University Of Texas At Austin

NIH Research Projects · FY 2026 · 2026-04

Project Summary Few therapeutic options exist for triple-negative and heavily treated, hormone receptor-positive metastatic breast cancer (BC) patients. Antibody-drug conjugates (ADCs) improve overall survival for BC patients with low-HER2 expressing tumors by [Trastuzumab deruxtecan (T-DXd), Datopotamab deruxtecan (Dato-DXd), or Sacituzumab govitecan (SG)], but these strategies fail when HER2 targeting is not possible. Moreover, most patients who qualify and respond to T-DXd, Dato-DXd, or SG will relapse or progress, leaving even fewer therapeutic options available. Novel treatments that improve outcomes for patients who relapse after the current standard of care ADCs remain a critical clinical need. We propose a novel ADC-based therapeutic platform for aggressive and resistant BC to address two issues that continue to hamper the development and adoption of ADCs for clinical use: high treatment-associated toxicities due to on-target, off-tumor effects and poor tumor penetration. We will overcome these challenges with two specific technical advances: (1) development of conditionally-active antibodies with enhanced binding to antigens in the tumor microenvironment to minimize impacts on healthy tissues, and (2) drug conjugation via cyclic peptides, including an Arg-Gly-Asp tumor-penetrating peptide, and a neutrophil-elastase resistant linker (EGCit) to enhance drug penetration into tumors. To evaluate our ADC, we will target CD44 variant 9 (CD44v9), a marker associated with poor patient outcomes and overexpressed in the subgroup of BC patients who are resistant or unresponsive to HER2-targeted therapy; notably >50% of all BC are Her2-low and CD44v9+. We hypothesize that antibodies binding the novel CD44v9 target and engineered for optimized selectivity and payload penetration will effectively home to and kill aggressive breast cancer. We will achieve this objective by developing tumor-selective antibodies binding CD44v9 (Aim 1), creating anti-CD44v9 ADCs equipped with tumor-penetrating peptides (Aim 2), and evaluating the efficacy of the ADCs and CD44v9 antibodies in orthotopic and humanized mouse models of HER2-negative BC and BC that is resistance to FDA-approved ADCs (Aim 3). This project is technically innovative for its development of conditionally-active antibodies, the use of tumor- penetrating peptides, the introduction of a novel linker, the evaluation of CD44v9 as a novel target, and the combination of these elements into a single ADC. The results will provide proof of concept for a novel ADC platform for aggressive or drug-resistant BC and provide mechanistic insights into the biological role of CD44v9 in BC. When successful, the newly developed ADCs will offer a critical path forward for patients with aggressive BC who have limited treatment options.

NSF Awards · FY 2026 · 2026-04

This award supports participation by U.S.-based researchers in the special trimester program Mathematical Developments in Geophysical Fluid Dynamics at the Institut Henri Poincaré in Paris, France, from April 13 to June 10, 2026. Geophysical fluid dynamics underpins weather forecasting, ocean circulation, and natural hazard preparedness. By convening mathematicians, physicists, and geoscientists from universities and research institutions, the program will accelerate the exchange of ideas, strengthen the scientific workforce through training opportunities for students and early-career researchers, and broaden participation in this scientifically important field. The activities will be open to anyone interested in the program themes. NSF support will provide travel assistance for U.S. participants and enable broad dissemination of educational materials, increasing access and impact for the U.S. research community. The program will advance the analysis and modeling of rotating and stratified flows governed by nonlinear partial differential equations, with thematic emphases on vortices and vorticity dynamics, waves and instabilities in geophysical flows, and geophysical turbulence. Goals include fostering collaboration between applied scientists and experts in mathematical analysis; improving theoretical understanding of stability, instability, and singularity formation; connecting rigorous analysis with computation and observations; and identifying priority directions for future research. Activities will include an introductory school at Centre International de Rencontres Mathématiques, three week-long workshops at IHP, weekly seminars, round-table discussions, and collaborative working groups. Expected outcomes include accessible lecture notes, curated problem lists, and publicly available seminar recordings. Event information is available at https://indico.math.cnrs.fr/e/GeoMaths2026. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2026 · 2026-04

Discovery in science, machine learning, and artificial intelligence (AI) invariably employs computations with matrices and their higher-dimensional extensions, tensors. Examples include the design of new antibiotics or cancer drugs, the development of quantum computers which can rapidly solve even the harder problems in chemistry and physics, or the construction of novel artificial intelligence architectures which can work more effectively alongside human researchers while reducing energy and hardware costs. These operations typically demand a major part of the computational resources, necessitating the use of fast computers and high-performance software. Convenience and flexibility are also of great importance, as cutting-edge applications often require new functionality and rapid development. The project investigates and delivers a new, adaptable framework for a broad class of matrix and tensor computations, targeting the entire high-performance hardware stack while vertically integrating the software layers. This supports innovation in science and engineering and the application of advanced models to real-world problems. The software is available under open-source license. It is designed to conveniently support existing and future computational tools, while reducing barriers to entry and facilitating the training of the next generation of computational and data scientists. Dense linear algebra software libraries, developed over the past four decades, have had an arguably unparalleled impact on scientific computing and, more recently, machine learning, data science, and AI. While much innovation has happened over this time, the fundamental approach and exported interfaces have changed little. The Framework for Advanced (Multi)Linear Infrastructure in Engineering and Science (FAMLIES) project leverages highly successful prior research and development, sponsored by the National Science Foundation and industry, to develop, design, and deploy a new, vertically integrated dense matrix and tensor software stack. The library targets the entire hardware stack, including single and multi-core, GPU-accelerated, and massively parallel compute environments. It is simultaneously backward compatible via its support of widely used interfaces and forward compatible because it is a framework for synthesizing new functionality. The effort builds on decades of experience by the research team turning fundamental research on the systematic derivation of algorithms into practical software for these domains. This project implements key linear algebra and tensor operations, highlighting the flexibility and effectiveness of the new framework. The software is shared via GitHub, allowing contribution from and dissemination to the broader community. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NIH Research Projects · FY 2026 · 2026-04

Project Summary Visual processing transforms sensory inputs into signals used to guide behavior. These transformations occur over multiple stages of the cortical hierarchy to generate representations that are increasingly invariant to nuisance features such as size, angle and lighting. Such invariance is fundamental for scene segmentation and object recognition. Thus, understanding the cellular and circuit mechanisms underlying these transformations will provide key insights into cortical function. In this project, we will dissect invariant coding of motion direction to the specific components of the stimulus. For instance, when multiple drifting gratings are superimposed to form a plaid, the perceived direction is in the pattern direction, rather than that of the individual components. “Pattern cells” that encode this direction have been identified in the visual system of primates, carnivores and most recently rodents, suggesting that these cells are important for the perception of motion direction. The predominant model proposes that pattern cells are constructed through a series of sequential transformations: direction selectivity is established first, followed by spatial invariance and finally motion invariance. Our preliminary data from mice and marmosets argue against this clean hierarchical transformation, revealing the presence of spatially dependent pattern cells in V1 of mice and marmosets. Here we will test the hypothesis that the foundation for pattern motion is first laid by spatial inhomogeneities in the feedforward pathway and that the major computation is to generate a spatially invariant representation in the higher visual areas (HVAs). In Aim 1 we will examine how thalamic drive supports pattern selectivity in V1 of rodents and primates. We will use extracellular recordings to compare how spatial receptive fields create pattern selectivity and then use intracellular recordings in conjunction with optogenetics to determine how thalamic inputs sculpt these representations. These data will be used to build a receptive field model of cortical processing that explains cellular responses to plaids in V1. In Aim 2, we will determine how motion and spatial invariance increase from V1 to the HVAs in marmosets and mice (areas MT and AL). We will interrogate this transformation by using pre- and post-synaptic imaging approaches to test the hypothesis that spatially variant signals in V1 converge onto individual neurons in the higher areas to generate invariance. In Aim 3, we will investigate the contribution of local inhibitory circuits to pattern selectivity. We will use two-photon imaging to assess the plaid direction selectivity of inhibitory interneurons, and whole-cell recordings to determine how inhibitory inputs are integrated. Finally, these data will be used to elaborate our computational model to determine the role of recurrent circuits in the generation of pattern selectivity. Together, these experiments will provide a mechanistic explanation for the construction of an invariant cortical representation, from the level of synaptic inputs onto molecularly defined cell types within and across cortical areas in two species.

NIH Research Projects · FY 2026 · 2026-04

Project Summary / Abstract Cell polarity is a fundamental feature of eukaryotic cells, and must be coordinated between cells and regulated to allow for normal animal development and tissue homeostasis. Despite genetic identification of proteins involved in cell polarity and a large body of knowledge about their interactions in vitro, it remains unclear how polarity proteins are organized into signaling complexes in cells. This lack of knowledge has prevented the field from understanding mechanisms of developmental control of polarity signaling in vivo. The long-term goal of the proposed research is to resolve the network of protein-protein interactions that supports animal cell polarity and to understand how this network can respond to developmental signals. To enable progress towards this goal, the applicants have developed innovative experimental tools that allow single-molecule measurements of native protein complex abundance in single cells. This project focuses on an evolutionarily conserved protein kinase, called aPKC, that plays a central role in polarity by localizing to one end of a polarized cell and dictating polarized cell behaviors. The applicants will make use of the C. elegans early embryo, in which cells reproducibly polarize in response to multiple spatial and temporal cues, to discover mechanistic links between developmental signals and the polarity machinery. The central hypothesis of this work is that developmental signals control cell polarity by altering the molecular complexes in which aPKC resides. This hypothesis will be explored by elucidating mechanisms that regulate assembly of aPKC into different complexes in the zygote; by determining how polarity is entrained to cell-cell contacts in 8-cell embryos; and by determining how translation of new protein components remodels the polarity system between these two stages. The work proposed in this application is significant because it will reveal fundamental mechanisms controlling cell polarity, and because it places these mechanistic studies in a developmental context. The proposed work is innovative, in the applicant’s opinion, because it uses novel experimental methods to perform biochemical, mechanistic studies in vivo. By studying the biochemical control of aPKC in multiple cellular and developmental contexts in a single experimental system, this work will identify fundamental mechanisms of PAR polarity signaling and to learn how these mechanisms are deployed to achieve different outcomes during development.

NIH Research Projects · FY 2026 · 2026-03

Project Abstract Over the past 10 years, the rates of multiple sclerosis (MS) have nearly doubled in the United States. This chronic, neuroinflammatory, and neurodegenerative disease is most often diagnosed between the ages of 20-40. In many countries, it is the main cause of nontraumatic disability in young adults. Cognitive impairment affects up to 70% of those with MS, in whom the incidence of early onset dementia is 7 times higher than it is in adults without MS. Cognitive- based rehabilitation, however, can improve memory and learning as well as symptoms of depression and anxiety, which may reinforce cognition. Traditional approaches to cognitive rehabilitation use restorative (drill and practice) and compensatory (management) strategies. Yet most cognitive interventions for persons with MS are predominantly visual or speech focused, which eliminates the possibility of stimulating multiple senses. Playing a musical instrument provides multisensory stimulation and feedback to enhance neuroplasticity in the learning process making it superior to traditional cognitive rehabilitation strategies. Music training is a multimodal activity that involves coordinating of sensory and motor sequences with planned actions that require higher cognitive resources. Music training has been associated with frontal lobe function and higher visuospatial, working memory and executive function performance across the life span. The purpose of this study is to determine the feasibility of Music-4-MS, a 12-week music-based, eHealth intervention. The specific aims are to 1) determine the feasibility and acceptability of delivering the Music-4-MS eHealth intervention among individuals with MS over 12 weeks; 2) evaluate the preliminary effect of Music-4-MS on cognitive (objective performance, subjective function), psychosocial (anxiety, depression, social function), and functional (physical function, fatigue, hand dexterity) well-being over time compared to an active control group; and 3) explore participants’ perceptions of the motivation, engagement, connection, and usefulness of Music-4-MS in their daily lives.

NSF Awards · FY 2026 · 2026-03

Reliable, safe, and affordable energy storage is essential for maintaining a resilient electric grid and supporting economic growth. Many rechargeable batteries fail due to the formation of needle-like metal structures called dendrites. Dendrite formation reduces storage capacity and shortens battery life. Aqueous zinc-based batteries use abundant materials, have low safety risks, and offer cost advantages. However, uncontrolled zinc metal growth on the battery electrode limits their practical use. This project will study a new class of electrolytes. They combine liquid-like ion transport with solid-like mechanical strength to resist dendrite formation. The project will identify processes that control metal deposition and long-term performance. The results will support the development of safer, long-lasting, reliable energy storage systems. The results will also benefit manufacturing of high performance energy technologies. The goal of this project is to understand and control zinc dendrite formation by linking electrolyte mechanical properties to interfacial metal growth dynamics. The project will design quasi-solid electrolytes containing swelling clay particles and tune their physical and chemical properties to regulate zinc ion transport and deposition behavior. Specialized in-situ battery cells will be developed to enable three-dimensional X-ray imaging of zinc nucleation, growth, and degradation during battery operation. This non-destructive approach allows direct visualization of early-stage metal growth under realistic conditions. Because dendrite formation is localized and sporadic, machine learning methods will be applied to analyze large image datasets, enabling automated detection of nucleation events, identification of growth patterns, and recognition of failure indicators. By integrating electrolyte design, operando imaging, and data-driven analysis, the project will establish fundamental design principles for suppressing dendrite growth and improving the stability and lifetime of zinc metal batteries. 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 · 2026-02

PROJECT SUMMARY The overall goal of this application is to provide the principal investigator with targeted training in advanced methods for capturing and analyzing daily family dynamics relevant to child mental health. In the long term, the applicant intends to establish an independent research career focused on how everyday family processes shape children’s development, with the goal of informing interventions that strengthen family functioning and support mental health. To support this trajectory, the proposed training plan includes focused development in three key areas: (1) interdisciplinary collaboration to integrate technology into the study of family interactions, (2) advanced statistical methods for analyzing large, multimodal datasets, and (3) the conceptualization and dissemination of findings on adaptive and maladaptive family processes in daily life. Together, these components will lay the groundwork for a research career focused on advancing the science of family processes and enhancing interventions that support child and family well-being. Children’s exposure to family conflict can significantly shape their development, influencing both mental health symptoms and how they manage conflict in the future. While some conflicts promote problem-solving and emotional growth, others escalate into maladaptive patterns that increase the risk for mental health symptoms. Yet, key questions remain about the core features that distinguish adaptive from maladaptive conflict. This study addresses that gap by examining how conflict unfolds in daily life, focusing on natural escalation within individual episodes as well as broader day-to-day conflict patterns over two months. Findings will identify the features that contribute to maladaptive conflict, shedding light on when and how conflict becomes harmful. By identifying these distinguishing features, this research will inform the development of targeted interventions aimed at supporting healthier family relationships. This project addresses two complementary aims, offering both detailed and broad perspectives on parent-child conflict. The first aim takes a “zoomed-in” approach, examining naturally occurring conflict episodes captured through at-home audio recordings. It focuses on specific features such as baseline intensity and the trajectory of escalation, and how these dynamics are linked to same-day and next-day changes in child mood and parent- child interactions. The second aim takes a “zoomed-out” view, using ecological momentary assessment surveys to track daily patterns of conflict intensity, frequency, and duration over a two-month period. This broader approach will examine how families’ overall conflict patterns relate to child mental health symptoms and parent-child relationship quality. By integrating these two levels of analysis, the project will provide a more complete picture of how parent-child conflict unfolds in everyday life—offering valuable insights to guide interventions that strengthen family relationships and promote child well-being.

NSF Awards · FY 2026 · 2026-02

With the support of the Chemistry of Life Processes Program in the Chemistry Division, Jie Li from the University of South Carolina is studying how nature uses enzymes to incorporate and modify sulfur centers in microbial secondary metabolites. These natural products provide a competitive advantage to the producing microbes while also conferring significant fundamental value in medicinal, manufacturing, agricultural applications. In contrast to extensively studied sulfur incorporation in primary metabolism (e.g., nucleotides and amino acids), there is a gap in the general understanding of the installation of sulfur in secondary metabolites. Thus, this study seeks to develop a molecular-level understanding of this life process, which will provide not only valuable sulfur-containing natural products but also opportunities to employ relevant enzymes in practical applications, including synthetic biology and green chemistry. Furthermore, this research synergizes with education and outreach activities aimed at conveying the importance of the chemistry of living systems to diverse groups of students, particularly members of underrepresented minority communities including students at historically black colleges and universities in South Carolina. An important outreach activity will involve a Backyard Chemists program, through which backyard microbial samples will be collected in the larger community, thereby engaging community members in the discovery of new natural products. This research project is directed at the investigation of the enzymology of sulfur incorporation and modification in the biosynthesis of microbial natural products, with initial efforts focused on two unique types of sulfur-containing natural products: sulfenic acids and sulfonolipids. The goal of the research includes understanding how a sulfur center is transferred, incorporated, and modified in sulfenic acids and/or sulfonolipids through a series of enzymatic steps, as part of both primary and secondary metabolism and from both organic and inorganic sulfur sources. A flavoprotein and two pyridoxal phosphate-dependent enzymes and their mechanisms will be studied. A combination of in vivo genetic manipulation, in vitro enzymatic reconstitution, site-directed mutagenesis, enzyme kinetics, and protein crystallography will be employed. Considering the remarkable diversity of sulfur reactivity, this work is expected to both facilitate discovery of sulfur-containing natural products and advance fundamental understanding of enzymatic sulfur incorporation and modification in natural products biosynthesis. This work has the potential to yield new strategies to unleash the power of enzymatic reactions in natural products chemistry. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2026 · 2026-02

With support from the Chemical Measurement and Imaging program in the Division of Chemistry, Professor Jennifer S. Brodbelt of the University of Texas at Austin will develop innovative methods to characterize nucleic acids using mass spectrometry. Nucleic acids are being developed for many purposes, including therapeutics, sensors, diagnostics, and data storage. Dr. Brodbelt and her group will design and implement novel analytical methods that integrate high performance mass spectrometry and gas-phase separations to determine the structures of nucleic acids and elucidate their interactions with other molecules. Laser-based photoactivation will be used to dissociate nucleic acid ions to pinpoint modifications with high sensitivity. Students at multiple levels will participate in collaborative, interdisciplinary research and engage in professional development to support their future careers. Training students as innovative scientific leaders is a key driver for this research program. The enormous degree of programmability of nucleic acids, meaning the ability to modulate shapes, sizes, and properties in order to modify functions, affords many opportunities for development of new functional materials. The broad range of structures and properties of nucleic acids results in many analytical challenges that must be solved in order to determine the sequences, modifications, structures, conformational shapes, and functional motifs, and therefore understand structure/functional relationships that are critical for harnessing nucleic acid materials. These challenges motivate the Brodbelt research group to advance new mass spectrometry strategies to decipher the structural features of nucleic acids. This research effort focuses on the development photodissociation and ion mobility methods to provide extensive structural insight into nucleic acids and their assembly with other molecules. Charge reduction methods and charge detection measurements will increase the sensitivity and resolution of the mass spectrometry analysis. Numerous collaborations with both academic and industrial partners will expand and disseminate both innovative analytical methods and software while also increasing interdisciplinary familiarity with advanced mass spectrometry methods for solving biological problems. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2026 · 2026-01

With support from the Chemical Measurement and Imaging Program in the Division of Chemistry, Professor Michael Marty and his group at the University of Arizona are collaborating with Genentech, Inc. to develop new algorithms and experimental methods for analysis of complex mass spectrometry data. Analysis of intact proteins by mass spectrometry is widely used in a variety of industries. It is especially important for quality control of proteins produced as biopharmaceutical products. However, data analysis for these large proteins is a key bottleneck. To overcome this challenge, the Marty team is developing new computational tools and integrating these tools into a unified open-source software project, which will enable widespread use by academic researchers, core facilities, and industry labs. These research objectives are complemented by educational activities to provide online resources to teach general coding skills and how to use the new software developed by this project. By solving key bottlenecks in data analysis and enhancing professional development, this project will enhance the national infrastructure for research, improve economic competitiveness, and foster partnerships with academic, industrial, and governmental researchers. Data analysis is a critical bottleneck limiting more widespread use of intact mass spectrometry (MS) in industry applications. To address these data analysis challenges, under this GOALI (Grant Opportunities for Academic Liaisons with Industry) award, the Marty group at the University of Arizona and industrial collaborators at Genentech, Inc. are developing novel algorithms for analysis of mixed resolution spectra and two-dimensional data sets generated by coupling online chromatography with MS. They also will develop multiplexed injection strategies, both experimental and computational, to couple these online separations with charge detection-MS. Each of these objectives is expected to advance knowledge by providing new types of data analysis not currently possible, and each integrates dimensions of the data that have been previously inaccessible. To enable practical implementation of these new tools in a unified package, the algorithms are being integrated into an open-source software package with a user interface. These research objectives are accompanied by educational objectives to develop online resources to teach the use of these new software workflows and to teach fundamental skills related to MS science. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2026 · 2026-01

Cosmic radiation on the earth’s surface over long timescales creates rare forms (isotopes) of many minerals. These isotopes are known as cosmogenic nuclides. Measuring the relative abundance of these minerals provides insights into current and past processes that have shaped the earth’s surface, including erosion, tectonic processes, glaciation, and sea level changes. The scientific data on these processes is normally collected and measured by independently working groups of scientists, so having the capability to share the data in a consistent way is extremely important for reproducing scientific results, reusing data for new research questions, and (as the coverage of the collected data on the earth’s surface becomes significant) tackling large-scale or even global research problems. The Informal Cosmogenic-nuclide Exposure-age Database (ICE-D) project enables this research by facilitating community access and engagement with the continually growing dataset of cosmogenic nuclide geochemical and field measurements used for exposure dating applications. The project expands the capabilities of prior work on ICE-D by implementing support for sophisticated surface processes such as dating now-buried surfaces, in addition to exposure dating. This project expands capabilities and trains a wider audience of geoscientists for the Informal Cosmogenic-nuclide Exposure-age Database (ICE-D) Project. ICE-D is a computational infrastructure project aimed at facilitating synoptic data discovery and analysis of geochronologic measurements that constrain numerous Earth surface processes. Transformative components of the project - the transparent computational middle-layer and users-as-developers model - are currently enabling higher-order analyses of cosmogenic-nuclide measurements that critically underpin several fields in Earth surface processes research, namely reconstructing past contributions to sea level fluctuations from ice sheets, assessing seismic hazards along major fault systems, and constraining global climate patterns that caused past alpine glacial fluctuations. Expanded capabilities targeted in this iteration of the project will additionally aid in analyzing fluvial landscape evolution processes, biological applications such as tracking species evolution and the dispersion of ancient humans across the world during the Quaternary, among other impactful Earth surface and biological processes. By centralizing the detailed datasets of cosmogenic-nuclide measurements - including field observations and laboratory measurements - required to compute geologically meaningful parameters from samples collected in a variety of environments worldwide, ICE-D removes several bottlenecks in the community. To further increase engagement, the project undertakes a workshop program to train the community of users to contribute their data, help maintain the database and ultimately use the database for synoptic analyses. Finally, project investigators institute an undergraduate research program aimed at training undergraduates as well as fostering engagement with the geoscience community. This award by the Office of Advanced Cyberinfrastructure is jointly supported by the Division of Earth Sciences and the Division of Research, Innovation, Synergies and Education in the Directorate of Geosciences. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2026 · 2026-01

The Arctic’s permafrost — persistently frozen ground — holds an enormous amount of carbon, roughly twice the amount currently in the Earth’s atmosphere. As permafrost thaws, the associated release of dissolved carbon into the ocean causes acidification that threatens marine life, disrupts ecosystems, and puts coastal communities and food security at risk. This project brings together geoscience and artificial intelligence (AI) experts to develop new tools that will help us better understand and predict the impacts of thawing permafrost and the associated release of carbon into oceans. Through a combination of data-driven and physics-driven approaches, model development will overcome limitations of data sparsity that have hindered previous attempts to quantify and predict sources and rates of permafrost-driven carbon release. This project also offers hands-on research opportunities for graduate and undergraduate students and develops a new course on using machine learning for subsurface flow modeling. All software and training datasets will be made publicly available alongside documentation that will foster broad reuse for energy and environmental research and education. This project aims to develop efficient surrogate models to accurately capture the multi-physics processes within the thawing permafrost and to quantify permafrost carbon dynamics and associated uncertainties across the Arctic shelf. The research team will develop novel progressive neural operators (PNOs) that leverage multi-level, reduced-physics training datasets while addressing challenges such as catastrophic forgetting in machine learning. The developed PNO models will be integrated with a wide range of compiled datasets to predict seabed methane fluxes across the Arctic shelf and quantify the associated uncertainties. This project will also generate time-dependent, three-dimensional distributions of permafrost dynamics (e.g., temperature, ice content, pore water pressure, and salinity) and carbon transformations (e.g., organic carbon decomposition rates, methane concentration, and relict organic carbon content) beneath the sediment-water interface. These results will significantly enhance our understanding of the Arctic permafrost and provide new insights into how permafrost carbon affects ocean chemistry and the global carbon cycle. 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 · 2026-01

PROJECT SUMMARY Craniofacial differences are caused by a large variety of genetic factors, and represent some of the most common inherited disorders in humans. Cilia are abundant eukaryotic organelles whose proper biogenesis and continued homeostasis are crucial to embryonic development and tissue function in vertebrates, greatly contributing to craniofacial morphology. Mutations that disrupt ciliogenesis prevent proper ciliary construction, beating, and signaling, resulting in a diverse array of birth defects and lifelong diseases. A protein complex called CPLANE is required at multiple steps of ciliogenesis, and disease-causing mutations in its constituent subunits have been identified in clinical settings. However, the disease etiology of Jbts17, the largest and most enigmatic CPLANE subunit, remains unknown. Here, I propose to study the molecular mechanism of action and interactions of Jbts17, which is clinically associated with the diseases Oral-Facial-Digital Syndrome, Joubert Syndrome, and Meckel-Gruber Syndrome. In Aim 1, I will use a vertebrate model organism to directly test the role of seven disease-associated Jbts17 variants in protein stability and localization to the basal body. I will also test their role in basal body migration to and docking at the plasma membrane, along with the recruitment of Intraflagellar Transport proteins. In Aim 2, I will further leverage these disease variants with preliminary computational results to identify the role of Jbts17 during Distal Appendage Vesicle recruitment and Ciliary Vesicle formation, two early steps in the process of ciliogenesis. Aim 3 will explore Jbts17 protein-protein interactions on an amino acid-level resolution using mass spectrometry-based proteomics. By determining the molecular mechanism and interactions of Jbts17, this work will add new depth to our understanding of Jbts17 disease-causing alleles in craniofacial ciliopathies. Furthermore, the proteomics experiments proposed here will potentially implicate new proteins in ciliogenesis and craniofacial morphology. Impact: Experiments proposed here will lead to an improved understanding of the genetics and cell biology of ciliogenesis and ciliary vesicle trafficking. The results will aid in diagnosing craniofacial genetic disease and will help lay the foundational knowledge for future development of therapies to restore tissue function in patients with ciliopathies.

NIH Research Projects · FY 2025 · 2026-01

PROJECT SUMMARY Cystic fibrosis (CF) is a life-threatening genetic disorder that affects >120,000 individuals worldwide. Mutations in the CFTR gene cause CF, with 10% of people with CF (pwCF) having two copies of mutations that are unre- sponsive to FDA-approved CFTR modulators. Exon 12 contains the most common modulator-ineligible mutations, affecting nearly one in five pwCF who are not able to receive modulator therapies. This subpopulation of pwCF urgently needs a corrective gene editing therapy to restore CFTR function. Gene editing the endogenous CFTR presents a promising path to cure CF by correcting the pathogenic mutations. However, current approaches face significant limitations: they target only one or few mutations at a time, demonstrate low efficiency and risk unintended off-target effects. To overcome these barriers, this proposal aims to develop a new gene editing strategy that replaces the entire exon 12 without a DNA break, termed retron editing. Successful completion of the proposed aims will establish a retron editor that can safely and efficiently replace CFTR exon 12. More broadly, successful retron-mediated exon replacement will transform the lives of pwCF and those with other rare monogenic diseases.

NIH Research Projects · FY 2025 · 2025-12

PROJECT SUMMARY Research on early language acquisition has long focused on how infants learn nouns, but far less is known about how they acquire verbs, which are critical for structuring sentences and conveying relational meaning. Verbs present unique learning challenges because actions are transient, context-dependent, and highly variable across instances. To successfully acquire verbs, infants must resolve two computational problems: 1) word-referent ambiguity—determining which action a verb refers to; and 2) word-meaning ambiguity — inferring the meaning of a verb and generalizing its meaning across different objects and motion variations. Despite these challenges, verbs—particularly action verbs with concrete meanings—are a significant part of early vocabulary. The overarching objective of the present study is to understand how infants learn verbs from everyday contexts with noisy, natural input. The proposed research tests the hypothesis that infants resolve word-referent and word-meaning ambiguity by tracking statistical regularities across verb instances. The project takes a three-pronged approach—naturalistic, experimental, and computational—to determine how the structure of real-world input, and infants’ attentional and cognitive abilities, support verb learning. Aim 1 is to quantify the input statistics that can be used to resolve word- referent ambiguity in one everyday activity – meal preparation. I will analyze how often verbs in parent speech are aligned with their manual actions, and more critically, I will use head-mounted eye tracking to measure whether infants attend to the relevant referent when hearing an action verb. Aim 2 is to test infants’ ability to extract verb meaning via cross-situational learning. I will design and conduct a cross-situational learning experiment by systematically manipulating the co-occurrence statistics of manual actions and target objects across several experimental conditions. Aim 3 is to use computational models to test verb-learning mechanisms. The models will help to determine whether statistical input alone is sufficient for verb learning or whether additional cognitive mechanisms, such as attentional biases, are required. By comparing model performance to infant learning, we will identify which aspects of the learning environment best support generalization. Uncovering the mechanisms that support early verb learning will bridge the gap between controlled experimental findings and real-world language acquisition. By identifying how infants track statistical regularities to resolve word-referent and word-meaning ambiguity, this work will advance theories of word learning and contribute to a broader understanding of how children extract meaning from complex natural input. The findings will also have implications for parenting and behavioral interventions aimed at supporting language development in children at risk for language delays, such as those with developmental disorders.

NSF Awards · FY 2025 · 2025-10

The Geotechnical Extreme Events Reconnaissance (GEER) Association provides for investigation, data collection and public dissemination of information from natural disasters. GEER’s mission to “turn disasters into knowledge” benefits the geotechnical engineering profession by providing information that can improve design and construction practices to reduce poor performance in extreme events; benefits the engineering profession by fostering innovative methods to perform reconnaissance to learn from extreme events; and benefits society by improving the management of risk in future extreme events. This project intends to identify opportunities for improvement moving forward to ensure the objectives remain relevant and new and emerging technologies are leveraged. The methodology will be to conduct a two-day workshop to discuss and brainstorm opportunities for GEER. Specific objectives include identifying priorities for future efforts, developing best practices for incorporating new technology, and reshaping governance to provide adaptability and transparency. The workshop will include invited talks by professionals (academics and practitioners) with GEER experience, as well as panels shaped around specific questions and objectives. It will involve 30 participants intended to provide representation from senior and junior levels, people who have led and/or participated in GEER activities for many years and people who have not, people with a broad range of perspectives about different types of extreme events, and people with knowledge about a variety of geographic regions. The workshop will be preceded by an online survey among GEER members to collect additional input on critical topics that should be addressed during the workshop. The product of the workshop will be a report summarizing the discussions and providing guidance for the future of GEER. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2025 · 2025-10

Many important properties of materials, from light absorption and emission to electrical resistivity and superconductivity, arise from complex interactions between electrons, crystal vibrations, and light. Accurately predicting these properties requires advanced many-body theoretical and computational methods beyond the standard approach in computational materials science based on density functional theory. However, these many-body methods remain difficult due to their mathematical complexity and fragmented software landscape. This project enables broader access to advanced computational methods for materials modeling and design at the atomic scale by developing MATCSSI 2.0, a cloud-integrated platform that streamlines complex many-body calculations. By combining user-friendly tools, interoperability among widely used software, and interactive learning resources, this project lowers barriers to entry and promotes reproducibility and transparency in computational materials science. This project advances the predictive power and accessibility of many-body electronic structure methods that go beyond density functional theory (DFT). This project develops MATCSSI 2.0, a platform that combines a cloud portal hosted at the Texas Advanced Computing Center with interoperable software (such as EPW, BerkeleyGW, and SternheimerGW), a universal abstraction layer (EPWpy), intelligent user support, and end-to-end learning modules. This platform enables widespread adoption of many-body materials modeling and design methods by connecting to a broad array of high-performance computing infrastructure. MATCSSI 2.0 enables researchers to study complex quantum phenomena involving coupled electrons, phonons, and photons, both in and out of equilibrium. It supports research into advanced materials for optoelectronics, superconductivity, and quantum technologies, and facilitates the generation of high-quality many-body datasets for AI-driven materials discovery. The platform is deployed on the cloud via the Texas Advanced Computing Center and is accessible to all researchers without restrictions. Broadening access to these advanced quantum simulation methods helps accelerate the discovery of new materials for advanced applications, such as microelectronics and quantum technologies. It facilitates training a competitive STEM workforce with interdisciplinary expertise in materials science and high-performance. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2025 · 2025-10

Generative Artificial Intelligence (AI) has revolutionized visual content creation, enabling the production of highly realistic, synthesized video at an unprecedented scale. However, adapting current image-based diffusion methods for real-time, long-form video editing faces significant challenges, including latency, maintaining consistency between frames, and hardware efficiency. These challenges restrict the wider application of generative AI systems in various fields such as research, education, healthcare. This project introduces a new AI framework designed for real-time, scalable, and precise generative video editing, with a strong focus on rigorous algorithms, efficient system-level operations, and verifiable results. This project will help advance national interests in economic competitiveness, education, and public welfare by making generative visual AI systems more efficient and broadly accessible to a variety of users. This project advances the state of the art in generative visual AI systems through four key innovations: motion-adaptive cross-frame attention, pipelined frame scheduling for multi- graphics processing unit (GPU) systems, formal verification of semantic consistency, and system-level validation on real hardware. Motion-adaptive attention tracks scene changes, trimming computation while preserving spatio-temporal coherence. A pipelined, concurrency-optimized scheduler distributes encoding and decoding across multiple GPUs for high throughput. Linear Temporal Logic (LTL) verifies object consistency, automatically flagging semantic errors between frames, thus enabling automated detection of visual inconsistencies. These innovations collectively offer a unified solution for low-latency, high-precision generative editing across long-form video, while also providing correctness guarantees and practical deployment strategies that could be extended to other visual systems. Finally, the project will undergo rigorous system-level validation on both consumer-grade and datacenter-grade GPUs and will be released as open-source software with accompanying benchmark datasets and courses to broaden participation in generative 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 · 2025-10

Generating nuanced yet interpretable hypotheses from noisy and high-dimensional observations is a core activity across numerous scientific disciplines. For example, across neuroscience, genomics, biomechanics, and ecology, researchers analyze images and videos to study phenomena ranging from the genetic expression of lab mice to global species distributions of birds. Useful scientific hypotheses are typically instantiated on symbolically interpretable concepts and attributes (such as stride periods and center of mass oscillations when studying kinematics of walking). A key challenge is to extract such symbolically interpretable hypotheses from raw high-dimensional observational data. To address this challenge, this project aims to develop a novel neurosymbolic programming framework, called foundation model programming, for generating symbolically interpretable scientific hypotheses from high dimensional observational data. The main idea is to represent interpretable hypotheses as neurosymbolic programs that use symbolic primitives as well as neural modules, including foundation models. The use of neural and foundation models allows the hypotheses to be full-stack, modeling both the extraction of relevant patterns and motifs from high-dimensional raw data and reasoning over those patterns. The core technical benefit of this approach is that it inherits both the contextual flexibility of modern foundation models given high-dimensional inputs, and the rigorous reasoning abilities offered by neurosymbolic approaches. The proposal is backed by a substantial amount of prior work on both neurosymbolic learning and applying foundational models in scientific domains, including foundation model-enabled hypothesis generation for low-dimensional data, self-supervised symbolic feature extraction from high-dimensional data, data-efficient expert-in-the-loop training approaches, and deep deployment into real scientific workflows. Importantly, the use of foundation models enables building methods that are more autonomous, and require fewer manual annotations by the expert. This project will develop algorithms that can jointly reason over what are the most useful symbolically interpretable concepts or motifs that can be extracted from the raw data, as well as how to compose those concepts into coherent hypotheses. This paradigm mirrors how humans develop hypotheses, by jointly establishing a discrete vocabulary of concepts (from continuous high dimensional descriptions) and reasoning over those concepts, thus enabling interpretability. This project will benchmark the developed methods on several scientific tasks and domains and 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 2025 · 2025-10

The primary goal of this project is the development of broadly applicable foundational tools and new mathematical theories to advance the state of the art in Generative Artificial Intelligence (AI). Although AI systems are now pervasive across disparate domains, core algorithmic challenges for building and deploying large models remain. It is critical that training algorithms make the most of available computational resources and that resulting models are accurate, robust, and interpretable during inference. Data sets must be curated and network architectures tuned depending on the modality of the task at hand. This research will focus on new frameworks for formally modeling these problems in order to create efficient solutions. In addition, this project will help thousands of students and working professionals acquire AI expertise through a large-scale online masters initiative and through activities targeting high-school students. The project's technical research is divided into four foundational thrusts. The first, algorithms and optimization for generative models, focuses on better training and inference for large models and looks beyond first-order methods. The second, a mathematical theory of foundation models, aims to understand how to specialize foundation models for new domains using as little additional data and compute as possible. The third thrust is on diffusion, now a cornerstone of Generative AI, and studies how to learn distributions without memorization and solve associated inverse problems. The last thrust looks at improving the robustness and safety of generative models through the lens of distribution shift. All of these thrusts are coupled with use-inspired projects in medical imaging, generative biology, and AI for mathematical theorem-proving. A particular emphasis is on open-sourcing AI in order to provide transparent models for use in multiple domains. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2025 · 2025-10

Decades of research has focused on how to simulate the fracture of objects into multiple fragments. Relative to this forward problem, the inverse problem of fractured object reassembly---recovering the complete underlying object from its fragments---remains largely open, despite its high-impact applications in fields including reconstruction of broken artifacts, bone fracture reduction, docking of proteins, DNA sequencing, fossil reconstruction, document restoration and forensics, geoscience, and assembly planning in robotics. The state of the art in practice is to manually reassemble fragments using experience and domain knowledge - an expensive and labor-intensive ordeal. By combining cutting-edge research on geometric deep learning from the computer graphics and machine learning communities, together with well-validated physical simulation algorithms from computational mechanics, this project will study the first practical automatic algorithm for reassembling an object from its fragments, and to evaluate the effectiveness of this algorithm on 3D scans of real-world pottery and bone fragments. The core idea of this project is to overcome the dearth of fracture data by joint learning of fracture simulation and fractured object reassembly. The research plan has three thrusts. The first thrust introduces a fast but physics-aware fracture and weathering simulation network that can be trained from a modest number of accurate simulation results. The resulting trained simulator will be used to produce large-scale synthetic data. The second thrust features the first end-to-end trainable pipeline that incorporates multiple geometric cues for fractured object reassembly. The third thrust examines how to perform fracture simulation and object reassembly jointly. This joint learning paradigm is critical, as it shifts the goal of physical simulation from maximizing the predictive quality for specific individual objects subjected to specific loading conditions towards generating useful training data for learning the reassembly inverse problem. The proposed project seamlessly bridges the physical simulation community and the shape analysis community in computer graphics and creates ample outreach opportunities. Collaborations with experts in archaeology, geoscience, and medicine amplify the broader impacts. 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 · 2025-09

PROJECT ABSTRACT Alcohol use disorder (AUD) is a leading cause of disability in the US, affecting 28.9 million people as of 2023. Despite the prevalence of AUD, treatment options remain limited and ineffective, highlighting the critical need for the development of novel therapeutics for AUD. Although AUD is often linked to liver, heart, and brain damage, recent studies have also implicated immune dysregulation in AUD. One potential link between AUD and immune dysregulation is the gut microbiome, a dynamic network of microorganisms essential in maintaining host homeostasis. The gut microbiome is known to regulate central nervous system (CNS) and immune functions through the gut microbiome, a two-way communication pathway between the gut and the CNS. As chronic heavy alcohol consumption is known to cause gut dysbiosis (imbalance of the gut microbiome) and the gut microbiome is critical for the development and maturation of the innate and adaptive immune systems, it is possible that the gut microbiome may play a role in driving both AUD and its associated immune deficits. Our research leverages high drinking in the dark mice (iHDID), a genetic rodent model for heightened alcohol consumption, to explore the role of the gut-brain axis in AUD. The overall objective of this proposal is to determine whether: 1) iHDID gut microbial species are not significantly altered by alcohol consumption and/or 2) iHDID gut microbiota contribute to heightened alcohol consumption. In Aim 1, using shotgun metagenomic sequencing, we will elucidate the extent to which bacterial/fungal gut microbiota shift following alcohol exposure in iHDID mice, relative to C57BL/6J (a popular strain used by alcohol researchers) and HS/Npt (the iHDID founding strain) controls. We will also quantify volatile organic compounds in the blood/feces to determine secretion of potentially immunomodulatory microbial metabolites. In Aim 2, we will determine whether the iHDID microbiome may drive increased drinking behavior using fecal microbiota transfer (FMT) from either alcohol-naive iHDID or C57BL/6J mice to alcohol-naive C57/BL6J mice and measuring recipients’ subsequent performance in a drinking procedure. Further, to examine whether FMT-induced increases in alcohol consumption stem from immune changes in the gut, I will quantify differential gene expression post-FMT in the brain using RNA 3’ tag-sequencing. This proposal employs several innovations: (i) iHDID mice, (ii) metagenomic sequencing, (iii) analysis of volatile organic compounds, (iv) 3’ RNA-tag sequencing, and (v) FMT. Our results will i) address a critical gap in knowledge on the interplay between alcohol, the gut microbiome, and immune function in AUD, ii) pave the way for the use of gut microbial analyses/manipulations in diagnosing/treating AUD, and iii) provide guidance towards improving outcomes for millions of AUD patients who lack effective diagnostics and therapies. Additionally, through the completion of this proposal and the guidance of my sponsors, I will receive extensive training to develop my professional and scientific skillset towards becoming an independent scientist.

NIH Research Projects · FY 2025 · 2025-09

PROJECT SUMMARY Human milk (HM) provides the critical nutritional composition and non-nutritive bioactive factors that promote the healthy development of infants. HM is often stored by refrigeration or freezing and then thawed and/or warmed to body temperature (37 oC) prior to serving. Additionally, HM provided by a milk bank is required to undergo pasteurization. All these processes create opportunities for degradation of vital HM components that can reduce its value for the health and growth of babies. Although there have been studies considering the impact of processing on certain HM components, conditions and conclusions vary widely. We will execute a rigorously controlled and comprehensive analysis of the response of a wide array of HM components to standard pasteurization protocols, freeze/thaw methods, and rewarming approaches. This is significant because it will provide clear guidance on HM handling of this entire process to maximize preservation of important milk components. Another innovation of the proposal is that it will characterize effectiveness of our unique Rapid Thermal Control of Liquids (RealCooL) platform technology invented by our group to control temperature and preserve HM components. RealCooL is capable of more spatially-uniform temperature, 20-fold increased cooling rate, and 18-fold increased heating rate compared to conventional methods of thermal processing. We hypothesize that current processing methods cause degradation of HM components and that RealCooL will enhance preservation of HM. To test our hypothesis, we will execute the following specific aims. Aim 1: Determine the effect of standard thermal processing methods on HM composition. This aim provides a comprehensive and standardized assessment of the impact of thermal processing methods by commercially- available devices (refrigerator, freezer, pasteurizer, and bottle warmer) on selected HM components. We will measure somatic cell count, lactoferrin, lysozyme, lipase, sIgA, IgM, IgG, interleukins, and bacteria in raw milk and milk that has undergone a range of heating and cooling processing characteristic of procedures used at home, milk banks, hospital nurseries, and daycares. The impact of individual and combined processes will be documented. Aim 2: Determine the effect of RealCooL thermal processing methods on HM composition. This aim will evaluate our innovative RealCooL system that rapidly and precisely pasteurizes, thaws, cools, and rewarms milk by directly warming and cooling it using a heat exchanger with a novel control system. We will evaluate its capability to execute desired processing of HM with a single device compared to separate traditional devices used in Aim 1. This project is impactful because knowledge gained will include the potential degradation of HM components for both standard thermal processing methods as well as the novel RealCooL system. Results will guide the development of improved devices and processing standards to optimize the quality of HM that is provided to babies, ensuring they receive maximum value which should result in better nutrition and growth.

NSF Awards · FY 2025 · 2025-09

Computers, be it those in mobile phones or servers, are increasingly being designed with heterogeneous processors and memory that is shared across all of these processors. The heterogeneity enables greater performance, with different processors tailored for specific purposes (e.g., graphics), and the shared memory facilitates easier programming. These processors are already being used to support critical computational tasks, including artificial intelligence (AI), robotics, and medical research. While these processors offer great potential, they pose two problems. First, it is difficult to understand how to compose them. Specifically, different types of processors use different communication protocols, and composing these protocols is complicated. Second, it is also challenging to verify that the processors will behave correctly in all situations. The composed protocols have a vast number of possible interactions, and verification techniques do not scale up to meet this challenge. This project addresses both challenges by developing a systematic way to compose processor protocols and a new, scalable technique for verification of these processors. These contributions can offer many benefits, including shorter time to market, confidence that processors will behave as expected, and a lower barrier to entry for startups and researchers seeking to create new processors. By providing a foundation for the correct design of heterogeneous shared memory processors, the project will help to enable the coming generation of high-performance computing systems. These systems will sustain American economic competitiveness, supporting breakthroughs in AI, medicine, science, defense, and many other fields that will enhance the lives of all Americans. This project will make three important contributions to the theory and practice of processor design and verification. First, it will provide, for the first time, a mathematical foundation for defining and reasoning about the interaction of programs sharing memory in a heterogeneous system. This understanding will be crucial for designing the coming wave of heterogeneous systems-on-chip that will drive system performance for consumers and industry in the era of AI. Second, the work will provide an understanding of the large design space of heterogeneous coherence protocols and the first automated tools for correctly synthesizing the protocol converters needed to connect diverse local and global protocols. Third, the project will develop the first compositional approach for verifying heterogeneous coherence protocols and the first application of translation validation to cache coherence protocols. It will integrate verification as part of the protocol design flow, enabling designers to realize cost-effective proofs, and provide an exemplar for making formal methods practical in systems design. 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.