University Of New Mexico

NSF Awards · FY 2026 · 2026-10

This Faculty Early Career Development Program (CAREER) award supports research and education aimed at innovating how advanced ceramic composite materials are manufactured for demanding applications. Ceramic matrix composites combine reinforcing phases with ceramic matrices to provide strength, toughness, and resistance to heat, corrosion, and wear for uses in aerospace, defense, energy, semiconductor, and biomedical technologies. However, conventional fabrication methods are often multi-step and provide limited control over microstructure, while existing additive manufacturing methods can suffer from weak interfacial bonding and uneven reinforcement. This project develops a new ultrafast laser-based manufacturing strategy for polymer-derived ceramics that enables precise local control of structure and properties, creating a pathway to ceramic composite materials with tailored performance and functionality. By advancing fundamental understanding of laser-matter interactions and opening new routes for manufacturing high-performance ceramic materials, the project serves the national interest by promoting the progress of science, supporting innovation in advanced manufacturing, and contributing to technologies important for space exploration, energy-efficient systems, semiconductor devices, and other societally important applications, while strengthening the nation’s technological competitiveness. The project also integrates research and education by training students in emerging ultrafast laser manufacturing technologies, incorporating research into undergraduate and graduate courses, and engaging local K–12 students and educators through accessible, hands-on outreach activities, all of which contribute to STEM workforce development. The project investigates two complementary ultrafast laser fabrication modes for polymer-derived ceramics and examines how laser processing can be used to control material conversion, microstructure evolution, and resulting properties. Specifically, the research addresses three connected goals: to determine the mechanisms governing ultrafast laser-driven modification and conversion in preceramic polymers, including the coupled effects of laser energy deposition, thermal confinement, chemical transformation, and stress development; to establish relationships among processing conditions, resulting material structures, and performance through in situ diagnostics, ex situ characterization, and multiscale computational modeling; and to integrate these insights with reinforcement phases to create graded ceramic matrix composites with enhanced interfacial bonding, programmable microstructures, and tailored properties. By combining experiments and modeling, the project will generate validated principles for ultrafast laser interactions with preceramic polymers and establish transferable manufacturing knowledge for laser-based ceramic fabrication across a range of material systems. These advances will provide a foundation for precise, scalable fabrication of high-performance ceramic composites and broaden the scientific and technological reach of advanced ceramic manufacturing. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2026 · 2026-08

Automation and artificial intelligence are transforming society, increasing productivity by improving the speed, efficiency, and reliability of conducting complex tasks. Despite these advances, current approaches to autonomous decision-making require enormous computational resources, driving the expansion of data centers and placing growing strain on national electricity and water infrastructure. These high computational demands also limit where autonomous systems can be deployed, preventing low-cost or resource-limited applications from benefiting. This Faculty Early Career Development Program (CAREER) grant supports research that will create novel algorithms to dramatically reduce the computational demands of autonomous decision-making. The research will exploit symmetries, which are repeated patterns that frequently occur in large-scale human-engineered systems built from many similar components, including energy storage systems and logistics networks. By identifying and leveraging these patterns, this project will develop computational tools that enable symmetry-aware algorithms for extreme-scale autonomous decision-making, reducing both computing and memory requirements. Applications such as active battery balancing and resilient manufacturing logistics will strengthen national infrastructure by reducing the economic and environmental costs of computation. All algorithms and tools will be released as open-source software, broadening access and fostering innovation. Educational and outreach activities will promote interdisciplinary collaboration by bringing concepts from autonomy and artificial intelligence into curricula for students in majors outside control systems engineering. Project-based educational materials will be drawn from a broad range of real-world applications to engage students and demonstrate how these emerging tools are reshaping control and automation. This project will exploit symmetry to reduce the computational demands of extreme-scale autonomous decision-making. Extreme-scale refers to decision-making problems whose state and action spaces and constraint sets are so large, and whose decision times are so short, that conventional algorithms cannot meet real-time requirements on practical hardware. Central to this project is the notion of symmetry: transformations of the decision variables under which the decision problem remains invariant. These methods systematically identify and exploit these symmetries to reuse information across symmetric components rather than treating each as unique, enabling scalable autonomy with substantially lower computational and memory requirements. Although conceptually straightforward, efficiently exploiting symmetry in real-world applications is non-trivial and requires advances at the intersection of mathematics, optimization, and autonomous systems. The transformative potential of this research lies in applications such as vehicle electrification, smart grids, and resilient manufacturing, where it mitigates the growing economic and environmental costs of computation. 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 Faculty Early Career Development Program (CAREER) award will advance the science and engineering of fire-resistant construction materials to improve the resilience of buildings exposed to extreme heat and wildfire. Increasing wildfire intensity has created urgent challenges for protecting homes and infrastructure, particularly in vulnerable regions. This project will develop advanced geopolymer materials as an alternative to conventional fire protection materials by using innovative material architectures that combine structural strength and thermal insulation. The project will establish fundamental knowledge needed to design materials that better resist heat-induced damage and improve structural performance during fire exposure. The research will promote the progress of science by advancing understanding of fire-resistant materials, support national welfare by improving safety and resilience of the built environment, and contribute to economic competitiveness through innovation in construction materials. Integrated educational activities will train students in advanced materials and fire resilience, create new learning opportunities through curriculum development and online education, and increase participation in STEM through outreach to schools, industry, and communities. The research will develop a systematic framework for engineering fire resistant geopolymer materials that combine load bearing and thermal insulating capabilities. The project is organized around four technical goals: optimizing dense and foamed geopolymers derived from regional aluminosilicate precursors; engineering layered microstructures that control heat transfer and mechanical response; characterizing phase evolution, pore structure, and failure mechanisms under extreme temperatures using advanced experimental methods; and developing predictive, multiscale models that link chemistry, processing, structure, and fire performance. The research methodology integrates statistically designed experiments, controlled microstructural tailoring, in situ high temperature characterization techniques, and computational modeling of heat transfer and degradation behavior. By coupling experimental observations with physics-based models, the research moves beyond empirical fire testing approaches toward predictive material design. The expected outcomes include quantitative structure–property relationships, validated design tools, and scalable material strategies that enable performance driven fire protection solutions. This integrated approach will advance fundamental understanding of geopolymer materials under extreme conditions and provide a scientific basis for next generation fire resistant construction 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 · 2026-07

Malware threats have become increasingly sophisticated, often using techniques that often enable evasion, and are quite often persistent. The growing use of Virtual Private Networks (VPNs) and the encrypted VPN traffic allow attackers to potentially bypass current network defense techniques. The project’s novelties include the creation of new approaches to detect and mitigate protocol misconfiguration which could be exploited as an attack surface and to prevent threat actors from exploiting VPN weaknesses through malicious scripts, authentication bypass, covert access, and related techniques. The project's broader significance lies in helping cultivating VPN security expertise at both undergraduate and graduate levels through hands-on modules, and empowering VPN providers and IT organizations to adopt secure VPN practices. The project takes a comprehensive, cross-layered approach to securing VPN ecosystems through three tightly integrated thrusts: Thrust I introduces the first platform- and version-aware knowledge graph for OpenVPN, enabling interpretable reasoning over directive semantics, dependencies, and mis-configurations. Thrust II advances the field by developing a sandboxed VPN ecosystem to estimate the impact of VPN mis-configurations across stakeholders and to map observed behaviors to the CIA triad and MITRE ATT&CK tactics for structured risk assessment. Thrust III builds a hybrid detection system that combines directive signatures with host behavior, enabling early and interpretable detection of malicious VPNs. This facilitates platform-agnostic, timely, and robust malware defense. Altogether, these thrusts provide a novel, explainable, and scalable foundation to identify, understand and mitigate VPN-based threats across diverse platforms. 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

Chondrocytes derived from cranial neural crest cells give rise to cartilaginous structures that form the craniofacial skeleton. These cells must undergo dynamic cellular processes of differentiation and maturation before forming a template that will serve as a scaffold for subsequent bone formation. The gene regulatory networks (GRNs) and signaling pathways, controlling these processes need to be tightly regulated. Any alteration to the GRNs or signaling modules during chondrocyte maturation and differentiation can compromise the skeletal integrity of the developing craniofacial tissues and contribute to the etiology of congenital defects including but not limited to cleft lip with or without cleft palate, mandibular hypoplasia among others. Our preliminary data suggest that in mice, two chromatin modifiers, PRDM3 and PRDM16, function together to facilitate development of craniofacial cartilage, specifically Meckel’s cartilage, which supports mandibular bone development. Combined loss of both Prdm3 and Prdm16 using the Wnt1-Cre driver leads to a complete absence of the Meckel’s cartilage, likely due to misregulation of Wnt/β-catenin transcriptional activity. The specificity of this phenotype varies with the alleles lost as combinatorial mutants exhibit varying phenotypic severity. The goal of this proposal is to further define the mechanisms by which these two PRDM paralogs function to facilitate craniofacial cartilage development. The aims outlined in this proposal will utilize a combination of genetic mouse models with in vitro cell culture systems and molecular tools to test the hypothesis that precise levels of PRDM3 and PRDM16 fine-tune activation and repression of canonical Wnt/β-catenin transcriptional activity and its downstream GRN to control chondrocyte differentiation. In Aim 1, the molecular mechanisms controlling chondrocyte differentiation through Wnt/β-catenin transcriptional activity in the developing murine mandibular process will be defined across the Prdm3;Prdm16 combinatorial mutant allelic series through comprehensive histological assessments over developmental time. Additionally, a thorough evaluation of global transcriptomic changes will be performed, and direct gene targets will be identified through CUT&RUN experiments. Aim 2 will determine if precise modulation of PRDM protein levels refine Wnt/β-catenin activity and contributes to the dosage-dependent craniofacial chondrocyte phenotypes observed in mice. We will develop and apply the degradation tag (dTAG/BromoTag) systems to precisely control PRDM protein dosage in an in vitro model of cranial neural crest development. Together, the results from these studies will provide the tools and concepts to further explore these mechanisms in an R01 application. Completion of these aims will provide insight on how these epigenetic modifiers control specific GRNs and signaling modules (Wnt/β-catenin) to facilitate proper chondrogenesis in formation of the craniofacial skeleton. Importantly, this project will also provide mechanistic insight behind how loss of these factors contributes to the development of craniofacial disorders.

NIH Research Projects · FY 2026 · 2026-06

Project Summary and Abstract I am applying for an NLM Grant for Scholarly Works in Biomedicine and Health to complete my history of medicine monograph: Bringing Up Baby: Race, Infant Mortality, and the Creation of Prenatal Care, 1900-1930. This book will trace the intertwining threads of public health, eugenics, racial science, Progressive Era philanthropy, and professionalizing obstetrics in a story of how Americans became aware of and sought to fix the problem of infant mortality in the early twentieth century. In the 1910s and 1920s myriad groups and organizations, both those interested in health and those interested in social reform, studied the extent and causes of infant mortality, lobbied state and federal governments for maternal and infant welfare funding, and attempted to convince the American public that pregnancy was a condition that required medical surveillance and intervention. This will be the first work of history to dive into these movements and determine how nationalism, race, and medical professionalism efforts shaped the development of prenatal health care in this country. There have been no historical studies devoted solely to prenatal care and my findings into the emergence of this medical specialty and public health concern show it to be rooted in particular racial politics and national health concerns of the early 1900s. Relying on a range of sources including federal infant mortality studies, public health journals, personal correspondence, medical reports, meeting transactions, sociological reports, and popular health pamphlets, I illustrate that prenatal health care originated in a time of eugenics, Jim Crow, and medical misogyny, and perhaps never fully left those values behind. Investigating the history of prenatal health care will expand the historical field of American reproduction and medicine as well as inform current racial disparities in maternal and infant health care and mortality. In addition, this study draws together and speaks across multiple fields in history including women’s history, medical history, political history, and social history. I have plans to publish with Rutgers University Press, the press that published my well-received and widely-read first book Lost: Miscarriage in Nineteenth-Century America.

NSF Awards · FY 2026 · 2026-06

The recognition and binding of nucleic acids by proteins is a central phenomenon in biology, governing processes that range from the flow of genetic information to viral infection and propagation. Despite the importance, our current understanding fails to capture the conformational complexity and dynamic nature of the recognition and binding process. Molecules like RNA exist as an ensemble of accessible conformations, including rare conformations that are not observable using traditional measurements but can be biologically active. Furthermore, a huge span of timescales can be relevant in the process, from microsecond conformational motions to binding/unbinding dynamics occurring over hundreds of seconds. This study leverages advanced spectroscopy and computational modeling to quantitatively map protein-RNA binding dynamics with unprecedented sensitivity and resolution. It will provide a new perspective in protein-RNA interactions that will advance our fundamental understanding of basic biomolecular processes and bring to light the role of hidden states that mediate biomolecular recognition and binding. The study will also strengthen the training of young biological engineers in skills that are critical in research but rarely part of educational curriculum, such as the design and operation of home-built spectroscopic equipment. The central hypothesis of this study is that protein-RNA binding occurs over a range of time and energy-scales, with some interactions being mediated directly through thermally excited conformations of the target molecule. The recent development of new single-molecule spectroscopic methods, in conjunction with non-perturbative fluorescence labeling using non-canonical nucleotide analogues, has enabled the observation of biomolecular dynamics over a temporal range of microseconds to hundreds of seconds and with a sensitivity of < 0.1% of an ensemble population. The study will investigate the binding of the TAR RNA, a small 30 nucleotide hairpin RNA dominated by local secondary fluctuations, and the stem loop A RNA, a large 80 nucleotide RNA structure with secondary and tertiary fluctuations. Taken together, the two systems provide an elegant exploration of how conformational motions of different time and energy scales contribute to protein-RNA binding. The proposed interdisciplinary approach will provide a quantitative (thermodynamics and kinetics), nucleotide-resolved, and temporally resolved picture of protein-RNA binding. The information-rich experimental data, interpreted with the aid of computational modeling, seeks to build the foundational molecular scale knowledge required to understand and predict protein-RNA interactions. 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

This REU Site award to the University of New Mexico, located in Albuquerque, NM, will support training of 10 students for 10 weeks during the summers of 2026- 2028. Students will live and work at the UNM Sevilleta Field Station located on the Sevilleta National Wildlife Refuge in Socorro County, New Mexico. Students will work with a faculty mentor to learn how to conduct research by identifying a research question and developing an independent project in an area of dryland ecology, building upon and contributing to the accumulated knowledge from nearly 40 years of research of the Sevilleta Long-Term Ecological Research Program. Completing a challenging research project, in the company of students with similar interests, builds professional skiils, establishes connections with colleagues of similar interests, and develops transferrable skills in critical thinking and oral and written communication. Students will learn to conduct research and many will present the results of their work at scientific conferences. This program will be assessed using program surveys and by tracking student career outcomes after they leave the program. Students should apply to the REU site using NSF ETAP (Education and Training Application: https://etap.nsf.gov). The program’s independent research experience inspires students while providing training in public speaking, data analysis in R, conservation management knowledge, and workshops on research ethics and professional development. PIs and mentors emphasize collaboration, the research setting, and linking science to action. Mentors with expertise in ecology and conservation biology across disciplines and a range of taxa including plants, animals, and fungi, form collaborations to enhance participant engagement in formal and informal settings. The Field Station environment further facilitates close student-mentor interactions throughout the summer, while the Sevilleta LTER provides students with greater understanding of the research process through observation and interaction with other researchers. Understanding of hypothesis-testing, data analysis, and interpretation increase as students progress through their projects. Student confidence and scientific identity grow under mentor guidance as they design and implement research, assist peers, report results, and contribute meaningfully to research. The project integrates research and education at every interaction level, enabling students to address national scientific workforce needs. It inspires continued interest in environmental science and STEM careers and prepares students for graduate school and research careers after graduation. 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

This long-term program seeks to understand how food webs respond to environmental variability in semi-arid ecosystems, where rainfall pulses drive dramatic but short-lived bursts of plant growth. Drawing on more than 37 years of continuous experimental research, the team will investigate how small mammal populations respond to rainfall pulses and how their foraging behaviors shape the structure and function of the ecosystem. Better understanding how droughts, extreme rainfall events, and shifting resource availability shape populations and communities will enable informed responses to critical challenges related to desertification and ecosystem management. The project will generate broadly applicable insights into how pulsed resources shape consumer behavior and organismal interactions across arid ecosystems worldwide. In addition, the project supports education and workforce development through international collaborations between U.S. and Chilean institutions, hands-on training opportunities for students and early-career researchers, and outreach programs that engage audiences across career stages. These efforts will help prepare a skilled workforce equipped to excel across multiple career paths in STEM with the use of advanced data analysis using machine learning and hands-on experiences in metabarcoding and high-thoughtput analysis, thereby advancing the national interest in science, ecosystem management, and societal well-being. This project advances NSF’s priorities in Biotechnology and Artificial Intelligence The project will integrate long-term field data with complementary analytical approaches, including stable isotope analysis and dietary DNA metabarcoding, to quantify how small mammals utilize distinct “fast” (forb-based) and “slow” (shrub-based) energy channels across both short-term seasonal cycles and longer-term interannual climate variability associated with El Niño-Southern Oscillation (ENSO). By combining isotopic and genetic methods, the research will generate detailed, individual-level dietary profiles that reveal patterns of trophic niche partitioning, dietary specialization, and temporal shifts in resource use across multiple trophic levels. These fine-grained empirical data will be used to parameterize and test dynamic, process-based models that link resource pulses, consumer population dynamics, and foraging behavior to emergent patterns of food-web stability and temporal variability. The project will also incorporate advanced data science approaches, including machine learning techniques, to identify nonlinear relationships and improve predictive capacity in this highly variable system. Methodological innovations in biotechnology, such as high-throughput sequencing and isotope-enabled analytics, coupled with open-access data libraries that are required to support both taxonomic and functional interpretations, will provide unprecedented resolution of the structure of food webs. Associated training programs will include hands-on course-based undergraduate research experiences in dietary metabarcoding, stable isotopes, bioinformatics, and reproducible practices in data science. Together, these approaches will allow the team to disentangle the relative roles of abiotic forcing and biotic interactions in structuring ecological communities. The resulting models and datasets will provide a robust framework for predicting how ecosystems respond to increasing climatic variability and will contribute broadly to ecological theory, long-term ecological research, and the development of transferable tools for analyzing complex, data-rich environmental 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 · 2026-03

This project supports the acquisition and operation of a dedicated graphics processing unit (GPU)-based, large-memory, parallel high-performance supercomputing cluster, as a state-of-the-art resource for AI- and quantum-driven chemistry and materials research at the University of New Mexico (UNM). The computing resource will enable simulations algorithm development by faculty and researchers from chemistry, physics, and engineering, with shared affiliations through the Center for Computational Chemistry (C^3) and Center for Quantum Information and Control, an NSF Focused Research Hub in Theoretical Physics. The system will serve as a critical resource for advancing understanding of the complex quantum phenomena underlying the computational design of new chemicals, biomolecules, reactions, and materials, and the development of novel quantum computing algorithms for simulating complex many-body systems. The broader societal impact of the instrument will be achieved via its use for education and training of the next generation of interdisciplinary chemistry, chemical biology, and materials researchers through the NSF Research Traineeship (NRT) Quantum Photonics and Quantum Technology (QPAQT) graduate program, the Quantum Undergraduate Research Experience at the Center for High Technology Materials (QU-REACH) summer program, and new computationally-driven courses. The research enabled by the supercomputing cluster addresses the need to significantly deepen understanding of correlations and emergent quantum phenomena at both the electronic and interacting atomic length scales, using the tools and techniques of ‘classical’ molecular and materials modeling, intersecting with AI, machine learning (ML), and quantum information science (QIS) perspectives. The supported projects will span quantum chemistry applications to small molecules, molecular complexes, proteins, nucleic acids, and polymers; quantum dynamics; quantum information and entanglement; quantum computing; and quantum materials design. Concurrently, the instrument will foster development of significantly deeper circuit simulations incorporating new models of logical qubits for noise mitigation and quantum sensing; exploration of a new generation of many-body simulation methods for quantum computing on molecules and materials; and advancement of hybrid quantum-classical algorithms. The availability of this dedicated, high-performance computational resource serves as a focal point for encouraging new collaborations, new science, and algorithmic knowledge exchange to grow GPU-enabled AI/ML expertise within quantum-focused research. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2026 · 2026-01

The composition of rocky planets orbiting other stars (exoplanets) can differ according to silicon, magnesium, calcium, and aluminum content. These differences relative to Earth may impact their planetary evolution and habitability. In previous lab experiments on materials like those expected for rocky exoplanets, the project team found small changes in composition could suppress the cycling of elements necessary for life and cause a long-lived lava ocean. In this project, high pressure and temperature experiments are carried out on materials with a wider variety of element ratios to cover the potential spread of exoplanet properties. As a result, astronomers will be able to target stars whose planets are most likely to have Earth-like geochemical conditions. A graduate student and several undergraduate students will be trained in laboratory methods. Training in science communication for the project’s students will culminate in disseminating the project via public outreach events and an episode of “Strange New Worlds: A Science and Star Trek Podcast.” Petrologic experiments on hypothetical bulk silicate exoplanet compositions different from Earth or other solar system bodies will be performed to acquire fundamental data about how planet compositions influence long-term habitability, as well as planetary cooling and structure. This project consists of five tasks: (1) conduct initial set of experiments on four hypothetical exoplanet compositions to determine shallow mantle melting curves, melt compositions, and melting reactions; (2) use results from Task 1 to guide selection of compositions for a second set of experiments; (3) conduct a second set of experiments on four hypothetical exoplanet compositions selected in Task 2; (4) package data for incorporation into the geochemical modeling code exoMELTS; and (5) prepare a prescriptive filter of FGK-type stars that are likely to host geochemically habitable exoplanets on the Hypatia Catalog website. 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

Artificial Neural Networks (ANN) are increasingly being employed to monitor and control Cyber-Physical Systems (CPS), as in autonomous ground and aerial vehicles. With increasing complexity and safety criticality of these systems, formal-verification techniques that provide rigorous guarantees are urgently needed. The broad goal of the research is to develop novel algorithms and software tools for formal verification of ANN-controlled CPS (ANN-CPS). One of the main challenges of existing techniques is their scalability to large number of neurons and complex physical dynamics. Using the novel concept of Interval Neural Networks, coupled with ideas from formal methods such as counter-example guided abstraction refinement and approximate bisimulation, the project investigates scalable formal verification techniques for ANN-CPS. The results of the project will enable rigorous analysis of complex ANN-CPS possible, thereby enhancing their reliability in applications such as autonomous driving. Further, the PI is engaged in course development, mentorship of undergraduate and graduate students, and outreach activities for K-12 students, with the broader aim of motivating and building the workforce for formal analysis of cyber-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 2025 · 2025-11

Water shortages occur in many places in the world due to increasing water demand and changing water availability. Navigating changes in water resources, therefore, requires a global workforce with the intellectual depth and breadth of expertise to respond to water-related challenges. The Partnerships Along the Headwaters of the Americas for Young Scientists Program (Pathways Program) develops such a workforce with training to work in interdisciplinary, international, and interorganizational (I3) settings throughout the Americas. Specifically, the Pathways Program provides educational and research experiences at research sites in the Andean Mountain Range (Ecuador, Peru, Chile, and Argentina). Within the I3 context, the Pathways Program provides U.S. students with experiences in problem-based learning and teamwork-oriented collaboration, equipping them first to cross disciplinary borders, then organizational and international borders. Students acquire the skills necessary to excel in a wide range of water-related careers. Shifting climate patterns, changes in land use, escalating water demand, and rigid water policies have amplified the risk of enduring ecological damage and political conflict in river basins worldwide. Addressing and resolving water conflicts requires a robust understanding of various disciplines within varied interdisciplinary contexts. For instance, students investigating the human dimensions of water resources often require a solid understanding of the hydrological landscape and the physical barriers and opportunities that may influence water policy and norms. Conversely, hydrologists and ecologists are more likely to produce and communicate actionable science if they can interpret the social and political factors exacerbating water management issues. The Pathways Program facilitates collaborations between U.S. students and international researchers, who will provide research expertise, regional knowledge, and access to data and field sites. This will enable the exploration of water resource system connections across the Americas and provide opportunities for research collaborations and the development of intellectual capacity through educational activities. Objectives of the Pathways Program are to: (1) Build, deliver, and disseminate a novel model for graduate-level international research experiences including research-related professional development; (2) Use and expand an international research network to advance fundamental knowledge of socio-ecological systems; and (3) Enhance competency in I3 with community engagement to evaluate socio-ecological patterns and climate adaptation. Student research results improve society's ability to build adaptive capacity to changes in climate by expanding an interdisciplinary network of researchers. The training plan prepares U.S. students for I3 research to develop a set of elite water resources professionals, with scientific and cultural sophistication to work effectively in international settings. Through the multi-disciplinary Pathways program and its focus on internationalizing research and professional development this project grows international research capacity for U.S. graduate students and early career faculty. 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 · 2025-10

Project Summary/Abstract American Indian and Alaska Native (AIAN) communities experience significant alcohol-related problems, further propagated by obstacles to beginning effective, high-quality treatment for alcohol use disorder (AUD). Precision medicine approaches to AUD treatment may make treatment more efficient and effective by individually tailoring AUD treatments to common etiological and maintenance mechanisms underlying AUD. For example, recent precision medicine efforts have identified that individuals who drink primarily in the context of reward (i.e., drinking for enhancement and social interaction) may respond better to naltrexone, whereas those who drink primarily in the context of relief (i.e., drinking to relieve negative physical and affective states) may respond better to acamprosate. However, extant measures used to classify reward and relief drinking tendencies do not account for the contextual factors associated with these constructs in AIAN peoples. Identifying relevant measures of reward and relief drinking tendencies in AIAN peoples may inform precision medicine research and reduce burden related to alcohol problems in AIAN. The proposed work will utilize a multi-method approach grounded in a community-engaged research framework to develop a tailored measure of reward and relief drinking by refining and expanding on several existing measures of reward and relief drinking and assess the construct validity of the newly developed measure in AIAN peoples. First, we will recruit AIAN people with AUD (N=20) to examine the content validity of three existing measures of reward and relief drinking and use cognitive interviewing methods to refine existing items and/or develop additional relevant items. Second, we will administer these items and any participant-identified additional items to a geographically varied sample (N=100) of AIAN with AUD. Using these data, we will then examine structural validity by identifying the hierarchical contributions of individual items to respective reward and relief drinking factors by conducting nonparametric item response theory (IRT) analyses. Lastly, we will utilize the refined measure to identify reward and relief drinking subgroups and examine comparative associations with convergent constructs to assess external validity. Ultimately, the proposed research will produce a construct-valid measure of reward and relief drinking informed by the lived experiences of AIAN peoples with AUD. The tailoring of an AIAN-specific measure of reward and relief drinking will play a direct role in improving alcohol problems in this population.

NSF Awards · FY 2025 · 2025-10

The theory of C*-algebras, which originated in the 1930s in the study of quantum mechanics, is now a vital part of modern mathematical analysis, with applications across the mathematical sciences. C*-algebras arise naturally in connection with a variety of mathematical objects of interest, including groups, dynamical systems, and discrete graphs. This project concerns the structure and properties of C*-algebras associated to quantum graphs. A relatively recent generalization of the classical notion of a discrete graph, quantum graphs have proven to be useful in quantum information theory: just as classical discrete graphs encode confusion due to noise in a classical communication channel, quantum graphs encode confusion due to noise in a quantum channel. The project will generate new methods for analyzing the structure of quantum Cuntz-Krieger algebras and their underlying quantum graphs, and explore their interplay with quantum information theory, a topic of growing global interest. Educational opportunities for undergraduates will be provided through research projects, and a new, interdisciplinary certification program in introductory quantum information theory at the PI’s home institution. Student researchers and visiting speakers will be recruited with a focus on diversity and representation. Given a simple discrete graph, the Cuntz–Krieger algebra for the graph is a universal C*-algebra which encodes the graph’s edge relations. The Kubo-Martin-Schwinger (KMS) states on a C*-algebra can be physically interpreted as states of thermal equilibrium for a quantum system. The KMS states on the Cuntz–Krieger algebra of a simple discrete graph were classified by Exel in 2003 using an isomorphism between the Cuntz–Krieger algebra and the graph’s Exel crossed product, which is a universal C*-algebra that encodes natural dynamics on the graph’s infinite path space. For a quantum graph, an analogue of its Cuntz–Krieger algebra, called a quantum Cuntz–Krieger algebra, was defined in 2021. The principal investigator and her collaborators have since constructed Exel crossed products for some classes of quantum graphs and shown these Exel crossed products to be isomorphic to a quotient of the corresponding quantum Cuntz–Krieger algebras. The first major objective of this project is to design a canonical construction of an Exel crossed product for an arbitrary quantum graph and study its relationship to the corresponding quantum Cuntz–Krieger algebra. The second major objective of this project is to classify the KMS states on the Exel crossed product for a quantum graph and, following Exel’s techniques in the classical setting, use this relationship established in the first objective to classify the KMS states on the associated quantum Cuntz–Krieger algebra. 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

From designing processes to writing reports and modeling complex systems, Artificial Intelligence (AI), especially generative AI (GenAI), is transforming how engineering work is done, yet engineering students are often unprepared for this new reality. Additionally, because much of the technology used in industry is proprietary, protected by intellectual property laws, or cost-prohibitive, educators often lack access to up-to-date information about how AI is actually being used in the field. This limits their ability to teach students the ethical, technical, and professional skills they need to succeed in AI-integrated workplaces. This project will respond directly to this challenge by creating new ways for faculty and students in chemical engineering to learn how AI is used in the workforce and how to use it responsibly. The project supports NSF’s goals to strengthen the U.S. STEM workforce, promote economic competitiveness, and ensure that emerging technologies like AI benefit all Americans. By creating tools that help bridge the gap between industry practices and classroom instruction, this project will help prepare students for high-tech careers while reinforcing responsible innovation and excellence in engineering education. This project will investigate how GenAI is used across different levels of chemical engineering practice and explore how students, instructors, and professionals perceive and interact with GenAI tools. The research team will conduct interviews and focus groups with chemical engineering professionals, faculty, and students to understand their beliefs and practices related to GenAI. Using discourse analysis and qualitative analysis, the team will examine how individuals display agency over, share agency with, attribute agency to, or offload agency onto GenAI tools. These findings will inform the development of a novel survey instrument designed to help faculty stay up to date with evolving professional GenAI practices while considering workplace privacy and proprietary constraints. The results will support timely curricular updates, enabling faculty to embed relevant and ethical GenAI instruction into engineering education. The project will generate guidance for curriculum developers, offer insights into workforce readiness, and advance research on human-AI agency. The research will be conducted by universities in states that serve a high proportion of first-generation and veteran students, expanding educational opportunities for those students and aligning with NSF’s commitment to broadening participation without exclusion. Ultimately, this work will lay the foundation for a scalable, research-based approach to keeping engineering education aligned with the fast-evolving landscape of AI-integrated professional practice. 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) award will reveal how biophysical signals at multiple length scales can be used to engineer personalized heart tissues. These engineered tissues could replace damaged heart muscle in adults. Heart attack causes permanent loss of heart muscle and can lead to heart failure. Current treatments cannot regenerate damaged heart muscle. Induced pluripotent stem cells, which come from a person’s own cells, can become any cell type. As such, these cells offer new hope for personalized engineered heart tissue regeneration. However, heart tissues made from these cells to date lack the functionality of adult tissues. This is because heart muscle cells generated from stem cells are immature in shape, size, and function. There is a critical need to improve heart muscle cell maturity for engineering adult heart tissue. Drawing inspiration from the heart’s multiscale structure, this research will explore how three-dimensional biophysical signals can be used to improve heart muscle cell maturity and how this knowledge can be leveraged to manufacture personalized adult heart tissues. The complementary education program will use this research context to promote equity in engineering education through curriculum innovation. This work will also broaden access to engineering for those who have been historically excluded through scalable K-12 programs. The goal of this research program is to develop fundamental mechanistic understanding of how multiscale biophysical cues can be used to influence the structural and functional maturity of engineered heart tissues. Using a novel multiscale platform that permits orthogonal control of stiffness, viscoelasticity, and three-dimensional geometric confinement, this research will determine how these microscale and mesoscale cues influence cardiomyocyte maturation defined by cellular ultrastructure, contractile force, and conduction velocity. Through bioinformatic analyses, this work will uncover mechanisms by which these cues have influence individually and collectively. Additionally, this work will determine if biophysically mediated maturation introduced in unit biomanufacturing processes persists in the hierarchical formation of large-scale engineered heart tissues. This inclusively designed research will identify differential effects of biophysical stimuli associated with cell sex in engineering adult heart tissues. The integrated education program will create systems to advance equity in engineering education and broaden participation of historically excluded racial and ethnic groups. This award will present a major step toward transforming mechanobiological insights into biomanufacturing processes enabling the creation of personalized, fully functional engineered tissues and to measurably impact equity, diversity, and inclusion in engineering with new faculty-driven change models. 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

Water scarcity is one of the greatest environmental challenges facing the western United States, where prolonged drought, warming temperatures, and reduced snowpack are straining rivers that support both people and ecosystems. The Rio Grande, a lifeline for millions of people and home to endangered fish and forests, is experiencing especially severe stress. Water demands for agriculture, urban areas, and interstate water agreements often leave little for the river itself, resulting in dry riverbeds and degraded habitats. This project brings together scientists, conservation groups, and water managers to answer a critical question: how can we sustain river ecosystems with much less water? This will be accomplished by testing when and how to release limited water to benefit species and ecosystems while meeting human needs. The project will help chart a path toward a more resilient future for rivers in dry regions. The project also provides education and hands-on experiences for students from middle school to graduate researchers, ensuring the next generation of scientists and citizens is engaged in solving real-world environmental problems. The Rio Grande in New Mexico is one of the most heavily managed rivers in the western US. This project will study how a 280-kilometer stretch of this region is affected by different water flow patterns, especially those designed to mimic natural spring floods, affect fish, plants, soil, and carbon storage in the river and its floodplain. Experiments will include field monitoring, laboratory analyses, and outdoor test systems called mesocosms that simulate river-floodplain interactions under different conditions. Scientists will track the survival of cottonwood seedlings, the development of aquatic food webs, and how organic carbon moves and is stored across the landscape. These findings will be used to improve a water management model called the Rio Grande Futures Model, which helps predict the ecological outcomes of various water delivery strategies. This model will be tested with water managers to explore trade-offs and design environmental flow strategies that balance ecological benefits with agricultural and municipal water demands. Together, these efforts will help restore critical habitat, support endangered species, and ensure the long-term health of a vital river system in a time of growing water scarcity. This project is jointly funded by the Divisions of Environmental Biology and Integrative Organismal Systems through the Partnership to Advance Conservation Science and Practice 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

Virtual Private Networks (VPNs) are one of the most fundamental security and privacy tools that have found their way into regular Internet users' toolboxes. Despite widespread reliance on these tools, the mobile VPN ecosystem is rife with VPNs, posing several privacy and security issues. There is no visibility into the extent of the problems in mobile VPN apps beyond a few isolated examples based on manual reverse engineering efforts. The project’s novelties are new tools and methods to uncover hidden risks in mobile VPNs, which many people use to stay private online. The project’s broader significance and importance are in bringing together researchers, advocates, and VPN providers to better understand the risks and strengthen the safety and privacy of VPN services for everyone. This effort advances technical solutions needed for assessing and mitigating the risks associated with the mobile VPN ecosystem. The current Reverse engineering tools are less developed for mobile environments, and their effectiveness is further limited by the complexity of the mobile VPN ecosystem. This project facilitates systematic investigation into the mobile VPN ecosystem by understanding current practices and designing and building novel frameworks capable of conducting large-scale analysis. The project also uses this framework to explore and evaluate different components of the mobile VPN ecosystem, such as free VPNs. Finally, the project also focuses on advancing the understanding of detection and interference attacks on mobile VPNs. 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

With support from the Improving Undergraduate STEM Education: Hispanic-Serving Institutions (HSI Program), this Implementation and Evaluation Project aims to improve how students learn and stay engaged in mechanical engineering courses at the University of New Mexico. The project introduces the idea of organizational citizenship behavior into the classroom, meaning that it encourages students to go beyond just doing their own work to help each other, much like good teammates in a workplace. Engineering students often gain strong technical skills, but they perform even better when they feel welcome and supported by their classmates. This project will create learning environments where students are encouraged to work together, share ideas, and support one another. Such interactions can improve motivation, build confidence, and strengthen students’ connection to engineering. This project will design activities that promote collaboration and peer support. Through structured learning activities, the project will demonstrate how cooperative behaviors, such as sharing ideas, offering encouragement, or assisting classmates, can lead to improved student learning, greater confidence, and a stronger connection to the engineering field. The expected outcomes of this project are to help all students succeed by creating supportive, collaborative classrooms and developing teaching and learning strategies that can be shared widely to prepare students for their engineering careers. This project will (1) introduce the concept of organizational citizenship behavior (OCB) into undergraduate engineering education, (2) implement structured pedagogical interventions that encourage prosocial and voluntary student behaviors in the classroom, and (3) rigorously assess the impact of these interventions on key student outcomes. The project will employ a mixed-methods approach. This includes surveys, interviews, classroom observations, and analysis of academic performance data. The research team will examine both short-term and long-term outcomes of OCB-based instructional practices on student engagement and success. Expected results include improved student academic performance, more supportive behaviors, and a stronger sense of belonging and engineering identity. Resources and tools developed, along with key findings, will be shared through an open-access platform and academic dissemination efforts, so that other colleges and universities can adopt them to improve their engineering programs. This project is funded by the HSI Program, which aims to enhance undergraduate STEM education and increase capacity to engage in the development and implementation of innovations to improve STEM teaching and learning at HSIs. 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 aims to serve the national interest by implementing evidence-based teaching practices in biology. Recognizing the ongoing need to advance the quality of undergraduate biology education, the research team plans to develop training for administrators, STEM faculty, and staff (Scholars). Through their involvement, the Scholars will, in turn, prepare graduate and undergraduate teaching assistants at their institutions. The initiative will be designed to promote the adaptation and application of purposeful, validated teaching practices by graduate teaching assistants and undergraduate learning assistants through participation in teacher professional development (Teaching Assistant – Teaching Professional Development, TA-TPD). A set of six regional workshops are planned, extending the team's prior work supported by the NSF Research Coordination Networks for Undergraduate Education initiative. A change theory for teaching guides this undertaking. Research and evaluation activities will investigate the effectiveness of the workshops and the connection of project activities and elements with the anticipated outcomes of the project. There are three interconnected objectives: (1) Develop regional workshops that will each host Scholar teams from at least ten unique institutions, reaching over 50 institutions over four years, while minimizing travel costs; (2) Engage each Scholar team in a contextually relevant reflective process to develop or refine existing TA-TPD activities at their institutions; (3) Evaluate the efficacy of workshops and TA-TPD using a mixed-methods research design for formative and summative purposes and the generation of new knowledge. Drawing on teaching change theory in parallel with the evaluation, the research team will conduct a mixed-methods research effort guided by the following research question: What are the impacts of the program on Scholars, their teams, and their institutions? What are the results for TA who participate in TPD activities? A comprehensive set of data will be collected through pre-post surveys, focus groups, interviews, and document analysis using quantitative and qualitative approaches and frameworks that are grounded in empirical data and the change theory (including contextual factors, personal factors, teacher thinking and beliefs, and instructional practices). The NSF IUSE: EHR Program supports research and development projects to enhance the effectiveness of STEM education for all students. Through the Institutional and Community Transformation track, the program supports efforts to evolve and improve STEM education across institutions of higher education and disciplinary communities. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2025 · 2025-10

With support from the Improving Undergraduate STEM Education: Hispanic-Serving Institutions (HSI Program), this Institutional Transformation project will scale and sustain a number of student and faculty programs at the University of New Mexico (UNM). Early undergraduate STEM courses can be challenging for both students and educators, and as such can become a barrier to students looking to pursue a degree in STEM. Universities often offer academic support for students and professional development centered on teaching for faculty. However, it can be challenging to build awareness of these opportunities, encourage participation, and grow and sustain promising efforts. This project will scale successful pilot programs for UNM students and faculty across STEM programs and build bridges from the classroom to support resources. Faculty will be introduced to tools and interventions that will allow them to creatively support student engagement and success. The project will benefit all students in the impacted classes and programs, with a particular focus on improving STEM degree completion for lower income students. This project integrates multiple evidence-based interventions into an institutional strategy to improve STEM degree persistence and completion for all UNM students. The project will design degree pathways that engage students and include curricular safety nets to improve completion rates. Curricular transformation will enable immediate access to problem-solving in a student's area of STEM interest and reduce math pre-requisite barriers. Faculty professional development will encourage evidence-based practices and allow educators to bring university resources, such as peer tutoring and wellness programming, directly to their classes. In addition to scaling and institutionalizing effective practices, the project will research how an improved student experience and contributes to an improved faculty experience. Results will be disseminated through conference presentations, annual reports, and scholarly publications. This project is funded by the HSI Program, which aims to enhance undergraduate STEM education and increase capacity to engage in the development and implementation of innovations to improve STEM teaching and learning at HSIs. 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

Any instances of “women” have been changed to “females”. Information regarding gender discrepancies has been removed. Research has consistently demonstrated that alcohol use is a particularly salient risk factor for sexual assault among college females. Additionally, recent work on alcohol and cannabis co-use, particularly simultaneous co-use (i.e., use on the same occasion so effects are likely to overlap), has indicated that alcohol and cannabis co-use is associated with increased substance use-related negative consequences, including sexual assault victimization and revictimization among college females. However, limited empirical attention has been directed toward integrated alcohol and cannabis co-use and sexual assault protective behavioral strategies (PBS). This F31 application addresses an important gap in the literature in investigating PBS for substance use (alcohol and cannabis co-use) and sexual assault in a deliberately integrated fashion, in that it focuses explicitly on substance use-specific behavior attenuation that may be most useful for college females in mitigating risk for sexual assault. The long-term goal of the proposed research is to inform clinical assessment and intervention research focused on substance-use facilitated sexual assault prevention among young females. This three-phase study will provide the data necessary to develop and refine a novel alcohol and cannabis co-use PBS measure that is specific to the prevention of sexual assault among college females. Specific Aim 1 involves community-based participatory research (CBPR) to gather in-depth qualitative data on how college females use alcohol and cannabis PBS in the context of sexual assault prevention. Specific Aim 2 comprises the pilot testing of initially developed scale items elucidated from Aim 1 to finalize a new measure of co-use-related sexual assault PBS. Lastly, Specific Aim 3 will assess scale performance (i.e., reliability, validity) through investigating the scale’s associations with substance use and sexual assault victimization. This proposed study directly corresponds with NIAAA’s most recent strategic plan for fiscal years 2024-2028 in that its overarching aim is to better understand the effects of alcohol use and misuse among females (i.e., within the context of sexual assault) while informing the development of a tailored PBS measure to address their needs in research and clinical contexts. The proposed award is also consistent with NIAAA’s NOSI (NOT-OD-24-079) on health issues that affect young females, including the etiology, prevention, and treatment of alcohol misuse.

NIH Research Projects · FY 2025 · 2025-09

Risky alcohol use is widespread among United States military service members and veterans with over half of veterans reporting past-month alcohol use and an estimated 1.7 million veterans endorsing criteria for current alcohol use disorder (AUD). Veterans also experience complex biopsychosocial complications including co-occurring mental health diagnoses such as post-traumatic stress disorder (PTSD), depression, chronic pain, and diabetes which all complicate AUD treatment and create added risks for alcohol use compared to veterans without AUD. Further, the majority of veterans who acknowledge a problem with their alcohol use do not seek formal treatment, largely because they do not want to abstain from alcohol. Providing accessible treatment options that address whole-person recovery from complex biopsychosocial conditions and that do not require abstinence are critical for veterans. These needs are highlighted by recent strategic plans and notice of special interests posted by the National Institute on Alcohol Abuse and Alcoholism that call for increased studies of interventions that target whole-person outcomes, especially among military and veteran populations. One potential way to address these needs and calls for funding priorities is to leverage existing interventions and technology to reach all veteran communities nationwide, including health professional shortage areas. The goal of the present study is to enhance an existing mobile app called VetChange that currently targets alcohol use reduction and abstinence goals and PTSD symptom improvement. The current version of VetChange is based on empirically-supported treatments and previous iterations of the intervention have demonstrated preliminary efficacy. The enhancements proposed may extend the reach of VetChange to veterans who want to decrease alcohol-related harms, not solely decrease their alcohol use alone. Further, User Experience methodology has not yet been performed with VetChange and is a critical next step towards app development and refinement. In the second phase of the proposed study, a randomized clinical trial will compare the enhanced VetChange mobile app to a control condition app. The ultimate goal of the present study is to capitalize on existing resources to further the field’s offering of accessible and effective treatments to US veterans that require accessible and flexible treatment options. If effective, this project has the potential to reach a broader population of veterans who engage in harmful alcohol use, compared to both in-person interventions and existing apps that focus on abstinence or drinking reductions, in a way that is culturally appropriate to veterans and their complex biopsychosocial, whole-person needs.

NSF Awards · FY 2025 · 2025-09

Nontechnical description Whiteness in conventional paint coatings arises from broadband light scattering across the visible spectrum. However, because light scattering alone is not highly efficient at reflecting light, such coatings are typically thick and often require multiple applications. This project aims to reduce the required coating thickness by approximately a factor of 100 through a fundamental study of optical transport in submicron-thick nanostructured films. In these films, optical transport results from a combination of light scattering and interference, which together dramatically enhance reflectivity beyond what scattering alone can achieve, while also suppressing the iridescent colors typically produced by interference. The proposed submicron-thick bright white coatings will be realized by optimizing the balance between structural order and disorder in the nanostructures, leveraging the interplay between scattering and interference. In addition to advancing knowledge in optical transport, this project will offer important societal benefits through educational components such as visualizations of structural ordering and by significantly reducing the use of white titania pigments—materials that are strategically important to the national economy due to their widespread use and reliance on imported mineral feedstocks. Technical description This project aims to achieve unprecedented submicron-thick bright white coatings through a fundamental study of optical transport in ultra-thin films featuring controlled disorder in nanorod-based photonic structures. In this intermediate regime of order and disorder, both multiple scattering and optical interference govern light propagation. Gaining a deep understanding of how light interacts with such structures in thin films is essential for developing ultra-thin, highly reflective white coatings. To achieve this goal, the research will establish a theoretical framework tailored to these nanostructured systems, perform numerical simulations, and conduct experiments using colloidal fabrication techniques that enable controlled disorder in the position, diameter, and direction of nanorods. The theoretical framework will generate knowledge about (i) how various types of disorder influence the distribution of photonic states across the spectrum, and (ii) how reflectance correlates with film thickness across different regimes of optical transport. Numerical simulations will serve to validate the theoretical predictions. With solid understanding of disorder effects established, the fabrication process will be optimized to achieve unprecedented reflectance throughout the visible spectrum in submicron-thick coatings. Position disorder will be controlled by manipulating the eccentricity of polymer coatings on the nanorods and removing them after self-assembly; diameter disorder by mixing nanorods of different sizes; and direction disorder by tuning magnetic fields applied to the magnetically sensitized nanorods during layer-by-layer assembly. The resulting advances in understanding optical transport and developing fabrication strategies are expected to enable next-generation ultra-thin white coatings with substantial industrial impact. 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.