University Of Houston

NSF Awards · FY 2026 · 2026-06

Many important physical systems involve complex processes spanning multiple physical scales, including those arising in carbon storage, hydrogen containment, and groundwater management. Accurate prediction of these systems is essential for sustainable energy technologies, environmental protection, and national economic competitiveness. Over the past decade, physics-informed artificial intelligence methods have shown strong potential for accelerating scientific discovery and enabling rapid simulation of complex physical processes. However, existing approaches often lose accuracy and stability when applied to realistic multiscale systems. This project develops a new class of scientifically grounded artificial intelligence methods for reliable modeling of complex fluid flow and transport phenomena. The work advances foundational research at the intersection of computational mathematics, artificial intelligence, and scientific computing, supporting national priorities in AI-enabled scientific discovery and high-performance computing. The project will also train graduate and undergraduate students through research, mentoring, outreach, and open-source software development, strengthening the nation’s technical workforce in artificial intelligence and computational science. The project develops an energy-stable neural basis framework for multiscale partial differential equations arising in porous media flow and transport. The research addresses fundamental limitations in physics-informed machine learning approaches by identifying how inappropriate residual metrics degrade approximation quality and stability in multiscale settings. Building on Petrov-Galerkin theory and classical numerical analysis, the project develops operator-aware, energy-consistent residual formulations with expressive neural basis representations. Fractional Sobolev boundary treatments are further incorporated to achieve physically faithful and efficient approximations with reduced mesh dependence and computational complexity. The research further extends to parametric systems through enhanced learning of mappings between physical parameters and neural solution coefficients, enabling stable, accurate, and fast predictions. Additional investigations will address coupled flow-transport dynamics and applications beyond porous media flow. The resulting framework aims to establish a mathematically grounded and scalable paradigm for AI-enabled simulation of multiscale physical systems. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2026 · 2026-06

DNA mutation is a fundamental biological process that drives evolution, adaptation, and human health challenges such as cancer and antibiotic resistance. Understanding how and why mutation rates vary across cells, organelles, and species remains a major open question in biology. This project investigates how mode of reproduction shapes the evolution of mutation rate. By determining how reproductive strategies influence the origin of new genetic variation, this research provides foundational insights that can help predict how natural populations will adapt to novel environments and assist in managing invasive species, which are frequently clonal or highly self-fertilizing. Because human cells proliferate clonally, understanding mutational processes in clonal lineages sheds light on aging and cancer development. This project also supports education and public engagement by providing STEM activities for communities in Iowa and Texas. This initiative will also train graduate, undergraduate, and high school students, offering them vital career development and mentorship in science and community outreach and building a biotechnology workforce. The primary goal of this project is to model and empirically test the evolutionary consequences of reproductive mode variation on mutation rates. The project aims to build on the drift-barrier hypothesis to develop new theory exploring the short- and long-term impacts of reproductive mode variation, polyploidy, and beneficial mutations on mutation rate evolution. Empirically, the research measures base-pair substitution and structural mutation rates across three different types of organisms that have undergone independent transitions in reproductive mode. The study systems include the snail Potamopyrgus antipodarum (outcrossing to obligate clonal), the ciliate Tetrahymena (facultative outcrossing to clonal), and plants in the Brassicaceae family (outcrossing to highly selfing). The investigators will use mutation accumulation experiments and parent-offspring analyses, combined with whole-genome sequencing, to estimate de novo mutation rates and mutational spectra. By comparing mutation parameters in closely related lineages with multiple independent transitions in reproductive strategies, this project will help illuminate factors driving mutation rate variation across the tree of life. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NIH Research Projects · FY 2026 · 2026-06

PROJECT SUMMARY Visual attention is one of the most robust early indicators of autism, a neurodevelopmental condition characterized by persistent difficulties in social communication - including joint attention (JA), which refers to the ability to simultaneously attend to a target of interest with another individual. Recent research has given considerable focus to studying JA experiences among infant siblings of individuals diagnosed with autism (HFL - high familial likelihood for later autism diagnosis), given the documented link between JA experiences and later developmental outcomes (e.g., autism diagnosis) in this clinical population. However, studies of JA among HFL infants are often limited to screen-based eye-tracking tasks (that are devoid of social context) and subjective observational coding during structured play tasks (which are prone to reliability issues or rely on explicit cues to elicit JA). Importantly, the characterization of JA relies on simple shared attention to a target and does not accurately reflect the extent of the cognitive resources deployed by the infant to process the target. Recent electroencephalography (EEG) studies with screen-based eye-tracking tasks have revealed neural indices associated with attentiveness that may serve as critical differentiators between infant engagement and disengagement during JA episodes - however, these studies are limited to infants with low familial likelihood for later autism diagnosis (LFL), despite considerable EEG work linking neural activities within the first year of life to later autism diagnosis. Consequently, there is a lack of understanding about how infants’ brains learn to pay attention within complex, dynamic, and ecologically valid social settings - such as during parent-infant play, where parents are known to actively support JA experiences. To address these issues, the proposed goal of the current study are to implement a new technology that combines head-mounted eye-tracking with EEG recordings to simultaneously record HFL infant attention and neural activity within a social parent-infant play session to explore the following questions: (1) how parental social scaffolding supports JA, (2) how neural indices are related to JA, and (3) how infant neural activity during JA responds to parental social scaffolding. This study aligns with the National Institute of Health’s (NIH) vision and mission of seeking fundamental knowledge about cognitive mechanisms that have the potential to advance early parent-mediated autism interventions offered to families and their children. Ultimately, this project aims to provide essential and innovative research in the area of infant siblings of individuals with autism, while also training a promising new investigator in ethical, methodological, neuroinclusive, and interdisciplinary research practices.

NIH Research Projects · FY 2026 · 2026-06

Project Summary Sustained attention (SA)—the ability to maintain engagement with people or objects over time—is a foundational skill that supports early learning across domains, including language, cognitive development, and social interaction. Disruptions in early SA have been linked to later difficulties in academic achievement, emotion regulation, and mental health. Yet, little is known about how SA naturally emerges, stabilizes, and becomes self-directed in infancy, particularly in everyday social contexts. This project investigates how SA develops through dynamic coordination among behavioral, neural, and autonomic systems, captured in real time during naturalistic parent–infant interactions. We will conduct a longitudinal study of typically developing infants between 6 and 30 months, integrating head-mounted eye tracking (ET), electroencephalography (EEG), and heart rate monitoring (ECG). This multimodal design enables precise identification of SA episodes and their physiological signatures as they unfold in the real world. We hypothesize that caregiver scaffolding—such as holding—shapes the salience and structure of infants’ attention, triggering coordinated multisystem engagement that supports the emergence of self-directed SA. We will characterize age-related changes in SA, examine its variation across social contexts, and assess whether early multisystem patterns predict later individual differences in attention control, language development, and neural function. This project is innovative in its longitudinal, ecologically grounded approach to studying attention, combining first-person ET with neural and autonomic measures during live interaction. Findings will advance theories of developmental attention and clarify how early multisystem dynamics contribute to variation in cognitive and social outcomes.

NIH Research Projects · FY 2026 · 2026-05

Systemic Autoimmune Rheumatic Diseases (SARDs), including Rheumatoid Arthritis (RA), Systemic Lupus Erythematosus (SLE), Sjogren’s Syndrome (SS) and Systemic Sclerosis (SSc). are chronic, debilitating systemic autoimmune diseases without cure, affecting ~400/100,000 Americans and >30 million people worldwide. SARDs result in significant physical, mental and social impairment, and economic burden, costing $20,000->$60,000 USD per patient per annum. Individual SARDs have been examined in silos without an attempt to discern shared underlying features at the molecular level. Current understanding of the initial (and likely shared) origins SARDs is only rudimentary, but urgently needed to develop means for prevention and treatment. Loss of tolerance to nuclear antigens leading to the development of anti-nuclear autoantibodies (ANAs) is an early phase of SARD, present in >50% of these subjects. However, the Gene X Environmental risk factors and molecular pathways underlying this early phase of autoimmunity, and subsequent development of rheumatic disease remain unclear. Here, we dissect out these triggers, leveraging the NHS and NHSII cohorts. Whereas Aim 1 focuses on the initial development of autoantibodies, Aim 2 focuses on the subsequent development of disease. In both Aims, plasma samples from the prospective cohort will be screened for ANAs, comprehensive proteomics and environmental exposures using an immunoexposome array at baseline and at 10-yr follow-up. Genetic loci, exposome triggers. Gene X Environment interactions, and functional protein modules associated with ANAs, SARDs, or future ANA development or SARD onset will be identified. Collectively, these studies will help identify the genetic, environmental and GXE factors that are operative at the 2 steps of SARD development, namely ANA emergence and disease onset. More importantly, these studies will highlight functional molecular pathways (and mechanisms) that may be operative at each step. There is an increasing focus on the pre-disease phase in SARDs, but the inciting events and specific molecular triggers are unknown. The proposed studies address this gap by providing a higher resolution molecular map of the earliest functional pathways that precede and initiate ANAs/SARDs. This may also offer opportunities for intercepting the disease at its incipience.

NSF Awards · FY 2026 · 2026-05

This NSF CAREER project aims to develop a quantum–classical optimization framework to improve the integration of data centers into power systems. The project will bring transformative change by enabling power systems and data centers to coordinate their operations more efficiently, helping ensure a stable and affordable power supply as hyperscale data centers expand over the next decade. This will be achieved by modeling how data centers can flexibly adjust their energy use, developing advanced optimization tools, and exploring new quantum computing approaches to address complex operational challenges. The intellectual merit of the project includes advancing optimization theory and algorithms for integrating flexible data centers into power systems, overcoming key limitations of existing classical methods, and establishing foundations for new quantum-enabled optimization techniques. The broader impacts of the project include supporting more cost-effective and reliable power grid operations, accelerating energy-efficient data center deployment across the United States, and preparing students with interdisciplinary skills at the intersection of power systems, optimization, and quantum computing. This project addresses the technical challenge of co-optimizing power system operations and data center flexibility under uncertainty. It develops: (1) new mixed-integer linear and convex optimization models that capture the spatiotemporal flexibility of data centers within security-constrained unit commitment; (2) hybrid quantum–classical optimization frameworks that embed quantum subroutines based on quantum dynamics within classical decomposition methods to solve large-scale problems; and (3) adaptive quantum evolution techniques that improve convergence and robustness through optimized Hamiltonian scheduling. By combining advanced mathematical modeling and innovative algorithm design, the project paves the way for a more reliable, affordable, and resilient power grid. 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 · 2026-05

PROJECT SUMMARY Exosomes are nanoscale extracellular vesicles (30–150 nm in diameter) secreted by nearly all cell types into biological fluids such as blood, urine, saliva, and cerebrospinal fluid. Exosomes are known to play important roles in a variety of basic science and applied research fields such as cancer biology, neurological disorders, regenerative medicine, and dermatology. Exosomes are also heavily pursued for various diagnostic and therapeutic applications such as liquid biopsy and drug delivery. Current methods of analyzing exosomes have many limitations such as inadequate sensitivity and specificity, insufficient resolution for single exosome profiling, demanding sample volume, and heavily reliant on DNA amplification and sequencing techniques. In addition, most existing exosome pre-analytical sample preparation protocols require a multitude of steps involving purification, isolation, concentrating, and sophisticated labeling. These barriers have hampered the advances in exosome-centric basic and applied research and technology development. To address such an unmet need, the PI proposes to develop an Integrated Nanophotonic imaging and Spectroscopy Technology (INSPECT) for scalable, multi-parametric Single Exosome Analysis (SEA). The core is a nanophotonic chip to enable multi-modal imaging and spectroscopy based on three well-known nanoplasmonic enhancing mechanisms demonstrated in PI’s previous works: localized surface plasmon resonance (LSPR), plasmon- enhanced fluorescence (PEF), and surface-enhanced Raman scattering (SERS). These three techniques are highly complementary and can provide strong synergy in acquiring structural as well as compositional/molecular information from individual exosomes. Using LSPR-imaging, the PI has demonstrated individual exosomes can be detected, counted, and sized with 20 µL of undiluted and unpurified blood plasma. The same exosome population can then be interrogated by PEF using various fluorophore-labeled antibodies and molecular beacons to reveal exosomal surface and intravesicular molecular targets such as proteins and microRNAs. SERS will then provide a comprehensive, “non-targeting” molecular fingerprint.

NIH Research Projects · FY 2026 · 2026-04

PROJECT SUMMARY This proposal aims to elucidate how the dynamic oligomeric transitions of Copper Transporter 1 (CTR1) couple copper (Cu) homeostasis with neuronal developmental pathways. Cu is an essential micronutrient for neuronal function, and a deficiency of Cu in early life can have devastating impacts on development. We recently discovered that CTR1 can reversibly transition between trimeric and monomeric states to regulate Cu uptake. Moreover, a CTR1 mutant that fails to undergo these de-oligomerization events impairs growth factor–activated signaling pathways. These findings challenge the conventional view of CTR1 as a stable trimeric transporter and suggest that Cu-induced allosteric changes in CTR1 oligomerization directly influence neuronal health and development. Our primary objectives are to determine the mechanisms driving CTR1’s oligomeric-state transitions and to clarify how these shifts impact CTR1’s function in Cu regulation and stem cell differentiation. Using innovative single-molecule assays, such as single-molecule localization microscopy and in-cell oligomer stoichiometry assays, we will visualize and quantify CTR1’s oligomeric states in situ. Human embryonic stem cell (hESC)-derived neurons will provide a physiologically relevant model for these studies. In addition, we will conduct comprehensive proteomic and biochemical analyses to uncover key protein interaction networks that modulate CTR1 trafficking and function. This proposal aims to address two main research directions: (1) identifying the triggers behind CTR1’s transition from trimeric to monomeric forms under conditions of excess Cu, and (2) exploring CTR1’s role in stem cell differentiation, with emphasis on its interactions with Laloo and SNT1 to regulate neuronal maturation. This multidisciplinary effort will be supported by collaborations with experts in neurobiology, membrane trafficking, Cu homeostasis, and stem cell research, thereby ensuring a robust and integrative approach. The insights gained will provide significant contributions to understanding CTR1’s role in Cu regulation and cell development, as well as elucidating its broader impact on neuronal health and disease. Additionally, the methodologies developed will have broad applicability for examining the dynamics of other membrane proteins. This project is innovative for several reasons: it introduces novel single-molecule assays for in situ studies, uses a more physiologically relevant hESC model, and addresses a critical gap in our understanding of allosteric regulation mediated by CTR1 in neurons. The insights gained from this research could have far-reaching implications in neurobiology and may inform strategies for treating Cu-related neurodegenerative diseases.

NSF Awards · FY 2026 · 2026-04

This grant provides support for 40–60 researchers to participate in the workshop Grand Challenges in Mechanics, which will be held in Houston, Texas. Mechanics serves as a foundational discipline for modern science and engineering, enabling advances in infrastructure, energy systems, biomedical technologies, and emerging fields such as stretchable electronics and advanced materials. As the scientific landscape evolves, the field is uniquely positioned to address national and global priorities, including the National Academies’ Grand Challenges. The workshop will convene a balanced cohort of senior scholars, mid-career researchers, and early-career innovators from academia, industry, and national laboratories. The meeting will emphasize facilitated dialogue and forward-looking discussion, fostering deep intellectual exchange and cross-sector collaboration. The primary goal of this grant is to produce a community-driven roadmap that connects mechanics to emerging frontiers, including quantum technologies, nuclear fusion, space exploration, artificial intelligence, and brain–machine interfaces. Participants will articulate key research questions and identify high-impact directions that will shape the discipline over the next decade and beyond. In addition to the roadmap, the project will generate a strategic report, commissioned perspective articles, and educational tutorials aimed at lowering barriers to entry for researchers seeking to engage in these transformative areas. Through these coordinated efforts, the workshop will strengthen the intellectual leadership of mechanics, catalyze new interdisciplinary collaborations, and help recruit and prepare the next generation of innovators to address complex societal challenges. 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

The objective of this proposal is to establish a multidisciplinary Clinical Research Center (CRC) on Developmental Language Disorders (DLD). Through the CRC, we propose to identify and estimate the prevalence of DLD (and Late Talking, LT) in a linguistically and culturally representative cohort of young children representative of the general population of the Houston Metropolitan Statistical Area (HMSA). To sample the targeted population of children aged 18-24 months (who will form the sample upon which cases will be ascertained), Core C (Recruitment and Retention) will work with the Texas Children’s Hospital (TCH) pediatric clinic network, the city’s largest organization of pediatric health clinics and hospitals. The children will be assessed for language development status and classified as Late Talkers (LTs) or not Late Talkers (non-LTs) for sampling purposes. For Project 1 (Prevalence), a TCH population sample (n ~ 3,600) will be used to identify a CRC cohort of about 2,400 children in the first 18 months of the study, half of whom (n~1,200) will have screened as LTs and half as non-LTs (n~1,200). This cohort will be followed until the children are 48-59 months of age and assessable for DLD in Project 1. Project 2 (Etiology) will investigate social and genetic determinants for LT and DLD (macro-phenotypes). In Project 3 (Pathways), a subcohort of children speaking Spanish-, English-, or both (n=300) will participate in a longitudinal study of developmental pathways of LT and DLD and their convergence/divergence; these pathways will be characterized with developmentally traced motor, cognitive, and linguistic indicators (micro-phenotypes). Sophisticated longitudinal designs, data management, and statistical analyses are supported by Core B (Data). Core A (Administration) explicitly addresses communication and coordination across projects, progress monitoring, and quality control, thus promoting team effectiveness, usage of the Cores, and synergy of the Projects. The central theme of the proposed CRC is “Evaluating Sources of Heterogeneity and Risk Factors in DLD.” The representativeness of the population of the HMSA makes it an ideal location for investigating heterogeneity and risk factors across different languages, cultures, and socioeconomic strata. This heterogeneity culminates in an aggregate indicator assessed when the children are at least 4 years old, namely school readiness. To understand social, genetic, cognitive, and linguistic factors that predict and characterize DLD and prepare children for school, individual and group differences substantiating the heterogeneity of DLD will be systematically studied from multidisciplinary perspectives.” This theme is refined through three synergistic Projects, three well-organized and efficient Cores, and three Specific Aims on DLD, LT, and their relations: (1) Implement large-scale LT and DLD prevalence studies; (2) Implement converging projects to characterize LT and predict DLD and (3) Conduct multidisciplinary research anchored by strong methodologies, enhanced by complex analytics, and supported by an expert team of clinical researchers.

NSF Awards · FY 2026 · 2026-03

This grant provides support for students to participate in the 2026 Institute of Industrial and Systems Engineers (IISE) Annual Conference and Expo to be held in Arlington, Texas, 16-19 May 2026. The IISE Annual Conference & Expo is interdisciplinary, bringing together engineers from different disciplines which can foster innovation and increased engagement from students. This conference aligns with the scientific areas supported by NSF, addressing the opportunities and challenges posed by engineering science in the next industrial revolution. Travel support to attend the IISE Annual Conference & Expo can increase participation in engineering by providing students with access to a professional network, mentorship opportunities, and exposure to cutting-edge research and industry practices. Engaging with a community of professionals and peers at various career stages helps students build valuable connections, gain insights into career pathways, and develop a sense of belonging within the engineering field, ultimately encouraging their continued growth and participation in the profession. The broad range of topics presented at the conference will provide students with opportunities to learn more about the methods and tools needed to create globally competitive industries focused on the creation of manufactured products and systems. This grant supports students' conference registration costs with the goal of promoting student participation at the conference. The selection process of the awardees prioritizes students who do not otherwise have sufficient funds from other sources (e.g., advisor, department, other travel awards) to attend the conference. The conference offers career development opportunities through special sessions, workshops, networking activities, and mentoring from experienced professionals representing a variety of career stages, institutions, and geographical regions. Participation is open to all, and the lineup of speakers and organizers fosters an environment designed to empower students to engage meaningfully in discussions on the future of industrial and systems engineering. 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

Flows in the atmosphere and ocean exhibit complicated patterns that include swirls, gusts, and waves. Underwater and aerial vehicles must contend with variability in the flow surrounding the vehicle, which often reduces their efficiency. However, many animals can take advantage of flow variability to improve their propulsion. This project will use mathematical modeling, computer simulation and wind tunnel experiments to find ways to exploit flow variability in locomotion. Results will improve the performance and reliability of underwater and aerial vehicles. Vehicles that can adapt to flow variability will improve ocean exploration and observation, underwater inspection and cleanup, aquaculture, and defense applications. The project will engage students in hands-on STEM research and outreach to encourage students to pursue careers in STEM fields and contribute to a strong national workforce. This project will identify ways to harness environmental flow heterogeneity and unsteadiness in the context of locomotion. The research will focus on swimming, but it will exploit similarities between swimming and flight. Aim 1 will study how to harness interactions between swimmers and complex boundaries by relating boundary shape to the dynamics, efficiency, and propulsive performance of swimmers. Aim 2 will study how structured ambient flow disturbances can be harnessed for propulsion. Aim 3 will study how to maintain the stability and cohesion of large collectives of swimmers. A combination of mathematical modeling, computer simulations, and water tunnel experiments will be used to study these effects across a range of conditions. This layered approach will help identify physical mechanisms and define limits of simplified models. By extending research in bio-inspired locomotion from highly idealized flow environments to realistic ones, this project will help understand how locomoting organisms and machines interact with real flows. Results will aid the design of next-generation underwater and aerial vehicles, foster collaboration across engineering and life sciences, and engage students in research and education at the intersection of physics, biology, and robotics. 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

The goal of the Nuclear Physics from Multi-Messenger Mergers (NP3M) Focus Research Hub is to create a national effort in nuclear physics and astrophysical simulations to systematically probe the properties of hot and dense strongly interacting matter with multi-messenger observations of neutron stars. Neutron star mergers probe the nature of matter at densities and temperatures far beyond those present in atomic nuclei. The NP3M Hub will form a new interdisciplinary collaboration across nuclear physics, astrophysics, and gravitational-wave physics to drive the coordinated advances in each of these sub-fields needed to interpret observations. The NP3M Hub will develop theoretical models of dense and hot matter to connect multi-messenger observations of neutron stars to the underlying merger dynamics by means of numerical simulations. Hub members will lead a coordinated effort to study the impact of theoretical uncertainties in the nuclear physics and the numerical modeling on the prediction of multi-messenger signals from merging neutron stars (end-to-end). They will lead a parallel effort to back propagate observational constraints to nuclear physics, connecting astronomical observations with laboratory results. The Nuclear Physics of Multi-Messenger Mergers (NP3M) collaboration is comprised of researchers across the US and aims to understand how atomic nuclei and the interactions between neutrons and protons impact the merger of two neutron stars. The evolution of neutron stars, the compact stellar objects which are the final stage in the evolution of stars between about 8 and 20 times the mass of the sun, is controlled by nuclear physics. Some neutron stars in our universe are created in pairs, and these pairs of neutron stars eventually merge in a cataclysmic event which emits gravitational waves, neutrinos, and photons, creating either a more massive neutron star or a black hole. These mergers are the likely origin of a significant fraction of the atomic nuclei heavier than iron which are present on earth. This grant supports the training of early-career scientists who will work on different aspects of this problem. The NP3M hub will create pages and videos that discuss how any new discoveries in multi-messenger astronomy impact nuclear astrophysics. The hub will develop a curriculum on nuclear physics and multi-messenger astronomy for K-12 educators, and host two summer schools that will educate students in hub-related science. This project advances the objectives of "Windows on the Universe: the Era of Multi-Messenger Astrophysics", one of the 10 Big Ideas for Future NSF Investments. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NIH Research Projects · FY 2025 · 2026-02

Project Summary: The dental pulp is the vital microenvironment in the tooth, harboring blood vessels and nerves, not to mention odontoblasts that interface with the dentinal tubules. Trauma or bacterial infection may inflame the dental pulp, creating extreme pain. Extirpating the inflamed pulp (and potentially replacing it with inert materials) ameliorates the pain, but the procedure leaves a devitalized tooth. An alternative is possible in juvenile patients, called over-instrumentation (OI). During OI, the pulpal chamber is exposed to the peripheral circulation post-pulpectomy. As long as the apical papilla is intact, some tissue regeneration takes place in the pulpal canal subsequently — although the disorganized tissue does not mimic native soft tissue. In adults in particular, OI results in non-functional pulpal ossification. Another concern in endodontic procedures is occurrence/recurrence of colonization by oral bacteria. Such infections may prolong and exacerbate pulpal inflammation. A material- based formulation is proposed that can (a) promote vascularized soft-tissue regeneration in the pulp, while (b) resisting bacterial infection. Our strategy rests on self-assembling peptide hydrogels — a class of supramolecular materials that can be injected in vivo while keeping their gel-like properties. The materials consist of canonical amino acids and are biocompatible. Such materials need to provide both mechanical support and biological cues for tissue ingrowth. Somewhat counter-intuitively, a self-assembling peptide hydrogel, without added growth factors or exogenous cells, demonstrated formation of vascularized soft-tissue in a canine pulpectomy model in 28 days. In a separate study, a different cationic amphiphilic hydrogel belonging to the same platform, showed efficacy in inhibiting bacterial growth via membrane permeabilization. In this proposal, a combinatorial treatment modality will be tested for its effectiveness in achieving the dual goals described above. A mechanistic puzzle that these projects would help solve is the lineage/source of infiltrating cells and evolution of the cellular milieu in the pulpal canal after pulpectomy and implantation of soft biomimetic hydrogels. Characterization of the long- term maturation of the vascularized soft tissue promoted by such hydrogels is another target. The multi- disciplinary project proposed in this Bioengineering Research Grant application would bring together a chemist and bioengineer (PI V.A.K., an early-stage investigator), a specialist in oral bacterial colonies (co-I C.C.), and an endodontist (co-I E.S.), to solve an enduring challenge: regenerating biomimetic vascularized soft tissue post- pulpectomy. In vitro mechanistic analyses, in vivo characterization of infiltrating cells, and histologic/radiographic identification of long-term evolution of the pulpal soft tissue and the pulp-dentin complex would build on published studies and extensive preliminary data. Even if the proposed experiments are only partially successful, we would learn about tissue-material interaction in the context of dental pulp. Success of the aims would produce compelling data for a cell-free, growth-factor-free, off-the-shelf material formulation ideal for application in endodontic settings and improve clinical outcomes in millions of patients needing pulpectomy.

NSF Awards · FY 2026 · 2026-02

Entangled materials—such as polymer networks, textiles, and steel-cable structures—are found across length scales and exhibit remarkable mechanical properties driven by both fiber properties and the complex ways fibers entangle and self-contact. However, their behavior remains difficult to predict and design due to the lack of simple models capturing their intricate geometries and physical interactions. This Designing Materials to Revolutionize and Engineer our Future (DMREF) project will address that gap by developing quantitative metrics of entanglement through experiments and microscopy across scales. These metrics will connect entanglement geometries to physical properties, enabling the creation of simplified digital network representations of complex entanglements. These representations will guide the design of future entangled materials with user-defined properties. The project will provide open-source tools and data to support scalable design and optimization of fabrics, textiles, and knits, particularly at industrial scales. Broader impacts include educational integration of network science across institutions and a public art exhibit that will focus on visualizing networks, aiming to raise awareness of network-science-driven materials engineering. The project will establish a closed-loop framework for describing entangled matter using physical networks, correlating structural features with mechanical performance, and using these insights for targeted design. It consists of three unified thrusts that combine theory, computation, and experimentation. To span multiple length scales, the team will use testbeds made of 3D-printed textile architectures and woven metamaterials. Quantitative mechanical measures of entanglement will be obtained both experimentally and numerically. This data will inform the development of network models in which filaments are converted into skeleton and contact networks with geometric and topological attributes. These models will then be used to optimize entanglement geometries for desired performance using graph neural networks and gradient-based refinements. The result will be new material prototypes with engineered entanglements and mechanical properties, along with a broadly applicable design methodology for entangled filament-based 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 2026 · 2026-01

Precise measurements of Earth’s surface motion and deformation are critical for understanding and mitigating natural hazards such as earthquakes, volcanic eruptions, and landslides. On land, the Global Navigation Satellite System (GNSS) and several other geodetic techniques have enabled high-precision, affordable, and continuous monitoring of surface deformation. In contrast, humans’ current ability to monitor the ocean floor – where many hazardous events originate – remains severely limited due to technical challenges and high costs. As a results, large portions of the solid Earth, particularly offshore regions prone to earthquakes, tsunamis, and submarine volcanic activities, remain unmonitored. This project addresses the gap by developing new, low-cost methods to measure seafloor motion over time. These methods reduce reliance on expensive seafloor instruments by employing innovative surveying strategies and alternative seafloor components. The approach has the potential to significantly lower the cost of seafloor geodesy and expand its spatial coverage. In addition to advancing scientific capabilities, the project provides hands-on training for students and early-career researchers, contributing to the development of a skilled geodetic workforce. Global Navigation Satellite System–Acoustic ranging (GNSS-A) is a major technique for seafloor geodesy. The conventional GNSS-A method requires at least three acoustic transponders per site, each costing tens of thousands of dollars, thereby limiting the number of deployable sites. These multi-transponder systems are also vulnerable to individual instrument failure because all transponders at each site need to function normally to form effective geodetic observations. In addition, a traditional GNSS-A site requires flat seafloor relief to mitigate errors associated with oceanic variability. This project aims to overcome these limitations by developing alternative GNSS-A configurations using only one transponder or a passive acoustic corner reflector per site, combined with symmetric survey designs using multiple semi-autonomous Wave Gliders. Five GNSS-A stations (four with transponders and one with an acoustic corner reflector) will be deployed at water depths between 85–1100 meters offshore Oregon and southern California. Additionally, one existing site deployed by the Near-Trench Community Geodetic Experiment will be utilized in this project. These sites will be surveyed multiple times over a three-year period to assess the repeatability of seafloor positioning. A Wave Glider-based winched ocean profiler will also be tested to improve sound speed modeling for improved GNSS-A data analysis. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2026 · 2026-01

Plants live with a large number and diversity of microbes that influence plant health. Some microbes help plants by providing nutrients, whereas others can have negative effects like causing disease. Plants, in turn, influence the presence or absence of certain microbes in the soil, with individual plant species creating unique microbial signatures. This allows for a situation where plant-associated changes in microbial communities feedback to influence plant health. These plant-soil feedbacks can play an important role in processes ranging from structuring natural plant communities and impacting crop performance. Practically all microbes are sensitive to changes in the environment, and changes in weather, such as drought, can impact plant-soil feedbacks. While experiments have shown that plant-soil feedbacks are mediated by microbes, it is often unclear which microbial taxa or what microbial functions are causing the feedbacks. This knowledge gap limits the ability to generalize results or make targeted management recommendations. To help fill this gap, the investigator will pair a relatively new molecular tool, metatranscriptomics, with classic plant-soil feedback experiments that manipulate the presence of drought. Metatranscriptomics will provide a way to analyze microbial gene expression in the soil, identifying active microbes and their functions that change with drought. Results of this work will have direct applications for predicting and mitigating the effects of drought on an economically and culturally important ecosystem, Texas grasslands. This work will give a mid-career scientist the opportunity to learn a new molecular tool, enhancing the scientist’s career goals harnessing plant-microbe interactions to develop targeted solutions for environmental problems. This project will also provide training to graduate and postbaccalaureate students on bioinformatics and the analysis of metatranscriptomic datasets. This project has two major components. First, a greenhouse-based plant-soil feedback experiment will test how drought influences the composition of active microbial taxa and microbial gene expression associated with plants growing in conspecific and heterospecific soils. Second, an experiment will test for functional redundancy in microbial responses to drought using soils from across a natural precipitation gradient in Texas. This mid-career advancement award will provide protected time and training for the PI to learn metatranscriptomic techniques and data analysis, as well as provide training opportunities for graduate and postbaccalaureate students. Given plant-soil feedback theory's success at integrating plant-microbe interactions into the framework of plant community ecology, deeper insights into microbial mechanisms underlying plant-soil feedbacks may transform our understanding of how plant communities are structured and plant diversity is maintained. 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 · 2025-12

This project focuses on special materials called "polymer brushes," which are tiny, hair-like chains attached to surfaces. These brushes can change how they behave based on their environment — such as changes in acidity or salt levels. Because they are low-cost, flexible, and easy to make, polymer brushes have the potential to be used in water purification and environmental sensing. However, scientists do not yet fully understand how to design these brushes to control how small molecules move in and out of them. This project will identify how characteristics of the brush — such as whether they carry an electric charge, how they interact with water, the size of the building blocks, and how many chains are present — affect their structure and behavior. The goal is to better understand how the makeup of these brushes affects their response to environmental changes and how they allow molecules and particles to pass through. Project outcomes could help improve materials used in water purification systems and biological separation processes. The project will also provide training for undergraduate and graduate students at Rice University and the University of Houston. In addition, the team will lead public outreach activities on filtration and clean water at local centers and science festivals. This project will help understand the influence of charge state on the transport of penetrants within charged polymer brushes. The team will synthesize random copolymer brushes with charged, neutral hydrophilic, and/or neutral hydrophobic monomers. Polymer structure and charge distribution in various solution conditions will be characterized using in situ (wet) ellipsometry, neutron scattering, and molecular simulation. This information will be used to test theories coupling penetrant transport, to be assessed through microscopic imaging and simulation, to the dynamics of polymer brushes under quiescent conditions. Finally, penetrant transport will be quantified under flow conditions using microfluidics and simulation. This project will thus provide the fundamental knowledge needed to molecularly design polymer brushes to control penetrant transport. This information will be used to control the local monomer interactions within brushes and thereby tailor the interfacial properties of separations and sensing devices. Results will be disseminated at local meetings that attract participants from Houston’s petrochemical, biomedical, and materials industries, including the Texas Soft Matter Meeting. The PIs will partner with the Rice Office of STEM Engagement (R-STEM) to develop outreach modules on water purification for the Energy Explorations Academy to present hands-on demonstrations at the annual Houston Energy Festival. 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 · 2025-12

PROJECT SUMMARY This project aims to develop the Resonant Voice Index (RVI), an objective tool for assessing vocal resonance in clinical voice evaluation. Voice quality depends on the interaction between vocal fold vibrations (source) and the vocal tract (filter). When optimally coupled, this interaction creates forward-focused resonance, a resonant voice quality characterized by clear tone, minimal effort, and efficient use of the resonators. Current clinical practice relies only on subjective perceptual ratings that lack reliability and sensitivity. The RVI will be a multiparametric model incorporating acoustic features that quantify forward-focused resonance, offering clinicians an objective measure that matches the complexity of expert perceptual rating. The project has three aims: 1. Determine causal relationships between acoustic features and resonance perception: Use speech resynthesis of standard voice evaluation stimuli to systematically manipulate selective acoustic features and determine their specific impact on perceptual ratings of resonance. 2. Validate acoustic predictors of resonance through natural voice analysis: Examine a diverse corpus of natural voice recordings to identify which acoustic features consistently correlate with expert perceptual ratings of resonance. This will confirm which features identified in Aim 1 generalize to real- world voice samples across speakers. 3. Develop the Resonant Voice Index: Apply machine learning techniques to integrate validated acoustic features into comprehensive, clinically applicable indices that objectively quantify resonance quality. The RVI addresses a critical gap in clinical voice care by providing clinicians with an objective tool for reliable and sensitive evaluation, clinical decision making, and tracking of resonance-related outcomes. Due to the popularity of resonance-focused treatment (e.g., resonant voice therapy, semi-occluded vocal tract exercises, Vocal Function Exercises), the RVI can benefit millions of patients. This research directly supports the NIDCD's strategic goal of improving evaluation and treatment of voice disorders to enhance the quality of life for these patients.

NSF Awards · FY 2025 · 2025-11

Industries of the Future (IotF) is comprised of artificial intelligence (AI), quantum information science (QIS), advanced manufacturing, advanced communications, and biotechnology. This three-year RET Site will focus on research experiences in biotechnology, AI and machine learning (ML), Internet of Things and communication, quantum computing, and advanced manufacturing. The goal of this project is to provide a six-week summer research experience for nine high school STEM teachers to engage in IotF research each year. These project experiences will help teachers develop and implement innovative course modules by translating cutting-edge research in IotF into their high school’s course curriculum using hands on teaching methodologies. The lessons will also meet the Texas Essential Knowledge and Skills (TEKS) standards. Additional professional development activities, including industry field trips, will allow teachers to connect academic research with real-world engineering and technology for their students. Academic-year follow-up includes workshops and module discussion and feedback. Through partnerships with schools in the Houston metropolitan area, the project will assist teachers with positively influencing the learning and career paths in STEM of their high school students. The course modules will be disseminated through TeachEngineering.org. The RET project will be shared with middle and high school students in summer camps organized by the project team in collaboration with partners including local ISDs and the UTeach Association. IotF research areas provide growing opportunities for creating the next generation of the STEM workforce. 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 examines how the designers of artificial intelligence (AI) tools influence user interactions. A key focus is on the work of conversation designers and user experience (UX) professionals who write AI dialogue and determine how AI systems respond. The project includes fieldwork among these researchers and designers to understand how their choices affect interactions between AI and users. Project findings are adapted into new educational resources to improve digital interactions. The project involves a mixed-methods ethnographic study that uses participant observation and interviews with conversational user experience professionals across multiple field sites. The study population represents various company sizes and approaches to AI development. By employing the embedded laboratory observation methods pioneered by anthropology and science and technology studies (STS), the project asks: how professionals define usability for conversational systems without visual interfaces; how cultural contexts influence AI conversation design practices; and how expertise in conversational user experience evolves as the field develops. The project team intends to examine the sociocultural practices that shape human-AI interaction design. The project contributes to user experience education along with translational work in the development of pedagogical resources and industry partnerships that prepare students for careers designing AI systems. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2025 · 2025-10

Understanding how to compare and interpolate complex data, such as images, shapes, and network structures, is a fundamental challenge across science and engineering, especially in the context of artificial intelligence. This project develops new mathematical and computational tools that extend the theory of optimal transport, a well-established framework for measuring distances between probability distributions. The proposed methods are tailored to settings which more closely reflect specialized real world data structures than those considered in classical optimal transport. These advances will enable more adequate quantitative analysis methods for medical images, dynamic crowd movements, and biological network structures. An integral outcome of the project will consist in the production of robust and open-source software packages, which will make these generalized optimal transport methods accessible to researchers and practitioners in biomedical imaging, machine learning, and network analysis. Importantly, these algorithms will be firmly grounded in mathematical theory. The project will also train graduate and postdoctoral researchers through cross disciplinary collaborations, foster community engagement via a workshop, and engage with the broader community via a coding-focused course and K-12 outreach activities. The project pursues three interlocking aims. First, it formulates a new Constrained Unbalanced Optimal Transport model for comparing general positive measures under integral and parametric constraints; this involves rigorous well posedness results and efficient numerical solvers, targeted at shape analysis and population/crowd modeling. Second, it introduces an Optimal Riemannian Metric Transport framework, which blends ideas from optimal transport and infinite-dimensional geometry to compare Riemannian metrics on a fixed manifold; this framework is anchored in geometric connections to the well-established Wasserstein-Fisher-Rao metric, which has been proven successful in applications in machine learning and data science. Third, it investigates the Alexandrov and Riemannian geometry of Gromov–Wasserstein distances; this will result in geometry driven computational tools for comparing structured data on different domains, with applications to the analysis of astrocyte cell morphology. Together, these efforts will yield new methodological insights and a suite of software libraries. 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 Faculty Early Career Development (CAREER) grant will investigate the elastic behavior of structures that can unfold and deploy from compact configurations into useful 3D geometries. Deployable structures are used in several areas pertaining to robotics (e.g., soft robots used in minimally invasive surgeries and in search-and-rescue), to aeronautics and astronautics (e.g., smart airfoils, deployable mirrors and solar panels and sails in space missions) and to smart structures (e.g., deployable shelters and rooftops). The relationships that exist between the global behavior of a deployable structure and the local behavior of its elementary constituents (bars, plates and membranes) are governed by a mixture of geometrical and mechanical laws. The research aims to establish a fundamental understanding of such local-to-global relationships and of the way they dictate how a deployable structure changes shape under external loads so as to improve current modeling, design and control paradigms. The theme of geometry provides aesthetic and appealing connections with several areas of artistic and fashion design and will be leveraged to increase public engagement with science and technology through outreach activities which, with the help of an institutional center, will particularly target K-12 and underrepresented minorities. The main purpose of the project is to initiate a theory of the finite, geometrically non-linear, elastic deformations of deployable structures that are tailored on small space scales as in architected materials and metamaterials. The technical objectives of the research efforts are to (i) characterize the macroscopic deformation paths compatible with the small-scale kinematics of a deployable structure composed of rigid or inextensible elements; to (ii) compute the generalized, strain-gradient or enriched, elasticity functionals that govern the equilibrium geometries of a deployable structure and particularly so in cases where standard Strength of Materials theories break down; (iii) formulate conceptual control problems and solve them for actuation parameters understood as the boundary data that deform the deployable structure into a target shape; and (iv) establish inverse design paradigms that allow us to find deployable structures with pre-programmed deformation paths. To do so, the project will develop analysis methods marrying differential geometry, asymptotics and homogenization theory. This project will allow the PI to advance the knowledge base in solid mechanics and to support his long-term career within that field. 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 Noyce Track 4 project aims to serve the national need of improving STEM teacher retention and effectiveness in high-need school districts (HNSDs) by fostering a collaborative Community of Practice (CoP) among multiple institutions. The initiative addresses persistent challenges in recruiting, preparing, and retaining secondary STEM teachers in HNSDs by leveraging a network of teacher preparation programs across the United States. Through a combination of data-driven research and community engagement, this project will systematically study factors influencing teacher persistence and effectiveness while developing strategies to enhance STEM teacher preparation. The project includes large-scale data collection, quantitative and qualitative analysis of teacher experiences, and dissemination of findings to refine teacher education practices. By promoting collaboration and knowledge sharing, the project has the potential to improve STEM education for all students and strengthen the STEM teacher workforce. This project at a consortium of universities, including Alabama A&M University, Central Washington University, Middle Tennessee State University, North Dakota State University, University of Colorado Colorado Springs, University of Houston, University of Nevada-Reno, University of Texas at Austin, and University of Texas Rio Grande Valley, is designed to investigate and enhance STEM teacher persistence and effectiveness in HNSDs. Key project goals include analyzing the impact of STEM teacher preparation programs on teacher retention, identifying programmatic features that contribute to long-term effectiveness, and evaluating early career STEM teachers using the nationally normed Tripod student perception survey as well as a classroom observation protocol. Using a convergent parallel mixed-methods approach, the project will integrate quantitative analysis of STEM teacher placement and retention trends with qualitative interviews and focus groups. Findings will contribute to understanding the key elements of effective STEM teacher preparation and inform evidence-based practices for recruiting and supporting teachers in high-need schools. The CoP framework will facilitate knowledge exchange and professional development among teacher educators, fostering a common vision of effective STEM teaching. Evaluation efforts will track the impact of the CoP on teacher preparation programs and retention rates, ensuring sustainability and broad dissemination of results. This Track 4: Noyce Research project is supported through the Robert Noyce Teacher Scholarship Program (Noyce). The Noyce program supports talented STEM undergraduate majors and professionals in becoming effective K-12 STEM teachers and experienced teachers in becoming STEM master educators in HNSDs. It also supports research on the effectiveness and retention of K-12 STEM teachers in these districts. 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 addresses the growing national need for a skilled workforce in the critical minerals sector, which supports technologies essential to national defense, economic growth, and energy production. By engaging high school and community college students in immersive, hands-on learning, it is positioned to cultivate early interest in science, technology, engineering, and mathematics (STEM) and expand pathways into geoscience, engineering, and resource management. The project will focus on students from the Greater Houston area with limited prior STEM exposure, leveraging regional energy and mineral industry infrastructure to deliver relevant, industry-tailored experiences. Through mentorship, cohort-based learning, and academic-industry partnerships, impactful educational opportunities will be offered that align with workforce needs. The project supports NSF’s mission by promoting scientific progress and enhancing national prosperity and security. Aligned with the Explorations track of NSF’s Experiential Learning for Emerging and Novel Technologies (ExLENT) program, this project plans to develop and evaluate a learning framework that integrates artificial intelligence, geoscience, and mineral processing into early-stage STEM education. Led by the University of Houston with industry partners, the program plans annual support of 20 students through field-based activities, lab experiments, and structured mentoring. The curriculum designed emphasizes active learning, digital tools, and peer support. The evaluation plan includes measures of student learning, career interest, and persistence. Planned outcomes include a validated experiential model, impact data, and publicly available resources to inform broader STEM education and workforce development efforts. Public dissemination of outcomes and instructional materials can support national efforts to build capacity in critical minerals education and provide a scalable model for experiential STEM training. 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.