University Of Washington

NSF Awards · FY 2026 · 2026-09

Many-body quantum systems exhibit entanglement patterns that make tasks such as predicting outcomes, certifying device behavior, or succinctly describing the ground state/space computationally prohibitive. This project will develop a theory of computational complexity that explains when physically motivated quantum states (especially ground and low-energy states of local Hamiltonians) admit efficient description and verification, and when they provably do not. These insights are particularly timely due to the growing capabilities of near-term quantum devices, whose behavior increasingly probes regimes of entanglement that are classically intractable. This project will also strengthen quantum-information training by developing graduate-level curriculum in quantum complexity theory and by creating mentored research pathways for undergraduate and graduate students, with the goal of broadening participation in theoretical quantum computing. Technically, this project formulates and proves lower bounds on the description complexity of many-body states arising from local Hamiltonians, focusing on the resources required to (i) represent these states succinctly, (ii) verify or test their properties from local measurements or interactive procedures, and (iii) perform state transformations such as cloning. The particular focus on the description complexity of ground states of local Hamiltonians yields a lens that connects the peculiar nature of entanglement with established tools from theoretical computer science, such as query complexity lower-bounds, classical and quantum error-correction, and boolean function analysis. Expected outcomes include improving our understanding of core quantum complexity classes (e.g., QMA vs QCMA), bettering our understanding of quantum query complexity and state transformations, and making progress towards the quantum PCP conjecture, the major open problem in quantum complexity theory. In addition, these contributions are expected to influence multiple subfields: in theoretical physics, by clarifying how entanglement behaves in realistic models; in quantum cryptography, by enabling constructions like public-key quantum money and unclonable schemes from first principles; and in engineering, by identifying principled computational obstructions to simulation and verification in near-term devices. 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 · 2026-09

Large language models (LLM) are increasingly used by the public to seek health information, but current LLM-based systems can still generate inaccurate information due to the well-known problem of LLM hallucinations, while expressing it with high confidence. The issue of confidently representing erroneous data creates risks in high-stakes settings. This project addresses that problem by developing artificial intelligence methods that reduce hallucinations and improve the reliability, transparency, uncertainty estimation, and information-seeking behavior of large language models. The project focuses on women’s health as an application area because it provides a testbed for a broad range of conditions, including breast cancer, osteoporosis, cardiovascular disease, autoimmune disorders, and mental health. By improving the ability of language models to reduce hallucinations, communicate uncertainty, and ask clarifications questions, the project aims to accelerate the adoption of AI technologies in high-risk domains that require stable LLM behavior such as the medical domain and law, among many others. This project develops new multilingual natural language processing methods for language models operating in high-stakes environments. First, it will create methods to curate and structure evidence from heterogeneous sources into an evidence-aligned, reliability-scored knowledge repository in English, Spanish, and French, together with dynamic benchmarks that test reasoning, attribution, abstention, and clarification under evolving conditions. Second, it will develop new model training and inference methods for long-form non-hallucinating generation, fine-grained attribution, calibrated uncertainty estimation, abstention when confidence is low, and proactive follow-up questioning when user queries are ambiguous or incomplete. Third, it will establish a staged validation framework for deployment in health applications, including retrospective evaluation, expert review, user pilot studies, and continuous monitoring. The resulting methods, datasets, benchmarks, and evaluation protocols will advance the science of stable behavior of language modeling and support safe deployment of language models in health and other high-stakes domains. 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 · 2026-09

Quantum computers will fundamentally change the landscape of cryptography. On the one hand, they challenge the security of widely deployed encryption schemes by efficiently solving problems such as factoring and discrete logarithms. On the other hand, the ability to process and exchange quantum information opens the possibility of realizing cryptographic functionalities that are entirely unattainable with classical capabilities alone. In recent years, the field has entered a new phase focused on understanding how quantum information and computational hardness can be combined to unlock these new capabilities. Motivated by this shift, the overarching goal of this project is to investigate how uniquely quantum phenomena, such as the no-cloning principle and entanglement, interact with computational hardness to enable new forms of security and verification. These investigations will strengthen our understanding of quantum information as a resource in cryptography and in computation more broadly, and may reveal new connections with complexity theory and algorithms. In parallel, the investigator will develop coursework in quantum computation and cryptography, mentor undergraduate and graduate researchers, and organize outreach activities that introduce high school students to quantum computing. This project will pursue three main directions. First, the investigator will design novel protocols to realize cryptographic functionalities that are unattainable classically, including copy-protection of software, and stronger forms of encryption with unclonable ciphertexts. Second, the investigator will study inherently quantum computational problems that can serve as minimal building blocks for cryptography, with the goal of characterizing the computational assumptions necessary and sufficient for quantum cryptography. Third, the investigator will develop simple and efficiently verifiable protocols for demonstrating quantum advantage using cryptographic techniques, with the goal of reducing the complexity of existing protocols and enabling implementations on near-term devices. 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 · 2026-09

NON-TECHNICAL SUMMARY Plastic waste is a growing environmental and societal challenge, with millions of tons accumulating in landfills and oceans each year. These plastics persist for centuries, harming ecosystems and public health. This research addresses this problem by creating sustainable plastics that can naturally decompose, using plant-based materials called cellulose nanofibers. These nanoscale fibers are abundant, break down safely, and derived from the non-edible parts of plants, making them a promising alternative to current commercial plastics. However, due to limited methods to modify plant nanofibers at scale, plastics made from these materials have constrained properties and functionality, preventing their widespread use. This research tackles these limitations combining eco-friendly chemistry with artificial intelligence and machine learning (AI/ML) to develop plant nanofiber-based plastics with “programmable” properties, meaning their resistance to breakage, water barrier abilities, and adhesiveness to other surfaces, etc., can be customized for applications in electronics, packaging, and biomedical devices. The approach uses water-based chemical processes instead of harmful solvents, while AI/ML accelerates innovation by predicting how chemical changes affect material performance. This research supports national interests by reducing plastic pollution, advancing U.S. leadership in sustainable manufacturing building on domestic feedstocks, and promoting biotechnology for a circular bioeconomy. Beyond environmental benefits, it invests in education and workforce development. Students will gain hands-on experience in sustainable materials and data-driven design in relevant courses, participate in industry internships, and create short educational videos for platforms like TikTok and YouTube to engage K–12 learners and the public. These efforts will enhance STEM education and prepare future leaders in biotechnology and advanced materials. TECHNICAL SUMMARY Global plastic production exceeds 400 million tons annually, with less than 10% recycled, creating severe environmental challenges. Cellulose nanofibers (CNFs), derived from non-edible plant residues, are abundant, biodegradable, and mechanically robust, making them promising candidates for sustainable plastics. However, their adoption is constrained by limited surface chemistry and the absence of scalable functionalization strategies, limiting performance tuning for diverse applications. This research develops a scalable, aqueous phase “click” chemistry platform for modular CNFs functionalization, enabling bioplastics with programmable properties. The research integrates green chemistry, polymer engineering, and machine learning through three objectives: (1) Establish catalyst-free reaction in water for selective attachment of diverse functional groups to CNFs, (2) Fabricate and characterize CNF-based bioplastics with tunable mechanical, interfacial, and physical properties, mapping structure–property relationships. (3) Implement an invertible machine learning (AI/ML) framework for bidirectional design—predicting material properties from functionalization parameters and generating functionalization “codecs” for target performance. The experimental framework includes systematic variation of functional group type and density, combined with advanced characterization to build a comprehensive dataset for predictive modeling. Educational components include a research-integrated undergraduate course, student internships, and datasets generated by student projects that feed into the machine learning model, creating a closed-loop system linking education and research. The results of this research are aimed at establishing fundamental design rules for property-programmable biotechnology-based polymers and enabling multifunctional bioplastics for advanced applications such as flexible electronics, soft robotics, and smart packaging. These outcomes are expected to advance predictive materials engineering, biotechnology and sustainable 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 · 2026-06

Inspection of confined spaces, such as aircraft wings and ship compartments, is essential for maintaining safety and reliability in critical infrastructure. However, these environments are difficult and hazardous for human workers, and existing robotic methods often struggle to operate efficiently in tight, complex spaces filled with structural constraints. Current exploration strategies tend to generate motion patterns that work well in open areas but are poorly suited for confined environments with limited maneuverability and restricted communication. The research funded by this award seeks to develop new coordination strategies that enable teams of robots to explore and inspect constrained spaces more effectively and safely. By improving inspection efficiency and reducing the need for human entry into confined structures, this research supports US competitiveness in the aerospace and ship manufacturing sectors, enhances workers safety and lowers maintenance cost of industrial and civil infrastructure. The project will also advance workforce development by providing specialized research training and education to undergraduate and graduate students in decentralized control of networked systems and multi-robot systems. Building on prior advances in robotic exploration, the goal of this research is to develop a new mathematical framework for coordinating multiple robots in topologically constrained environments. The central technical contribution is the design of time-discounted ergodic controllers on spatially discretized region graphs, enabling efficient exploration in confined spaces where continuous-space methods are difficult to apply. The research addresses the following challenges: (i) developing decentralized methods for estimating and updating time-varying target distributions over graphs using shareable information metrics, without requiring transmission of all the collected data; and (ii) accounting for agent heterogeneity by incorporating differences in mobility, sensing, size and accessibility through agent-specific graph structures that may restrict access to specific regions. Collectively, these developments advance the theory of ergodic control for multi-agent systems and provide scalable graph-based approaches applicable to inspection, cleaning, data collection for learning, target localization and surveillance, and other coordinated multi-robot tasks in constrained environments. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NIH Research Projects · FY 2026 · 2026-06

Project Summary Genital herpes (GH) is caused by herpes simplex virus (HSV) infection of epithelial cells, which leads to permanent infection of neurons in anatomically connected ganglia and then periodic recurrences with genital lesions, pain, live virus shedding, and potential transmission. Currently, GH is incurable. Responding to RFA- AI-24-068 “New Therapeutic Strategies for Genital Herpes”, we propose the optimization and advanced pre- clinical development of an immunotherapeutic to boost the levels and localization of HSV-specific T cells. This could lead to a functional cure. T cells are mobile cells the express hypervariable, specific surface T cell receptor (TCR) molecules, which, like antibody molecules, specifically bind short regions of HSV. We target T cell boosting for several reasons: T cell immune deficiency pre-disposes to severe GH, T cell candidates can be effective in animal models, and specific T cells can be programmed to traffic to infected sites and to persist at sites of HSV-2 epithelial lytic infection and ganglionic latency. Importantly, antibody-targeting GH immunotherapeutics have had low or no efficacy in clinical trials. T cells occur in two major types expressing the CD4 or CD8 surface co-receptor. The prior antibody-targeting GH products have elicited/boosted CD4 Th1 T cells. No data have been reported for a GH immunotherapeutic from a clinical trial using a format suitable for boosting CD8 T cells. In the 2025 competitive landscape, trials of the Moderna mRNA candidate that is somewhat CD8-targeting is underway. Based on our in-house data obtained during NIAID Large Scale Epitope Discovery contracts, we believe that we can improve on CD8 T cell human population coverage and HSV-1 cross-reactivity compared to this candidate. NIH and WHO preferred product profiles (PPP) prioritize covering HSV-2 and HSV-1. We have therefore defined several hundred CD8 T cell epitopes in HLA-diverse and therefore race/ethnicity-diverse persons and have designed a prototype poly-epitope “string of pearls” HSV-2 immunotherapeutic and performed initial in vitro/in vivo tests. Preliminary data are positive for human population coverage, HSV-1 cross-reactivity, and in vivo antigenicity. In addition to boosting CD8 T cells, a GH immunotherapeutic GH should boost specific CD4 T cells. For this, we plan to use a mutated version of HSV-2 gD2 unable to perform a putative immune evasion function. gD2 has shown safety and immunogenicity in human trials and has been a component of prior candidates with some clinical activity. The R21 Aims are to optimize a polyepitope CD8 candidate to cover diverse HSV strains and human populations while avoiding immunodominance, self cross-reactivity, and potential cytotoxicity pitfalls, and to test prototypes in vivo in an immune memory context to rank nucleic avid formats including novel mucosal homing strategies. Milestones for the R21 → R33 transition are relevant to these Aims are provided. The R33 Aims encompass more advanced preclinical development in the realms of further optimization in the murine model, and efficacy in the guinea pig GH therapy model with support from the NIAID Pre-Clinical Services (PCS) program.

NIH Research Projects · FY 2026 · 2026-06

Abstract Brachial plexus and other peripheral nerve injuries often lead to severe functional impairments, with impacts on one's independence and quality of life. Nerve reconstruction surgeries offer the best chance for restoring motor function, however, return of muscle strength occurs slowly over the course of 1-2 years. Rehabilitation and hand/physical therapy is critical for recovery, but many patients lose motivation and engagement over this long period of time. We previously developed a wearable surface electromyography (EMG) device, which detects electrical signals from underlying muscle activation to provide biofeedback of muscle function, and integrated this with a mobile app-based game. In this project, we propose a two-center, randomized, interventional pilot study to examine the use of surface EMG-driven therapeutic gaming in individuals with brachial plexus and upper extremity peripheral nerve injuries who have undergone nerve reconstruction surgery. We seek to describe differences between receiving standard-of-care hand therapy versus the addition of surface EMG- driven therapeutic gaming, as measured by 1) improvements in muscle strength and 2) changes in patient reported outcome measures over a six-month period. The results of our study will generate the necessary preliminary data and provide critical insights to inform the design of a multicenter randomized controlled trial assessing the efficacy of this adjunctive intervention for postoperative rehabilitation, as well as greatly improve our understanding of the patient experience in the postoperative recovery period with the use of patient reported outcomes in addition to standard clinical tests of muscle strength.

NIH Research Projects · FY 2026 · 2026-06

ABSTRACT Opioid use disorder (OUD) represents a major public health crisis in the US, with over 550,000 people dying from opioid-involved overdoses between 2014 and 2023. Hospitals are increasingly adding services to offer medications for opioid use disorder (MOUD) like buprenorphine, however, there are profound gaps with linkage to outpatient programs, with up to 50% of patients failing to continue MOUD after discharge. Interventions are urgently needed to support linkage to outpatient buprenorphine treatment after discharge. Through a prior NIDA R34, we developed and pilot tested the MHealth Incentivized Adherence Plus Patient Navigation intervention (aka, MIAPP intervention). The intervention includes financial incentives to increase medication adherence and linkage to outpatient MOUD services, and a patient navigator who offers support during the transition out of hospital, including care coordination, motivational interviewing and medication adherence coaching. These services are delivered through an mHealth platform that facilitates remote video-based monitoring of medication adherence, delivery of financial incentives, and video-, text-, and voice-based communications with the patient navigator. The intervention is guided by the Information-Motivation-Behavioral Skills (IMB) Model – a well validated model of medication adherence in the context of other chronic conditions (e.g., HIV). Our pilot randomized clinical trial demonstrated the intervention and randomized trial procedures were feasible and acceptable, thus we propose to conduct a fully-powered, Hybrid Type I Effectiveness- Implementation study using randomized controlled trial design that will (1) test the effectiveness of the MIAPP intervention for improving treatment linkage and medication adherence during a critical period of transition from inpatient- to outpatient-based OUD treatment, and (2) illuminate early implementation insights that will inform future efforts to integrate this mHealth-based + patient navigation approach in hospital settings. In Aim #1 we will conduct a randomized controlled trial (n=160) to test the hypothesis that MIAPP, relative to usual care, increases 30-day linkage to outpatient MOUD treatment (primary outcome), increases 30-day medication adherence and 90-day MOUD coverage (secondary outcomes), and reduces 180-day hospital readmissions and ED encounters (tertiary outcomes). In Aim #2 we will characterize barriers, facilitators, and adaptations to the MIAPP intervention that would facilitate its future implementation in hospital settings through short surveys and semi-structured interviews with patients, clinical staff/providers, and other key informants. Data collection will be grounded in the Consolidated Framework for Implementation Research (clinical staff/providers, key informants) and IMB model (patients) to identify key influences on experiences. If effective, this approach that combines patient navigation with mHealth could provide a transformative service model that helps reduce substantial gaps in MOUD initiation and retention for people initiated on buprenorphine in hospital settings.

NSF Awards · FY 2026 · 2026-06

The BIOPODE (Biomechanics, Integrative Ocean Physiology, and Organismal Development and Evolution) REU Site award to the University of Washington, located at Friday Harbor Marine Laboratories (FHL) in Friday Harbor, WA, will support the training of 15 students for 8 weeks during the summers of 2026–2028. FHL is a world-class marine research station in Washington's San Juan Islands, where students live and work alongside a community of scientists and engineers. The BIOPODE REU site immerses undergraduate students in hands-on, mentored research at the intersection of biology, engineering, oceanography, and environmental science. This interdisciplinary approach reflects the real-world complexity of modern scientific challenges and prepares students for careers and graduate study across STEM fields. The program has a strong track record: over 80% of alumni have presented at national conferences, and more than 60% of graduates have gone on to graduate school. Students will learn how research is conducted and how to present their work to a broad audience. Assessment of the program will be conducted through pre- and post-program surveys, tracking of student outcomes, and mentor evaluations. Students should apply to the REU site using NSF ETAP (Education and Training Application: https://etap.nsf.gov). The training students will receive is aligned with NSF priorities in Biotechnology. The intellectual focus of the BIOPODE REU site spans marine ecology, developmental biology, comparative physiology, biomechanics, and bioinspired engineering, areas where FHL has historical strengths and a mentor pool of over 50 faculty from 11 departments, including Biology, Oceanography, Mechanical Engineering, Civil Engineering, and Fisheries and Aquatic Sciences to name a few, and 30 institutions. Each student is paired one-on-one with a mentor who has an active, externally funded research program, and engages in the full research pipeline from experimental design through data analysis and public presentation. Professional development workshops cover scientific communication, career pathways, and research ethics. A parallel program supports faculty in designing effective independent projects with undergraduates. Students contribute to open-access data repositories and deposit their papers into the UW Library system, maximizing the reach of their work. By training students to interact and communicate across disciplinary boundaries, BIOPODE builds the interdisciplinary workforce needed to address complex challenges in marine science, renewable energy, and environmental resilience. 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/ABSTRACT Despite concerted efforts by states, counties, agencies, and researchers to promote evidence-based treatments (EBTs) for youth in community mental health (CMH) settings, youth mental health outcomes are often still poor. Implementing regular assessment of outcomes, or “progress monitoring” (PM), alongside EBTs can support EBT implementation and improve youth outcomes. However, barriers such as competing demands and fear of PM data potentially being used punitively against clinicians hinder the widespread adoption of PM in routine practice. Many studies have investigated ways to address PM implementation barriers, though most neglected addressing important barriers that are perceived to be low in feasibility to address, potentially jeopardizing successful PM implementation. Additionally, system leaders could have valuable insights on solutions for addressing these barriers, but they are rarely included in implementation research efforts. Guided by the Exploration, Preparation, Implementation and Sustainment (EPIS) Framework, the current study engages constituents from both the inner (clinic partners) and outer context (system leaders) to understand barriers to PM identified as priorities by clinics and implementation strategies needed to address these barriers. Findings will be used to develop a tailored implementation blueprint, or plan of action, for PM across the Preparation, Implementation and Sustainment phases in one CMH center serving youth. This study leverages an ongoing Washington (WA) state-wide EBT training initiative called CBT+ that trains youth- focused CMH clinicians on the common elements of cognitive-behavioral therapy (CBT) for anxiety, depression, trauma and behavior disorders. For the proposed project, we will partner with a WA state payer, Community Health Plan of Washington. We use innovative methods from the NIMH-funded ALACRITY Center, IMPACT. Study aims are to: Aim 1. (1a) Identify which barriers to PM implementation are priorities to address using the IMPACT Center barrier prioritization method and (1b) Understand the goals and perceptions of PM from constituents at multiple levels through qualitative focus groups and quantitative assessments; Aim 2. Collaboratively match implementation strategies to prioritized barriers from Aim 1. Clinic partners and system leaders will brainstorm strategies using Ideation (IMPACT method) for prioritized barriers, with a particular focus on engaging system leaders to identify strategies for high importance, low feasibility barriers. Participants will match strategies to barriers and develop enactment plans for a subset of the strategies; Aim 3. Develop and evaluate a 3-phase tailored implementation blueprint for PM at a CMH center that serves youth. Findings from this study will be used to develop practical methods for implementing PM in CMH settings that are scalable and generalizable to other settings, with the ultimate goal of improving youth mental health services and outcomes.

NIH Research Projects · FY 2026 · 2026-06

Project Summary/Abstract Metabolic programming and nutrient utilization are increasingly recognized as key determinants of immune cell function. Advances in understanding of immunometabolism have led to important therapeutic applications in oncology and rheumatology. In these diseases, changes in metabolic programming are associated with immune activation or suppression. Despite the critical role of the immune system and the significant maternal metabolic changes that occur during pregnancy, there is limited understanding of immunometabolism at the maternal-fetal interface. Immune cell function is highly dynamic during pregnancy, with pro- and anti- inflammatory phenotypes occurring at different gestational weeks. Our preliminary data demonstrate significant differences in placenta metabolite composition in each trimester. We hypothesize that metabolic changes at the maternal-fetal interface contribute to immune phenotype in pregnancy. The overall goal of this proposal is to examine how nutrient availability and utilization impacts immune cell function at the maternal-fetal interface. Our experiments will assess the combined metabolic and inflammatory phenotypes of immune cells at the single-cell level at each trimester during normal pregnancy. In Aim 1, we will determine the role of glucose utilization in immune cells throughout gestation, while in Aim 2, we will evaluate glutamine metabolism in immune cells during pregnancy. To address these aims, we will use high parameter single-cell flow cytometry assays to determine the metabolic phenotypes of human immune cells from placenta and uterine decidua in each trimester during normal pregnancy. In addition, we will analyze transcriptomics and metabolomics from decidual immune cells to determine how glucose and amino acid metabolism in immune cells changes as pregnancy progresses. Importantly, we will also directly assess potential metabolic drivers of immune cell phenotype with ex vivo stimulation assays to assess mechanism. Our team consists of experts in Maternal Fetal Medicine, Immunology, Metabolism, and -omics including single-cell analysis. This combined expertise as well as access to an extensive pregnancy biorepository containing well characterized placenta and decidual samples throughout gestation ensures that we are well poised to address this critical gap in understanding immunometabolism during pregnancy.

NIH Research Projects · FY 2026 · 2026-06

ABSTRACT: Interleukin (IL)-15, a proinflammatory cytokine, has garnered significant interest for its immunotherapeutic potential, and recently for its role in mediating a protective immune response to a candidate HIV vaccine. IL-15 is a predicted upstream regulatory for the protective immune response elicited by a cytomegalovirus vector- based vaccine (RhCMV/SIV) in simian immunodeficiency virus (SIV) challenged rhesus macaques (RM), which is highly correlated with a robust whole blood transcriptional signature (wbPPTS). Modulating key pathways in the IL-15/wbPPTS signaling axis, pharmacologically or through vector engineering, may improve vaccine protective efficacy. However, our limited understanding of IL-15 signaling networks, and their connection to the wbPPTS and vaccine protection, represents a significant knowledge gap. The overall objective of this proposal is to comprehensively characterize IL-15 signaling networks in RM myeloid and lymphoid cells, and identify linkages between key networks, the wbPPTS, and vaccine protection. Studies in RM and human blood cells indicate monocytes are the major contributor to the wbPPTS after IL-15 stimulation. Other cell types that likely contribute to the IL-15-mediated wbPPTS include subpopulations of natural killer cells and cytotoxic CD8+ T-cells. The central hypothesis is IL-15 signaling activates previously undefined signaling networks in these cell populations, which are critical for RhCMV/SIV-mediated vaccine protection. This hypothesis will be tested with the following Aims: 1) Defining cell-specific IL-15 associated transcriptional networks and chromatin accessibility changes; 2) Defining cell-specific IL-15 signal transduction pathway engagement; and 3) Identifying key signaling networks associated with vaccine protection through multiomics in vitro and in vivo data integration. These studies are highly significant because they will further our understanding of IL-15 immunobiology and the RhCMV/SIV vaccine protective mechanism, as well as identify therapeutic targets for improving vaccine efficacy. The proposed Mentored Research Scientist Career Development Award will provide the necessary protected research time, training, and mentorship for Dr. Isaac Barber-Axthelm to transition into an independent investigator utilizing NHP models. As one of the top research universities with extensive resources, globally recognized faculty, and a collaborative work environment, The University of Washington is an ideal location to conduct this work. Dr. Barber-Axthelm’s career development plan builds on his background in immunology and NHP biology with coursework and training in molecular biology, bioinformatics, and systems biology. This, along with guidance from his mentors and a multidisciplinary advisory committee, will provide Dr. Barber-Axthelm with the critical knowledge, experience, and skills to develop an independent research program focused on innate signaling pathway regulation of the adaptive immune system.

NSF Awards · FY 2026 · 2026-06

Videos are central to how people learn, communicate, and understand the world, capturing subtle differences in actions that distinguish expert performance from novice attempts. From surgical procedures and athletic movements to everyday tasks and instructional content, these fine-grained motions carry critical information about intent, skill, and outcome. However, today’s artificial intelligence systems struggle to interpret such nuanced dynamics, limiting their effectiveness in real-world applications such as education, healthcare, robotics, and assistive technologies. While existing systems can recognize broad activities, they often fail to identify precise actions, track objects over time, or explain how events unfold. They also struggle to efficiently analyze long videos or multiple videos at once, which are common in practical settings. This project addresses the urgent need for open and efficient tools that can better understand how actions unfold over time in videos. By advancing the ability of AI systems to interpret dynamic visual information, the project will promote progress in science and engineering, support workforce development through improved training technologies, enhance accessibility through assistive video understanding systems, and broaden participation in AI by releasing openly available resources for researchers, educators, and developers. This project develops a new class of open video-language models designed to overcome fundamental limitations in how current systems represent and reason about video. The research focuses on three tightly integrated innovations. First, it introduces trajectory-based video tokenization methods that represent videos using motion and object-centric units, reducing redundancy while preserving important temporal and spatial structure. Second, it designs flexible encoder architectures that can process multiple videos at varying resolutions, allowing models to dynamically allocate computational resources to both long-term temporal patterns and fine-grained visual details, and enabling few-shot reasoning across multiple examples. Third, it develops multimodal decoding methods that produce both textual and spatial outputs, such as object tracks, segmentations, and event descriptions, enabling grounded reasoning about dynamic scenes. Together, these components form a unified framework for understanding complex real-world video data. The project will develop new algorithms and architectures, train models on large-scale datasets, and evaluate them on benchmarks for fine-grained action recognition, temporal reasoning, and grounded video understanding. This work is expected to establish a new foundation for video-based AI systems capable of supporting applications in robotics, scientific analysis, education, and beyond. 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

Summary and expected impact Our understanding of retinal processing, built from decades of work with artificial stimuli, fails to account for responses to natural images and movies. Current mechanistic models fail to generalize across stimuli, while deep learning models with better generalization lack interpretability. This proposal will help close this gap by investigating time-dependent nonlinearities with particular emphasis on bipolar cell synapses—critical sites that exhibit strong synaptic plasticity and serve as convergence points for parallel pathways. We hypothesize that current models fail because they do not capture how diverse time-dependent mechanisms at bipolar synapses and other circuit locations work together during natural viewing. Aim 1: Standard retinal models trained on artificial stimuli fail to engage the time-dependent nonlinearities that dominate responses to natural stimuli. We will develop stimuli that strongly engage time-dependent nonlinearities specifically at bipolar cell output synapses, probing synaptic depression and recovery dynamics. We will then develop dynamic models incorporating time-varying nonlinearities, including our validated biophysical photoreceptor model coupled with synaptic plasticity models, to isolate and quantify bipolar synaptic contributions relative to other adaptive mechanisms. Aim 2: We will determine how time-dependent nonlinearities differ across retinal pathways. First, we will characterize time-dependent nonlinearities in rod versus cone bipolar synaptic outputs at mesopic light levels. Second, we will systematically map time-dependent nonlinearities in commonly encountered RGC types in both primate (midget, parasol) and mouse (alpha) retina, measuring spike outputs, excitatory inputs, and inhibitory inputs to determine where nonlinearities arise and how pathway interactions create emergent properties. This work will advance understanding of retinal computation by creating interpretable models that incorporate the diverse time-dependent nonlinearities at bipolar synapses and other circuit locations. The resulting models will aid in the design of retinal prostheses that must function under natural viewing conditions and provide insights into visual processing deficits.

NIH Research Projects · FY 2026 · 2026-06

ABSTRACT / SUMMARY Reactive oxygen species (ROS) and regulation of redox pathways are critical to human health and disease, as they influence cellular metabolism, signaling, and stress responses. Disruptions in redox homeostasis contribute to the pathophysiology of numerous disorders, including neurodegenerative diseases, muscular degeneration, and drug-induced cardiotoxicity. However, the tools for monitoring redox dynamics in living human cells remain limited in dimensionality, sensitivity, and applicability to disease-relevant models. To overcome these challenges, my research program aims to develop a next-generation, multiplexed optical platform for quantitative redox phenotyping and apply it to disease modeling and drug screening in human induced pluripotent stem cell (iPSC)- derived systems. Over the past five years, my lab has engineered two advanced genetically encoded hydrogen peroxide (H₂O₂) sensors, oROS-G and oROS-HT, exhibiting improved dynamic range, kinetics, and spectral flexibility. We established a high-throughput optical screening platform and integrated machine learning approaches to accelerate protein sensor engineering. These sensors have been applied in diverse host systems, including iPSC-derived neurons and cardiomyocytes, and have revealed new aspects of redox signaling in cell health. Building on this foundation, our future research will continue along three complementary directions. First, we will complete the development of a fully multiplexed, intensity-based TreDox sensor suite to simultaneously monitor oxidative pressure and antioxidant capacity with single-cell resolution in real time. Second, we will engineer lifetime-resolved redox biosensors and use fluorescence lifetime imaging microscopy (FLIM) to enable robust, expression-independent quantification of intracellular redox states. Third, using single-cell optical phenotyping, we will apply these tools to profile redox imbalances and early cytotoxicity signals in human iPSC- derived cardiomyocytes, neurons, and skeletal muscle cells. We aim to detect subtle cellular imbalances in redox pathways that precede cellular dysfunction and are often missed by traditional high throughput assays. This research program will fill critical gaps in our ability to study redox biology in human-derived host systems by integrating state-of-the-art protein engineering, advanced imaging, and human stem cell models. The tools and knowledge generated will improve our understanding of redox-linked disease mechanisms, enhance the predictive power of preclinical drug testing, and establish a flexible, generalizable platform for functional phenotyping at single-cell resolution.

NIH Research Projects · FY 2026 · 2026-06

Project Summary Children with cerebral palsy experience motor impairments, including spasticity and altered gait, which negatively impact mobility and quality of life. Transcutaneous spinal cord stimulation has emerged as a promising non-invasive intervention to improve motor function for children with cerebral palsy. Preliminary studies suggest that spinal stimulation, when combined with activity-based neurorehabilitation or treadmill training, may significantly reduce spasticity and enhance gait performance. However, a key challenge in translating this novel technology into clinical practice is a lack of standardized methods to determine and optimize stimulation intensity. Currently, stimulation intensity is largely set through trial-and-error, which limits consistency across clinical trials and hinders mechanistic understanding. This research aims to systematically evaluate the effects of spinal stimulation intensity on gait biomechanics for individuals with cerebral palsy. In Aim-1, we will quantify changes in gait kinematics, kinetics, and muscle activity in response to varying stimulation intensities applied at the lower spine. Twenty ambulatory children with bilateral cerebral palsy will complete structured treadmill and overground walking trials across two visits, allowing us to assess sensitivity and repeatability of gait responses to spinal stimulation. In Aim-2, we will compare optimal stimulation intensities from biomechanical, clinical, and patient perspectives. Participants will provide their preferences during walking trials, while clinicians will assess gait improvements through video ratings. Focus groups with individuals with cerebral palsy and clinicians will refine these comparisons, leading to evidence-based guidelines for future research with spinal stimulation. This research will provide critical insights into how spinal stimulation intensity modulates gait and establish foundational methods for standardizing stimulation intensity. By integrating biomechanical analysis with patient and clinician perspectives, we aim to advance neuromodulation strategies for cerebral palsy, paving the way for future clinical trials and personalized, data- driven approaches to spinal stimulation therapy.

NSF Awards · FY 2026 · 2026-06

Many deaths in the U.S. each year are attributed to end-stage organ failure. Organ replacement could prevent many of these deaths. However, many patients added to transplant waiting lists do not receive organ transplants. There is a shortage of organ donors, and many organs are discarded because they cannot be preserved for more than a few hours. Long-term preservation requires storage at very low temperatures. Tissue damage can occur during cooling and rewarming because of ice formation among other issues. An alternative approach is vitrification, which preserves samples in a gel-like state. This CAREER project will develop new technologies for organ vitrification by applying electromagnetic waves to suppress ice formation and avoid thermomechanical stress fractures. A combination of experimental and theoretical studies will lead to an effective vitrification strategy that overcomes problems associated with preservation at low temperatures. The CAREER project will provide opportunities for students to participate in the research. The research team will conduct outreach to K-12 schools, community colleges and local communities in collaboration with the Point Defiance Zoo and Aquarium. These efforts will empower young talent in STEM fields and promote public understanding of challenging problems in biomedical engineering. Long-term cryopreservation and biobanking of human organs can address the severe organ shortage crisis in transplantation and save the lives of millions of patients with organ failure each year. Owing to the complexity and large size of most organs, vitrification remains the most promising approach for achieving long-term organ cryopreservation. However, organ vitrification remains an unsolved problem due to grand challenges, including crystallization and devitrification during cooling and rewarming, cytotoxicity associated with highly concentrated vitrification solutions, and thermomechanical stresses that lead to fractures and damage, particularly during rewarming. This CAREER project will overcome longstanding barriers and establish technologies for organ cryopreservation through three objectives: (1) investigating the fundamental quantum physics underlying vitrification which include electromagnetic field interactions, and dielectric polarization and relaxation in multiscale biological systems; (2) advancing technologies that synergistically utilize single-mode electromagnetic resonance and cryoprotective agents to suppress ice formation for vitrification, and (3) integrating the resulting technologies and assessing their effectiveness through vitrification and rewarming of a rabbit kidney model. Outcomes will result in long-term organ preservation that substantially increases the availability of transplant organs and benefits fields including bioconservation, food preservation, and biobanking. 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/ABSTRACT This project requests funds to purchase a state-of-the-art electron paramagnetic resonance (EPR) spectrometer. EPR spectroscopy is an important technique that provides detailed insight into the structure of function of biomedically relevant proteins. The resulting data are essential for understanding the molecular basis of life and of disease and for providing the knowledge base necessary for developing new drugs. This instrument will advance the research of several NIH-funded projects that investigate metalloenzymes and their mechanism of action and study conformational flexibility of proteins and their functional relevance.

NIH Research Projects · FY 2026 · 2026-06

Orofacial cleft (OFC, primarily cleft lip and/or cleft palate) is a relatively common structural birth defect with environmental and genetic contributions to etiology. Genome wide association studies (GWAS) and linkage studies have identified many gene variants, most in non-coding DNA, that are associated with elevated risk for isolated OFC. However, our understanding of the pathogenic mechanisms underlying this disease remains poor because, one, only a fraction of the heritable risk lies is derived from common variants, two, we have yet to distinguish the non- coding variants that directly influence risk for OFC (i.e., causal or functional variants) from those that are merely in linkage disequilibrium with them, three, it has never been directly shown that a common variant can affect the gross phenotype of an embryo. In Aim 1 we will conduct statistical analyses to identify de novo non-coding mutations that are likely to be functional. In Aim 2 we propose to identify the OFC-associated SNPs that are functional by filtering them against enhancer marks, testing them for allele-specific effects in reporter assays in vitro, and finally by engineering them singly or in combination into induced pluriopotent stem cells, differentiating the cells in to embryonic oral epithelium, and assessing allele-specific effects on gene expression and transcription factor binding. In Aim 3, we will engineer the genome of mouse strains that are genetically predisposed to cleft lip, cleft plate, or both, to be homozygous for risk or non-risk alleles of proven functional SNPs that are conserved in mice and humans, expecting the risk allele to increase the penetrance or expressivity of the cleft phenotype. The expected outcome of the proposed experiments is identification of the mechanisms by which genetic risk variants cause a common birth defect.

NIH Research Projects · FY 2026 · 2026-06

SUMMARY The correct cis/trans amide bond configuration at prolines is vital for protein folding and altered configurations are associated with the onset of several diseases. At present, determining the cis/trans state of prolines necessitates either high-resolution structural determination or extensive NMR experiments, which are often not feasible for larger or more complex protein assemblies. For this reason, it is highly desirable to develop new analytical tools to enable direct identification of proline cis/trans states across a protein sequence. We propose development of tandem proteolysis applying a combination of cold liquid chromatography, proline specific proteases, ion mobility mass spectrometry, and ultraviolet photodissociation (UVPD) to resolve and directly monitor proline configurations across a protein sequence. Integrated with existing bottom-up proteomics pipelines, this approach will provide a much-needed tool for analyzing proline cis/trans isomerization that is capable of handling sample complex samples and far faster than current methods.

NIH Research Projects · FY 2026 · 2026-05

PROJECT SUMMARY Perinatal brain injury such as hypoxic-ischemic encephalopathy (HIE) is associated with a high risk of mortality and morbidity including intellectual disability, cerebral palsy, epilepsy, hearing or vision impairment. No therapies currently for late preterm(34-36 weeks’ gestation) HIE , which falls in a research gap between term (37-40 weeks’ gestation) HIE treated with therapeutic hypothermia and preterm white matter injury frequently seen in infants born <32 weeks’ gestation. Importantly, even when the etiology of injury is similar, there is limited evidence to suggest that the same therapeutic strategy will be effective for late preterm and term infants born with 2-4 weeks’ difference in gestational age at birth. Our recent preliminary data in late preterm and term equivalent rat models of newborn brain injury suggests there is a loss of neuroprotective effect of an anti-inflammatory, anti-oxidant agent with increasing developmental age at onset of injury. To determine if developmentally specific therapies are needed for neonatal brain injury, our objective is to evaluate curcumin-encapsulated biodegradable nanoparticles with high curcumin loading (40% by weight) and controlled release in late preterm and term brain injury. Curcumin has antioxidant, anti-inflammatory, and anti-apoptotic effects; however, curcumin’s therapeutic applications are restricted due to low aqueous solubility, low bioavailability, instability in light, and rapid first-pass hepatic metabolism. Polymeric nanoparticles can provide high drug encapsulation efficiency and sustained release of a therapeutic, in addition to improving drug solubility, circulation kinetics, and parenchymal distribution and cellular uptake in the brain. We have shown upon systemic administration that curcumin-loaded poly(lactic- co-glycolic acid) poly(ethylene glycol) (PLGA-PEG) nanoparticles localized in injured regions of the brain and reduced acute neuronal and global brain injury after cerebral hypoxia-ischemia (HI) in a late preterm rat. Our overall hypothesis is that curcumin-loaded nanoparticles will lead to long-term reduction in neuroinflammation and oxidative stress, and improved behavioral outcomes after neonatal HI brain injury, with greater efficacy in the late-preterm model than the term model. In Aim 1, we will evaluate nanocurcumin for neuroprotection in late preterm and term neonatal HI brain injury. We will measure oxidative stress and inflammatory markers, and quantify PLGA-PEG and curcumin uptake and localization as a function of blood-brain-barrier impairment. We will then evaluate the neuroprotection afforded by two dosing schemes of cucumin-loaded PLGA-PEG in the late preterm and term neonatal HI model to determine the most effective dosing scheme for reducing global brain injury and total area loss, improving neuropathological outcomes, and mitigating inflammation and oxidative stress. Using the most effective dose from Aim 1, in Aim 2 we will assess long-term neuropathology and behavioral outcomes of curcumin neuroprotection in late preterm-equivalent rats after HI. Successful completion of this study will result in a promising nanotherapeutic platform for late preterm brain injury and inform therapeutic development for different developmental ages at injury onset.

NIH Research Projects · FY 2026 · 2026-05

PROJECT SUMMARY Many neurodegenerative disorders (NDDs) preferentially affect neurons with long or complex axonal arbors. However, our understanding of this specific vulnerability is limited. We hypothesize that axon length represents a molecularly definable source of vulnerability common to many NDDs, and indeed we found that Drosophila neurons with long axons are intrinsically more vulnerable to degeneration than same-type neurons with short axons. In preliminary studies we have defined molecular markers whose expression covaries with axon length and have developed a robust platform for single cell transcriptomics, in vivo analysis of axon length-dependent effects on synaptic connectivity, and behavioral diagnostics of length-dependent axon degeneration. We will leverage the significant advantages of this system to systematically identify genes whose levels covary with axon length and assay functions for these genes in length-dependent axon degeneration. Altogether, our studies will provide fundamental insight into mechanisms of axon length-dependency in degeneration, addressing a fundamental gap in our understanding of NDDs.

NSF Awards · FY 2026 · 2026-05

This award supports participation in the international research conference "Recent Developments in the Dynamics of Nonlinear Partial Differential Equations" held August 3-7, 2026, at the University of Washington in Seattle, Washington. This event is especially timely in light of recent breakthroughs in both qualitative and quantitative analysis of nonlinear dispersive partial differential equations (PDE). The conference serves as a satellite event for the 2026 International Congress of Mathematicians taking place July 2026 in Philadelphia, Pennsylvania and represents an excellent opportunity for graduate students and other early-career researchers, particularly those in the Pacific Northwest region of the United States, to engage with leading experts and share their research. The conference showcases the interactions between harmonic analysis, probability theory, and mathematical physics, with an emphasis on the techniques and concepts shaping current advancements in nonlinear and stochastic PDE. By bringing together researchers working across these areas, the event aims to promote collaboration and drive progress on key open problems in the field. Information about the conference is available at https://sites.google.com/view/icm2026pnw/home. 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

Antarctica holds vast, hidden reservoirs of salty groundwater beneath its ice and frozen soils; an extensive network that may influence earth system variability, ocean ecosystems, and ice sheet stability. This project will directly measure groundwater discharge and potential associated gas seeps along the Antarctic coast, revealing how these subsurface waters transport nutrients, trace metals, microorganisms, and atmospheric-reactive gases to the Southern Ocean. Understanding these exchanges is vital because they can shape marine productivity, influence carbon cycling, and control the release or storage of gases. The project strengthens U.S. and New Zealand scientific collaboration in alignment with the “Antarctica InSync” initiative, supporting coordinated, sustainable research in one of the world’s most logistically challenging environments. Insights from this work will help improve predictions of how Antarctica both responds to and influences global environmental variability. This collaborative RAPID project investigates how Antarctic groundwater drives ecosystem connectivity across the McMurdo Sound coastal zone, focusing on the Cape Evans and New Harbor regions of the Ross Sea. The team will identify groundwater discharge using in situ gamma radiation sensors, deploy seepage meters and OsmoSamplers for fluid and gas flux measurements, and collect water and sediment samples for detailed geochemical and microbial analyses. These data, combined with land-based geophysical, SCUBA, and ROV surveys by New Zealand partners will quantify groundwater pathways, flux rates, and biogeochemical properties. The project tests the hypothesis that Antarctic groundwater significantly affects coastal geochemistry, microbial diversity, and glacial flow, influencing the sensitivity of Antarctic coastal margins to earth system dynamics. The findings will provide foundational data for future multinational monitoring, modeling, and management of Antarctic critical zones. 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 Heart failure affects million adults in the U.S., is associated with high mortality, and is a leading cause for hospitalizations. The long-term objective of this project is to understand the impact of an existing, trauma- informed, community-based, nurse-led heart failure disease management program that provides care to heart failure patients with adverse social determinants of health and unmet social needs (poverty, unstable housing, substance use, mental illness). The Community Heart Failure Program (CHFP) is operated out of a safety-net hospital in the Pacific Northwest and delivers an innovative model of care, where staff provide clinical care in the location of the patient’s choosing, often a shelter, tent, apartment, or other non-clinic-based location. Point- of-care labs and ultrasound support clinical decision-making. Using trauma-informed care principles, including safety, trustworthiness, collaboration, empowerment, and choice, the CHFP and this research project were designed to engage patients with adverse social determinants of health and unmet social needs. This project advances health equity by removing barriers to high quality clinical care and clinical research participation. The short-term objective of this project is to establish feasibility of research study protocols that were designed to evaluate the impact of this innovative existing program. The proposed study is a prospective, longitudinal design (N=40). The specific aims of this project are to: 1) evaluate feasibility of research protocols, 2) compare healthcare utilization 6 months pre- and post-CHFP enrollment, and 3) compare guideline-directed medical therapy (GDMT), biomarkers, and patient-reported outcomes (PROs) at baseline and 3- and 6-months post-enrollment, and 4) examine associations between CHFP conceptual model key components (trust/relationship building, shared-decision making, care coordination, harm reduction) and outcomes (healthcare utilization, GDMT, biomarkers, and PROs). Descriptive statistics will be used for Aim 1. For Aim 2 and 3, paired t-tests (or Wilcoxon signed-rank test) will be used to compare outcomes pre- and post- enrollment, and effect sizes will be calculated to inform future intervention studies in this patient population. For aim 4, correlations (continuous variables) and chi-squared (categorical variables) will be used to examine the direction and strength of associations between key components of the conceptual model and model outcomes, in addition to multivariate regression to determine the independent effect of key components on outcomes. The goals of this project align with the strategic mission of the National Institute of Nursing Research to prioritize research that advances health equity by removing barriers to research participation, optimizes health for individuals and communities, and addresses pressing health challenges.