University Of Texas At Austin

NSF Awards · FY 2025 · 2025-04

Humanity’s technological achievements are extraordinary, from the invention of basic tools to the creation of self-driving cars and smart devices. These advancements rely on the ability of humans to build on previous generations’ cumulative knowledge. Central to this is innovation, creating new tools or finding novel uses for old ones to solve problems. Yet, young children often struggle to invent tools, raising important questions about how innovation develops. This project explores the cognitive processes and educational experiences that shape human capacity for tool innovation. By understanding how humans learn to innovate, this research helps inform efforts to enhance STEM education and foster creativity in the next generation. This research brings together an international group of researchers to collaboratively study the development of innovation. The investigators use behavioral methods to evaluate children’s tool innovation skills across a variety of educational contexts. Specifically, this research examines the role of access to education and other contextual factors in the development of innovation skills. This project aims to deepen an understanding of how innovation develops and how humans can nurture it in a rapidly advancing technological world. This proposal is awarded under the SBE-UKRI Lead Agency Opportunity. 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 · 2025-04

Theories about how humans create language depend on what language structures are observed. Less is known about sign languages compared to spoken languages. The sign language examined in this doctoral dissertation project is important because it is a young language. Studying young languages helps scientists understand how languages develop and change over time. In some theories, the structure of new languages depends on ways in which human brains are hard-wired to learn language from birth. In other theories, the structure of new languages comes mainly from the ways people communicate and interact with each other. This doctoral dissertation project investigates a young sign language that has never been studied in detail by linguists before. The results reveal a timeline of how a language develops that could provide evidence for or against these theories, thus advancing a more comprehensive scientific understanding of human language. Other benefits to society include educational opportunities and workforce development for students involved in the research. This doctoral dissertation project yields a sign language grammar and an archive of language samples. Data are gathered by interviewing three groups of people: those who were born before the language began, those who were children during the early period of the language, and those who grew up after the language was established. Past research has found that people keep using language features they learned when they were young. Studying the language of different generations is used to infer language change over time. This study collects information about linguistic structures by having participants do communication tasks such as describing what they see in a photograph, reversing a sentence from a positive to a negative, or filling in a gap in a sentence. These tasks elicit specific expressions, including verbs in different tenses or different types of clauses. The researchers also gather data on language transmission at the level of individual language users. Data and language samples are made available in a public archive, providing an important resource for education and research. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2025 · 2025-04

Dissolved organic nitrogen (DON) is a large pool of reduced nitrogen that influences chemical and biological processes. DON plays important roles in primary production, nutrient availability, and carbon storage in the ocean. The compounds and structures in DON influence its role in these processes. This project uses novel methods to study amino acids, a group of small organic nitrogen compounds that make-up proteins. Specifically, the study will focus on how amino acids are converted to different forms and “refractory DON.” Refractory DON is important because it can resist degradation and persist in the ocean for several thousand years. This study will use stable isotopes to trace the fate of nitrogen in amino acids and address important questions about marine nitrogen cycling. Findings from this study will be useful for understanding the composition of DON and how marine organisms use this form of nitrogen. In addition, this project will train graduate and undergraduate students, offer education programs to K-12 students, and enhance ocean literacy through public presentations and radio programs. This project investigates the fate of nitrogen (N) in amino acids and the processes that contribute to forming refractory dissolved organic nitrogen (DON) in the ocean. Using stable isotope probing with 15N-labeled amino acids and controlled incubations, the study will trace the fate of different amino acids with diverse chemical structures in waters from productive coastal zones to oligotrophic open ocean waters. The proportions of amino acid-derived N that are remineralized into inorganic forms (e.g., ammonium, nitrite and nitrate), converted to labile DON, and transformed to refractory DON, will be traced and quantified. Through the comparison of labeled and unlabeled incubations, advanced high-resolution mass spectrometry techniques will be applied to characterize the molecular composition of amino acid-derived DON and compare it to naturally occurring refractory DON. The overarching goal of the project is to improve the understanding of N cycling and the processes that influence the marine DON pool. Results from this project will enhance our understanding of marine N dynamics and the molecular-level composition of the oceanic DON pool. This project will also promote STEM education by training graduate and undergraduate students through a semester by the sea program and summer internships. K-12 students will have educational opportunities through a summer science program at the University of Texas Marine Science Institute. Results will be disseminated through publications, national and international conference presentations, public lectures and radio programs. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NIH Research Projects · FY 2025 · 2025-04

PROJECT SUMMARY/ABSTRACT NOT-DA-19-048 Adolescent females in juvenile justice settings engage in multiple health risk behaviors that place them at risk for HIV and pregnancy affected by alcohol and marijuana. Specifically, they engage in frequent sexual risk behaviors, placing them at risk of pregnancy, STIs and HIV, while also using marijuana, and alcohol. With nearly half of U.S. pregnancies being unplanned, females unaware of their pregnancy will continue to drink or use marijuana during the early and critical weeks of gestation, which places them at risk of substance-exposed pregnancy. The long-term goal of this proposed line of research is to develop efficient and opportunistic interventions that reduce the risk of substance-exposed pregnancy (SEP) and HIV/STIs for justice involved female youth. Therefore, the overall objective of this study is to test the efficacy of CHOICES-TEEN (CT) for reducing the risks of SEP and HIV/STI in young women involved in community probation or diversion programs. CT was adapted from the CHOICES preconception intervention and its shorter version, CHOICES-PLUS, which have a robust history of efficacy in reducing the risk of alcohol and tobacco-exposed pregnancy with high-risk adult women. CT utilizes Motivational Interviewing (MI), which has demonstrated significant promise with adolescents and criminal justice populations. Our recent pilot study (R03DA034099; CHOICES-TEEN; CT-P), in which we adapted CHOICES for teens and tested its feasibility with youth on community probation, produced promising results. CT was modified based on this pilot work to 1) focus on marijuana (reported by 89% in CT-P study) rather than tobacco given the low prevalence and sporadic nature of nicotine use reported by the teens; 2) add a mobile health application to increase engagement with the daily journal and; 3) incorporate a post-CT self-regulation component targeting behavioral processes of change (POC). This study will move the field vertically by elucidating important factors influencing youth health behavior change, while testing an intervention designed to reduce individual and societal costs for this high risk, underserved adolescent population. The next logical step is to conduct a rigorous RCT to assess the efficacy of this gender-responsive, tailored bundled risk reduction intervention for young, primarily minority, women involved in a community-based juvenile justice diversion or probation program. A stage II behavioral intervention efficacy trial will: 1) Primary Aim: Test the efficacy of CHOICES-TEEN (CT) on reducing the risk of substance-exposed pregnancy (SEP) and HIV/STI among high-risk female youth involved with the juvenile justice system by reducing alcohol use, increasing marijuana cessation, reducing pregnancy risk, and increasing condom use. Aim 2: Test the efficacy of CT, compared to SC, in increasing cognitive self-regulation abilities; Aim 3: Test proposed intervention mediators/mechanisms of action for CT overall and by race/ethnicity; Aim 4: Test the moderating effect of initial readiness to change on risk of SEP and risk of HIV/STI. If efficacious, CT is readily scalable and has the potential for dissemination not only to juvenile justice settings, but to a wealth of settings that serve young adolescent women at risk of substance-exposed pregnancies and HIV/STI.

NSF Awards · FY 2025 · 2025-04

With support from the Chemical Structure and Dynamics (CSD) program in the Division of Chemistry, Professors Sean Roberts, Narayana Aluru, and Michael Cullinan of the University of Texas at Austin are combining sophisticated experimental and computational methods to study how strain affects singlet fission and energy transport in layered heterostructures. Singlet fission is a unique process wherein an energetically excited molecule shares half its energy with a neighboring molecule. This energy redistribution can enable improved systems for energy harvesting, photocatalysis, and quantum sensing, but achieving this goal requires an improved understanding of how a material’s structure impacts its ability to both undergo singlet fission and transport energy. Professors Roberts, Aluru, and Cullinan will address this challenge by developing micro-electromechanical (MEMS) devices that will be used mechanically strain molecular crystals to alter their internal structure. Computational modeling performed in concert with time-resolved microscopy measurements will identify how strain-induced changes in the structure of a crystal impacts its behavior to uncover structure-function relationships expected to guide the design of singlet fission materials for applications in energy harvesting and light detection. This project will support the training of three graduate students as well as two undergraduate student researchers from Austin Community College. Singlet fission enables individual photons to be transduced into pairs of spin-triplet excitons. However, developing technologies that utilize singlet fission to boost their performance requires singlet fission materials that direct the spatial transport of the triplet excitons they generate. To address this, a research team led by Professor Roberts will map how the structure of molecular crystals dictates their ability to undergo singlet fission and transport triplet excitons by using mechanical strain to systematically alter their intermolecular structure. By mapping how singlet fission and exciton transfer depend on a material’s structure, the research team will establish benchmarks for refining theoretical models for singlet fission and exciton transfer while simultaneously guiding the design of new compounds that can meet current challenges in energy harvesting. To achieve its objectives, the research team will employ MEMS devices to strain molecular crystals and thin film heterojunctions in concert with transient absorption microscopy experiments that will track exciton diffusion with femtosecond time resolution and tens of nanometers spatial resolution. These measurements will be modeled using electronic structure calculations that will both predict how materials deform under applied strain and how these structural changes impact a material’s ability to generate and transport triplet excitons. 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 · 2025-04

This I-Corps project is based on the development of devices to study the behavior of light at the nanometer scale and the interaction of nanometer-scale objects with light. These devices may be used to improve the performance of optical systems in a broad range of applications including communications, medical diagnostics, and quantum technology. In addition to optical systems, the technology also may advance the semiconductor manufacturing capability in the U.S., as well as benefit the development of quantum technologies. The technology uses lithium niobate crystals, which are considered one of the most important materials for modern optics, even though there are efficiency limitations with current devices using these materials. This technology may serve as the foundation to improve the performance of optical systems that will transform and improve current computing, security, and communication systems. This I-Corps project utilizes experiential learning coupled with a first-hand investigation of the industry ecosystem to assess the translation potential of nanophotonic lithium niobate devices. The large second-order coefficient of lithium niobate forms the foundation for numerous optical applications in both classical and quantum regimes, including expanding the wavelength of light sources and generating quantum states for quantum communications. Nonlinear efficiency is one of the most critical metrics. To further increase nonlinear efficiency, chip-scale lithium niobate devices made with nanofabrication technology have been developed. Unfortunately, despite intensive efforts in the past decade, such chip-scale lithium niobate devices failed to deliver the promised high nonlinear efficiency due to non-uniformity. To overcome this issue, an adapted poling technique has been developed to compensate the non-uniformity, and it was shown that the ideal phase-matching condition can be recovered. Further, it was demonstrated that the nonlinear efficiency may be increased by over ten-fold. Chip-scale lithium niobate devices may provide important benefits in system size, weight, and power consumption and change the landscape of lithium niobate applications ranging from laser sources and optical sensing to quantum communications. 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 · 2025-04

This I-Corps project focuses on bringing a new nanomanufacturing technology to market that can create tiny patterns and structures on materials. This nanopatterning can leading to a rapid and low-cost alternative to the nanomanufacturing systems used today. Currently, nanopatterned technologies exist almost entirely within the semiconductor market and must meet the standards of Complementary Metal-Oxide-Semiconductor (CMOS) compatibility, necessitate clean room infrastructure, and can require upwards of $10 million - $100 million of capital investment. This new nanomanufacturing technology platform uses a process called self-assembly, where the tiny building blocks automatically arrange themselves into organized patterns without much external help. This approach significantly reduces production costs and increases manufacturing efficiency. This approach may help pioneer new markets for nanotechnology, unlocking the potential of previously demonstrated nanotechnologies that were unprofitable due to the high capital requirements, while also helping to ensure that the U.S. remains a global leader in nanotechnology. This I-Corps project utilizes experiential learning coupled with a first-hand investigation of the industry ecosystem to assess the translation potential of a low-cost, large-area, and highly scalable nanomanufacturing platform. This solution leverages the self-assembly of colloids to create and define nanopatterns across a variety of substrate materials and substrate curvatures, with various colloidal materials, and without the need for a clean room environment. This self-assembly based nanopatterning system varies considerably from current existing technologies, such as photolithography and nanoimprint lithography, where strict clean room infrastructure is required, flat wafers are needed as substrates, and Complementary Metal-Oxide-Semiconductor CMOS-compatible processes must be used. This manufacturing technique could enable new fields of nanotechnology enhanced products, such as bactericidal medical implants, anti-reflection and light trapping nanostructured photovoltaics, and high efficiency thermal management devices for data centers. 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 · 2025-04

Geohazards pose large risks at geologically active continental margins. These geohazards are interconnected and thus difficult to study in isolation. The goal of this project is to bring together experts to develop plans for an integrated array of instruments to observe these hazards. The array will be designed using Chile as a case study. This is a unique location where frequent events and existing networks provide a global understanding of interacting hazards. Teams of experts in computer modeling and technical planning will design sub-arrays for earthquake, volcanic, and landslide observations. Teams will also compile new catalogs of earthquakes and landslide susceptibility in the study area. The teams will meet in a 3-day workshop to synthesize results. Broad input from the scientific community will be solicited through a series of webinars. New models and catalogs will be shared openly through the SZ4D website and data repositories to benefit communities exposed to subduction-related hazards in the U.S. and internationally. Subduction of ocean lithosphere results in the largest earthquakes, volcanic activity, and landscapes highly prone to destructive landslides. For decades research related to subduction and related geohazards has proceeded piecemeal. This research will provide the basis for an overarching framework for integrated studies that can directly address the linkages between earthquake, volcano, tsunami, and landslide geohazards. This award will support a series of modeling studies and technical planning that will be used to design three overlapping arrays of instrumentation at the Chile Subduction Zone. Chile is unique in combining a high level of geological activity and good logistical access. The instrument array will be designed to observe a broad range of earthquake, volcanic, and landslide processes. The work is organized into ten work packages. Five will assess and plan various aspects of the seismic detection and geodetic network. Two will address sediment and hydrologic transport for landslides. Two will address using seismicity to forecast volcanic processes. The final work package will bring together the others with a three-day workshop and with scientific community input via a series of webinars. The connection between this research and the SZ4D initiative makes very clear the connection of this planning activity to benefit people who live with subduction-related geohazards in the U.S. and globally. 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 · 2025-04

PROJECT SUMMARY/ABSTRACT Adolescent girls with autism spectrum disorder (ASD) are at high risk for depression due to risk factors associated with female sex and ASD-related challenges. However, little empirical evidence is available on the psychosocial mechanisms of depression onset and change for girls with ASD, leaving no effective intervention to treat depression in this extremely vulnerable group. While social interaction and relationship challenges are particularly pronounced during adolescence, sufficiently nuanced longitudinal data is needed to understand how momentary and cumulative social and emotional experiences impact psychological well-being during this critical time in order to inform treatment development. The current proposal aims to investigate longitudinal trajectories and social mechanisms of depression in ASD during adolescence with a focus on girls. Three complementary studies are proposed to achieve the research objectives and provide necessary training to equip the candidate with advanced methodological and professional skills to transition into an independent research position. During the mentored K99 phase, two secondary data analyses will leverage existing data to generate initial evidence on depression and social experience in adolescent girls with ASD to: (1) Map trajectories of depression symptoms in girls with ASD and typical development (age 10 to 13) (Aim 1), using group-based longitudinal structural equation models (LSEM); (2) Profile daily social experiences relevant to negative mood and depressive symptoms in girls with ASD (Aim 2), using daily social experiences data collected with a cross-sectional sample of transition-age youths with ASD. To examine social-emotional protective and risk factors of depression (Aim 3), during the R00 phase, an accelerated longitudinal study with measurement bursts using ecological momentary sampling (EMA, study protocol piloted in K99) will be conducted to collect four waves of original data on 120 adolescent girls (60 ASD and 60 TD, age 14 to 16) over two years. Findings from the novel multi-method longitudinal study will directly inform development of targeted intervention by identifying how momentary social experiences, emotions, and coping strategies impact mood in the “moment” and in the long term, while considering the impact of individual- level characteristics. Through the carefully designed training activities and the institutional resources available at UCSF, the candidate will receive focused training on developmental psychopathology and pubertal development in ASD, hands-on clinical experiences in identifying and treating depression in girls with ASD, and methodological training in the state-of-art design and analyses of EMA during the K99 phase. With the strong support from a team of leading experts in their respective fields, the candidate will successfully establish her own line of research and become an independent researcher with unique expertise.

NSF Awards · FY 2025 · 2025-04

This award provides partial support for US-based participants in activities related to the thematic program "Topological and Geometric Structures in Low Dimensions", which will occur July 2 through September 12, 2025 at Centre de Recherches Mathématiques, Montréal (CRM), with some activities held at Université du Québec à Montréal (UQAM). The primary purpose of the is to disseminate the most current results in low-dimensional geometry and topology and to foster interaction between researchers in some of the most active and rapidly developing areas within the field. Participation of early-career researchers in these activities will provide opportunities to make new professional connections, collaborate with peers, establish access to experts in their field, and learn the details of distinct topics of contemporary interest through attending mini-courses, conferences, and other program activities. One component of this collaborative project is support through the lead organization (Temple University) for participation in the thematic program, including three focused activities, each for 40 to 50 participants, consisting of a week of mini-courses followed by a week-long conference: "Topological 4-manifolds" (CRM, July 2-11), "Low-dimensional topology and Floer theory" (UQAM, Aug. 18-29), and "Hyperbolic manifolds of dimension 4 (and more)" (CRM, Sept. 2-12). The second component, through non-lead organization the University of Texas at Austin, is support for participation in the conference "Knots, groups, and manifolds" (UQAM, Aug. 11-15) centered around current developments in low dimensional topology; this conference is attached to the thematic program but organized as a separate event of a significantly larger scale, with over 150 total participants expected. Broadly speaking, the scientific theme of the activities is contemporary classification problems in low-dimensional topology and the interplay between the algebraic, topological, geometric and analytic structures that arise in these classifications. Central focus will be on emerging areas, new progress in traditional areas, investigating conjectured connections between disparate structures on low-dimensional manifolds, and applications of these methods to the topology of manifolds of dimension 3 and 4.The workshop website is at: https://www.crmath.ca/en/activities/#/type/activity/id/3951 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 · 2025-03

PROJECT SUMMARY Admixture, natural selection, and changes in life history are fundamental biological processes that shape genetic variation, drive the evolution of complex traits, and influence the distribution of disease risk across human populations. The advent of Ancient DNA (aDNA) technology now presents us with an unprecedented opportunity to examine these mechanisms in a manner not previously possible by directly allowing changes in gene frequency over time and space. Over the past decade, the field has grown dramatically, with publicly available aDNA samples expanding more than 100-fold to over 10,000. However, this rapid expansion of data has also led to major increases in complexity and scale that have outgrown existing analytical methods to detect admixture and natural selection. My research program addresses this gap by developing computational and statistical methods that the integrates aDNA with large-scale genome-wide association studies (GWAS) to uncover the mechanisms underlying genetic and phenotypic evolution. First, we will develop highly scalable methods for automatic reconstruction of admixture graphs and large human pedigrees, unlocking large sample size analysis of both population and familial dynamics. These tools will have broad applications beyond just time series data and help enhance association analyses for both rare and common diseases by improving resolution of population structure and relatedness particularly in populations with complex demographic histories. Second, we will extend recent models we have built to study natural selection in ancient populations, including the evolution of complex traits, while addressing challenges posed by population structure. Third, we will expand the scope of evolutionary analysis beyond DNA to include DNA methylation, providing insights into the evolution of life history traits such as age at death over long time scales. Finally, by leveraging the thousands of skeletal samples already analyzed for aDNA and combining this information with climatic and biochemical information, we will develop predictive models for DNA preservation, which will inform future sampling efforts and importantly advance our understanding of why DNA remains stable over long time scales in certain natural environments but not others. These insights will be broadly applicable, from improving studies of ancient pathogens to enhancing study of genetic disease risk while deepening our understanding of fundamental biological processes shaping human genetic variation. The computational tools built will broadly benefit the scientific community across medical domains.

NSF Awards · FY 2025 · 2025-03

The stiffness of the heart’s right ventricle (RV) is a critical determinant of cardiac health. For example, RV stiffness is a strong predictor of disease progression in pulmonary hypertension. It may even be a more reliable indicator of clinical outcomes than traditional measures of RV function, such as contractility. This research addresses the current lack of fundamental understanding of how intra- and extra-cellular mechanisms contribute to the stiffening of the RV. This topic has largely been overlooked in favor of studies focused on the structure and function of the heart’s left ventricle (LV). Given the significant differences in physiology between the RV and LV, this critical gap in knowledge presents a barrier to advancement in the diagnosis and treatment of RV-related conditions. Thus, by filling this gap, this project will provide insight into the mechanobiology of RV stiffening while supporting the development of new diagnostic tools, prognostic markers, and therapeutic strategies. This research is well-aligned with NSF’s mission to advance scientific progress and contribute to public welfare by addressing a significant health challenge. Furthermore, the project will provide educational and training opportunities for underrepresented student populations and enrich open science initiatives through publicly accessible content, such as live-streamed cardiac anatomy lessons. This project aims to delineate and model the intra- and extra-cellular mechanisms contributing to RV myocardial stiffening using a combination of experimental and computational approaches. Experimentally, micro-scale mechanical tests will be conducted on individual cardiomyocytes, followed by mechanical testing of combined cardiomyocyte-extracellular matrix strip assemblies. Primary myocardial samples isolated from the RV of both healthy and diseased sheep with established pulmonary hypertension will be included. Among the many potential biological mechanisms of RV stiffening, investigation will begin on variations in titin isoform expression and phosphorylation states and changes in endomysial collagen composition, density, and cross-linking. Computationally, machine-learning-based surrogate modeling approaches will be used to bring micro-scale models of cardiomyocytes and extracellular matrix up to the organ scale, where ultimately the role of each stiffening mechanism on tissue-scale measures, such as RV diastolic function, will be interrogated. The primary outcome of this work will be a multi-scale model that enhances understanding of RV physiology and diastolic dysfunction, thus contributing both valuable mechanobiological insights into RV remodeling and a set of open-source computational tools for future cardiovascular research. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2025 · 2025-03

From the weather to human health to fighter jets, there are many complex systems whose outcomes we would like to predict and control. To achieve these goals, scientists and engineers often build digital twins---computer models that emulate and interact with the underlying physical systems. The current project describes fundamental research into the generalization ability of digital twins: to what degree can digital twins predict outcomes under conditions they have not previously encountered? For example, if the digital twin for an airplane has only seen data collected under normal operating conditions, can it accurately predict the plane's response to turbulence? By combining mathematical tools from nonlinear dynamics and computational tools from machine learning, this project aims to develop fundamental theories on generalization and build robust digital twins that can perform well in extreme or unexpected conditions. While the proposed framework applies to a broad class of complex systems, it is first being applied to circadian rhythms, which are the internal timekeeping mechanisms of the human body. Human biological clocks are increasingly subject to disturbances introduced by modern lifestyles such as long-haul air travel and nighttime computer use. Predictive digital twins can give personalized recommendations on effective interventions, such as optimal strategies to speed up recovery from jet lags. The project will also provide opportunities to teach modern mathematical concepts to a diverse population of undergraduate and graduate students. Through this project, students learn valuable skills in mathematical modeling, data analysis, science communication, and gain first-hand experience in building and managing state-of-the-art machine learning pipelines. Current domain-agnostic digital twins based on deep neural networks are very expressive but can struggle when generalizing beyond their training conditions. Physics-based digital twins, on the other hand, generalize better to unseen conditions thanks to the strong inductive bias built into the model. On the other hand, they are often not sufficiently flexible to fully capture the rich dynamics in data. This project develops a new class of hybrid digital twins with tunable physics-based and domain-agnostic components, allowing practitioners to balance expressivity versus generalization, depending on the available data and the nature of the task. Utilizing concepts such as basins of attraction in multistable dynamical systems, a key objective of the project is to quantify how the generalization ability of the digital twin changes as the weights assigned to the two components are adjusted. In particular, the project explores the possibility that a properly weighted domain-agnostic component in the hybrid digital twin can sometimes improve out-of-distribution generalization, especially when the inductive bias provided by the physics-based component is imperfect. Digital twins that generalize to unseen conditions are crucial to applications such as finding optimal interventions for restoring disrupted circadian rhythms. For example, to find optimal strategies to speed up recovery from jet lags, a digital twin needs to predict the dynamics of a severely perturbed circadian clock based on data gathered mostly from normally operating clocks. These investigations will guide the creation of more robust digital twins and help inform critical decisions under new or uncertain conditions. 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 · 2025-03

Advances in artificial intelligence and machine learning provide the opportunity for using autonomous multi-agent systems to solve important social and economic problems, such as the application of multiple robots in wildfire monitoring, search-and-rescue, manufacturing, etc. In these systems, agents autonomously cooperate to make decisions in real-time to perform complex tasks. Reinforcement learning, a data-driven control method that enables agents to autonomously learn desired tasks by interacting directly with the environment, has emerged as one of the predominant frameworks for this kind of real-time decision making. While reinforcement learning provides a powerful and flexible framework, it suffers fundamental challenges in its scalability and resilience. Specifically, existing methods require a vast amount of data and computational power and can be unstable in the presence of various types of errors and adversaries. These challenges are the main barriers to the wide applicability of reinforcement learning for real-world problems. This CAREER project will develop new foundations of scalable and resilient distributed reinforcement learning for real-time autonomous cooperation in open multi-agent systems. The overarching goal is to design new learning and control methods that enable agents to interact effectively in open systems, adapt gracefully in time-varying environments, and be resilient to unexpected failures and adversaries. The project will also contribute to education and workforce development by integrating the research findings with rigorous educational and outreach activities, course development, student training, and public partnerships. The central idea of this project is to establish new fundamentals of two-time-scale stochastic approximation for non-monotone systems. The key approach is to leverage extrapolation techniques in optimization and singular perturbation theories in control to address the instability issues of stochastic approximation under non-monotone settings. New theoretical principles will be studied to characterize the finite-time complexity of the proposed methods. By leveraging these new results of two-time-scale stochastic approximation, this project will advance several foundational aspects of distributed learning and control in open multi-agent systems. The focus is to develop new scalable and resilient distributed multi-time-scale reinforcement learning methods that allow agents to cooperate efficiently in real-time under diverse practical considerations, including time-varying numbers of agents, unexpected failures, communication constraints, and adversaries. During the course of this project, the proposed research activities will be evaluated systematically through a series of simulations and field experiments of multi-robot navigation. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2025 · 2025-03

The nation’s ability to maintain global economic competitiveness depends on its efforts to prepare a diverse workforce trained in scientific computing and data science that will tackle grand challenges in multidisciplinary environments. The rapidly changing technology landscape further necessitates increased training using the national Cyberinfrastructure (CI) and training students as early as possible. Advanced computing - data, high performance computing (HPC), artificial intelligence (AI), machine learning (ML), visualization, and analytics - is required to keep pace with the accelerating rate of scientific discoveries. In response to the National Science Foundation's mission to promote the progress of science; advance the national health, prosperity and welfare; and secure the national defense, the CI Research for Societal Advancement REU project is actively engaging 10 undergraduate students each summer for nine weeks in solving real-world problems of national relevance. The REU project is preparing a future scientific workforce using advanced CI resources and building capacity in areas that support major advances in predictions across societal challenges. The inclusive research environment encourages creativity and collaboration amongst diverse social groups to develop innovative solutions and prepare students for careers that will ensure the country’s prosperity and security. The REU aims to meet three objectives: 1) train students to use national CI by integrating the learning of computational science, AI, data-enabled science, and multidisciplinary science in preparation for graduate programs and the workforce; (2) train students to apply advanced computational skills, critical thinking, and creativity to research problems that advance society; and (3) increase the number of diverse and computationally trained students in science, technology, engineering, and mathematics (STEM) disciplines. The REU includes cutting-edge research in science and engineering disciplines, training using TACC resources, mentoring by The University of Texas (UT) at Austin and TACC researchers, social and team-building activities on the UT Austin campus, professional development and graduate school preparation, and opportunities to enhance communication skills. Research projects emphasize advanced computing as a tool to power discoveries that will impact social change for future generations. Students use AI to support decision-making in resource management, and apply preference-based planning in AI to help develop theory and algorithms for designing and verifying autonomous systems at the intersection of computing, control theory, and learning theory. Additionally, students enhance research resources for the modeling and prediction of porous material properties in the fields of petroleum, civil and environmental engineering, and geology. Other projects include simulating and controlling turbulent fluid flows, with a particular emphasis on creating large-scale turbulent simulations and other complex phenomena; investigating the application of constrained optimization models and algorithms to enforce fairness and reduce bias in machine learning models; and developing a bioinformatics pipeline in support of new, cleaner chemical processes. The REU aims to recruit at least 50% of students who identify as Black, Latinx/Hispanic, Native Hawaiian, and Pacific Islander, and also targets women, first-generation college students, and students from institutions without doctoral programs including Minority-Serving Institutions and community colleges. 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 · 2025-03

This NSF CAREER project aims to advance the autonomy of power grids by developing the fundamental theory for enhancing decision speed, resilience, and societal and sustainability awareness of distributed grid management models and algorithms. The fusion of ubiquitous autonomy and connectivity, along with the widespread adoption of distributed energy resources, is transforming our legacy grid into a multi-agent and distributed infrastructure. The project brings transformative change to power grid management by developing innovative strategies to orchestrate the autonomy of cyber-enabled components (agents) to enhance the grid's efficiency, resilience, and sustainability. This is achieved by addressing three critical research questions: how to leverage agents’ autonomy for faster decision-making, enhance the resilience of managing a multi-agent power grid, and integrate societal awareness and carbon footprint considerations into distributed power management models. The intellectual merits of the project include the development of novel machine learning (ML)-assisted optimizers to rapidly solve complex decision-making problems for individual power grid agents, the use of Artificial Intelligence (AI) and generative modeling to improve the resilience of collective power grid agents, and the design and integration of societal benefits and sustainability metrics into the multi-agent decision-making paradigms. The broader impacts of the project include enhancing power grid performance and resilience, educating the public through various media (print media and broadcast news), and offering educational and research opportunities for students, government, and industry professionals. The goal of this project is to enhance the efficiency of the power grid whose characteristics are increasingly multi-agent and distributed in nature. The efficient operation of future electric infrastructure depends on leveraging agent-level autonomy to enhance grid management functionalities in a timely, resilient, socially aware, and sustainable manner. To achieve this, the project proposes neural approximators for mapping optimization inputs directly to high-quality, feasible outputs, thereby eliminating agent-level iterations and significantly speeding up agents’ computations. In addition, this project seeks to enhance the resilience of a collection of agents by using AI to regulate inter-agent communication parameters, using generative models to address data gaps, and dynamically adjusting information dissemination rates during/post disruptions. Finally, the project plans to incorporate societal considerations into distributed power dispatch models by first studying how design parameters, such as computation granularity and strategies for managing struggling agents, affect the distribution of societal benefits and the carbon footprint of distributed computations. These findings subsequently guide the development of socially aware and sustainable models for distributed grid management. The expected outcome of this research is a set of novel analyses accompanied by algorithms, tools, and techniques that harness the autonomy of power system agents to enhance the efficiency and resilience of the nation’s power grids. 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 · 2025-02

This dissertation research studies how traditional resource management systems in coastal regions adapt to new regulations. The investigators specifically test for the social and cultural variables that impact how coastal fishing communities organize their resource management networks and for how these networks adapt to new regulations. In addition to providing training for a graduate student in anthropological science and community-focused anthropological methods, findings will inform the development of educational materials and training modules on resource management techniques for students and the public. In order to understand the impacts of new regulations on local resource management adaptations, the investigators utilize a range of qualitative research methods including interviews, archival analysis, behavioral observations, and oral history. Data will be analyzed using sampling strategies that account for demographic variations in adaptive behavior. The research contributes to studies of resource management, economic and environmental anthropology, and the science of adaptive capacity in fisheries and coastal communities. 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 · 2025-02

‘Cryptobenthic’ reef fishes are a group of thousands of tiny (<5cm), bottom-dwelling species that are difficult to see but occur on coral reefs worldwide in staggering abundances and diversity. In fact, half of all fishes on a typical reef are cryptobenthic, but traditional surveys do not usually include these species. Due to the limited geographic extent of scientific surveys, countless cryptobenthic fish species have yet to be discovered and described, and local biodiversity inventories do not exist in most countries. This project explores the biodiversity of cryptobenthic coral reef fishes in the Indo-Pacific by: 1) conducting a series of local and regional inventories through standardized survey and collection protocols, 2) describing new species by considering multiple high-resolution sources of information to delineate closely related species, 3) analyzing the evolutionary diversification of a particularly species-rich lineage (the dwarf goby genus Eviota) and 4) training a new generation of fish taxonomists with strong ties to coral reef nations to ensure reef fish biodiversity research and conservation for years to come. Inventorying the diversity of life on our planet is a critical challenge that intensifies with the rapid change of the biosphere. Cryptobenthic fishes, which include the world’s smallest and shortest-lived vertebrates, are a biodiversity taxonomic frontier. This research employs standardized sampling techniques across four locations in the Indo-Pacific that promises particularly rich biodiversity discovery (the Lakshadweep, Philippines, Solomons, and American Samoa) with a cutting-edge integrative taxonomy framework and immersive training of young scientists to boost description rates of cryptobenthic fishes for years to come. By implementing an innovative genomics approach to species delimitation, the project will also improve our knowledge about evolutionary processes in the sea, while establishing a promising new model system of small, short-lived, hyperdiverse vertebrates. Finally, the project will establish a dedicated online platform and a museum exhibit to engender scientific discovery and spark public interest in the world’s smallest marine vertebrates. 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 · 2025-02

The research path to clean energy technology and sustainable manufacturing of chemicals is increasingly turning to electrochemistry. Electrochemical approaches open the door to the use of electrical energy derived from sustainable sources such as wind and solar energy. Although significant advances have been made in electrochemical reactions promoted by catalysts (i.e., electrocatalysis), many challenges remain. Experimental studies alone often cannot unravel the complex chemical and physical processes underlying electrocatalytic reactions. The present project addresses this “complexity challenge” by adding theoretical and mathematics-based analysis of electrocatalytic reactions of importance to fuel cell technology and sustainable manufacturing of chemicals. Until recently, atomistic simulation methods, primarily rooted in density functional theory (DFT), have been used to provide insights regarding catalysis at the electrochemical interface. However, the complexity of chemistry, physics and transport mechanisms at the interface limits the models to small scales and special atomic configurations, which are often not realistic enough to accurately describe the interfacial processes. With regard to catalysis, DFT based models alone are challenged in providing information about reaction activation energies, thus preventing direct assessment of reaction rates. The project addresses these limitations by developing an accurate and efficient method to enable large-scale simulations for heterogeneous electrocatalysis. Machine learning force field (ML FF) is a promising approach to realize such simulations. The project will further enhance MLFF capability by adding features that describe the behavior of electrons under the grand canonical ensemble (GCE) in the catalyst. The expanded ML FF model (called “e-GCE-FF”) will be enabled by leveraging 'global state' features of graph neural networks developed previously by the research team. The integrated e-GCE-FF approach will be used to study pH and cation effects in heterogeneous electrocatalysis. Specifically, the project will address two long-standing questions: 1) why oxygen reduction on Au (100) proceeds through a two-electron pathway under acidic conditions, while changing to a four-electron pathway under alkaline conditions; and 2) why larger electrolyte cations promote the hydrogen evolution on Au while suppressing it on Pt? More broadly, the expanded MLFF code will be made open-source, and can be extended to other electrochemical systems (e.g. battery, corrosion). From the educational and outreach perspectives, the project will provide an excellent training opportunity for students interested in computational chemistry and data science. The research results will be integrated into the course curriculum, and the project leader will actively engage the general public through the university’s outreach programs such as “Explore UT”. 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 · 2025-02

Nontechnical Abstract: Two-dimensional materials are transforming science and technology by offering exceptional physical properties not found in bulk solids. Among these, semiconducting van der Waals magnets provide unique opportunities to study and manipulate optical and magnetic phenomena. When exposed to light, these materials generate excitons—bound pairs of an electron and a positively charged hole that transport energy across the crystal. Unlike conventional excitons, those in van der Waals magnets are deeply influenced by the material’s intrinsic magnetism, enabling novel functionalities. This project examines the properties of these exotic particles, exploring their microscopic interactions and developing control strategies to achieve long-range energy transport with minimal dissipation. By employing advanced optical techniques and tailored laser pulses, this research contributes to the emerging field of 'magneto-excitonics,' where the synergistic interplay of magnetic and optical properties enables innovative applications in quantum devices, energy-efficient nanophotonics, and information processing. Complementing these research efforts, the project addresses critical challenges in STEM education, inspiring and preparing the next generation of scientists and engineers. The activities engage over 30 undergraduate students and 50 K-12 educators in advanced research, fostering a pipeline of STEM professionals and cultivating sustained interest in science and technology from an early age. Strategic partnerships with Austin’s semiconductor industry ecosystem support a mentorship program impacting over 150 undergraduate students, offering valuable networking opportunities with role-model leaders and equipping participants with the knowledge and skills essential for success in the high-tech sector. These initiatives strengthen retention in STEM fields, contribute to workforce development, and bridge the gap between academia and the local semiconductor industry. Technical Abstract: Semiconducting van der Waals magnets host various classes of strongly bound excitons closely tied to the underlying spin order, showing exotic magneto-optical phenomena and high sensitivity to external stimuli. Unveiling methods to manipulate these spin-correlated excitons on ultrafast timescales is a critical goal in van der Waals materials research. Theoretical frameworks suggest that exciton properties can be dynamically tailored by engineering the magnetic or crystallographic environment out of equilibrium, and preliminary spectroscopic observations indicate that certain exciton species may exhibit coherent (i.e., wavelike) propagation over tens of picoseconds. The primary goal of this project is to gain a profound understanding of spin-correlated excitons in semiconducting van der Waals magnets by revealing their interactions with low-energy collective modes (e.g., phonons, magnons) and clarifying how exciton creation influences the underlying magnetic order. Building on these insights, the Principal Investigator aims to map the spatiotemporal propagation of these bound species and develop cutting-edge protocols—based on coherently evolving magnetic/crystal environments or optical microcavities—to enhance exciton coherence over macroscopic length scales. This research is integrated with a series of educational and outreach activities. Collaborations with UT Austin’s NSF Materials Research Science and Engineering Center and the Texas Institute for Electronics provide undergraduate students with hands-on research opportunities and connect them to Austin’s thriving semiconductor industry ecosystem through a dedicated mentorship program. The project also engages K-12 educators by offering workshops that introduce groundbreaking scientific discoveries and equip them with innovative teaching strategies to inspire the next generation of STEM leaders. 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 · 2025-02

Project Summary/Abstract Current prevalence rates from the Centers for Disease Control and Prevention indicate that 1 in 36 children have an autism spectrum disorder (autism). When considering the United States population, we find that approximately 26% of children are Latino. However, research on autism does not currently reflect the ethnic diversity of the population. Thus, recommendations and best practice guidelines for screening, diagnosis, and therapeutic support for autism may not reflect the needs of Latino children with autism. This results in significant disparities in identifying Latino children with autism, which can have long-lasting effects on developmental outcomes, well-being, and quality of life for Latino children with autism and their families. Underlying many of these disparities is the linguistic diversity observed in many Latino families and the limited knowledge about how language, communication skills, and autism symptoms manifest in early childhood. However, much of the autism research limits participation to English speakers, resulting in less knowledge about autism among non-English-speaking communities. Additional challenges arise when considering that bilingual (English/Spanish) environments may be more common among Latino children, and by extension, Latino children with autism. The current project will identify and measure the early developmental trajectories of language and communication skills of Latino children and assess and characterize the emergence of autism symptoms among Latino children with a high and low likelihood of autism. The proposed study's aims are to 1) identify and distinguish Latino children's language and communication profiles through a cross-sectional analysis; 2) assess and characterize the developmental trajectories of language and communication skills (English, Spanish) in Latino children between 1 and 5 years of age, and 3) assess and characterize the developmental trajectories of autism symptoms between 1 and 5 years of age in Latino children. We will use a cohort sequential design and multiple methods to capture comprehensive profiles of language and communication skills and autism symptoms to measure early development between 1 and 5 years of age among Latino children. The results of this study will help identify the developmental points when language and communication diverge between Latino children with a high and low likelihood of autism. Furthermore, the results will help clarify when and how autism symptoms can be distinguished between Latino children with a high and low likelihood of autism. These recommendations can have long-lasting effects on addressing the disparities Latino families of children with autism face in accessing culturally and linguistically informed services.

NIH Research Projects · FY 2026 · 2025-02

Summary/Abstract: Catalytic hydrogenations are the most frequently utilized chemical reactions in the synthesis of pharmaceutical ingredients, constituting 14% of GMP reactions (oxidations = 3% of GMP reactions). Metal-catalyzed cross-coupling and carbonyl addition are the most frequently utilized methods for C-C bond formation among GMP reactions. We seek to develop a family of catalytic methods for C=X/C=C (X = O, NR) and C-X/C-Y (X = Y = halide) reductive C-C couplings mediated by H2, 2-PrOH, NaO2CH that are as clean, scalable and cost-effective as catalytic hydrogenation. This includes catalytic hydrogen auto-transfer reactions that affect direct conversion of lower alcohols/amines to higher alcohols/amines via CH-XH/C=C (X = O, NR) C-C coupling. These methods inform parallel studies on the total synthesis of polyketide natural products with anti-cancer or anti-bacterial properties. Under the NIH-MIRA program, our work on catalysis (R01 GM069445) and total synthesis (RO1 GM093905) will be integrated to maximize their synergy.

NIH Research Projects · FY 2026 · 2025-01

Project Summary The brain is the most complex organ in the human body. Millions of interconnected cells communicate through an intricate network of pathways to give rise to our cognition, perception, and emotional experiences. At present we lack an accurate and complete map of the axonal projections that form the wiring of this network (the “pro- jectome”) in the primate brain. Such a map would be an invaluable resource for basic and clinical neuroscience, revolutionizing our understanding of the brain and advancing clinical applications in neurosurgery and neuro- modulation. Over the next five years, the BRAIN Initiative Connectivity Across Scales (CONNECTS) program is set to “develop research and techniques with the capacity to generate wiring diagrams that can span entire brains across multiple scales and species.” Five UM1 comprehensive centers (two in mouse, one in marmoset, and two in macaque/human) and several U01 specialized projects have been funded by CONNECTS to develop new technologies for data acquisition and analysis. We propose to establish the Axonal Projectome Exchange (APEX), a Data Coordinating Center (DCC) for the CONNECTS program, in response to RFA-NS-24-028. The APEX DCC will integrate and coordinate activities across CONNECTS data-generating UM1 and U01 projects that focus on multimodal imaging of axonal projec- tions in macaque and human. This includes two CONNECTS UM1s: the Center for Large-scale Imaging of Neu- ral Circuits (LINC) and the Center for Mesoscale Connectomics (CMC). The APEX DCC is a collaborative effort of investigators from the LINC and CMC consortia, and thus uniquely positioned to integrate data collected by these two centers and by any specialized projects funded by CONNECTS in this domain. The overarching goal of APEX is to enable a transformative shift toward petascale and eventually exascale primate axonal projectomes, laying the foundations for generating whole-brain wiring diagrams across scales in macaque and human at the end of this 5-year period. APEX will pursue two key objectives: First, it will coordinate consortium activities among CONNECTS projects that focus on multimodal imaging of axonal projections in macaque and human, and between these projects and other relevant efforts in CONNECTS, the BRAIN Initiative Cell Census Network, and BRAIN Initiative Cell Atlas Network. Second, it will integrate and harmonize analytic tools that are developed by its constituent UM1 and U01 projects to process multi-scale data from optical mi- croscopy, X-ray microscopy, and diffusion MRI. Rigorous standards and performance metrics will be established to benchmark analytic pipelines, ensure high data quality, and plan for scalability. APEX will contribute to building a unified knowledge base for brain connectivity across species and modalities. It will disseminate these re- sources to the scientific community, and it will establish a strong outreach and engagement strategy to com- municate the significance of the CONNECTS program's ambitious objectives to the general public.

NSF Awards · FY 2025 · 2025-01

Master’s-level engineers are critical for the technology workforce as the nation seeks to continue to advance national health, prosperity and welfare and to secure national defense. However, even though there are four times as many engineering master’s recipients as PhDs in the United States, we know almost nothing about their experiences, motivations, career planning, and skills required in industry. Most prior research on engineering graduate students has focused on doctoral students. This project will focus on this critical segment of the workforce, with an initial focus on mechanical engineering, so that we can systematically understand how to better prepare master’s students for their jobs so that they can make contributions in their careers from the outset. As workforce demands continue to increase for engineers who have master’s degrees, and as technology continues to change at a rapid pace, our research will use cutting-edge generative artificial intelligence (AI) techniques to illuminate the specific skills employers want from employees who have engineering master’s degrees, which can inform graduate curricular offerings. Our research will also help identify potential strategies for recruiting more students to engineering master’s programs, in particular domestic students, which is a critical need for the future workforce. The findings of this project will better inform students, employers, administrators, and those considering master’s degrees, about the skills desired and expected of mechanical engineering master’s recipients. This project will advance novel applications of natural language processing (NLP) coupled with interview research to understand the skills and benefits of terminal engineering master’s degrees, with a preliminary focus on the mechanical engineering discipline. The quantitative element of the project will involve analysis of a data set of over a decade of engineering job postings and apply an algorithm to extract skills from job advertisements to advance understanding of the engineering workforce, and of methodological development of NLP techniques. The qualitative element will involve collection and analysis of interviews with current master’s students about their reasons for pursuing a master’s degree, including desired skills. The project will mix these qualitative and quantitative analyses to identify mis(alignments) between what is communicated from the workforce about desired skills via job advertisements and current perceptions of the workforce from current master’s students. This research will fill an important gap in research on master’s-level engineering students, building knowledge about motivations for pursuing a master’s degree and employer expectations, including the most marketable skills. The NLP approaches developed in this project will apply to other employment sectors, disciplines, education research questions, and fields beyond engineering education research. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NIH Research Projects · FY 2026 · 2025-01

Project Summary/Abstract. In the rat hippocampus (HPC), memories are thought to be coded by coordinated sequences of “place cells”, neurons with receptive fields in specific spatial locations. During active behaviors, place cells representing successive locations within learned trajectories fire as organized sequences within cycles of the theta rhythm (~8 Hz). Place cell sequences that fire during active exploratory behaviors later reactivate or “replay” in a temporally compressed manner during awake rest and slow-wave sleep. Replay is thought to play a key role in memory consolidation and retrieval by transferring a compressed memory format from the HPC to downstream cortical regions that control memory-guided behaviors. Theta-coordinated place cell sequences and replay are widely believed to be important neuronal population mechanisms underlying HPC memory encoding, consolidation, and retrieval. Yet, prior studies of theta-coordinated place cell sequences and replay have focused on spatial memory operations in the dorsal HPC (dHPC). Whether coordinated place cell sequences emerge in populations of ventral HPC (vHPC) place cells remains an open question. It is also unknown whether vHPC place cell sequence replay occurs during sleep and rest. Moreover, the extent to which coordinated place cell populations in dHPC and vHPC integrate unchanging non-spatial information, such as emotional or motivational context, across different spatial locations remains largely unexplored. This project will address these critical gaps in knowledge by testing two overarching hypotheses. The first hypothesis emerges from prior studies of individual place cells and posits that coordinated place cell sequences in both dHPC and vHPC code specific spatial trajectories at different spatial scales. An alternate hypothesis is that coordinated sequences of dHPC place cells code specific spatial locations whereas vHPC place cell populations integrate shared motivational and emotional contexts across different spatial locations. The proposed experiments combine state-of-the-art neurophysiological methods for recording dHPC and vHPC place cell populations in freely behaving rats with sophisticated analytical methods for decoding place cell sequence representations. Experiments in Aim 1 will determine whether theta-coordinated sequences of place cells code longer trajectories in vHPC than in dHPC. Aim 1 will also test whether vHPC, but not dHPC, place cell populations integrate shared motivational or emotional information across different locations. Experiments in Aim 2 will test whether sequences of place cells replay representations of longer trajectories in vHPC than in dHPC. Aim 2 will also test the novel hypothesis that vHPC replay integrates representations of different spatial locations that share the same emotional or motivational context, while dHPC replay separates representations of different spatial locations that share the same nonspatial context. Results from this project will reveal whether dHPC and vHPC place cell populations differentially encode, consolidate, and retrieve spatial and nonspatial components of memories. Results will thereby produce a more comprehensive understanding of HPC mechanisms and function.