Texas A&M University

NSF Awards · FY 2026 · 2026-09

This award supports the study of carbon transport and transformation in river networks. Farmers apply crushed rock to enrich agricultural soils. Through a process known as enhanced weathering, the crushed rock also reacts with the atmosphere to remove carbon dioxide. However, some of the captured carbon may be lost as runoff into streams and rivers. This project will quantify that downstream carbon loss and improve carbon accounting associated with enhanced weathering. This work will also support geoscience and data science education. The project will develop a river carbon tracking model to quantify carbon transport and transformation in river networks following terrestrial enhanced weathering. The Mississippi-Atchafalaya River Basin serves as a natural laboratory for this study based on its history of agricultural liming. This project will integrate river hydrography, river and groundwater chemistry, watershed and climate data, lime application timeseries, machine learning, and reactive transport modeling. The relative effects of various carbon transformation mechanisms will be compared to understand the factors controlling rates of carbon loss. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2026 · 2026-08

This research will use mathematics to understand how living cells make decisions, such as when to divide or when to die. In living cells, many chemical reactions take place all the time. Scientists think of these reactions as forming a network, like a wiring diagram, with connecting wires describing the movement of nutrients and metabolites through a cell's chemical reactions. Knowing the entire wiring diagram is not necessary for understanding the fate of individual nutrients, but the many interconnected circuits make it difficult to isolate the part of the diagram important for certain activities, such as making the decision to divide or to die. This research will use mathematics (specifically, algebraic geometry and dynamical systems) to find and analyze simple components of a reaction network that allow cells to exhibit certain important behaviors. In addition, the project will provide interdisciplinary training opportunities for early-career mathematicians as well as undergraduate students, and thereby supporting the next generation of mathematicians and the STEM workforce. The dynamics observed in living systems is much more than the sum of its parts. Systems biology, therefore, seeks to understand how biological components come together to generate emergent, systems-level behavior. A current bottleneck in systems biology is the need for mathematical theory specialized to the field. Accordingly, this research will develop theory for reaction systems tailored to biological networks. The project will prove new theorems that predict dynamics from reaction-network structure and generate new insights about the dynamical behavior of biologically significant networks. In particular, the results will yield insight into how the dynamics of caspase proteins are tightly regulated and bring about the irreversible process of apoptosis (programmed cell death). Additional results will deepen our understanding of how certain biologically significant properties, specifically, absolute concentration robustness and multistability, arise in real-life applications — but also how they can be built from scratch. These results are expected to have strong potential impact in certain fields of biotechnology — in particular, in synthetic biology and molecular programming (e.g., DNA computing), where researchers aim to design functional objects using living molecules. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2026 · 2026-07

This award supports participation in the 2026 edition of SUMIRFAS held at Texas A&M University during the period from July 31 to August 2, 2026. SUMIRFAS, which started over 35 years ago as the SUMmer Informal Regional Functional Analysis Seminar, has grown into an anticipated and respected annual event that showcases worldwide experts and leaders in the fields of Analysis and Probability broadly construed, as well as promising Ph.D. students and postdocs. This is one of the focal points of the Workshop in Analysis and Probability held every Summer at Texas A&M University. One fundamental aspect of SUMIRFAS is to bring together senior and junior researchers and advanced graduate students in analysis and probability and related fields of mathematics in order to broaden the education of the early career participants and to promote interaction among researchers working in different areas. SUMIRFAS has a wide impact by providing intellectual stimulation for all participants as well as giving an opportunity to a diverse group of junior participants to increase the breadth and depth of knowledge and to interact with senior researchers in several areas of mathematics. There is also significant outreach to applied mathematics and computer science. This award will provide support to many researchers, including many not on sponsored research grants, who will benefit from contact with established leaders in the field and from being in a research-friendly environment. Emphasis is placed on the support of early-career researchers and graduate students. More information about the conference is available at https://people.tamu.edu/~florent/workshop/2026/sumirfas2026.html. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2026 · 2026-05

Approximation is a fundamental concept in mathematics, and a powerful tool in science and engineering, in which a core notion is that one may study an object of interest by modeling it with simpler objects and using a limiting process. The various approximation properties in mathematical analysis refer to the capacity of an object, such as an infinite-dimensional vector space, to be approximated in useful ways, for instance, by finite-dimensional subspaces. Such properties are central to a number of important fields, such as quantum probability theory, noncommutative geometry, and quantized calculus. This project will focus on Lp-approximation properties of von Neumann algebras and their applications in operator algebras, noncommutative analysis, and beyond. The project incorporates research opportunities for undergraduate and graduate students, as well as training for postdoctoral researchers. In this study, the principal investigator will explore Lp-approximation properties of group von Neumann algebras and their connections to the von Neumann rigidity property and Connes's quantized calculus. The major challenges of the proposed research include the absence of geometric/metric structure and a commutative product in the abstract setting. The proposed research necessitates a reinvention of classical theory on Fourier multipliers in a context with much less structure. Success in this proposed project will strengthen the existing connection between functional analysis and harmonic analysis and will enhance our understanding of the rigidity of discrete groups, the theory of noncommutative geometry, and its applications to quantum information theory. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NIH Research Projects · FY 2026 · 2026-04

Project Summary Damage to the nervous system results in the death of many neurons, leading to adverse outcomes such as locomotor and cognitive defects. Neural injuries may induce neuroplasticity responses in surviving neurons, where they extend their axons and dendrites and form new connections (undergo rewiring) to cope with the loss of their neighboring neurons. Neural circuits are composed of different neuronal types, each with unique intrinsic properties such as neural firing patterns, neurotransmitter identity, and synapses of different types and sizes. Whether different neuron and synapse types are equally capable of undergoing structural plasticity remains unclear. Furthermore, the role of non-neuronal cells (such as glia and muscles), intercellular signaling pathways, and cross-talk between neurons, glia, and muscles during the plasticity response are still under investigation. Addressing these knowledge gaps will help optimize therapeutic strategies, thereby maximizing the chance of the restoration of circuit function after neural injury. The objective of this proposal is to address these problems using the motor circuits underlying locomotion in Drosophila larvae. Two types of motor neurons (MNs) form neuromuscular junctions (NMJs) with larval body wall muscles: tonic-firing MNs with large NMJs and phasic firing MNs with small NMJs. These MNs receive input from excitatory and inhibitory premotor neurons (PMNs). In addition, different subtypes of glial cells in Drosophila are functionally and morphologically comparable to those found in vertebrates. The rich genetic toolkits in larvae provide an unprecedented opportunity to determine how PMN-MN-muscle circuits rewire in response to the death (or inactivation) of PMN and MN subsets and determine the role of glial and denervated muscles in mediating proper circuit rewiring. For the first time, strong preliminary data provided in this proposal demonstrate that in response to the death of MN subsets, the motor axon of a single surviving MN extensively sprouts and forms new NMJs on denervated muscles, recovering their contractility. Expanding on this experimental setup, Aim-1 will test the hypothesis that tonic-firing MNs and excitatory PMNs are more potent than phasic MNs and inhibitory PMNs to rewire and form new synapses upon loss of their neighboring neurons. Aim-2 will determine whether debris clearance and path formation by glial cells are prerequisites for sprouting and rewiring of PMN-MN-muscle circuits that survive post-injury. In parallel, Aim-2 will experimentally test a model based on which glial cells and denervated muscles release neurotrophic factors that instruct surviving MNs to form new synapses (i.e., new PMN-MN and MN-muscle connections) in proper locations. It is expected that this research will provide a deeper understanding of motor circuit recovery mechanisms mediated by synaptic plasticity that are conserved in mammals, and generate hypotheses to be tested in vertebrate locomotion.

NSF Awards · FY 2026 · 2026-04

Africa and the surrounding Atlantic region are home to some of the most consequential weather systems on Earth. The West African Monsoon and the African Easterly Jet drive large-scale environmental conditions that generate tropical storms, flooding rains, and other high-impact weather events affecting millions of people across Africa, the Caribbean, and the United States. As the world warms, understanding how these systems will change is critical for protecting vulnerable communities and improving the tools that forecasters and policymakers rely on. This project directly addresses that need by producing more realistic projections of future weather patterns over Africa and the Atlantic, with findings to be shared openly with researchers, agencies, and communities most at risk through conference talks and scientific publications. The work also supports the training of the next generation of atmospheric scientists, including students and postdoctoral researchers, contributing to workforce development in the geosciences. This project investigates how warming will alter the African Easterly Jet (AEJ), the West African Monsoon (WAM), African Easterly Waves (AEWs), and mesoscale convective systems (MCSs) — the interconnected weather systems that seed tropical cyclones and drive high-impact precipitation events across Africa and the Atlantic basin. The research employs a novel convection-permitting regional configuration of the Model for Prediction Across Scales-Atmosphere (MPAS-A), applying the PGW method for the first time in long-term regional convection-permitting simulations. Multi-year control and PGW simulations will be conducted to assess future changes in the AEJ and WAM mean state, AEW activity, and tropical MCS behavior under simulated warmer conditions, including changes in interannual variability. Model outputs will be made publicly available through the NSF NCAR GDEX archive, supporting the broader international research community and advancing open science. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2026 · 2026-03

This award supports the purchase of a new thermal ionization mass spectrometer (TIMS) that will advance science through high-precision measurements of chemical signatures of rocks, minerals, and sediments. These measurements will enable the documentation of changes in the oceans, atmosphere, and continents over millions of years. The studies will investigate ocean anoxic events, hydrothermal fluid evolution, continental weathering, and the environmental effects of volcanism. Documenting past environmental changes will help us to understand and refine models of future conditions. The instrument will also provide hands-on training for students and help build the future scientific workforce. The new TIMS will enable high-precision measurements of osmium and other isotopic systems in geologic and biogenic materials. It will allow analyses of small samples with low-level concentrations, and increase throughput and precision beyond the existing capabilities. The instrument will support ongoing NSF-funded research, including rhenium-osmium dating of black shales, isotopic tracing of paleo-oceanographic processes, and investigations of biogeochemical cycles during periods of intense environmental stress. The modern electronics and robust ion detection will enable measurements in negative-ion mode, opening new research directions and expanding into novel isotopic systems. The TIMS will also foster collaborations with national and international partners, while providing a platform for methodological innovation and training of undergraduate and graduate students, postdoctoral researchers, and visiting scientists. The new capabilities will enhance TAMU’s capacity to conduct high-impact geochemical research and advance the field of isotope geochemistry. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NIH Research Projects · FY 2026 · 2026-03

Project Summary The primary objective of this research project is to elucidate how circadian rhythms are integrated into neural circuitry important for reward-seeking behavior. Circadian timekeeping is an evolutionarily conserved system essential for the proper function of reward-seeking behavior. By aligning reward behaviors with optimal times of day, animals can seek food, mates, and safety in a manner that enhances survival. Investigating the circadian modulation of reward neural circuitry is therefore necessary to understand the fundamental properties that comprise the neural encoding of reward behavior. This research also has broad implications for human health, as maladaptive reward behaviors underlie various neuropsychiatric diseases, including addiction, bipolar disorder, and depression. Advancing the neuroscience of reward behavior has significant potential for informing the development of therapeutic and diagnostic tools for disorders affecting the brain's reward system. This study aims to determine how circadian rhythms are integrated within the brain's reward system by investigating the bidirectional relationship between rhythms in the ventral tegmental area (VTA) and nucleus accumbens (NAc), two key regions in the dopamine reward pathway. Aim 1 will examine how circadian clock gene expression in the VTA drives rhythms in the NAc, dopamine release, and reward-seeking behavior by selectively ablating critical circadian clock genes in the VTA and measuring downstream effects using long- term fiber photometry and behavioral assays. Aim 2 will investigate the reciprocal relationship, assessing how circadian clock gene expression in the NAc affects circadian clock gene expression in the VTA, dopamine release, and behavior using a similar approach. If these regions operate through a reinforcing feedback loop, it would suggest that circadian rhythms in reward behavior arise from coordinated communication rather than a unidirectional flow of circadian information. These findings could reshape the understanding of neural timekeeping in reward circuits and provide insight into how circadian disruptions manifest at the neural circuit level. This fellowship would support invaluable training opportunities toward the trainee's growth as a neuroscientist. Specializing in both circadian neuroscience and cutting edge tools like long-term fiber photometry, the Jones lab provides the ideal environment to conduct the proposed methodology. Additionally, this research plan will help develop expertise in the dopamine dynamics of reward through collaboration with Co-Sponsor, Dr. Rachel Smith, therefore preparing the trainee for a career at the intersection of circadian and reward neuroscience.

NIH Research Projects · FY 2026 · 2026-02

Proposal Summary Neural regulation of sleep, appetite and energy homeostasis is critical to an animal’s survival and under stringent evolutionary pressure. Despite the prevalence of disorders associated with metabolism and sleep, the neural and genetic processes that regulate interactions between these two systems is unclear. This proposal will investigate how sleep is regulated by blood feeding in the disease vector mosquito Aedes aegypti. We will systematically define sleep in mosquitos, and determine the dietary amino acids that promote sleep. We will then screen neuropeptides for regulators of sleep to test for neuromodulators required for sleep following a blood meal. These experiments leverage decades of sleep analysis in fruit flies, to define sleep in a blood feeding insect. The findings have potential to identify conserved regulators of sleep-feeding interactions, as well as providing the first investigation of how macronutrients regulate sleep in a blood-feeding insect. Further, the methodology used to define sleep can be readily applied to other mosquito species. Therefore, the completion of this work will establish Ae. aegypti as a model for studying dietary regulation of sleep.

NSF Awards · FY 2026 · 2026-01

A recent deep-sea volcanic eruption on the East Pacific Rise provides a rare, time-sensitive opportunity to investigate how deep-ocean ecosystems recover from catastrophic disturbance. Because this site has been studied for decades, researchers can compare current events to data from before the eruption and from past eruptions, offering insight into how ocean life responds to sudden natural change. Through this project, the scientific team is investigating how microorganisms and animals return to the area, how chemical conditions influence their survival, and whether early colonizers influence the long-term development of the communities. By examining a range of organisms from microbes to drifting larvae and newly-settled animals, the investigators are building a detailed understanding of how deep-sea ecosystems recover and how carbon and energy move through these systems after a disturbance. The project is supporting the training of graduate students and early-career researchers and is leveraging collaboration across multiple institutions. More broadly, understanding how seafloor communities recover is increasingly important as society considers the benefits and risks of deep-sea mining. On April 29, 2025, an eruption started at the 9°50'N vent field on the East Pacific Rise (EPR), presenting a unique chance to understand factors governing biological production in the ocean and to observe how vent ecosystems recover. Scientists have been monitoring this site for decades and these sustained data sets provide essential context for interpreting the influence of this most recent eruptive perturbation on the ecosystem. Through this project, the investigators are seeking to understand the biogeochemical and ecological processes that govern ecosystem recovery across microbial and animal communities. They are using integrative techniques to examine the eruption-related impacts on microbial activity and community structure, including measurements of microbial primary productivity, exoenzyme activity, and associated in situ fluid chemistry. When combined with a characterization of the microbial community’s diversity (amplicon-based), gene expression (metatranscriptomics), and protein production (proteomics), the investigators are identifying active microorganisms, quantifying their contribution to deep sea carbon cycling, and exploring their potential as settlement cues for pioneer animal colonists. The microbial studies are co-located with sampling of animal colonists on the seafloor to identify the pioneers and document their association with microbial consortia. The investigators are identifying and quantifying larvae in the plankton to compare pre-and post-eruption availability. The range of data collected is laying the foundation for understanding the drivers of post-eruption succession and for testing a biophysical model currently in development to explore the influence of mesoscale eddies on inter-segment vent larval dispersal. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2026 · 2026-01

Methane plays an important role in controlling climate on earth. Recently it has been found that methane is seeping from the seafloor into the Atlantic Ocean along the Eastern United States. Methane seeps like these may be common along similar coastlines and thus may affect our climate. The reasons for these methane seeps will be studied using geological, chemical, and biological methods. The project supports students at various levels and contributes to public understanding of the ocean through talks, publications, and videos. Summer camps designed to inspire the next generation of ocean scientists will be developed. The field work and community outreach will improve our understanding of methane's role in climate and contribute to public understanding of and appreciation for ocean science. This project will study biogeochemical and geological controls on recently discovered methane seeps in the northern US Atlantic Margin (200-1000m depth). Existing conceptual models of the origin, subsurface migration, characteristics, and history of methane discharge along continental margins would not predict the widespread presence and depth distribution of these seeps. This work will integrate geochemical, microbiological, geophysical, and geological data to investigate the genesis of seep fluids, fluid migration, and methane oxidation processes in a modern seep while reconstructing methane seepage characteristics and gas hydrate stability in the past. The project will combine geophysical characterization with the autonomous underwater vehicle Sentry at active seep sites. This will be combined with gravity and jumbo piston coring from ships as well as targeted sampling using the human occupied deep submergence vehicle ALVIN. This strategy will retrieve multiscale seafloor and sub-seafloor datasets, capturing a record longer than previously recovered at any other seep along this margin. The multi-pronged approach will contribute to an integrated and mechanistic understanding of continental margin methane seeps. This project is jointly funded by the Directorate for Geoscience’s Chemical Oceanography and Marine Geology and Geophysics programs and by the Established Program to Stimulate Competitive Research (EPSCoR). This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2025 · 2025-10

Flooding is among the costliest natural hazards in the United States (U.S.) and around the world. Addressing flood risk and the damage it causes requires an integrated investigation across different disciplines, sectors, and international boundaries. The International Research Experiences for Students (IRES) Global Flood Resilience Program (GFRP) between the U.S. and South Korea serves the critical need for comprehensive research education programs. The GFRP Program is designed to develop U.S. students as researchers and practitioners to address complex flood-related problems both in the U.S. and globally. It also trains the next generation of problem solvers to have a comprehensive set of visions and tools to succeed. Each year, 10 U.S. students participate in a six-week international research experience: six students travel to Korea, while four participate in Texas, engaging with visiting Korean students from Pusan National University (PNU). The research products and collaboration model established by the PI team through successful international research and education projects with the Netherlands are leveraged and expanded to provide students with a wide range of international experiences. Key U.S. partners include Texas A&M Galveston (TAMUG: Yoonjeong Lee; Samuel Brody) and College Station (TAMU: Jens Figlus) campuses, and the Univ. of Puerto Rico Mayagūez (UPRM: Ismael Pagán Trinidad). Korean partners are Pusan National University (PNU) and the University of Seoul (UOS). Urban and coastal flooding issues in Korea and Texas are examined through problem- and place-based case studies using the flood resilience framework developed through a decade-long collaboration between the U.S. and the Netherlands. Specifically, locations in Seoul/Busan in Korea and Houston/Galveston in Texas act as focal points to integrate expertise in engineering (TAMU/UPRM), planning (TAMUG/PNU), and public policy (TAMUG/UOS) that guide student research. Transdisciplinary place-based analyses address the following overarching research questions: 1) What are the differences in the driving factors of flooding and its associated damage between the two countries? 2) Which flood mitigation measures are more suitable for each study area and why? Students conduct research covering both surge-based and precipitation-driven flood problems. The project assesses the effectiveness and applicability of the integrative flood resilience framework in a global context, leading to a better understanding of when and where to implement specific strategies to mitigate adverse flood impacts. The participants leverage U.S. and Korean data, methods, and facilities related to flood management to generate new knowledge on global flood resilience. The program prepares the next generation of U.S. engineers, scientists, and policymakers in academia and practice to tackle the societal problem of flooding innovatively on a global scale. Furthermore, the program generates data on specific educational methods to improve learning outcomes in problem- and place-based research settings, as well as on how to enhance international experiences for students. These findings contribute to improving future international research programs and are incorporated into teaching strategies across all partner institutions. Emphasis is placed on recruiting students from all U.S. collaborating institutions, with a focus on providing them opportunities to collaborate with international students and researchers through an innovative program design. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2025 · 2025-10

This IUSE Level 1 project aims to serve the national interest by creating generative artificial intelligence (GenAI) learning assistants tailored to architecture, engineering, and construction (AEC) education, thereby helping students develop independent problem-solving skills. AEC is a unique, cross-disciplinary field in STEM entered on complex project-based learning, requiring deep information resources and personalized learning support. By focusing on the significance of providing real-time, course-specific feedback and personalized guidance, the GenAI learning assistants will help students understand complex concepts, boost their confidence, and foster more consistent independent learning. The project also aims to better understand how GenAI adoption strategies can be tailored to different AEC courses and academic levels. The project’s insights could be adapted to other STEM disciplines where project-based learning is integral. This proposed research involves the development and evaluation of three course-specific GenAI learning assistants: one for Construction Graphics (freshmen), one for Structural Systems (juniors), and one for Human-Building Interaction (seniors). The assistants will feature automated symbol identification in construction drawings, 3D visualization of load distributions, augmented onto real-world structures, and coding and hardware configuration support for developing intelligent building sensing systems. The core hypothesis is that adopting the GenAI learning assistants will enhance student learning and promote self-regulated learning (SRL). Each GenAI learning assistant is grounded in key SRL principles such as goal setting, self-monitoring, self-reflection, and strategic help-seeking—skills essential for preparing future AEC professionals. The immediate outcomes will transform AEC education by providing empirical evidence of the effectiveness of GenAI learning assistants. In addition, these results will help educators understand how GenAI-enhanced learning supports influence AEC student perceptions and use of AI after completing those courses. The NSF IUSE: EDU Program supports research and development projects to improve the effectiveness of STEM education for all students. Through the Engaged Student Learning track, the program supports the creation, exploration, and implementation of promising practices and tools. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2025 · 2025-10

The nuclear envelope (NE) is a physical barrier between the cytoplasm and the nucleus that is essential for the survival and function of eukaryotic cells. The NE has a complex geometry, consisting of two lipid membranes fused at hundreds of donut-shaped pores and maintained at a stable distance from each other. How the NE’s complex geometry enables its critical functions is not understood. Prior work suggests that double-layered membrane geometries have unexpected mechanical properties that are not found in manufactured materials. This award supports studies to develop new fundamental insight into the mechanical properties of the NE, with two broad goals: 1) discover the link between NE structure and NE mechanical properties, and 2) identify mechanical principles for the design of a new generation of biologically inspired complex materials with unique functions. Findings from this project will be used to develop physics-based games for a virtual mechanics and biomechanics lab (VMBL) for teaching students about the interplay between topology and mechanics in 2D materials. The project will train students from underrepresented groups and promote their success in research and teaching. The overarching goal of this experimental and computational project is to explain how passive forces, active forces, and geometry impact NE mechanics. The researchers will experimentally quantify spatial fluctuations in the NE under perturbations of passive load-bearing proteins, active force-generating cytoskeletal proteins, and ATP depletion. Monte Carlo simulations on a double membrane system with donut-shaped pores will be performed to interpret these experimental observations and quantify NE mechanics. Experimental data will provide snapshots of membrane geometry which will be interpreted with the computational model to develop insights into the underlying mechanics and forces. Overall, the study will unravel the interplay between geometry, topology, and mechanics in soft 2D materials. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2025 · 2025-10

Plates and shells have been used in diverse fields such as civil, mechanical, aeronautical, and marine engineering. A hallmark feature of these structures is their ability to support large loads despite their thin architecture. One such shell structure, responsible for guarding the genome inside our cells, is the nuclear envelope (i.e., the boundary of the nucleus). This structure has a unique geometry comprised of two concentric hollow spherical shells fused at thousands of sites with torus-shaped holes, and exhibits one order of magnitude amplification in flexural stiffness. Inspired by this finding, this study investigates a new class of optimal biomimetic shell structures, termed torenes, comprising concentric shell layers fused with torus-shaped holes. The torene architecture could enable new designs in aircrafts, submarines, and rockets to achieve high resilience in countering extreme natural forces. The discovered principles can guide the design of lightweight prosthetics, and protective gear for defense personnel and athletes to counter high impact loads. The research findings will be disseminated by hands-on pedagogical demonstrations, scientoons (science-based cartoons), virtual mechanics labs, journal publications and guest lectures for high school students. Poised at the interface of mechanics, geometry, and optimization, the research will investigate the mechanical properties and failure mechanisms of plate and shell structures with ultra-high genus. The study will perform finite element analyses to investigate force-deformation response and stability of torene structures under in-plane and out-of-plane loadings. This information will be used to construct proper objective functions and constraints to perform topology optimization of multilayer plates and shells. In particular, numerical optimization will be used to identify topologies that maximize performance of torene structures under different external loads and functional requirements. The study will apply the discovered geometric principles to design and experimentally test 3D torene architectures derived from 2D materials for achieving ultra-flexural stiffness. Overall, the work will disentangle the roles of differential geometry and associated geometric parameters in modulating the strength and stability of a new class of topological structures. This approach allows an investigation of structures at different length scales leading to the determination of scaling laws and scaling invariance. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2025 · 2025-10

This NSF IUSE: EDU project aims to serve the national need for a globally competitive STEM workforce by preparing undergraduate STEM education students to more effectively teach all K-12 students. The project will support future teachers by offering experiential learning through virtual reality artificial intelligence simulations and scaffolded peer teaching. These innovative teaching experiences are designed to help future educators develop the skills needed to facilitate discussions of scientifically relevant real-world problems. Through this work, the project aims to prepare undergraduate STEM education students to effectively teach science in K–12 schools to support all STEM learners in solving real-world problems. This project includes partners at the University of West Florida, Southern Methodist University, Texas A&M University, Kennesaw State University, and Drake University. Together, these institutions will investigate how undergraduate STEM education students can be effectively prepared to facilitate discussions of scientifically relevant real-world problems. Project goals include developing and comparing two instructional practice modalities - virtual reality artificial intelligence avatar simulations and scaffolded peer teaching - and evaluating their impact on approximately 250 undergraduate STEM education students across the five institutions. Using a mixed-methods approach, the study will collect data through rubric-based evaluations of teaching performance at three timepoints, participant surveys, and mentor feedback. The research will focus on preservice teachers preparing to teach science in K–12 schools. By preparing future STEM teachers to enact science instruction that encourages students to solve real-world problems, this project will provide empirical support for the use of scientifically relevant real-world problems in STEM teacher preparation programs. An external evaluator will provide formative and summative feedback about project objectives. Findings will be disseminated through peer-reviewed publications, national STEM education conferences, and a publicly accessible project website offering open educational resources. The NSF IUSE: EDU Program supports research and development projects to improve the effectiveness of STEM education for all students. Through the Engaged Student Learning track, the program supports the creation, exploration, and implementation of promising practices and tools. Partial funding for the project is from the Robert Noyce Teacher Scholarship program. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2025 · 2025-10

This project supports the BOSS (Best of Statistical Science) Workshop, a focused gathering of early-career and established researchers in Bayesian statistics, to be held in Spring 2026 at Texas A&M University. The primary goal of the workshop is to provide a dynamic platform for graduate students and junior researchers to engage directly with leading experts in Bayesian methodology, applications, and computation. The workshop is designed to foster cross-disciplinary collaboration, promote mentorship, and introduce participants to the breadth of modern developments in statistical science. NSF support will enable the participation of 20 early-career researchers, including postdoctoral scholars, tenure-track faculty, and advanced graduate students from across the United States. By connecting emerging scholars with senior researchers in an inclusive, discussion-oriented setting, the event aims to broaden participation and strengthen the national research capacity in modern statistics. The workshop will feature technical sessions on recent advances in Bayesian methodology, including modern computational approaches such as variational inference, shrinkage priors, and high-dimensional modeling; the use of AI in prior elicitation; and Bayesian machine learning. Applications will span the geosciences, biomedicine, and environmental sciences. Invited speakers will present cutting-edge research, while dedicated poster sessions and mentoring events will facilitate direct interactions between junior participants and senior statisticians. By showcasing a broad spectrum of theory, computation, and scientific applications, the project aims to stimulate methodological innovation and promote knowledge transfer across subfields of statistical science. Outreach efforts will include the dissemination of workshop materials and recordings to broaden participation, with particular attention to engaging institutions traditionally underrepresented in Bayesian research. More information may be found at https://calendar.tamu.edu/statistics/event/358551-2026-best-of-statistical-science-workshop-boss2026 . This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2025 · 2025-10

Federated learning (FL) enables Internet-of-Things (IoT) devices at the network edge to collaboratively learn a shared prediction model while keeping all personal data on the device. However, the current cloud-based FL fails to meet the latency requirements of delay-sensitive IoT applications due to the long-distance transmission between IoT devices and the cloud. This project aims to enable ubiquitous and time-critical FL at the wireless edge to support delay-sensitive and data-driven IoT applications. The project will fulfill the needs of many compelling applications with significant economic and societal impacts such as augmented reality, autonomous driving, mobile healthcare, and smart manufacturing. The project’s educational agenda includes outreach to K-12 with educational summer camps for high-school teachers, mentoring undergraduate and graduate students, especially from minority and underrepresented groups, in the research, and disseminating research outcomes to students and industry partners through new course development and seminars. This project develops a novel Federated learning (FL) framework based on cooperative mobile edge networking that can efficiently support learning and decision making on distributed Internet-of-Things (IoT) data with high accuracy, low latency, and guaranteed privacy. Three interconnected research thrusts are investigated in this project: 1) design of novel network-aware learning algorithms under a two-level network structure to ensure efficient and effective model training from decentralized data on IoT devices over wireless edge networks; 2) jointly optimize resource allocation and learning based on deep reinforcement learning to learn an accurate model rapidly under system heterogeneity and resource constraints; 3) develop novel differential privacy techniques to rigorously protect the privacy of personal data on IoT devices while maintaining high model accuracy and reducing communication cost. The proposed research will enable next-generation wireless edge networks that support a plethora of delay-sensitive and data-driven IoT applications. The proposed research will benefit not only the wireless networking but also machine learning research communities by bridging the gap between the evolving mobile computing and networking technologies and rapidly advancing machine learning techniques. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2025 · 2025-10

The Blue Economy consists of research focused on the sustainable use of ocean resources to drive economic growth. It covers areas such as marine resource management, ocean energy, sustainable fisheries and aquaculture, and maritime transportation. Artificial Intelligence (AI) holds transformative potential to advance Blue Economy research by enabling data analysis and predictive modeling. The targeted areas include the Mid-Atlantic region, Gulf coasts, and Hawaii. This conference proposal aims to tackle these regional issues through coordinated efforts that enhance Blue Economy research, expand funding opportunities for innovative projects, and strengthen cross-sector partnerships. The conference will bring students, faculty, and researchers from four academic institutions, along with participants from various fields such as geoscience, computer science, biology, and civil engineering. A core objective is to promote Artificial Intelligence (AI) education and workforce development to support the Blue Economy. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2025 · 2025-10

Enhancing Cyberinfrastructure-infused Research at Texas A&M University-San Antonio (ECR-TAMUSA) seeks to empower Texas A&M University San Antonio by building long-term capacity in high-performance computing (HPC) and artificial intelligence and machine learning (AI/ML) for its research and academic programs. The university will develop a support structure to meet the growing needs of advanced cyberinfrastructure (CI)-infused research in water-management, genomics, biology, chemistry, health, computing, humanities, social, natural, cybersecurity, and other applied sciences. This project addresses that gap through a sustainable and collaborative model that enhances multidisciplinary faculty collaborations on computational research, development of academic programs, and student research participation. The work supports national priorities by promoting the progress of science and expanding participation in the science, engineering, and entrepreneurial workforce. The project’s outcomes will help build cutting-edge computing. The project will develop a coherent campus-wide strategy that includes identifying the mechanisms by which researchers from different disciplines can form new research collaborations; determining the scope of joint research initiatives, including forming teams between CISE researchers and other domains scientists; incorporation of cybersecurity into research and student training activities; outlining a sustainability plan for growing computational research and campus CI; and developing training and education programs to facilitate research on advanced CI. The proposed work will provide the time, resources, input, research, and support from various stakeholders, ensuring that the resulting approach will be integrated intra-campus and with regional research programs. This work will better position the university to benefit from forthcoming regional and national initiatives that advance the use of AI/ML in research and academic programs. The outcomes will include innovative educational frameworks, enhanced research capabilities, and models of collaborative computational research that can be adopted by other emerging research institutions. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NIH Research Projects · FY 2025 · 2025-09

Project Summary/Abstract Circadian rhythms are physiological, physical, and behavioral cycles that occur over a 24-hour period in living organisms. These rhythms are regulated by the rhythmic expression of specific genes. Key scientific questions revolve around how the rhythmicity of these genes is influenced by genotypes, feeding types, and drugs. More specifically, identifying genes that exhibit differential rhythmicity can help elucidate the effects of these factors. Current statistical tools and software can identify genes that show differential rhythmicity under various experimental conditions. However, in this high-dimensional context, there is a need for methods that are both computationally efficient and capable of accurately controlling the false discovery rate. Furthermore, there are currently no suitable methods to cluster differentially rhythmic genes or to map the gene networks among them. To address these needs, we propose three novel aims, each supported by innovative modeling or computa- tional algorithms. In Aim 1, we will develop a false discovery rate (FDR) controlled F-test with Welch’s correction to identify differentially rhythmic (DR) genes. In Aim 2, we will use a Dirichlet process mixture model with a novel computational technique to cluster these DR genes. In Aim 3, we will develop fast Bayesian algorithms to identify the gene networks. Once successful, these statistical methods and software will help reveal salient features of the rhythmic expression of genes, thereby benefiting circadian research, understanding the fundamentals of many cardio- vascular diseases associated with the disruption of circadian rhythms, and drug development.

NSF Awards · FY 2025 · 2025-09

This project will contribute to the national need for well-educated scientists, mathematicians, engineers, and technicians by supporting the retention and graduation of high-achieving, low-income students with demonstrated financial need at Texas A&M University and Blinn College. A total of 75 scholars pursuing Associate and Bachelor's degrees in Engineering will receive scholarships up to $15,000 per year for up to five years. Scholars will receive faculty mentoring and the project will build strong scholar cohorts through a co-enrollment model intended to strengthen transfer outcomes for students transitioning from 2-year to 4-year college engineering studies. Additional activities for scholars will include a focused learning community and student seminars. The overall goal of this Track 3 Scholarships in STEM project is to increase STEM degree completion of academically talented, low-income undergraduates with demonstrated financial need. There is a significant national need to grow the STEM workforce and nurture key talent that will ensure economic competitiveness and provide domestic leadership across critical sectors. This project directly speaks to this need by supporting STEM student success, which will strengthen the workforce in semiconductor manufacturing and other key areas of need. The project will be assessed by an experienced evaluator, and the data generated will contribute to the knowledge base by examining the role of the co-enrollment model on retention and graduation outcomes for talented, low-income students in STEM. This project is funded by NSF's Scholarships in Science, Technology, Engineering, and Mathematics program, which seeks to increase the number of academically talented, low-income students with demonstrated financial need who earn degrees in STEM fields. It also aims to improve the education of future STEM workers, and to generate knowledge about academic success, retention, transfer, graduation, and academic/career pathways of low-income students. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2025 · 2025-09

With the support of the Macromolecular, Supramolecular, and Nanochemistry Program in the Division of Chemistry, Dr. Emily Pentzer and Dr. Jodie Lutkenhaus of Texas A&M University will design, synthesize, and characterize polymers for electrochemical energy storage. The polymers that will be developed could be created from domestic feedstocks and used in advanced battery technologies, such as flexible batteries. This work will answer the fundamental scientific questions needed to create new polymers for energy storage: how does polymer composition and structure impact the movement of electrons in and out of the polymer and how can this be improved. The answers to these questions will expand our understanding of polymers for energy storage, leading to the rapid development of new materials. Through this work, students will be trained in cross-disciplinary research such that they are prepared to be leaders in the next generation of the American STEM workforce. New educational modules on polymers for energy storage will be development for the public and shared at the Texas A&M Chemistry Open House. Non-conjugated redox active polymers will be synthesized in which redox active groups and highly polar dopant groups will be incorporated onto the same polymer scaffold. Different organization of the two types of groups will be used: random distribution, spatially defined organization, and block copolymers. Polymers will be synthesized by controlled polymerization strategies and the redox and dopant groups will be attached through high yielding click reactions. The redox active group used will be 2,2,6,6-tetramethyl-1-piperidinyloxy along with a polysiloxane backbone (for example, polydimethylsiloxane (PDMS)). The modular polymer design enables the use of azide-alkyne click chemistry to modify the PDMS-type backbones with cationic or anionic dopant units (imidazolium and trifluoromethanesulfonylimide, respectively) and/or neutral units (tetraethylene glycol). The electrochemical properties of the different polymers will be characterized in solution and in the solid state, and the heterogeneous and homogeneous rate constants and apparent diffusivity quantified and related to the polymer’s chemical structure and bulk physical properties. This research will advance our fundamental understanding of the effect of spatial arrangement and confinement on the electron transfer kinetics and overall properties of self-doping redox active polymers. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NIH Research Projects · FY 2025 · 2025-09

PROJECT SUMMARY Sleep disorders such as insomnia are common and have major public health consequences. Enormous resources have been invested in human sleep genomics, with data from several million individuals yielding genome-wide association study (GWAS) loci associated with sleep disorders. However, these associated genetic variants principally reside in non-coding regions of the genome and rarely pinpoint the precise location of the actual effector genes. Mounting evidence suggests that simply attributing GWAS signals to the genes within the closest genomic proximity is insufficient to identify the genetic variation associated with phenotypic changes. As such, GWAS results alone are limited to signal discovery, with only indirect implications for functional gene discovery. Here, we seek to address major barriers to further understanding sleep GWAS signals by applying genomic, computational, and behavioral approaches to study the basis of human sleep. We have developed a pipeline for functionally validating sleep genes from human GWAS, and used this to identify GPI-anchor biosynthesis as a critical regulator of sleep in animal models, as well as humans. In this application we will identify targets of this pathway in fruit flies, and the underlying mechanism of their impact on sleep centers in the brain. We will use then apply variant-to-gene mapping in human progenitor cells (NPCs) to identify how genetic variation associated with sleep-regulating genes impact their function. We will also characterize the impact of variation in insomnia risk genes by conducting in-depth human phenotyping of individuals harboring rare variants with predicted loss of function. Our global hypothesis is that genetic variation in the GPI-anchor biosynthesis pathway, and genes encoding for GPI-anchor proteins are critical regulators of sleep. Our cutting-edge molecular genomic approaches will elucidate the causal variants and the corresponding effector genes at these loci.

NSF Awards · FY 2025 · 2025-09

In the early Universe, all matter and radiation was concentrated in an extremely hot and dense fireball that expanded and cooled rapidly. During this evolution, several phenomena occurred that fundamentally shaped the world around us. In particular, during the first few microseconds, a plasma of elementary particles called quarks and gluons prevailed before converting into bound states called hadrons, which ultimately made up the atomic nuclei as we know them today. In this transition, at around two trillion degrees Kelvin, the quarks and gluons were permanently confined into hadrons, thereby generating about 98% of the visible mass in the Universe. The theoretical description of the confinement of quarks and gluons and the generation of hadronic mass remains an outstanding challenge in modern elementary-particle and nuclear physics. High-energy collisions of atomic nuclei can recreate the quark-gluon plasma (QGP) for a short moment in the laboratory, before it decays back into hadrons that can be measured in large detectors. In this project, rigorous theoretical analyses are carried out to deduce the properties of the QGP and its hadronization by analyzing the observed particle spectra. The goal of this project is to unravel microscopic mechanisms of the QGP-to-hadron transition by evaluating in-medium correlation functions. First-principle information on these is available from the theory of the strong interaction, Quantum Chromodynamics (QCD), using lattice-discretized computer simulations. However, these results are not readily applicable to experiment. This gap is bridged by utilizing the concept of spectral functions, which characterize the structure of matter. Spectral functions are calculated in both QGP and hadronic matter using quantum many-body theory, which can cope with the large interactions rates in the system. By focusing on spectral functions in the vector channel, a direct connection between lattice-QCD results (for correlation functions) and experimental data (for di-lepton spectra) is established. On the QGP side, novel techniques are developed to calculate quark-antiquark correlators at finite momentum and constrain them by lattice QCD. On the hadronic side, existing calculations of the vector spectral function are improved to incorporate mass degeneracies as predicted by QCD. A smooth matching of these calculations around the transition temperature and subsequent tests against experimental data are carried out. This project provides opportunities for students to carry out cutting-edge research, and involves outreach to high-school students. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.