverns. The project will study the extent of the reactions, the products formed, and the implications of the reactions on leaks into shallow aquifers. The project results will lead to improved efficiency, performance, and scalability of hydrogen storage, and protection of groundwater resources. The project team will train undergraduate and graduate students in research. The team will also create educational outreach programs to teach high school students about energy storage and the hydrogen economy. The United States is on the verge of a hydrogen (H2) revolution. Candidate storage reservoirs for H2 are mainly dissolution caverns in salt formations (either bedded or domal salts) and saline aquifers. However, H2 is an especially mobile and reactive molecule; it can embrittle metal, promote iron corrosion, and pass through nanofractures, leading to concerns that it will compromise well and/or formation materials and escape to overlying aquifers or to the atmosphere. This is especially concerning in salt caverns mined in shallow bedded salt formations or salt domes that are within several hundred meters of the ground surface with only thin beddings of overlying anhydrite or halite serving as caprock. An even greater concern is that stored H2 will transform to harmful by-products (such as hydrogen sulfide and methane) that reduce the quality and quantity of stored H2, more rapidly compromise well materials, harm drinking water aquifers, or escape into the atmosphere. The potential for such transformations has been identified, but the extent of these reactions under realistic biogeochemical conditions in salt caverns remains an open scientific question. This project will advance fundamental understanding of biogeochemical conditions that drive microbial-driven H2 consumption in salt caverns, and the risks that such reactions present to overlying potable-water aquifers and the atmosphere. This project comprises four objectives: (1) determine the biogeochemical conditions that promote H2 reduction with alternative electron acceptors; (2) determine shifts in microbial community dynamics and gene expression associated with H2 reduction coupled to alternative electron acceptors; (3) identify H2 reduction hotspots in salt caverns and the risk to H2 storage wells and seals; and (4) quantify and extend experimental results by using a biogeochemical model to simulate H2 reactions under a wider range of potential salt cavern conditions. Microbially-driven H2 consumption, reaction rates, and alternative acceptor consumption in evaporite materials will be measured. Microbial community dynamics and gene expression will be evaluated. Water transport, hydrogen consumption hotspots, and effects on storage security will be determined. A biogeochemical model will be developed and applied to a broad set of storage conditions. The project will train undergraduate and graduate students, and will create educational outreach programs to teach high school students about energy storage and the hydrogen economy. 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 · 2025-08

PROJECT SUMMARY The overarching goal of this research proposal is to (i) demonstrate a completely novel approach for cancer immunotherapy in which degradation of extracellular sphingosine-1-phosphate (S1P) enhances lymphocyte trafficking to and infiltration of tumors, especially those that are known to exclude and/or strongly suppress cytotoxic T cells and (ii) generate a developmental candidate therapeutic S1P degrading enzyme suitable for subsequent clinical development. Patients with tumors that have a “cold”, “immune excluded”, or “immune desert” phenotype typically have poor outcomes on immune checkpoint therapy (ICT). These immune “cold” tumors often result from impaired trafficking of lymphocytes from the sentinel lymph nodes into circulation. Extracellular S1P, a bioactive lipid arising from the tumor and/or tumor draining lymph nodes (TDLN) appears to play a significant role in blocking tumor specific T cell circulation and infiltration by stalling or sequestering lymphocytes in the TDLN, keeping T cells from entering circulation and migrating to tumors. Along these lines, elevated S1P synthesis arising from tumoral upregulation of a kinase that generates S1P, SPHK1, strongly correlates with resistance to immunotherapy and poor patient survival in multiple tumor types. In preliminary studies, we developed a prokaryotic S1P degrading enzyme tool compound (StS1PL) and demonstrated in murine cancer models that treatment with StS1PL depletes extracellular S1P, dramatically increases the accumulation of activated T cells in tumors, and strongly or completely inhibits tumor growth when administered as a single agent. We found that the anti-tumor effects of StS1PL strongly depend upon CD8+ T cells and that the CD8+ T cells found in the tumor are antigen-specific and appear highly activated. The work proposed here will test the hypothesis that the reduction of extracellular S1P is a powerful modality for releasing tumor specific T cells from TDLNs, allowing them to recirculate and enhance tumor immune surveillance. Through this work, we will determine if immune “cold” tumors can be made responsive to immunotherapy (Aim 1), and we will elucidate mechanisms by which S1P reduction facilitates T cell activation, differentiation, redistribution between tissues, and trafficking to the tumor microenvironment (Aim 2). Successful completion of these studies will yield a tractable strategy for treating tumors that are refractory to immunotherapies, as well as a more detailed understanding of the process by which impaired trafficking of T cells may occur. We also expect to deliver an optimized human therapeutic enzyme displaying the requisite catalytic function and pharmacological properties (stability, PK/PD) for subsequent clinical evaluation (Aim 3).

NIH Research Projects · FY 2025 · 2025-08

PROJECT SUMMARY/ABSTRACT----------------------------------------------------------------------------------- --- Greater than 80% of US adults do not meet physical activity recommendations leading to years of preventable disability, diminished well-being, early death, and annual healthcare costs estimated at upwards of $300 billion. Despite strong medical evidence that sedentary behavior is a health risk, current methods to increase physical activity rely on willpower and/or knowledge (e.g., doctors’ suggestions, public media) have been unsuccessful. Thus, there are currently no effective long-term methods to increase physical activity participation. Individuals who experience pain-relieving, destressing, antidepressant, and/or euphoric psychological affects during physical activity are more likely to participate. The source of these reinforcing affects has been linked to endogenously produced endocannabinoids. We hypothesize that enhancing the action of endocannabinoid receptors (CB1) during physical activity will increase these positive affective states leading to greater long-term participation. Under the mentorship of Drs. Jerrel Yakel and Guohong Cui at the National Institute of Environmental Health Sciences, along with the support of ten established scientists with expertise in the proposed studies, this Pathway to Independence Award offers an opportunity for postdoctoral fellow, Dr. Ayland Letsinger, to explore this hypothesis through three specific aims: SA1 (K99): To determine whether endocannabinoid release in the ventral tegmental area reinforces wheel running, SA2 (K99): To determine whether amplification of CB1 activity response to endocannabinoids by a CB1 positive allosteric modulator (PAM) will increase wheel running in mice that are innately low wheel runners, and SA3 (R00): To determine whether amplification of CB1 activity response to endocannabinoids via a CB1 PAM will increase wheel running in mice that have undergone an intervention to reduce wheel running. These findings will have a profound impact by identifying a target that can be acutely modulated to enhance long-term voluntary participation, thereby significantly improving human health. Moreover, this grant will support Dr. Letsinger’s transition to becoming an independent scientist in the field of addiction research. The award provides opportunities for training in photometry and pharmaceutical applications, comprehensive instruction in addiction-related study design and theory, mentorship from a diverse group of scientists in academia and government, and the production of high- quality data. This technical and professional foundation will support future research endeavors in addiction and related fields, enhancing Dr. Letsinger's potential to make significant contributions to science.

NIH Research Projects · FY 2025 · 2025-08

Project Summary / Abstract The long-term objectives of this work are to develop a platform for modeling disease transmission during outbreaks, and to deploy that platform into research laboratories and public health settings across the United States. Through these objectives we will accomplish our goal of providing critical insights into how disease spreads through different population compartments and the effects of various intervention strategies. Stratifying the population into compartments based on risk status or social determinants of health including including social vulnerability index, front line workers, pregnant individuals, children, and the elderly, will help us understand if targeting interventions toward areas of health disparity will have greater effect in slowing the spread of disease. These insights ultimately will give researchers and public health experts the tools to make real time decisions and better forecast uncertain outcomes when data is limited. The work proposed here builds off a previous collaboration and a prototype platform. The proposed work is divided into three Specific Aims. In Aim #1, we will transform and re-architect the front and back ends of the platform to standardize the data model to improve efficiency and compatibility with established visualization libraries, enable modeling of all US states at a range of scales – including statewide, metro areas, and cities, by county, zip code, or census tract, and add support for simulation concurrency and checkpointing, driving scalability and robustness. In Aim #2 we will write new functions to ingest additional data sources, including social vulnerability index and others, develop a toolkit to help other researchers augment the existing models with custom disease models, travel models, and intervention strategy models, and expand on the existing unit and functional tests to increase code coverage and improve documentation for future contributors. Finally, in Aim #3, we will leverage our existing network of epidemiological researchers and public health experts in order to perform hands-on testing, gather feedback, and refine the platform interface and functionality, and optimize the deployment strategies for single-user and multi-user environments in real-world settings to ensure reproducibility and foster a community-driven approach.

NIH Research Projects · FY 2026 · 2025-08

Project Summary Computational neuroscience has entered a new era of greatly increased capability to record neuronal activity and to construct large-scale computational models. The challenge before us is to connect theory and experiment by testing and comparing complex brain-computational models with massive measurements of neural activity. Con- necting experimental results to computational models is critical for deepening our understanding of the neural circuit computations that collectively give rise to perception, cognition, and behavior, basic brain functions with fundamental clinical implications. This project would launch a close collaboration between theorists and experi- mentalists to develop a rigorous and comprehensive methodology for evaluation and comparison of our new big models with our new big data. A central concept that has gained momentum over the past two decades is the concept of representational geometry. The representational geometry is the geometry of the points or trajectories in the multivariate neural population response space that are thought to represent the contents of brain compu- tations. Although a variety of estimators of representational distance have been proposed previously, none of them are well-suited for neural activity data. Adequate estimation of the representational geometry from neural response data requires carefully accounting for the marginal Poisson distribution of the noise, noise correlations, the context of the neural manifold, and biases caused by electrode sampling as well as by the sensitivity of es- timators to noise displacements. We will address key conceptual and technical challenges and conduct the first large-scale, objective evaluation of a broad range of options for evaluating representational models. Aim 1 is to develop a general-purpose methodology for testing theories implemented in neural network models with modern neurophysiological data. The methodology evaluates and inferentially compares models on the basis of their pre- dictions of neural representational geometries, and encompasses encoding models and representational similarity analysis, methods that allow different levels of flexibility in fitting model representations to neural data. Aim 2 is to validate the methodology developed in Aim 1 in the context of neuroscientific applications, where computational theories are evaluated with neurophysiological data. On the one hand, we will use model-based simulations and sub-sampling of neural recording data sets to validate the inferential methodology in scenarios where ground truth is known. On the other hand, we will seek empirical answers to exciting theoretical questions in the domains of vision and spatial navigation. Overall, the project will build a comprehensive methodology for linking theory to experiment in computational neuroscience in the new era of neural network models and modern neuroscience methods. The methodology involves formal inferential comparisons of brain-computational models that implement alternative theories. The project also launches the ongoing open-source development of this critical methodology through a series of workshops for theorists and experimentalists.

NSF Awards · FY 2025 · 2025-08

In recent years, driven by the impressive advancement in Artificial Intelligence, extremely powerful GPUs and software have become available. Meanwhile, with the increasing availability and importance of drones and robotic devices, the need for efficient algorithms to optimally control these devices has become more pressing. This project will develop novel algorithms with solid mathematical theories, bringing Artificial Intelligence to drones and robotic devices for mission-critical tasks, and pushing the frontier of scientific computing and simulations. In addition to drones and robotic devices, research outcomes will expand simulation capacity for a range of application areas, including nuclear fusion research. Regarding education and human resource development, research outcomes will be integrated into graduate-level courses and a new course series for undergraduates emphasizing the mathematical and numerical analytical foundations for machine learning. This project aims to develop and analyze innovative algorithms that integrate classical numerical schemes and the Deep Learning paradigm, including its software-hardware ecosystem, to address complex scientific computing challenges. By leveraging the flexibility of neural networks, the stability and convergence properties of numerical methods, and the computational power of modern GPUs, the proposed framework will tackle problems in high-dimensional, fully nonlinear differential equations, long-time simulation of Hamiltonian systems, and boundary integral equations. 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 · 2025-08

PROJECT SUMMARY Lung diseases such as Chronic Obstructive Pulmonary Disease (COPD) and idiopathic pulmonary fibrosis (IPF) represent major health issues in the United States, carrying significant economic and health burden. Despite efforts based on quantitative computed tomography (CT) to assess COPD severity and predict disease progression in IPF, current methods are limited by inherent variability, the influence of breathing effort on CT measurements, and an inability to capture the full complexity of disease progression. To address these challenges, we propose a novel biomechanical lung breathing model that we will leverage to infer patient-specific lung tissue material properties using well-established methods in numerical optimization and automatic differentiation. Our hypothesis is that early microvascular changes associated with lung conditions such as COPD, IPF, and radiation-induced pneumonitis can be detected and quantified with patient-specific material properties inferred from forced inhale/exhale CT (IE-CT) images. To test this hypothesis, we will utilize CT data fromthe Genetic Epidemiology of COPD (COPDgene) study, which is a multicenter observational study designed to identify genetic factors associated with COPD. This rich set of longitudinal data for 10k patients with varying degrees of COPD includes IE-CT scans. These scans will be used to 1) develop an inverse finite element method for estimating patient-specific material parameters and 2) assess their utility as predictive markers for COPD mortality. Our approach is the first to develop a forward finite element breathing model that takes inhale CT scans, pleural pressure boundary conditions, and lung material properties as inputs and generates an estimated exhale CT image. Using the forward model, we will construct an optimization framework based on well- established optimization and automatic differentiation methods to infer patient-specific material properties from IE-CT. Recognizing that ground truth biomechanical information is not currently available with paired IE-CT, our validation strategies leverage established approaches for deformable image registration validation. Moreover, the diagnostic and predictive utility of the recovered material properties will be assessed using longitudinal COPDgene mortality data. As opposed to quantitative CT approaches that attempt to normalize against the effects of breathing effort, we propose developing a new class of material property markers that are inherently independent of breathing effort variations. Our proposed models have the potential to better inform both clinical decision making and assessments of therapeutic efficacy.

NSF Awards · FY 2025 · 2025-08

This project focuses on developing new mathematical techniques that help us analyze signals more effectively by carefully isolating their distinct parts, whether in time, space, or frequency, without interference. Tackling this challenge enables researchers and engineers to build practical tools that clean up noisy signals and reveal essential details. These improvements directly enhance everyday technologies, such as delivering sharper and clearer MRI scans, boosting the reliability of our wireless communications, and speeding up accurate data reconstruction. By making these technologies better, the project not only pushes the boundaries of science and engineering but also makes real-world differences, improving healthcare diagnostics, ensuring emergency messages get through reliably, and enhancing national defense capabilities. Additionally, the project emphasizes education and community outreach, providing valuable research experience to undergraduate and graduate students in science and technology. Public workshops and outreach activities are planned to inspire broader interest in mathematics, encouraging future generations to pursue scientific careers and ultimately contributing to a stronger scientific workforce. This project aims to improve our understanding of special mathematical tools known as spatio-spectral limiting operators (SSLOs), which help analyze signals precisely in both space (or time) and frequency. Although mathematicians have thoroughly studied these tools in one dimension, the more complex case involving multiple dimensions still has many unanswered questions. Addressing these questions is important because it can significantly improve technologies that impact everyday life, such as MRI machines, wireless communication, and scientific imaging techniques. The research team will develop and study Gaussian wave packet systems, an advanced version of traditional tools like wavelets and Gabor systems. The use of these new systems will effectively manage the challenges of multi-dimensional signals by incorporating translations, modulations, and dilations. They will investigate how the properties of these mathematical tools change, particularly when dealing with regularly shaped domains like balls or other smooth geometric shapes, and how stable these properties are when applied to more complicated shapes, including those with small holes or gaps. To enable practical computation, they will specifically focus on radial wave packets for simple shapes like discs, making it easier and faster to perform analysis essential to signal processing tasks, such as interpolating and integrating data. Finally, the research team will extend these mathematical ideas to discrete-time scenarios, offering valuable insights useful in various applications, from digital signal processing to advanced imaging techniques. Overall, the project’s results will offer significant advances in mathematics while providing practical tools that enhance medical imaging, improve communications technologies, and enable more accurate scientific data analysis. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2025 · 2025-08

Viruses are experts at outsmarting the defenses of the animals they infect, but scientists are still uncovering exactly how they do this. This project investigates a group of viral proteins that help viruses hide from the immune systems of birds, reptiles, and insects. Studying how these proteins work across such a wide range of animals will reveal whether viruses have used the same tricks for millions of years—long before humans ever existed. This would unveil that different types of animals have been using a common virus defense that will help the biotechnology industry design better ways to fight infections in people, animals, crop pathogens, and the environment. The research will also create new laboratory tools and methods that can be used by other scientists, and it will train and provide research opportunities for the next generation of American students and young researchers. By supporting this work, federal funding will help uncover fundamental secrets of how viruses and their hosts have been locked in a battle for survival, key for any real understanding of how hosts and microbes interact. This proposal investigates the evolutionary conservation of viral DUSP11 (vDUSP11) activity across poxviruses infecting hosts from insects to vertebrates, focusing on 5’ triphosphate (5’PPP) RNA balance during infection. Preliminary studies identified 16 distinct vDUSP11s, with evidence that at least two avipox variants act on RNA polymerase III (pol III) transcripts to attenuate RIG-I-mediated antiviral responses. We hypothesize this proviral function is conserved among avipox and entomopox vDUSP11s, reflecting an ancient immune evasion mechanism targeting 5’PPP-RNAs, a known pathogen-associated molecular pattern (PAMP) in vertebrates and possibly broader phyla including insects. To test this, we will: 1.Determine if diverse vDUSP11s modify pol III RNAs in mammalian cells via northern blot; 2.Evaluate effects of vDUSP11 expression on RIG-I activation and gene induction using RT-qPCR; 3.Test whether vDUSP11s enhance vesicular stomatitis virus (VSV) infection in mammalian models; and 4.Assess whether vDUSP11s reduce 5'PPP-RNA PAMP-ogenicity in insect cells to evaluate proviral conservation. Stable cell lines expressing various vDUSP11s will enable transcript, gene expression, and viral replication assays. Anticipated outcomes include identifying universally or selectively conserved vDUSP11 activities, revealing convergent viral immune evasion strategies. These findings will advance understanding of triphosphate balance and reveal new paradigms for conserved host-pathogen 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 · 2025-08

On July 4, 2025, a devastating flood along the Guadalupe River severely impacted communities in Kerr County, Texas. It also caused widespread damage to infrastructure and natural vegetation, including centuries-old bald cypress trees. Because the growth of trees is marked by a growth ring formed during each year, tree rings provide natural records of how climate changed during the decades to centuries over which the tree grew. In some instances, these records can reveal extreme weather events such as hurricanes, droughts, and floods. The bald cypress is a long-lived species growing along rivers in central Texas that can be an important resource to reconstruct past climate conditions. Because central Texas is prone to both drought and flooding, trees damaged or uprooted during the July 2025 floods offer a rare opportunity to salvage wood samples that can be used to reconstruct patterns of extreme events that extend well beyond human memory or written records. Scientists leading this project will use samples of bald cypress trees salvaged from along the Guadalupe River to investigate long-term patterns of changes in the local climate and environment, including extreme weather events such as droughts and floods. Despite the disruption and loss, local community members and public officials are interested in supporting scientific efforts to recover this irreplicable material before it is destroyed during the recovery and cleanup efforts. The information provided by these unique and valuable records can inform water resource planning, flood risk assessments, and community strategies in this part of Texas known as Flash Flood Alley. This Rapid Response Research (RAPID) award supports the urgent collection of cross-sections, stumps, cores, and other anatomical material from mature trees uprooted by the July 2025 flood along the Guadalupe River. Samples will be collected in coordination with local authorities who are leading recovery efforts in this area. The samples will be preserved in a federally recognized tree-ring archive at the University of Arizona, providing a lasting resource for research that can build a history of the frequency and intensity of extreme events such as floods and droughts. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2025 · 2025-08

This project addresses the documentation and infrastructure developing for a low-resource language. It leverages traditional linguistic fieldwork, digital and computational tools for language documentation and linguistic analysis to document and analyze naturalistic speech data of monolingual and bilingual speakers. Low-resource languages have limited or no digital resources, and they often have few speakers. However, these languages offer valuable insights into language sciences and linguistics, as they serve as robust testing ground for formal linguistic hypotheses, which have traditionally been built on data from major, well-known languages. This research will focus on the documentation of an under-studied language. It will investigate structures found within the speech of monolingual speakers and compare it with that of bilingual speakers. By leveraging digital tools for linguistic annotations, the project will produce an annotated speech corpus as well as language datasets with previously undocumented language structures, such as specialized verb forms and noun markers, which offer valuable insights into language change and language variability. The machine readable infrastructure (database and datasets) that results from this project has the potential to provide new training data for AI systems, which rely on naturalistic language data from a variety of languages. The project advances models in language contact and grammatical restructuring and contributes to debates about language origins. Additionally, it promotes translational research by bridging academic study with real-world impact through language education. All materials will be archived for open access and future use in linguistic and cultural 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 2025 · 2025-08

Strongly correlated materials are an emerging class of materials where electron-electron correlations play an important role. The strong electron-electron correlations give rise to unique mechanical, thermal, chemical, optical, and electrical properties with applications in lightweight high-strength alloys, energy conversion, catalysis, and porous materials for sensing and separations. Despite their importance, the mechanical characterization of these materials including their properties, stress-strain behavior, surface mechanics, and dynamical response under loading remain elusive. The challenges in characterizing these materials originate from the lack of methods that can accurately determine the electron-electron correlation energies and scale reasonably with the number of electrons on classical computers. Quantum computers offer a potentially attractive approach to tackle these difficult problems. This BRITE PIVOT award supports fundamental research that seeks to develop algorithms to understand the mechanics of strongly correlated materials on quantum computers. The education and outreach plan includes training opportunities in the highly interdisciplinary areas of quantum computing, mechanics, and correlated materials, undergraduate research mentoring, a workshop for graduate students and postdocs pursuing careers in research, and public dissemination of project outcomes, especially a video module on introductory quantum computing concepts. Understanding the mechanics of correlated materials is challenging because of the strong role electron-electron interactions play in the total energy of a material. Quantum-mechanical methods such as the density functional theory are widely used to compute electron-electron interaction energy. However, density functional theory can be inaccurate for strongly correlated materials and beyond density functional theory methods such as, for example, coupled cluster, are needed. While beyond density functional theory methods can treat electron-electron interactions more accurately, they exhibit exponential scaling on classical computers and are not suitable for correlated materials. Quantum computers are a promising solution for correlated materials as they exploit superposition and entanglement principles and can potentially perform calculations in a computationally efficient manner. To accurately determine the electron-electron correlation energy and the total energy of a material, this research will perform computations that will use hybrid classical and quantum hardware and a fragmentation-based framework that integrates variational quantum eigen solver with post Hartree-Fock electronic structure methods like coupled cluster. Once the energy and its variation with strain is understood, fundamental mechanics studies focusing on mechanical properties, anharmonicity, stress-strain behavior, failure, surface adsorption, dynamic behavior under loading, and other phenomena will be performed. The knowledge generated looks to relate electron correlation effects to fundamental mechanics of this emerging class of materials. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2025 · 2025-07

Modern artificial intelligence (AI) techniques, including large language models (LLMs) like ChatGPT, have brought many benefits to our society, ushering in a new age of increased productivity and information accessibility. Despite these dramatic technological advances, modern AI techniques exhibit several drawbacks that limit their applicability and usability in many domains. They are trained on data that can easily become out-of-date in a world that currently generates over 400 million terabytes of new data every day. Modern AIs may generate answers that are difficult to explain or validate, and are therefore hard to trust. Finally, AIs are famously vulnerable to “hallucination,” producing answers that are simply wrong. The goal of this research is to address these shortcomings with a new computer system and software framework that enables efficient improvements to the reliability and applicability of modern AI. AI’s vulnerability to hallucination can be reduced using techniques that augment the context available to the AI with knowledge graphs. A knowledge graph is a way of representing information that represents not just individual data items, but connections between them. Graphs encode structure, hierarchy, and complex relationships, which, if accessible to an AI, can improve the correctness of its answers; at the same time, graphs can provide additional context which can help explain or validate answers, improving explainability. This research is necessary because current computer architectures and distributed computing platforms are not well suited to simultaneously supporting both LLM and large-scale graph computations. The GPU architectures that currently dominate AI are optimized for computations in which all data are laid out in a very regular, dense pattern, while graph computations have historically required a very different kind of optimization to support irregular data layouts. Additionally, advances in software are necessary to support multiple kinds of graph computations over distributed data to query the structure of graphs, analyze them, and make predictions based on those structures and analyses. This research will produce a system called Panther, that comprises a new, highly parallel architecture well-suited to both LLM and graph computations, a new memory system that efficiently supports the combination of large scale graphs and LLMs, and a distributed software framework and applications that collectively realize dramatic improvements for AI efficiency and reliability. We expect Panther to lay the groundwork for the next generation of high-performance trustworthy AI. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2025 · 2025-07

This project, called SMART (Spectrum-data for Machine Learning and Analysis through Robust Feature Transformations), aims to enhance access to radio frequency (RF) spectrum data through principled investigation in novel machine learning (ML) architectures that extract characteristic features from raw data. The project promotes the efficient use of spectrum resources critical for communications, national defense, and technological advancement, especially considering the growing interest in spectrum sharing between federal and commercial sectors. By providing a means to collaborate on spectrum data without compromising sensitive information, SMART opens new research and educational opportunities and trust within the ML researchers both in industry and academia. At a broader level, this democratization of data supports the goal of advancing national health and prosperity through improved communication technologies and informed policy decisions. By utilizing latent embeddings, i.e., higher-dimensional features generated through neural networks, instead of raw IQ (in-phase and quadrature) samples, the SMART project addresses significant challenges in spectrum sharing. The key project goals include reducing data storage requirements, facilitating opportunistic data collection, and ensuring the sharing of useful information without compromising sensitive data. SMART is composed of three interconnected research thrusts. The first thrust focuses on Offline Spectrum Feature Engineering, developing methodologies for generating and validating high-dimensional embeddings to support model training. The second thrust, Latency- critical Out-of-Distribution (OOD) Detection and Optimized Labeling, aims to create rapid detection systems for adapting ML models in real time without relying on raw data. Finally, the Toolkit for Feature-Model Validation will empower spectrum owners and regulators to verify the reliability of third-party ML models. By leveraging previous research in long-term evolution (LTE)-radar signal detection and integrating new data collections, SMART seeks to establish a robust framework for trusted spectrum sharing that can benefit both research and industry stakeholders. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2025 · 2025-07

Large systems of interacting particles arise in different contexts, from physics (in understanding e.g. boson stars) to social studies (when modeling social networks). ). Since the number of particles is often very large, rather than analyzing each particle, one would like to understand qualitative and quantitative properties of such systems of particles through some macroscopic, averaged characteristics. In order to identify macroscopic behavior of multi-particle systems, it is helpful to study the asymptotic behavior when the number of particles approaches infinity, with the hope that the limit - which is typically described by a nonlinear partial differential equation (PDE) - will approximate properties observed in the systems with a large finite number of particles. An example of an important phenomenon that describes such macroscopic behavior of a large system of particles is the Bose-Einstein condensation (BEC), which is a state of the matter of a dilute Bose gas at very low temperatures when the gas moves as a single particle. Although the BEC was predicted in early days of quantum mechanics by Bose and Einstein, the first experimental realization came in 1995 (subsequently recognized by a Nobel Prize in physics). Since then mathematical models have been developed to understand such phenomena. Those models connect large quantum systems of interacting particles and nonlinear PDE that are derived from such systems. In this project, the principal investigator (PI) and her team will work on advancing such models and developing new ones by relying on properties of underlying physical systems and on mathematical understanding of nonlinear PDE. In particular, recently remarkable progress has been achieved in understanding how the dynamics of various nonlinear PDE such as, e.g. nonlinear Schrodinger equation or Boltzmann equation arise from large systems of interacting particles. However, there are still many open questions that are motivated by novel analytical studies of these effective nonlinear PDE or directly by physics experiments. The PI addresses some of these questions and continues her work on advancing the connections between large systems of interacting particles/waves on one side and effective equations that stem from these systems upon certain averaging procedures. In particular, the PI and her team will focus on exploring origins of certain geometric and algebraic properties of effective equations, pursue a mathematical study of mixtures of bosons and fermions (which is directly inspired by recent physics experiments), and study derivation and analytic properties of wave kinetic equations that play role in wave turbulence. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2025 · 2025-07

Systems based on modern large language models (LLMs) play an increasing role in how users access information and compose text. For instance, a user executing a web search will increasingly rely on LLM-based systems to summarize their search results, rather than viewing individual web pages, and they might use LLM-based systems to “talk to” long documents like financial reports, rather than reading them in their entirety. To support these new paradigms, it is important that an LLM be able to generate responses that are factual, informative and safe. However, satisfying these criteria is not sufficient: a response should also be at the right level of abstraction or detail, in the right format, creative where appropriate, and aligned with other user needs. Current practice has neglected evaluation of these more subtle factors. This project proposes to address these shortcomings by identifying a set of “evaluation concepts” to indicate the kinds of areas where LLMs are failing, like “lack of detail in a list.” The project will then develop technology for automatically evaluating and improving LLM responses according to these concepts. This project aims to improve the evaluation and the functionality of LLMs in two ways. First, the project will discover a concept taxonomy and learn how to evaluate LLM responses according to the concepts in that taxonomy. This process will necessitate advances in reward models, which are themselves LLMs, customized to reliably score responses. Second, these reward models are applied to actually improve the LLMs’ responses. Specifically, the project will curate training data exhibiting the correct kinds of behavior for each concept, enabling training of LLMs that do better on those concepts. Finally, the project will develop methods for iteratively improving responses using our reward models. The project will open-source the concept taxonomy and reward models that will outperform closed-source, proprietary models. These models will enable the public to have a better sense of the performance of LLM systems across a variety of applications, and will drive the open-source community to build stronger, more reliable LLM systems. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2025 · 2025-07

This award supports student participation at the Thirty-Sixth Annual International Solid Freeform Fabrication (SFF) Symposium, scheduled for 10-13 August 2025, in Austin, Texas. The SFF Symposium is the longest-running academic conference dedicated to additive manufacturing (AM), hybrid manufacturing, and freeform fabrication technologies. The 2024 event hosted 517 attendees, including 243 students, demonstrating strong engagement across both US and international institutions. Student-focused programming is a defining strength of the symposium, with technical sessions, poster presentations, and career development activities tailored to support emerging researchers. The 2025 symposium will expand on successful initiatives introduced in recent years. A structured mentoring program will return, matching 2–3 students with mentors from academia, national laboratories, or industry to support personalized guidance and career exploration. The interdisciplinary session on Additive Manufacturing in Regenerative Medicine, first introduced in 2024, will continue to spotlight innovations in bioprinting and translational biotechnology. These components enhance the educational value of the symposium while facilitating broader engagement across fields and institutions. This award provides funding to provide full registration fee support for up to 60 domestic students from US-based institutions. Selected students must present their research in either oral or poster formats and participate fully in the conference. The selection process, overseen by the conference committee, will emphasize technical merit, institutional representation, and inclusion of first-time attendees to broaden access and professional development opportunities. Outreach efforts will include announcements on the official SFF website, direct emails to authors of accepted abstracts, targeted communication to past attendees, and promotion via professional networks. Supported students will also be invited to dedicated networking events and a career-focused luncheon panel exploring current research trends and future directions in additive manufacturing. By facilitating access, mentoring, and interdisciplinary collaboration, this initiative aims to prepare emerging leaders with the skills, perspectives, and connections needed to advance innovation in manufacturing. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2025 · 2025-07

Many companies now use algorithms or AI tools to make decisions about work—such as assigning tasks, setting pay, or rating performance. These decisions affect millions of workers nationwide. While these systems are fast and efficient, they also raise serious concerns about distribution. These concerns are especially amplified in gig work, where workers often face unstable pay, limited job security, and little ability to speak up. On platforms like Uber or DoorDash, workers often do not know how the app makes decisions about them. They also lack opportunity to question those decisions and must rely on whatever limited information the app provides to judge whether the system is fair and transparent. Understanding how workers make these judgments and how they affect key attitudinal and behavioral outcomes is important—not just for improving gig work but also for designing ethical AI and shaping public policy. Through two studies, this research project investigates how gig workers understand distribution and transparency in algorithmic decision making, and how those views influence their attitudes and behavior. Study 1 uses interviews and observations to explore workers’ perceptions and experience of algorithmic decisions. The study examines what workers can or cannot see and how that shapes their perceptions. Study 2 builds on these findings to create and test a new survey tool that measures algorithmic decisions. The second study also looks at how perceptions relate to outcomes like workers’ trust in the app and long-term work intentions, for this sizable sector of the economy. By centering workers’ perspectives, the project offers insights into building more transparent and fair algorithmic systems. The findings also inform ethical AI design and can guide policy initiatives such as the U.S. Algorithmic Accountability Act. 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 · 2025-07

Project Summary/Abstract A common intervention to prevent initial experimentation with drugs from continuing has been to inflict punishment, as a deterrent. But does punishment work? Studies in humans are surprisingly scant or biased, and rodent models offer the opportunity to test if punishment can deter future drug use, specifically during adolescence, which is when initial experimentation with drugs occurs. Preliminary data in rodent models show that male adolescent and adult rats equally suppress cocaine intake during punishment. However, the next day, when punishment is removed, adolescents resume cocaine intake to pre-punishment levels whereas adults continue to show suppressed intake. Punishment was in the form of contingent exposure to mild footshock during self-administration of cocaine. The goal of this proposal is to establish this phenomenon and its generalizability. Specifically, the first set of experiments will determine if the phenomenon is specific to males or if it generalizes to female adolescents; the second set of experiments will determine if the phenomenon is specific to cocaine or if it generalizes to a non-drug reward (sucrose). This small self-contained set of studies will establish the validity of this model and examine the generalizability of the effects across sexes and a non-drug reward. Results from these studies are significant, because they establish the futility of punishment as a deterrent for limiting future drug use in adolescents. This will prompt revisiting common parental and social interventions. Results from this study will also serve as the basis for a new line of research, aimed at understanding the neurobiological mechanisms underlying punishment resistance in adolescents, by focusing on brain circuits of punishment whose role in adolescent addiction has not been explored extensively.