University Of Texas At Austin

NSF Awards · FY 2025 · 2025-07

This award will support the student attendance at the International Conference on Electron, Ion and Photon Beam Technology and Nanofabrication (EIPBN) to be held May 27-30, 2025, in Savannah, GA. The EIPBN meeting is the premier lithography and nanofabrication symposium in the world and attracts participants from academia, industry, and government laboratories. This forum is instrumental in the generation and incubation of breakthrough and transformative ideas in the critical areas of nanofabrication, semiconductor manufacturing, and quantum computing. EIPBN is a key mechanism for linking basic science with industry stakeholders towards the development of nanotechnology-enabled products, which are critical for the US chip and defense industries. This conference offers a forum for the open exchange of new and previously unpublished scientific research and ideas. This award will support the participation of undergraduate and graduate students and training of domestic workforce for the semiconductor manufacturing industry. EIPBN is a major international meeting and will provide a single forum for discussion of the latest developments in lithography, fabrication, imaging, measurement and characterization of nanoscale electronics, devices and structures. The NSF funding will provide travel support to 24 US students so they may participate in the short courses, the commercial session, the technical program, and other career-developing events. There are also a number of student training programs, including a career mentoring session with academic and industry leaders, student breakfast with the organization committee, start-up competition to promote translational research, and the best student presentation awards to highlight student researchers. This award will expand student participation in STEM and strengthen the domestic workforce to fuel economic prosperity, national security, and global competitiveness. 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 scientific fields rely on high-performance computing in order to accurately simulate complex physical phenomena involving high dimensional tensors (multi-dimensional arrays). In particular, implementations of algorithms in the domain of computational quantum chemistry follow one of two paths: excruciatingly slow and error-prone expert hand-coding which results in very fast but inflexible code, or general, library-based (or code generation-based) development, which reduces development effort but often leaves significant performance on the table. The project's impact is to enable provably correct (correct by construction) code generation of key computational routines in a high-performance manner that can be incorporated into larger code bases. This contributes to the overall goal of whole-program verification of scientific applications. The project’s novelties are 1) new notations for describing operations involving structured matrices and tensors, 2) new insights to identify high-performance algorithms from their specification, and 3) integrating complex data movement into the specification of the algorithm to better identify and exploit optimization opportunities at various abstraction levels. Techniques and algorithms developed by the project will broadly impact the theory and practice of computational science, data science, and machine learning. All aspects of the work will involve the training of young scientists at the graduate and undergraduate student levels. This project leverages and extends the Formal Linear Algebra Methods Environment (FLAME), a proven methodology for the correct-by-construction development of dense linear algebra algorithms, to structured tensors and novel high-level linear algebra operations which commonly arise in computational chemistry. Real-world examples of linear algebra and tensor operations in computational chemistry applications will be used to develop new notations and abstraction levels. Desirable performance characteristics will be captured using the developed notations in the specification and at the loop invariant level to identify and derive high performance algorithms. The performance of the resulting implementations will be benchmarked against those in standard packages. 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

Abstract: The long-term goal of our research is to understand how human pathogens adapt to the environment of the host. These studies have focused on Shigella flexneri, a human intestinal pathogen that causes dysentery. Because this pathogen must transit the gastrointestinal tract, penetrate the mucous layer, and invade and replicate within intestinal epithelial cells, it must cope with multiple environments. Previous studies from our lab have shown that S. flexneri uses the availability of carbon sources or products of carbon metabolism to detect where they are within the host or external environment. In response to these signals, S. flexneri regulates the expression of genes, including virulence genes, to maximize survival and growth in a particular niche. A mechanistic understanding of these regulatory pathways is needed in order to design effective methods for preventing infection or disease caused by Shigella. Both human intestinal organoids and traditional cell culture methods will be used to study the interactions between S. flexneri and human cells. Bacterial mutants defective in carbon metabolism pathways or in carbon signaling (e.g. ppGpp or CsrA) pathways will be studied in association with human cells to determine the effects on invasion, intracellular replication, and spread of the bacteria to adjacent cells. Dual RNA-Seq will be used to determine the Shigella and host genes that are regulated during infection, and this will guide the construction of additional mutants for analysis. The use of human organoids for the experiments will provide additional information because these contain multiple cell types, which can be separated for analysis, and they have more complete innate immune responses than monolayers of epithelial cells. One innate immune signaling pathway that will be analyzed in detail is Type I interferon (IFN- β) signaling. IFN-β has a mild protective effect against Shigella infection, and fewer bacteria are able to invade cultured Henle cells or intestinal organoids. However, those bacteria that are successful in invading host cells spread more rapidly and cause larger lesions in the presence than in the absence of IFN. This indicates that S. flexneri has a mechanism(s) for not only overcoming interferon-beta induced protective responses but also increasing its virulence in response to interferon in human cell. How S. flexneri evades the protective effects of interferon on host cells will be determinized.

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.

NSF Awards · FY 2025 · 2025-07

Classical dynamics studies how systems change in time. Ergodic theory, a subfield of dynamics, focuses on the statistical behavior of dynamical systems. Applications of ergodic theory are widespread: from traffic modeling to aerospace engineering and population dynamics. It is natural and of practical importance to generalize the role of time in a dynamical system to more complicated groups of symmetries. This generalized notion of dynamics leads to applications in statistical physics, number theory and geometry. However, new tools are needed when the group of symmetries is non-amenable which means that boundary phenomena are too significant to be safely ignored. One such tool is the weak local (or Benjamini-Schramm) limit. These limits formalize the asymptotic local behavior of large, possibly random, mathematical objects. This project is concerned with fundamental questions: when do these limits exist (for natural families of low-dimensional geometric objects) and given an infinite mathematical object (such as a manifold or network), can it be identified as the weak local limit of finite objects? This research project has three main objectives. 1) In recent work with M. Chapman, A. Lubotzky and T. Vidick, the primary investigator (PI) settled the Aldous-Lyons Conjecture in the negative: there exist non-sofic unimodular random graphs. These are random rooted graphs which cannot be approximated by finite graphs in the Benjamini-Schramm sense despite satisfying the Mass Transport Principle. One goal of this project is to find explicit non-sofic unimodular random graphs which are either hyperbolic, the 1-skeleton of a CAT(0) cubical complex, falsify the analog of Gottschalk’s Conjecture regarding cellular automata or which have isomorphic Bernoulli shift spaces with different entropies. These may all be useful in finding non-sofic groups, a long-sought-after goal. 2) In recent work with K. Rafi and H. Vallejos, the PI proved that Masur-Veech random translation surfaces have a weak local (Benjamini-Schramm) limit as genus tends to infinity. The main tool is an elaboration on the surgery techniques of Eskin-Masur-Zorich. The PI intends to build on this by developing multi-parameter Siegel-Veech Theory and proving weak local limits of hyperbolic surfaces decorated with measured laminations, abelian differentials, quasi-Fuchsian embeddings and so on. The PI will investigate connections between these objects and Gaussian analytic functions, SLE curves, and the Curien-Warner Markovian triangulation. 3) In celebrated work, J. Friedman proved that random d-regular graphs admit an almost maximal spectral gap with high probability as the number of vertices tends to infinity. Recent work of the PI and student A. Embry generalize this to the degree-regular block model (DRBM). A third objective of this project to extend this to generalize the Bordenave-Collins Theorem (proving strong convergence of random permutation representations of free groups) to strong convergence of a related block model version. If successful, this may show that Koopman representations of measure-preserving actions of free groups determine C*-algebras that are matricial field: they can be strongly approximated through maps to finite-dimensional algebras. It should also determine precise spectral properties of this large and varied class of sparse random graphs. 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 · 2025-06

Rivers and deltas of the world have been extensively engineered for centuries. Past engineering projects have, collectively, enabled the prosperous economies of deltaic regions that are enjoyed today, but have also created conditions that limit the regions in the future. In many places, including along the United States Gulf Coast, planners and governments are implementing new projects that aim to restore coastal changes, enhance economic and environmental support, and mitigate risk to human lives and livelihoods. However, there is not a robust understanding of how past delta management has influenced human decisions and outcomes. This project uses novel numerical and computational modeling approaches to study how human engineering decisions cascade through space and time over centuries of landscape change to create system conditions that limit or enhance the portfolio of management decisions available at future times. This work develops tools with coastal planners to determine the portfolio of projects that maximize coastal survival. Delta engineering projects induce geomorphic change across space and time scales that impacts human lives and society. This project integrates cascading human decision making into landscape evolution models with three modeling approaches that inform one another and have complementary strengths: agent-based modeling, dynamical system modeling, and participatory modeling. Project research focuses on two testable hypotheses towards the tools and quantitative understanding of cascading decision making that are needed for delta planning: (1) local-scale engineering interventions can lead to less geomorphologically stable delta landscapes, when compared to a few larger system-scale interventions, and (2) human decisions can push the coupled system across tipping points, to both system benefit and detriment. Finally, research and development across all modeling approaches in this project is guided by community engagement. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2025 · 2025-06

Recent advancements in Artificial Intelligence have produced general-purpose Large Language Models (as used in AI chatbots such as chatGPT) that millions of people use daily. These models not only produce text that is startlingly like human writing and spoken language, but have shown remarkable ability to understand people’s emotions and even offer empathy. But we are unable to objectively gauge their true understanding of emotions, because we do not have rigorous definitions and tests that measure social and emotional understanding in AI. Without objective measures, we cannot assess progress in developing these models, identify blind spots or potential harms, or understand how to improve the emotional understanding of AI. This project addresses this knowledge gap, by defining what such social and emotional understanding entails, and devising new ways to measure the emotional capacity of AI language models. In parallel to the technical work, this project will also produce policy guidelines for designing and implementing AI that relies on understanding of human emotions, and educational materials to provide teenagers and adults with better AI literacy, and enable them to navigate the AI landscape with an awareness of its dangers. This project will ultimately enable practitioners to build more capable and safer conversational AI conversational that can better understand their human users. The project combines theoretical frameworks from psychology with rigorous methods from computer science to define several types of reasoning over emotions. These include predicting emotions from situation contexts and mental states; reasoning about interventions to change emotions; and how to include modalities like facial expressions and vocal expressions to inform emotionally-aware AI. The research will systematically define reasoning tasks in a Question-Answer format and generate benchmark datasets that researchers can use to assess various aspects of social and affective cognition. The project will also assess the progress of modern Large Language Models and develop techniques to improve their performance on tasks that measure social and emotional capabilities. 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 · 2025-06

Abstract Collective cell movements termed convergent extension (CE) drive the elongation of tissues and organs in essentially all animals. In vertebrates, CE is controlled by the planar cell polarity (PCP) signaling system, and defects in PCP-dependent convergent extension are associated with human neural tube defects and skeletal dysplasias. We feel that the overall lack of clarity regarding upstream inputs that guide CE as it occurs in vertebrate animals represents the key challenge in the field at this time. Building on our recent successes with in vivo imaging, in vitro assays of mechanosensing, structure modeling, and genomics, this proposal takes a holistic approach to this problem, focusing on both mechanical and molecular inputs and how they direct the action of PCP proteins, actomyosin and cadherin adhesion during CE.

NIH Research Projects · FY 2026 · 2025-06

PROJECT SUMMARY Bacterial communities associated with animals have substantial effects, positive and negative, on hosts and are critical factors in health status, including that of humans. The processes shaping these outcomes are complex, as they result from interdependent interactions among microbes and between microbes and hosts. Simple, tractable experimental models are essential to better understand how microbiomes are shaped and how they affect hosts. Insects offer such models and are used here to address (1) how interbacterial competitive interactions affect microbiome composition and the host, (2) how hosts choose beneficial over harmful bacterial colonizers, and (3) how intracellular symbionts escape destruction by host cells. Understanding forces shaping microbial communities is key to efforts to control microbiomes to improve health. The bee gut microbiome is dominated by fewer than ten bacterial species, all of which can now be grown in lab culture, manipulated with genetic tools, and visualized within the host. Although far simpler, this community has many parallels with the human gut microbiome, including restriction of members to the host gut, transmission through social contact, interactions with host immune systems, and presence in the distal gut where members help to digest dietary polysaccharides. This research program takes advantage of our past decade of progress in developing genetic and experimental tools for bee gut bacteria, enabling detailed interrogation of processes that govern microbiome composition and effects on hosts. In both humans and bees, a stable, healthy gut community bestows colonization resistance: the exclusion of foreign, potentially harmful, microorganisms. This project will identify sources of colonization resistance, which can include modulation of host immune responses and/or interbacterial antagonism via secreted toxins. Experiments will examine whether resident community members are more tolerant to immune effectors or antagonism mechanisms and how localization within the gut affects tolerance. These processes and how they affect host health will be investigated using newly developed genetic tools that allow targeted mutations and markers for visualization within bee guts. We also will develop genetic tools for newly described, host-specialized gut bacteria of Drosophila, with the goal of using this well- established genetic model for deeper understanding of host pathways affecting symbiont colonization. Intracellular symbioses offer the opportunity to examine intimate one-on-one interactions between host and symbiont, and to address how host cells respond to bacteria that enter the cytoplasm. This research will address how beneficial endosymbionts evade destructive lysosomal responses to intracellular pathogens. We will determine how the obligate symbionts of aphids deploy mechanisms derived from pathogenic ancestors to shut down the host response, thereby enabling their long-term persistence within the host cell. The broad goals of the research program are to identify which kinds of bacterial interactions and host responses are important in shaping symbiont communities and how these result in benefit or harm to hosts.

NSF Awards · FY 2025 · 2025-06

Nontechnical Description This NSF-supported workshop will identify innovative science use cases for the new, advanced semiconductor packaging facilities being developed now through the “Chips and Science Act of 2022.” This workshop will be hosted in late October 2025 in Austin, TX at The University of Texas at Austin. This site is chosen strategically because it is the site of the Department of Defense’s “Next-Generation Microelectronics Manufacturing” facility, a state-of-the-art advanced electronics packaging center. While several recent meetings have focused on how science can be used to make better chips and microsystems, very few recent meetings have focused on how access to improved chips and microsystems can be used to advance scientific research. This workshop will bring together leading researchers from many different scientific fields alongside electronics packaging experts in government and industry to chart the future of electronics systems development to make better devices and sensors for U.S. scientific research. Technical Description The workshop will engage stakeholders from defense, national laboratories, universities, and industry to explore how this national investment in advanced packaging can be leveraged for research across a broad range of scientific disciplines, including but not limited to biotechnology, geophysics, astronomy, quantum and AI hardware, chemistry, and physics. Experts in these fields will learn from packaging experts what it is possible to achieve using advanced packaging. In addition, this workshop will allow advanced packaging experts and packaging facility directors to learn about the types of packaging architectures and technologies that will be demanded by scientific researchers in the future. This bidirectional exchange of expertise is both timely and critical because scientific research is often a leading-edge market that predicts what future industry needs might be. Therefore, this workshop will provide significant value both to researchers by allowing them to better plan for their future research, and to packaging prototyping facilities by allowing them to find new, future customers that will be willing to produce innovative designs and who are risk-tolerant during the prototyping phase of the development of new packaging technologies. Specific tracks for the conference include: (1) sensors and detectors for physical and chemical science, where the ability to build 3D stacks of interconnected sensors and readout circuits would drive down response time while improving resolution; (2) 3D heterogeneous integration for biomedical and environmental applications, where sensors need to be packaged with memory and logic on-chip for preprocessing of huge amounts of data prior to transmission to a wireless receiver; and (3) quantum and AI hardware, where integration of qubits and microwave-to-optical transduction chips has become stalled out with the limitations imposed by traditional packaging. The core goals of this workshop will be to: (1) nucleate a national academic userbase for the advanced semiconductor packaging facilities being developed now and (2) identify quick-win opportunities for scientific research that leverage these advanced packaging facilities. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2025 · 2025-06

This project examines aerosol impacts on multivariate climate hazards that have great significance to society, including the concurrent extreme events of fire weather, humid-heat, and drought events. Aerosols have a larger impact on univariate extreme temperature and moisture events per unit global mean temperature change than greenhouse gases. However, their role in multivariate climate hazards is unknown and a framework on how to consider aerosols when attributing and planning for these hazards is also lacking. At the same time, aerosols are increasing because of increases in both anthropogenic and natural sources, accelerating the need to understand their role in climate hazards. This study addresses this need by 1) developing methodologies to uncover the influence of aerosols on multivariate climate extremes using state-of-the-art climate models; and 2) educating the next generation climate workforce with greater awareness of the factors contributing to multivariate climate extremes and the tools to enable robust societal planning for climate risk, taking into account these impacts. A key hypothesis of this work is that aerosols can have a greater impact than greenhouse gases on temperature and moisture and, thus, multivariate climate hazards in certain regions and for specific events. The project will examine this hypothesis using existing and emerging model output from the multi-model single forcing large ensemble (SFLE) aerosol intercomparison project and the Regional Aerosol Model Intercomparison Project (RAMIP). The SFLEs provide a decomposition for all pre-industrial through present day forcings into greenhouse gas-only forcing and anthropogenetic aerosol-only forcing, while one member additionally provides biomass burning (“black carbon”) aerosol-only forcing. The RAMIP, and a new black carbon-only forcing ensemble generated by this work, looks at how uncertainties associated with total aerosol emission, regional emission, and individual aerosol species impact near-term climate change. The project also develops novel aerosol-aware event attribution methodologies to better represent the impacts of aerosols on multivariate climate hazards both historically and in the future. To meet a growing need for a workforce trained in the computational and scientific theories specific to climate at the undergraduate level, this project integrates an undergraduate immersive, earth system modeling “field trip” framework and experiential earth system modeling curriculum for climate system science undergraduates with the goal of building the quantitatively climate-literate workforce needed to allow cross-sectoral operational climate planning. Finally, the project will mentor a doctoral student in aerosol processes and their role in multivariate climate extremes, climate hazard attribution methodologies, and undergraduate educational curriculum development. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2025 · 2025-06

Soft materials, including hydrogels, elastomers, and biological tissues, play an essential role in biomedical applications, flexible electronics, and soft robotics. However, understanding their fracture behavior remains a fundamental challenge due to nonlinear deformation and strain localization near crack tips. Current experimental techniques, such as methods based on full-field deformation measurements, provide valuable data but lack the resolution needed to capture localized damage. Additionally, existing computational tools to model fracture fail to efficiently integrate these full-field deformation measurements, leading to predictive inaccuracies. This award supports fundamental research that looks to overcome these limitations by developing an integrated experimental and computational framework for analyzing soft material fracture behavior. The outcomes of this research seek to advance the design of more reliable soft materials, enhancing the durability of medical implants, the resilience of soft robotic components, and the efficiency of energy-absorbing materials. Furthermore, the project looks to contribute to workforce development by integrating research findings into engineering courses, engaging students in interdisciplinary research, and supporting outreach programs to inspire the next generation of scientists and engineers. The objective of this award is to understand and model soft material fracture mechanics from full-field deformation measurements by resolving crack-tip inaccuracies. This research will develop high-resolution experimental methods for tracking crack propagation using improved digital image correlation and digital volume correlation techniques, combined with state-of-the-art machine learning algorithms. These measurements will be used to inversely calibrate phase-field fracture models, enabling precise calibration of material parameters such as stiffness and fracture toughness. The approach will also include development of an automated constitutive modeling framework based on physics-augmented machine learning to refine phenomenological models and minimize model form errors introduced by manual model selection. The modeling scheme will be validated through experiments on brittle hydrogels, demonstrating the effectiveness in capturing complex fracture behavior. By combining experimental and computational approaches, this effort looks to provide a robust framework for soft material fracture analysis, thereby establishing a foundation for future advancements in soft materials and their uses. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2025 · 2025-06

NON-TECHNICAL SUMMARY: Creating new materials with designer properties is critical for the advancement of science and our way of life. From a materials perspective, it is challenging to predict how a monomer structure will ultimately yield desirable material properties. On a biological side, it is possible to rewire the metabolism of cells to expand the repertoire of bio-based material building blocks. These new building blocks can unlock new materials that may be otherwise unfeasible. This proposal addresses the challenge of designing novel bio-based materials by enabling high-throughput characterization enhanced by Machine Learning (ML). Integrating ML and mechanistic model data is critical for establishing materials design criteria. Through a unique cycle, we can create, build, characterize, and understand the monomer-polymer function relationship for a specific class of microbial oil-derived polymers. Ultimately, this approach allows for the prediction of ideal starting molecules to produce desired materials properties. The proposed studies are significant since they address an unmet need in the field to develop a sustainable approach for materials production. Major outcomes of this work include: (1) high-throughput monomer production, (2) extensive materials preparation and characterization, and (3) data-driven models for predicting polymer properties. The outcomes of this work will have the broader impact of creating novel, sustainable processes for materials, as well as providing a deep understanding of materials design principles. Additionally, this collaborative research between Georgia Southern University and The University of Texas at Austin allows students to be exposed to a unique cross-over between genetic engineering, organic chemistry, and materials science not traditionally offered at the participating institutions. Student participation will demonstrate to the future generation of young scientists that positive, impactful, scientific advancement requires the combination of efforts from diverse fields. Through the incorporation of undergraduate students, their participation in an intense Summer program, and expansion of our ongoing collaboration with 3 local Schools (including the Deaf population), this work also has the broader impact of increasing interest in STEM fields, especially in underrepresented groups in the sciences. TECHNICAL SUMMARY: The proposed research aims to utilize an interlinked set of closed loop biological and material-design cycles whereby the cycle encompasses a Design-Build-Test-Synthesize-Measure-Model-Analyze paradigm. We have selected microbial-produced triglycerides with varied fatty acid chains as ideal candidates to test this approach given the great chemical diversity possible within these molecules. Polyunsaturated triglycerides can undergo free radical co-polymerization to yield thermosets. Chemical diversity is obtained by producing modified cells and allowing for high-producing, parallel bioprocessing to obtain bio-based monomers. Upon polymerization and characterization, data-driven modeling is used to link monomer chemical composition to the materials’ glass transition temperature, crosslink density, and storage modulus. This information is used to train an algorithm via ML. Combining material sciences, synthetic biology, and ML allows for a thorough investigation of combinatorial effects and holds potential for a paradigm shift in bio-based materials design. The proposed approach aims at achieving bio-based materials with crosslink densities higher than 77.7 x 10-4 mol/cm3, Tg higher than 85 ˚C, and storage moduli in the rubbery plateau higher than 2.2 GPa, as this will allow for unprecedented material performance from triglyceride-based co-polymers. These materials are suitable for applications in thermal insulation for the automotive and aerospace industries. The outcomes of this work will have the broader impact of creating novel, sustainable processes for materials, as well as providing a deep understanding of materials design principles. This collaborative research between Georgia Southern University and the University of Texas at Austin incorporates undergraduate students, offers an intense Professional Development Summer program, and expands ongoing collaborations with 3 local Schools (including the Deaf population). This work also has the broader impact of increasing interest in STEM fields, especially in underrepresented groups in the sciences. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NIH Research Projects · FY 2025 · 2025-06

ABSTRACT The goal of the current project is to establish the efficacy and mechanisms of exercise-enhanced fear extinction retrieval and generalization in posttraumatic stress disorder (PTSD). Exposure therapy is the gold standard treatment for PTSD, yet is only associated with remission rates of ~55% and in clear need of improvement. Exposure therapy is hypothesized to work through mechanisms of fear extinction learning, and as such, laboratory-based fear extinction paradigms are widely used as models of exposure therapy. Recent data demonstrates that moderate-intensity aerobic exercise, delivered specifically during or after fear extinction learning, can boost the consolidation of fear extinction learning. Consistent with emerging models of exercise’s pro-extinction effect, our pilot data among women with PTSD found that moderate intensity aerobic exercise delivered after fear extinction learning leads to a reduction in subsequent fear responding 24hrs later, an effect that was mediated by exercise-induced increases in peripheral BDNF. Our pilot data using multivariate pattern analyses (MVPA) also identified divided neurocircuitry organization of fear vs safety memories, and that this divided neural organization was altered in PTSD. Building on our pilot data, the current project would 1) compare the impact of different intensities of exercise delivered following fear extinction learning on multimodal measures of fear extinction retrieval and generalization, 2) identify the impact of exercise on MVPA representations of fear vs safety memories, and 3) demonstrate that spontaneous reactivations of extinction encodings in the acute consolidation window operate as candidate mechanisms by which exercise enhances extinction retrieval and generalization. Using a 3-day fear conditioning, fear extinction, and fear extinction retrieval and recognition task during fMRI, 200 adults with PTSD would be randomly assigned to either resting control or 30min of either light, moderate, or high intensity exercise. Testing dose-response relationships between exercise intensity and fear extinction will inform translation of this research to clinical settings. A one week-follow-up extinction retrieval test would investigate the impact of exercise on longer-term retention. This project would provide a critical evaluation of the impact of aerobic exercise on consolidation and recall of extinction learning in PTSD samples, thereby providing a strong foundation to translate this research to clinical care and enhance clinical outcomes for PTSD. The project would also provide general knowledge regarding dose-response relationships and neural mechanisms that support enhanced extinction, thereby informing development of additional novel treatments.

NSF Awards · FY 2025 · 2025-06

All languages have resources that allow their speakers to express how they have acquired the information they communicate, and how confident they are regarding the possibility and probability that the events they express have occurred or will occur. Yet these resources vary widely – languages like English use optional words like ‘apparently’ or ‘maybe,’ while other languages use complex combinations of obligatory affixes or other grammatical resources. These strategies are significant for communication – for example, a language that forces a speaker to be explicit about his or her information sources might lead to very different outcomes than one that does not. Moreover, knowledge may be expressed differently when it comes from the speaker, who can be sure about what they are thinking versus another person, whose thoughts we cannot know in the same way – and languages vary in their ‘rules’ for representing these differences. This project contributes to our understanding of the cross-linguistic possibilities in the expression of these domains, which in turn sheds light on the range of human perspectives on how knowledge is obtained and represented. This project addresses these questions in a set of language varieties spoken in remote rural communities, where speakers’ perspectives on knowledge and information source are likely to be maximally distinct from those represented in the urban and semi-urban contexts familiar to speakers of well-studied languages. The project will create a corpus of naturalistic speech and elicited data aimed toward exploring information source and levels of knowledge, and how they interact with the expression of time and other grammatical domains. The work will leverage questionnaires, interviews, and natural discourse recorded in audio and video, which will be transcribed, translated, and annotated. The project employs software such as ELAN and FieldWorks Languages Explorer (FLEx) for morphosyntactic analysis and ensures the availability of the linguistic data by archiving the corpus. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2025 · 2025-06

This Faculty Early Career Development (CAREER) award will support research that advances understanding of stretchable soft materials, a novel class of materials, and their ability to generate electricity through mechanical deformations. Traditional piezoelectric materials, which generate electricity when deformed, are typically limited to a narrow range of stiff, brittle materials that fail under even small strains. In contrast, this project explores the potential of using polymers, whose flexing mechanisms induce small-scale electrical polarization, the generate electricity. By investigating these mechanisms, this fundamental research intends to deepen our scientific understanding of soft materials and open the door to transformative applications, such as self-powered wearable and implantable technologies, active soft robotics, shape-conformal sensors, and energy harvesting devices. These innovations offer the potential to be both biocompatible and environmentally sustainable, thereby advancing national healthcare and energy solutions. Additionally, this project will create K-12 STEM teaching materials and provide educational opportunities for undergraduate and graduate students, with a special emphasis on supporting first-generation students in interdisciplinary fields such as computational materials science, nano/micro-mechanics, electromechanics, and soft materials. Key findings will be integrated into graduate courses, ensuring that the next generation of researchers and engineers is well-prepared to lead in these emerging areas. The CAREER project will support research that attempts to establish a multiscale framework to investigate how large strain gradients in polymer-based materials can induce electric polarization. Theoretical and computational models will be developed to link molecular-level polymer chain behavior to macroscopic electromechanical responses, accounting for both bulk and surface polarization effects. Advanced continuum models incorporating higher-order strain gradient theories will be used to capture the complex interplay between bulk and interface-driven mechanisms, which are particularly critical at small scales. These models will be validated through custom experiments conducted at submillimeter dimensions, where polymer specimens and 3D-printed architectures will be subjected to controlled loading conditions to generate measurable strain gradients and record the resulting electrical outputs. This integrated approach—combining theory, simulation, and experiments—will provide critical insights into the design and characterization of soft flexoelectric materials, paving the way for their use in sensing, actuation, and sustainable energy harvesting. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2025 · 2025-06

This Faculty Early Career Development (CAREER) award will support fundamental research focused on investigating fracture propagation in soft viscoelastic materials under a broad range of loading rates, from quasistatic to high-strain rates, and varying temperature conditions. Soft viscoelastic materials, such as polymers, hydrogels, and biological tissues, are integral to engineering and biomedical applications. However, their fracture behavior remains insufficiently understood, particularly under extreme loading rates and temperatures. Current fracture models primarily address slow-loading regimes, failing to capture the nonlinear, rate- and temperature-dependent fracture processes these materials undergo. Additionally, fractures under ultra-high strain rates often exhibit complex three-dimensional crack morphologies that diverge from conventional 2D or symmetric 3D models. This research project intends to develop integrated experimental and computational methods to bridge these gaps, providing a comprehensive, quantitative understanding of dynamic fracture behavior in soft materials. The results of this research look to have significant interdisciplinary impact, advancing mechanical and biomedical engineering fields, and contributing to applications in protective materials, soft robotics, and tissue engineering. Additionally, this CAREER project will provide research opportunities, curriculum development, a student symposium, K-12 outreach, and industrial and medical collaborations. The objective of this CAREER project is to develop innovative full-field experimental techniques and a unified theoretical and computational framework to understand fracture mechanics and material failure in nonlinear viscoelastic materials across diverse loading conditions. This project will introduce new 2D and 3D full-field deformation measurement techniques to quantitatively analyze fracture propagation at varying strain rates and temperatures. Experimental approaches will include quasistatic fracture tests, needle-induced cavitation, and laser-induced inertial cavitation experiments, with high-resolution full-field deformation measurements captured via 2D Digital Image Correlation (DIC) and 3D Digital Volume Correlation (DVC). These experimental datasets will inform the creation of a comprehensive theoretical and computational framework that integrates low-rate and high-rate fracture phenomena. The findings will enable more accurate predictions of soft material failure, advancing critical applications in advanced manufacturing, biomedical device design, and structural materials for extreme environments. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2025 · 2025-05

This I-Corps project is based on the development of a device to assist leg mobility that focuses on the hip. While hip flexor (bending) weaknesses are a common deficit experienced by many populations including those with neurological diseases such as multiple sclerosis, individuals with hip replacement, and healthy aging populations, few wearable devices exist that assist with hip bending. There is a challenge creating devices that work effectively due to differences in body shape and size. This technology is a form-fitting, soft robotic device called an exosuit that may be used for mechanical assistance and rehabilitation of the hip. The exosuit technology is a lightweight alternative to rigid exoskeletons used to assist mobility that is a clothing-like wearable device for movement assistance. In addition, this technology has been designed to be configurable, which allows the device to be adapted to people of different sizes and shapes. This customization may improve the assistance provided by the device. The goal is to increase the wearer’s mobility and gait quality and improve outcomes for people with mobility challenges. This I-Corps project utilizes experiential learning coupled with a first-hand investigation of the industry ecosystem to assess the translation potential of a robotic device for biomechanical assistance and rehabilitation of the lower extremity. The technology is a wearable, form-fitting, soft exosuit that is focused on the hip and may be configured to match the shape and functional needs of the user. Currently, the availability of hip flexion orthoses is limited due to the difficulty of fitting a device to the hip joint, where the geometry around the midsection is highly variable between individuals. To address this, a system was developed for modeling the interaction of the user and device, where individual surface geometry and functional abilities are incorporated into analyses that enable individualized optimization of device configuration. Simulation results demonstrate that these optimizations result in varied device configurations across individuals, and that for patient populations there is a positive correlation between the results of symmetry-based neuromuscular goals and thermodynamics-based metabolic goals. The aim is to provide physically minimal interventions that can affect meaningful biomechanical changes in people with a broad range of locomotor deficits. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2025 · 2025-05

This I-Corps project focuses on a portable water-quality sensing technology that provides fast, on-site detection of toxic mercury in water. Mercury contamination is a serious public health issue that can affect people throughout the U.S. Most current testing methods are slow, costly, and require special equipment and trained personnel, which makes real-time water monitoring difficult, especially in emergencies. This new technology offers an affordable and easy-to-use alternative that permits individuals and families to check water quality themselves. By making water testing more accessible, this solution helps protect public health and strengthens the ability of communities to respond to water challenges, especially during extreme weather events. This I-Corps project utilizes experiential learning coupled with a first-hand investigation of the industry ecosystem to assess the translation potential of the technology. This solution is based on the development of a compact, fast sensor that can detect very small amounts of mercury in water. The design uses a new approach that leverages optoelectric materials to achieve high accuracy and sensitivity without the need for large or expensive lab equipment. The sensor delivers quick results and can be easily integrated to existing water systems. What makes this technology unique is its use of low-cost and precise control methods with greatly improved 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 · 2025-05

This I-Corps project is based on the development of pulse oximeter designed for neonatal care. While pulse oximetry, electrocardiogram (ECG), and temperature monitoring are established practices during neonatal care, current devices can be unwieldy, requiring troubleshooting and wasting precious time due to difficulties like opening clenched neonatal fists, adhesive issues on wet skin, and complex application procedures. The accurate monitoring of oxygen saturation during neonatal care is vital to prevent complications from both excessive and insufficient oxygen levels. This technology is a rapidly appliable, multifunctional pulse oximeter designed specifically to address the challenges of neonatal care. The device's design allows it to be easily attached to a newborn's clenched fist using a flexible rounded post, enabling quicker and more reliable monitoring. In addition, the design includes a convertible clip that facilitates both short-term and long-term attachment without detaching the device. Eliminating the need for adhesive and reducing the complexity of application may serve to improve neonatal monitoring, potentially reducing the risk of complications from both excessive and insufficient oxygen levels and improving neonatal care. This I-Corps project utilizes experiential learning coupled with a first-hand investigation of the industry ecosystem to assess the translation potential of a pulse oximeter designed for neonatal applications. This technology is designed to provide accurate pulse oximetry, electrocardiogram (ECG), and temperature monitoring without the use of adhesives. The solution may be easily attached to a newborn's clenched fist using a flexible rounded post, enabling quicker and more reliable monitoring. The device also includes a convertible clip to strap design to facilitate both short-term and long-term attachment without detaching the device. Accurate and timely monitoring of vital signs in newborns is critical to preventing conditions such as retinopathy of prematurity, bronchopulmonary dysplasia, and neurodevelopmental impairment as a result of complications from both excessive and insufficient oxygen levels. In addition, this device is designed to integrate with existing hospital monitoring systems, which may facilitate adoption in hospitals and neonatal care units. The technology may improve neonatal care and may be suitable for use in other clinical environments, potentially extending its impact beyond neonatal care. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2025 · 2025-05

This Faculty Early Career Development Program (CAREER) grant funds research, education, and outreach activities aimed at developing new methods to understand and forecast the behavior of complex biological systems using data-driven approaches. Observing biological processes directly, such as genetic circuits and neuron activities, is often restricted by the limited capabilities of current experimental technologies. Nonetheless, dynamic systems theory offers mathematical principles that make it possible to glean significant information about these biological processes from limited data sets. The research activities funded by this award intend to create a new analytical framework that merges artificial intelligence with foundational concepts from nonlinear dynamics theory to develop principled algorithms for interpretable modeling of biological time series data. These algorithms use topological methods to overcome noise and errors in data, along with generative machine learning, an emerging approach that utilizes large data sets to construct probabilistic models that can predict future states. These data-driven techniques will be applied to study animal behavior and neuron activity, identifying recurring patterns associated with common behaviors such as navigation. Additionally, this CAREER award supports educational activities that will engage high school students in mathematical research, making complex mathematical concepts more accessible through scientific visualization. Furthermore, it will contribute to the creation of an open-source, online textbook on computational physics and engineering, bridging traditional computational methods with contemporary artificial intelligence techniques to advance science in the digital age. A central challenge in systems biology is inferring unseen dynamical systems from limited observations, such as measurements from a small number of genes, neurons, or species. This research introduces a generative machine learning algorithm that maps biological time series to a network of discrete dynamical motifs, that is, estimates of invariant solutions of the unknown dynamical system that governs a biological process. This approach requires no prior knowledge of the governing equations, instead leveraging strong theoretical constraints to enhance accuracy. Initially, topological data analysis is used to detect evidence of these invariant solutions directly from biological time series. This technique is integrated within an end-to-end generative machine learning architecture that maps complex time series to coarse-grained dynamic processes. This method will be applied to large-scale recordings of organismal behavior, addressing challenges posed by low-dimensional and highly heterogeneous measurements that vary widely across different organisms. By mapping various datasets of organismal behavior onto a shared space of latent orbits, this research will demonstrate how nonlinear dynamics can uncover conserved biological motifs across the tree of life. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2025 · 2025-05

Xuhui Huang of University of Wisconsin, Madison and Pengyu Ren of University of Texas, Austin are supported by an award from the Chemical Theory, Models and Computational Methods program in the Division of Chemistry to develope a new polarizable implicit solvent model for efficient and accurate RNA modeling. RNAs play a key role in regulating gene expression and many other vital cellular processes. Understanding how RNAs bind and fold is key to uncovering their molecular mechanisms and advancing RNA-based molecular design. Traditional molecular dynamics (MD) simulations, which do not account for electronic polarization, often fail to accurately model highly charged RNAs. In particulalr, water molecules, being highly polarizable, behave differently near these charged RNAs compared to in bulk water. In addition, explicit sovent based MD simulations are computationally expensive. To address these chanllenge, Huang and Ren will develop a new polarizable solvation model that represents water molecules around RNAs implicitly through statistical descriptions of water density and correlations. This model will adapt to changes in the local electrostatic environment, ensuring high efficiency and accuracy in modeling RNAs. Huang and Ren will apply this model to study RNA hybridization, RNA-small molecule binding, and ion-induced RNA folding. As part of the educational and component of this project, they will integrate their research findings into undergraduate and graduate courses to enhance STEM education. The developed software will be publicly available through the TINKER software package on GitHub and training workshops will be organized to educate the scientific community on the efficient use of the software. Compared to the explicit solvent models, the 3-Dimensional Reference Interaction Site Model (3DRISM) simplifies the all-atom description of solvation into a density-based representation of the solvent surrounding the solute. 3DRISM eliminates the need to sample explicit solvent configurations and enables the explicit inclusion of ions (e.g., Mg²⁺), which are crucial for accurate RNA modeling. However, current 3DRISM solvent models rely on pair correlation functions and cannot explicitly account for the many-body response in solvent. To overcome these limitations, Huang and Ren will develop a polarizable-3DRISM (p3DRISM) implicit solvent model to accurately model polarizable solvation for polarizable AMOEBA solutes. First, Huang and Ren will derive a new form of the 3DRISM equation that incorporates solute-solvent-solvent 3-body correlations and will develop an efficient implementation of this new equation, based on linear response theory. This approach accounts for changes in solvent-solvent correlation functions induced by polarization from the local electric field. Secondly, they will incorporate polarizable solute-solvent interactions through induced dipoles in the p3DRISM scheme. Huang and Ren will apply AMOEBA-p3DRISM to calculate free energies for RNA hybridization and RNA-small molecule binding, as well as to model Mg²⁺-induced folding of RNA k-turns. The software and algorithms developed will benefit the pharmaceutical and biotech industries, especially in RNA-based therapy and computer-aided drug discovery. Additional broad impacts include outreach to undergraduates at University of Wisconsin, Madison and University of Texas, Austin, integration of research findings into coursework, and training workshops for the scientific 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 · 2025-05

PROJECT SUMMARY/ABSTRACT The objective of this K23 is to support new mentored training in the computational neuroscience of higher-order threat learning and memory in posttraumatic stress disorder (PTSD) as the candidate transitions to an independent career as research faculty specializing in integrating lab work with real-world experience sampling in larger, heterogenous research samples to further translational efforts in conceptualizing and treating trauma and anxiety psychopathology. PTSD affects ~6% of the US population annually and is associated with substantial health and economic burden. RDoC-aligned threat conditioning paradigms have substantially contributed to etiological models of PTSD and exposure therapies, yet some patients fail to respond to treatment or experience relapse. One potential explanation is that exposure primarily addresses directly associated stimuli and experiences. Consequently, reductions in fear tend not to spread to more nebulous and indirect higher order networks of generalized threat associations commonly seen in pathological anxiety. Prolific nonhuman animal work has produced a neurobiological model of the mechanisms of higher-order threat learning, yet systematic translation to humans is almost nonexistent. Thus, we lack a compelling human neural model needed to anchor translational efforts that integrate higher-order learning principles into the theoretical framework of exposure. During this award, the candidate will build on initial functional MRI training and incorporate new training in advanced computational decoding applications of multivariate pattern analysis (MVPA) to test the complex interplay of aversive learning and memory mechanisms that result in dense and difficult-to-treat higher-order threat associations. Aim 1 will a) apply MVPA to test how memory integration mechanisms facilitate threat generalization across higher-order pathways in implicated medial temporal structures (basolateral amygdala, hippocampus, perirhinal cortex) and medial prefrontal cortex; b) determine the long-term durability of higher- order threat generalization biases memory retrieval at 24-hour or 1-month after initial learning; and c) test the hypothesis that higher-order threat generalization is heightened in PTSD compared with participants without psychopathology. Aim 2 will extend this work and address heterogeneity inherent to the PTSD diagnosis via subgroup and dimensional anxiety-related trait modeling. Aim 3 uses these lab-based neural metrics of threat generalization to predict real-world daily experience of PTSD and anxiety-related symptoms and physiology, which is needed to bring neural models in better alignment with the clinical reality of pathological anxiety. To facilitate this work, the candidate will receive extensive supervised training in longitudinal ambulatory assessment designs employing state-of-the-art wearable technology and experience sampling methods. The proposed work can advance neurobiological models of anxiety-related psychopathology and set the stage for addressing prominent treatment limitations. This award lays the groundwork for the candidate to achieve these research and training goals and to contribute to efforts optimizing treatments using lab and real-world data.

NIH Research Projects · FY 2026 · 2025-05

Abstract Electroactive bacteria couple changes in the extracellular reduction-oxidation (redox) environment to their central carbon metabolism through extracellular electron transfer (EET). Several electroactive commensals and pathogens have been isolated from the human gut, oral cavity, and other locations, but their influence on host biology has not been examined. Accordingly, the overall goal of the proposed work is to uncover the fundamental role that EET plays in human health and to exploit this understanding to develop new therapeutics and biological catalysts. Building off our previous efforts studying the electroactive bacterium Shewanella oneidensis, we will first select for new-to-nature reactivity by engineering electron transfer proteins that couple the desired chemical reaction to cell growth. We hypothesize that EET can significantly accelerate the rate of new biological reaction discovery. Next, we will examine the role of EET in the healthy and diseased gut microenvironment. We hypothesize that EET contributes to iron homeostasis and will explore this possibility by combining animal models of gut dysbiosis with a novel droplet microfluidic assay capable of detecting electroactive bacteria in complex ex vivo samples. Finally, we hypothesize that electroactive probiotics can facilitate EET-driven health outcomes. Towards this goal, we will transfer EET-capable proteins to therapeutically relevant bacterial hosts and lay the groundwork for future in vivo and therapeutic applications that exploit our understanding of EET’s role in disease biology. Overall, our proposal will examine the fundamental role of EET in human health and apply this knowledge toward advancements in biocatalysis and therapeutic development.