University Of Washington

NIH Research Projects · FY 2026 · 2026-02

Cannabis use during pregnancy has increased substantially, in conjunction with widespread decriminalization/legalization, changing public perceptions about harm, and evidence of cannabis’s antiemetic properties. Prior outcomes research on prenatal cannabis exposure is narrow in scope, as these older studies included research participants with polysubstance use (e.g., tobacco, alcohol, illicit drugs). In addition, prior research likely underestimated potential risks specific to cannabis use during pregnancy because modern strains are 10x more potent than they were 40 years ago. In our currently funded research, cannabis use is measured prospectively during pregnancy using weekly reports validated with labels and urine-based assays. Infants receive a neonatal neurobehavioral exam and multi-modal imaging (functional magnetic resonance imaging, myelin imaging, diffusion tensor imaging, and structural MRI) under natural sleep at 2-4 weeks-of-age and extensive neuropsychological follow-up assessments at 6 and 18 months. We propose to broaden the impact of this work by collecting longitudinal MRI scans concurrently with the 6- and 18-month neuropsychological visits. By focusing on the first 18 months of life, we aim to characterize cannabis-induced brain changes and dysregulated growth trajectories at a time when rapid synaptogenesis, axonal growth, and myelination is unfolding. In addition, we will test the hypothesis that prenatal cannabis exposure is more detrimental to females than males. This program of research aims to clarify potential health risks, enabling the general public to make better-informed choices surrounding cannabis use during pregnancy.

NIH Research Projects · FY 2026 · 2026-02

PROJECT SUMMARY Balance disorders are common and devastating – it is estimated that one third of US adults over the age of 40 (69 million) have vestibular dysfunction. A major underlying cause of vestibular dysfunction is damage to hair cells in the vestibular organs of the inner ear. Unfortunately in mammals including humans the consequences of this damage are irreversible. The zebrafish has emerged as a model system for studying vestibular function, with its small size and optical clarity advantages for studying hair cells in vivo. Moreover within the zebrafish inner ear, hair cells are able to fully regenerate after damage. Within vestibular organs hair cells are organized into distinct central and peripheral regions with functional and molecular identities highly conserved across species. We propose experiments to study how these spatial identities are initially established within the zebrafish ear. We will determine how hair cells are added during development, and how they undergo phenotypic switching, changing spatial identity during organ growth. We will test how spatial identity regulates regional differences in mitochondrial activity. We will test whether retinoic acid signaling plays a conserved role in zonal patterning. We will test whether transcription factors enriched in zonal expression underlie gene regulatory networks establishing spatial patterning. Together these studies will provide a comprehensive picture for how this core conserved feature of vestibular function is established.

NIH Research Projects · FY 2026 · 2026-02

More than 430,000 Americans receive maintenance hemodialysis treatments for end stage kidney disease. The health of the dialysis population remains sub-optimal: five-year survival for patients initiating in-center hemodialysis is only 42% with a course marked by frequent hospitalizations and debilitating symptoms. A major deficiency of prevailing intermittent dialysis treatments is the inability to control extracellular volume. Ongoing volume overload is present in >65% of patients receiving maintenance hemodialysis treatments. Residual volume overload promotes a vicious cycle of resistant hypertension, cardiac dysfunction, pulmonary congestion, and activity-limiting symptoms. Attempts to reduce extracellular volume with intermittent dialysis treatments are beset by hypotension and symptoms caused by overly rapid rates of ultrafiltration that exceed vascular refilling and the pervasive use of anti-hypertensive medications. Historical studies using frequent slow dialysis treatments and concomitant tapering of blood pressure medications achieved normotension without use of medications in >95% of patients. The intensive dialysis protocols used in these historical studies would be difficult to implement today. However, an abbreviated adaptation could yield clinically important and sustained benefits and be acceptable to providers and payers. We propose a randomized trial to test the effects of two concentrated ultrafiltration strategies in hemodialysis patients who have clinical suspicion of ongoing volume excess. Patients will be assigned to either usual dialysis care, a 4-week ultrafiltration strategy, or an 8-week ultrafiltration strategy. Each strategy will add two extra ultrafiltration treatments per week plus guided tapering of blood pressure medications to achieve blood pressures <130/80 mmHg without use of medications, unless prescribed for a non-blood pressure indication. Following the intervention periods, participants will return to usual dialysis care. We will test whether the concentrated ultrafiltration strategies are associated with sustained improvements in 24-hour ambulatory blood pressure, symptoms of congestion, medication use, and six-minute walk distance. In parallel, we will test whether these strategies are associated with more favorable hemodynamic responses to intermittent dialysis treatments, fewer symptoms of dialysis-related fatigue, and fewer episodes of intra-dialytic hypotension.

NIH Research Projects · FY 2026 · 2026-02

Project Summary Prior research from our groups and others has shown that Psl and Pel exopolysaccharides (EPS) play important roles within Pseudomonas aeruginosa (Pa) biofilms. These include initiating and maintaining cell-to-cell interactions, maintaining the structure of the biofilm via interactions with extracellular proteins and DNA (eDNA), conferring tolerance of antibiotics and antimicrobial peptides, enabling colonization of lung epithelia, and persistence against the host immune response. While Psl and Pel are known to exist in two distinct forms: a cell- associated and a cell-released form, the role(s) each variant of the polymer may play within biofilms has typically not been considered separately. Distinct roles for cell-associated and cell-released Psl/Pel suggest that these two forms of the polymers are likely chemically or structurally different. Little is known about how these polymers are retained on the cell surface and whether chemical modifications such as deacetylation of Pel may impact this association. In this proposal, we will test the hypothesis that Psl and Pel exist in several different isoforms of variable length and/or chemical modification. The first specific aim will examine these EPS for modifications. The Sia system positively influences the amount of cell-associated Psl and Pel in a c-di-GMP dependent manner but the mechanism by which it does so is unknown. Experiments in this proposal will explore how the Sia system controls Pel/Psl localization to the cell surface. Finally, we hypothesize that the cellular distribution of Psl/Pel controlled by the Sia system will impact adhesion and biofilm biomechanics, interaction of polymers with matrix proteins and eDNA, the susceptibility of Pa to antimicrobial and host defenses as well as pathogenesis. Thus, we will dissect the biological consequences of EPS localization and the contribution(s) each form of the polymer plays within the biofilm.

NIH Research Projects · FY 2026 · 2026-02

PROJECT SUMMARY The current application is responsive to the NIH-Wide Strategic Plan for Research on the Health of Women (2024-2028) led by the Office of Research on Women’s Health. It addresses Strategic Goal 1 (Research) under Objective 6 to advance women’s health through the study of key transition periods. Here, we focus on female pubertal development because of its impact on post-pubertal health and its potential to be targeted as a strategy for intervention to improve the long-term health of women. Earlier maturing girls are at risk for numerous emotional and social harms and for poor physical health, including the early emergence and worsening of cardiometabolic risk factors and increases in risk for obesity, type 2 diabetes, cardiovascular disease, and early mortality. Moreover, pubertal onset is hastened by prepubertal exposures such as excess body weight and adverse early life experiences. Yet, as evidence mounts revealing critical life course links between early life exposures, pubertal onset, and women’s cardiometabolic health, there are significant knowledge gaps about the mechanisms that account for these associations. Notably, the timing of pubertal onset has been hypothesized to itself mark aging, indicating the pace at which key social and biological milestones are met. However, few studies have examined this possibility directly by evaluating associations between pubertal timing and biological aging. Recent advances in biological aging biomarkers now make testing this hypothesis possible. The current study seeks NIH funding to recruit the female participants in the landmark birth cohort NICHD Study of Early Child Care and Youth Development (SECCYD) (expected: n=374; 80% return of 468; mean age=38 [35-40 yrs]) to complete a second FU visit (FU2), modelled after the first FU visit (FU1) which had an 81% return (n=378; mean age=28 [26-31 yrs]). This new FU2 data will be integrated with the original SECCYD and FU1 data, leveraging prospective (birth to midlife) gold standard measures, including Tanner staging for the assessment of pubertal maturation and epigenetic and ovarian aging biomarkers for the assessment of biological aging. A series of life course models will then be tested to determine whether the timing and pace of female pubertal maturation predicts worsening cardiometabolic health and accelerated biological aging both directly and as a mediator of effects of adverse prepubertal exposures. Models will also test whether the risk associated with these direct and indirect effects may be partially offset by post-pubertal protective factors (e.g., adult socioeconomic status, health behaviors, and psychological health) and whether accelerated biological aging may partially account for effects of earlier and faster pubertal maturation on worsening cardiometabolic health. Gaining new knowledge about whether pubertal maturation impacts the biological aging processes that underlie disease and mortality outcomes will inform opportunities for puberty-focused interventions to slow biological aging and prevent disease broadly. This work addresses the urgent need to enhance health and lengthen healthspan in women who, despite living longer than men, experience significantly more years of disability and are at increased risk for multimorbidity.

NSF Awards · FY 2026 · 2026-02

A major part of the STEM workforce, referred to as the Skilled Technical Workforce (STW), is composed of workers with less than a bachelor's degree performing jobs that require technical skills. This project uses a career approach to map the jobs in the STW by identifying the occupations that workers with technical skills, and less than a 4-year college degree, move through over the course of their careers. The project will identify the job ladders providing entry and mobility through the STW. Job ladders enable workers who acquire greater levels of experience to transfer developed skills across linked occupations, and achieve higher wages. Finally, the study will analyze variation in access to and mobility through the STW by investigating the role of teenage employment experiences, parental occupation, and living in rural, suburban, and urban areas. This research will result in the creation of an internet-based interactive tool that describes STW occupations and job ladders, in addition to the creation of research briefs and journal articles on the STW. The project will provide accessible information on how workers enter and move through the STW that will benefit students planning careers in the STW, employers looking to hire these workers, and community members seeking to strengthen the pathways into the STW. This research will advance the study of workers' careers by jointly studying occupation and wage mobility in the context of the STW. The analysis builds on previous research to identify job ladders between pairs of occupations that facilitate upward wage mobility, and operationalizes the concept of occupational internal labor markets. The project refines previous measures of occupational linkages using the O*NET database and job changers in the Current Population Survey to focus on STEM knowledge and technical scores and workers without a bachelor's degree. The project studies the effect of these occupational linkages on workers' mobility by analyzing whether workers who gain experience are more likely to move to a strongly linked occupation and achieve upward wage mobility. The project uses an advanced longitudinal method, a multinomial conditional logit model (a form of discrete choice model), to jointly model occupational and wage mobility. The project uses nationally representative longitudinal data from the National Longitudinal Study of Youth 1979 and 1997 cohorts, and the 1996-2014 panels of the Survey of Income and Program Participation. This project is supported by NSF's EDU Core Research (ECR) program. The ECR program emphasizes fundamental STEM education research that generates foundational knowledge in the field. Investments are made in critical areas that are essential, broad and enduring: STEM learning and STEM learning environments, broadening participation in STEM, and STEM workforce development. 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

Fluid-Structure interaction (FSI) refers to physical systems whose behavior is dictated by the interaction of an elastic body and a fluid mass. The study of FSI is relevant to various applications, ranging from aerodynamics to biomechanics. To address the inherent numerical and physical uncertainties in these applications, it is common to introduce stochastic influences into mathematical models. This project takes an initial step in investigating the effects of stochastic forces on FSI models arising in biofluidic applications that describe the interactions between a viscous fluid, such as human blood, and an elastic structure, such as a human artery. Depending on the specific application, such as the location, roughness, and size of the vessel, various mathematical models will be explored. The proposed program opens a new class of problems in mathematics involving the study of stochastic partial differential equations (PDEs) posed on randomly moving domains, particularly when the displacement of the domain boundary is not known a priori. The aim of this project is to prove that the proposed stochastic FSI problems are well-posed and to study the properties of the solutions. Education and mentoring are important components of the project, with students involved in research activities. The writing of an expository book will also be undertaken. The goal of this project is to provide existence results for a class of nonlinearly coupled stochastic FSI problems that includes a range of possibilities, such as compressible and incompressible fluid flows within thin or thick, linear or nonlinear elastic structures. Additionally, distinct coupling conditions, including the slip and no-slip kinematic coupling condition at the random and time-dependent fluid-structure interface, will be examined. Multiplicative white-in-time noise, applied both to the fluid as a volumetric body force and to the structure as an external forcing on the deformable fluid boundary, will be considered. The existence proof is based on semi-discretizing the multi-physics problem in time, decoupling the approximate problem using a penalty method, and employing an operator splitting strategy to split the fluid from the structure sub-problem(s), with the aid of a novel cut-off function approach coupled with a stopping time argument. The results of this research will shed light not only on the analytical properties of the solutions but also on the stability of the partitioned numerical schemes for stochastic FSI problems, ultimately providing insights into the robustness of these models against external noise. This study integrates tools from probability, differential geometry, and fluid dynamics. 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

This project will contribute to the national need for well-educated scientists, mathematicians, engineers, and technicians by supporting the retention and graduation of high-achieving, low-income students with demonstrated financial need at University of Washington. A total of 33 scholars pursuing bachelor’s degrees in mathematics, Environmental, Biomedical and Computer Science, Information Technology, and Computer, Electrical, Mechanical and Civil Engineering will receive scholarships averaging $15,000 for up to five years. Scholars will receive faculty mentoring and the project will build strong scholar cohorts through an on-campus STEM Living Learning Community and a Success in STEM seminar. Additional activities include and early Fall Precalculus program and an engineering or environmental Course-based Undergraduate Research Experience. The overall goal of this Track 2 Scholarships in STEM project is to increase STEM degree completion of academically talented, low-income undergraduate students with demonstrated financial need. There is a significant national need to grow the STEM workforce and nurture key talent that will ensure economic competitiveness and provide domestic leadership across critical sectors. This project directly speaks to this need by supporting STEM student success, which will strengthen the workforce in Science, Engineering and other key areas of need. The project will be assessed by an experienced evaluator that will use a mixed-methods approach that would leverage data obtained from quarterly self-report surveys and student institutional outcomes. These data will contribute to the knowledge base regarding effective strategies to support talented, low-income students in STEM. This project is funded by NSF’s Scholarships in Science, Technology, Engineering, and Mathematics program, which seeks to increase the number of academically talented, low-income students with demonstrated financial need who earn degrees in STEM fields. It also aims to improve the education of future STEM workers, and to generate knowledge about academic success, retention, transfer, graduation, and academic/career pathways of low-income students. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NIH Research Projects · FY 2025 · 2026-01

PROJECT SUMMARY/ABSTRACT Opioids are the most widely used analgesics for chronic pain and post-operative care. However, opioid misuse has resulted in a national overdose crisis fueled by untreated opioid use disorder (OUD) and the infiltration of the potent synthetic opioid, fentanyl, into the drug supply. Fentanyl primarily signals at the µ-opioid receptor (µOR), a G protein-coupled receptor (GPCR) expressed throughout many subcortical brain regions responsible for regulating pain perception, reward, stress, and emotion. However, the pharmacological and spatiotemporal profile of fentanyl in the brain is currently poorly understood. Traditional tools to monitor opioid distribution lack specificity and spatiotemporal resolution. Previously, the Berndt Lab developed a fluorescent opioid sensor, μMASS, which couples real-time opioid detection at the µOR to an increased fluorescence response. However, like the µOR, μMASS detects a variety of opioids, including endogenous met- enkephalin and exogenous fentanyl, making it difficult to discern signals between specific ligands. My goal is to engineer a fentanyl-specific fluorescent opioid sensor to investigate fentanyl pharmacology and pharmacokinetics in real-time. Previously, I combined AI-guided structure determination and molecular docking to study µMASS-fentanyl binding in silico, and observed µMASS did not maintain a critical salt bridge with amino acid D1473.32. Mutating this residue to a glutamate (D1473.32E) rendered the sensor specific only for fentanyl, which I dubbed FentMASS1.0. To engineer a next-generation fentanyl sensor with enhanced sensitivity, specificity, and expression, in Aim 1.1 I will bolster my current approach using updated structure prediction models and molecular dynamics simulations to observe sensor-fentanyl interactions and identify new mutation targets. In Aim 1.2 improved sensor variants will be rapidly generated and screened using a high-throughput protein engineering approach established in the Berndt Lab and optimized for neuronal expression in Aim 1.3. Finally, in Aim 2 I will validate the optimized fentanyl sensor (or FentMASS1.0) in µOR-expressing brain regions in ex vivo brain slices and determine fentanyl pharmacology in real-time. In the end I will have an enhanced µOR-based fentanyl sensor with optimized expression in neuronal tissue, which can then be applied to study fentanyl pharmokinetics across brain regions in vivo in freely behaving animals. This proposal is significant because there is currently no available µOR-based sensor to study fentanyl pharmacology with reliable sensitivity, kinetics, and neuronal expression. The approach is innovative as it combines AI-guided structure determination and in silico molecular modeling in combination with next-generation high-throughput sensor engineering techniques to rapidly optimize a GPCR-based sensor. This approach and resulting sensor technology will aid in studies to characterize the progression of fentanyl induced OUD while laying the groundwork for developing future specific opioid sensors.

NIH Research Projects · FY 2025 · 2026-01

PROJECT SUMMARY / ABSTRACT The primate retina’s output is split into 15-20 Retinal Ganglion Cell (RGC) types, each processing and transmitting different aspects of the visual world. Different RGC types are thought to have distinct roles in visual processing and behavior, and type selective impairment is implicated in optic neuropathies and developmental disorders. The response properties of numerically dominant RGC types have been described using predominantly linear models, and a large body of psychophysical work uses these models to propose visual stimuli that can selectively drive a given RGC type. However, these stimuli face three important limitations: 1. Recent studies challenge the accuracy of key model assumptions, 2. They lack direct neural population testing and the underlying models focus on single cells, and 3. They are predominantly simple artificial stimuli and the underlying models fail to capture responses to naturalistic stimuli. A lack of validated, ethologically relevant RGC type selective stimuli limits the ability to answer key questions in visual neuroscience such as whether Midget RGCs dominate ventral stream processing and Parasol RGCs dominate dorsal stream processing. The central aim of this proposal is to acquire the necessary training in sensory neuroscience, psychophysics, and computational neuroscience to directly test existing approaches, refine and evaluate new models of RGC responses, and leverage new models to generate effective, ethologically relevant RGC type selective stimuli. Preliminary data from in vitro population recordings of peripheral Macaque retina suggest that stimuli based on assumed contrast gain differences between Parasol and Midget RGC types fail to achieve their proposed selectivity. I will confirm and extend this to other stimuli based on assumed response property differences between RGC types, which will aid interpretation of behavioral results in healthy and diseased subjects tested with these stimuli. Further, I will refine and evaluate newer mechanistic and deep neural network models of RGC responses that can capture nonlinear circuit components driving responses to naturalistic stimuli. I will leverage these models for precise manipulation of RGC population outputs by systematically generating stimuli that minimize modulation of one RGC type population while maximizing modulation of others. This will provide validated tools for studying the downstream contributions of the numerically dominant RGC types in naturalistic contexts with applications in basic research and the clinic. Through this training and research plan, I will develop the necessary expertise and skills for contributing substantially to sensory neurophysiology and pathology as a physician scientist.

NSF Awards · FY 2026 · 2026-01

Cyclones occur in both the tropics (as hurricanes) and middle latitudes (as, e.g., Nor'easters), posing significant threats to lives and economies. While weather forecasts have improved overall, the evolution and hazards of some of these high-impact storms remain poorly forecast and understood, partly because different types of storms are often studied in isolation. However, real-world storms can transform and interact with other weather systems, as seen with Hurricanes Sandy (2012) and Helene (2024). This project addresses this challenge by using advanced Artificial Intelligence (AI) to study the full life-cycle of cyclones, viewing them not as separate categories but as an interconnected spectrum of weather systems. The ultimate goal is to uncover the early warning signs of dangerous storms, leading to more accurate and trustworthy long-range predictions. This will give communities more time to prepare, potentially saving lives and reducing economic damage. The project will also train a new generation of scientists at the cutting edge of atmospheric science and AI, and by making its tools and findings openly available, it will help accelerate improvements in forecasting for all. This project aims to advance fundamental understanding of the long-range predictability and environmental dependency of cyclones using new observational datasets and AI tools. The research leverages an interdisciplinary approach to: 1) Delineate the predictability limits of high-impact cyclones using newly developed AI weather forecasting models and identify the initial conditions that control their development; 2) Adapt AI algorithms based on robust representation learning to automatically flag atmospheric precursors that influence long-range forecasts; 3) Conduct and co-develop physical and physics-informed AI simulations to test hypotheses about the factors controlling cyclone evolution; and 4) Use AI and statistical models to probe the contributions of ocean, sea ice, and land properties to the predictability of cyclone activity. This work will generate new insights into cyclone dynamics and provide a robust framework for improving weather and climate prediction 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 · 2026-01

The Urban Housing Systems (UHS) IRES program is designed to prepare U.S. undergraduate students in urban studies and civil engineering to address real-world urban development challenges, with a particular emphasis on housing. Participating students conduct research at Universidad Diego Portales in Santiago, Chile, developing skills in qualitative data collection, multi-disciplinary collaboration, problem-solving, and systems thinking and modeling to analyze complex socio-technical systems. Students work closely with local faculty mentors, engage in cultural exchange, and contribute to innovative research focused on how housing policies and practices can better meet community needs through a systems-based approach. By fostering connections with housing experts and planners in both Chile and the U.S., the program supports shared learning and cross-cultural dialogue, generating insights that can help housing authorities in both countries, and globally, develop more effective and systemic solutions to urban housing challenges. The global shortage of affordable urban housing is a complex, systemic issue driven by factors such as limited land availability, inadequate infrastructure, restrictive zoning, gentrification, and financing barriers. Despite growing awareness of these challenges, a significant gap remains in research and practice that models the interconnected social, environmental, economic, and technical factors influencing urban housing outcomes. To address this gap, the UHS IRES program provides civil engineering and urban studies students with essential skills in qualitative research design, systems thinking, cross-cultural collaboration, and stakeholder engagement through participation in mixed-methods research in Santiago, Chile. As part of the program, students working in multidisciplinary teams analyze case studies reflecting three distinct urban housing approaches, contributing to a broader understanding of effective housing policies and practices worldwide. Through a phased research design - impact assessment, systems modeling, and leverage point identification - U.S. students collaborate with faculty at Universidad Diego Portales to develop socio-technical systems models based on these case studies. The program's research approach combines qualitative data with network analysis, participatory systems modeling, and fuzzy-set qualitative comparative analysis to rigorously identify leverage points for improving urban housing outcomes. By combining engineering, social science, and architectural perspectives, the research provides valuable insights to shape impactful policy recommendations. Collaborations with urban housing planners in Chile and the U.S. promote shared learning and meaningful dialogue. A multi-phase external evaluation - comprising feedback, post-program surveys, and a follow-up Zoom focus group assesses students' growth in research skills, collaboration, urban housing expertise, and the UHS IRES program's long-term impact on their educational and career trajectories. 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 · 2026-01

Summary TDP-43 pathology occurs in the majority of individuals with high Alzheimer's disease neuropathologic change (ADNC). This accumulation of cytoplasmic hyperphosphorylated aggregates of TDP-43 (pTDP-43) in neurons has been termed limbic predominant age-related TDP-43 encephalopathy neuropathologic change (LATE-NC). LATE-NC occurs in similar brain regions that are affected by ADNC, however the underlying mechanisms of polyproteinopathies in the context of AD, including how they develop, if and how they interact, and the involvement of the diverse cell types of the CNS, are not well understood. We recently described a family with a pathogenic variant in the AD-associated gene SORL1 where several variant carriers underwent autopsy at the University of Washington Alzheimer's Disease Research Center. This variant, SORL1 R953C, segregated with high ADNC and a TDP-43 pathology that was characteristic for LATE- NC, but occurred in cases with much younger ages of onset. SORL1 has defined roles in endosomal trafficking and regulation of amyloid precursor protein (APP) processing, but how SORL1 in particular, and endosomal dysfunction in general, may contribute to polyproteinopathy in neurodegeneration remains to be explored. In this study we will use human induced pluripotent stem cell (hiPSC)-derived neural cells generated from SORL1 variant carriers and controls to investigate how dysfunction in endosomal trafficking and cellular stress may contribute to the accumulation, mis-localization, and phosphorylation of TDP-43. We will also test whether cells that harbor pathogenic SORL1 variants are more susceptible to modulation of TDP-43 expression. Because pathologic TDP-43 has been described in both neurons and glia, we will generate cortical neurons and astrocytes from hiPSCs for these experiments. We will perform a comprehensive characterization of endosomal pathology in post-mortem brain tissue from donors with ADNC+LATE-NC vs. ADNC or LATE-NC only. We will also analyze endosomal pathology from post-mortem samples of SORL1 variant carriers. For these studies we will use our newly established pipeline for high-resolution imaging of endosomal morphology in post-mortem tissue. Our goal in this exploratory R21 proposal is to test the hypothesis that endosomal dysfunction is a driver of TDP-43 co- pathology in AD and to develop a model of LATE-NC in a tractable, human in vitro system. Our studies will elucidate the molecular mechanisms of how dysfunction in SORL1 and endosomal pathways may lead to TDP- 43 pathology and provide a comprehensive analysis of endosomal pathology in brains of subjects with LATE-NC which, if successful, could open novel therapeutic avenues.

NIH Research Projects · FY 2026 · 2026-01

Project Summary/Abstract The proposed R13 grant seeks support for a conference entitled Harnessing Knowledge from the Social and Behavioral Sciences to Develop Sociobehavioral Interventions to Reduce Children’s Oral Health Inequities to be held at the University of Washington School of Dentistry in Seattle, WA in Spring 2026. The objective of the proposed 3-day conference is to gather a community of scholars, connecting social and behavioral scientists with applied dental researchers. We will convene 24 researchers (1 applied dental researcher paired with either a social or behavioral scientist from outside of dentistry). Each pair will engage in preparatory work on their topic before the conference, with the goals of (a) presenting a critical paper on the current state of science on their topic and proposed immediate next steps relevant to sociobehavioral interventions needed to move the field forward and (b) integrating relevant findings from the conference into a final paper. The papers presented during the conference will be part of a special issue on sociobehavioral interventions, co-edited by the MPIs (Dr. Donald Chi and Dr. Cameron Randall) and published in the International Journal of Paediatric Dentistry. Using a model that drives interdisciplinary collaborations at the Center for Advanced Study in the Behavioral Sciences (CASBS) at Stanford University, the R13 grant will bring together social and behavioral scientists and applied researchers in dentistry to advance promising interventions that address the key social and behavioral causes of children’s oral health inequities, reduce unnecessary pain and suffering, and improve the oral health and quality of life of socioeconomically vulnerable children. Children’s oral health inequities will be used as a case study to generate a more broadly generalizable model on how to systematically harness knowledge from the social and behavioral sciences to address health inequities through sociobehavioral interventions.

NSF Awards · FY 2026 · 2026-01

Benthic nepheloid layers (BNLs) are persistent layers of enhanced particle concentrations near the seafloor. Intense BNLs of a few hundred meters thick have been observed globally and may significantly influence the cycling and overall budget of sediment-sourced trace elements and isotopes (TEIs). BNLs can act as elemental sources or sinks, potentially enhancing or suppressing elemental fluxes across the sediment-water interface. However, sampling of BNLs has been limited and very few chemical measurements have been made. In conjunction with a previously-funded research expedition in the Labrador Sea, this project will conduct high-resolution sampling of particle composition and isotopes in BNL particles and surface sediments. The overall goal of the project is a deeper understanding of the role of BNLs in regulating deep-ocean chemistry. Beyond the scientific contributions, the project will train graduate and undergraduate students, engage the public in collaboration with a science media specialist, incorporate scientific and outreach content into undergraduate teaching curricula, and foster international collaboration. The overarching goal of the proposed research is to generate process-level understanding of how BNLs change the net flux of TEIs into the overlying water column. To achieve this, the team aims to address three key questions: (1) What is the benthic flux of TEIs from sediments in the Labrador Sea? (2) How do BNLs modify net benthic fluxes of TEIs? (3) What characteristics of the BNL are the most important controls on TEI concentrations? The investigators hypothesize that BNL regions will exhibit a higher benthic flux of TEIs at the sediment-water interface, as determined by radium-thorium disequilibrium, but that the net benthic flux of particle-reactive TEIs will be lower in BNLs with more manganese oxides due to their greater scavenging capacity. This project will evaluate different scavenging intensities by estimating partition coefficients using particle composition data and investigate the particulate Mn mineral phases responsible for scavenging using synchrotron techniques. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2026 · 2026-01

Wildfire is a key process for many types of ecosystems. Over the past century, however, fire activity has changed dramatically. One important driver of this shift has been the accumulation of flammable materials, such as fallen leaves and dead wood, that provide fuel for wildfires. Despite its importance, fuel accumulation remains difficult to model because it involves processes unfolding on very different timescales: the slow buildup and decay of fuels and their rapid combustion during wildfire. This project investigates how fuel accumulation and fire activity are changing over time in a range of landscapes in western North America. This includes places where fire is limited by fuel availability as well as those where fire activity is driven more by flammability. The project uses these insights to improve models that predict future fire regimes and works closely with land managers to co-develop model improvements and scenarios that support decision-making. This project: (1) examines, quantifies, and reduces uncertainty in models of the slow but steady process of fuel accumulation, and (2) uses improved models to investigate how changes will influence rates of energy conversion at both slow and fast time scales and across watersheds. The project first investigates the process of decomposition, which reduces a critical source of uncertainty in projecting future fuel accumulation. The project also uses long-term empirical datasets of litter decomposition to perform uncertainty analysis and multi-objective assessment of several decomposition models. In parallel, the research team engages with management partners to co-produce model development and to design management-relevant scenarios. Then, the team uses those scenarios across several watersheds in western North America to project how combined scenarios of environmental variability and management alter wildfire regimes. Coproduction efforts align the team’s modeling efforts with management needs and provide managers access to critical technical infrastructure. The project engages the next generation of scientists in the theory and practice of fire ecology, biogeochemistry, ecohydrology, and fire regime modeling. The project also delivers a workshop to train modelers at the graduate student and early-career researcher level. This convergence research- spanning biology, geosciences, and mathematical and physical sciences- advances theories of ecosystem energetics in the context of natural and managed fire regimes. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NIH Research Projects · FY 2025 · 2025-12

The goal of this proposal is to define the cellular sources of atypical age associated thymic epithelial cells (aaTECs) and uncover molecular pathways that promote their emergence with aging. The thymus is a critical primary immune organ responsible for giving rise to a diverse, yet self-tolerant, T cell pool. This critical T cell development process is holistically governed by a group of thymic epithelial cells (TECs) that express the master transcription factor Foxn1. There are two major groups of TECs that are functionally and regionally distinguishable. Cortical TECs are responsible for the early stages of T cell development, while medullary TECs can express peripheral antigens and are tasked to delete autoreactive T cells. A strong population of effective thymic epithelial cells is critical for optimal thymic function and proper T cell immunity. With aging, both major TEC cell types operatively decline through a phenomenon called thymic involution. Here, the organ atrophies throughout lifespan and functional thymic epithelial cells are diminished. Despite thymic involution being well documented, the key mechanisms within the thymic stromal compartment that influences this phenomenon are incompletely understood. Our lab’s preliminary data describes that nonfunctional atypical aaTECs emerge and limits thymic function in aged mice. These aaTECs do not retain the classic thymic epithelial cell markers that are present within canonical TECs. Specifically, we have characterized novel markers for two aaTEC populations: aaTEC1 (marked as UEA1 neg, Ly51 neg, Claudin3 pos) and aaTEC2 (EpCAM neg, PDGRFa neg, and Podoplanin pos). Early tracing experiments indicate that aberrant age associated TECs come from bonafide thymic epithelial cells that have expressed Foxn1. Bioinformatic interactome analyses suggests that protein and cellular interactions are skewed away from conventional TECs, in favor of aaTECs. Furthermore, atypical aaTECs can respond to critical growth factors and co-opt these proteins away from classical TECs, likely contributing to their eventual decline. Lastly, we find that aaTECs appear to downregulate Foxn1 expression and highly express androgen and estrogen receptors, suggesting that these two pathways are relevant to their emergence with aging. Our initial characterization of aberrant aaTECs notwithstanding, the stromal cells that transition to aaTECs and vital molecular cues that promote them have not been fully explored. Therefore, with our exciting findings, the goal of my proposal is to know the key thymic epithelial cells that serve as pre-cursors to aaTECs (Aim 1) and discover major pathways that induce aaTEC formation with aging (Aim 2). Through the F31 fellowship, I will not only continue to enhance my skills as a PhD student, but my findings will also contribute to efforts in creating therapeutics to boost thymic function within aging populations. This prestigious fellowship will also provide me with the necessary training and support system to further my goal in becoming an independent researcher and leading my own scientific enterprise.

NIH Research Projects · FY 2025 · 2025-12

ABSTRACT In 2018, there were over 100,000 severe infections and 50,000 deaths from yellow fever globally. Case fatality rates are estimated to be as high as 20-60%, though mild cases and diagnostic challenges make estimating disease burden difficult. Though a vaccine is available, vaccination rates vary significantly both between and within countries. The 2016-2019 yellow fever epidemic in Brazil was the country’s most significant in 70 years, with the outbreak shifting towards population centers in the east. Despite an estimated national vaccination coverage of 70%, yellow fever has continued to be a public health threat, due partly to heterogeneous vaccine coverage at the municipality level. Yellow fever in Brazil is primarily sylvatic, with transmission between non- human primates and humans. Higher levels of forest fragmentation and low levels of native vegetation are associated with increased risk of outbreaks in non-human primates. Studies of land use and human risk have often used proxies such as vegetation indices and areas impacted by fire. More research is needed to clarify the relationship between land use, especially land use heterogeneity, and the risk of human yellow fever cases. Furthermore, as climate change intensifies, the risk of vector-borne disease is shifting along with a shift in areas most suitable for vector survival and proliferation. These changes need to be incorporated into efforts to model future yellow fever risk. This study will evaluate the association between land use, climate, and the risk of human yellow fever cases in Brazil. Aim 1 will use machine learning to characterize fine scale land use change from 2016-2022 in 6 Brazilian states with varying histories of yellow fever infection, including changes in land use types and landscape heterogeneity. Aim 2 will assess associations between land use, environmental degradation, and incident yellow fever cases via the development of spatial models of risk. Aim 3 will combine data on land use with predications of climatic variables under various climate change scenarios to predict areas at future risk for yellow fever in Brazil. Findings from these analyses will allow the identification of landscape features associated with yellow fever cases in Brazil at an improved level of granularity and improve understanding of yellow fever risk to humans under current and future conditions. This will result in comprehensive risk estimates relevant to yellow fever control programs and the efficient allocation of vaccines. Development of this novel approach will also allow for future expansion of this predictive work to other countries and diseases which are sensitive to climatic and land use changes.

NSF Awards · FY 2025 · 2025-10

Teenagers are increasingly using tools that depend on generative artificial intelligence (generative AI). Social media companies are rapidly integrating generative AI into their platforms to create agents that they describe as assistants, companions, and coaches. However, research on how the use of generative AI agents affects teen well-being lags behind teens' exposure to these systems. In this project, the research team will study how teens are interacting with generative AI agents today and how this use impacts teens' well-being. The team will also explore how technology designers can use this knowledge to create more useful, safer generative AI agents for teens. The contributions of this work will include new scientific understanding of how teens are using generative AI and new guidance for maximizing the benefits of these systems for young people. The research is organized around three main activities. In the first activity, the team will recruit teenagers who use generative AI who are willing to share selected parts of their interactions with these systems. Through analyzing the chat histories and using them to guide personalized interviews with teenagers, the team will develop a nuanced understanding of what kinds of conversations teens have with generative AI systems that impact their well-being. In the second activity, the team will ask therapists who work with teens to themselves interact with generative AI systems and review findings from the first study. This will allow the research team to develop an expert perspective on the benefits and risks of current generative AI systems as well as design principles for systems that are more likely to improve teens' well-being. In the final activity, the team will conduct co-design activities with teens to create generative AI agents based on the new knowledge and principles developed. These agents will be assessed through a comparative field study where teens interact with these agents versus commonly available ones and report on their interactions and well-being during the study. 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

Industrial internet-of-things (IIoT) technologies spark growing interest in manufacturing security and resilience. However, current solutions lack a holistic understanding of cyber-physical resilience in complex systems, failing to connect IIoT network vulnerabilities with dynamic manufacturing processes for effective detection and control. To address these gaps, this Faculty Early Career Development (CAREER) project aims to develop novel methodologies that integrate modeling, detection, and control measures for understanding the cyber-physical resilience of continuous critical manufacturing systems. This study will work to eliminate barriers to the development of new policies, regulations, and standards for IIoT applications in manufacturing. In collaboration with industry stakeholders, this project promises long-term benefits by extending its methods and tools to other critical infrastructures, thereby enhancing national cyber-physical resilience. Meanwhile, the education and outreach activities in this project foster sustained awareness of cyber-physical resilience among both future and current manufacturing professionals. Introducing new courses and training materials enhances students' exposure to advanced manufacturing technologies and improves their data science and cybersecurity skills. K-12 outreach initiatives boost understanding of IIoT and cyber-physical resilience, promoting manufacturing careers. A specially designed training software addresses the need for intuitive cybersecurity training in engineering language. These endeavors align with the National Strategy for Advanced Manufacturing by contributing to the goal of ensuring national security. This study addresses critical challenges in continuous manufacturing systems' cyber-physical resilience. The research objectives include (1) Development of Generalizable Tools: The project aims to build generalizable tools for cyber-physical resilience quantification. By creating stochastic models that integrate cyber connectivity and system dynamics of heterogeneous components, a novel quantification metric will be established. This metric considers both IIoT network features and manufacturing system dynamics through stochastic optimization, revealing system-level risks induced by IIoT connectivity. (2) Rethinking Anomaly Detection: The project will rethink cyber-physical resilience-driven anomaly detection by incorporating system-wide resilience quantification into process-based anomaly detection algorithms. This involves designing novel semi-supervised learning algorithms that incorporate resilience, with a focus on understanding the theories governing detection accuracy and resilience enhancement in high-dimensional data-driven anomaly detection. (3) Collaborative Learning-Based Resilient Control Strategies: The study aims to create collaborative learning-based resilient control strategies. Leveraging reinforcement learning and system connectivity, these strategies enhance a system's adaptability to cyberattacks. This involves exploring the under-explored area of vertical federated reinforcement learning and generating new knowledge regarding the trade-off between the control performance of individual machines and the system's adaptability to adversaries. 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 will combine large flow-cytometry datasets with novel machine learning models to reveal the geographical distribution of phytoplankton and show how the environment shapes these patterns. Neural network methods for flow-cytometry data analysis will be applied to data from over 100 cruises across the Pacific and Atlantic Oceans. The project will develop computationally efficient mixture of neural network models, a generative model framework for changepoint detection, and spatially dependent convolutional neural networks. These methods will make oceanographic data analysis more automatic and efficient while also allowing for model-based rediscovery of ocean provinces as well as predictive mapping of ocean microbe populations and traits. The proposed methodology will advance AI and statistics, data science, and oceanography while also being useful across a broad range of disciplines that deal with complex high-dimensional dependent data such as environmental science, ecology, agriculture, epidemiology, and econometrics. The methodology will also be useful for various data science industries that handle high-dimensional mixture data or flow cytometry. Public-use software packages will be created. The project will develop computationally efficient neural network models that automatically classify cell level data with environmental covariates. This will streamline the analysis and reveal biological responses to changing environments. Generative neural networks will be used for changepoint detection. Latent variables will identify shifts in phytoplankton communities and help redefine ecological ocean provinces. Finally, convolutional neural networks will be applied to density regression and spatial interpolation of flow cytometry data. This predicts complete cytogram “images” extending data value beyond cruise tracks, to help create global phytoplankton biogeographies. 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

Non-technical description: The goal of this project is to build and steer swarms of micron-scale magnetic colloidal particles that come together and move cooperatively through complex environments, much like schools of fish, flocks of birds, or swarms of insects. These swarms are activated by a time-varying magnetic source (for example, an electromagnet or a moving permanent magnet) which functions as an external remote controller. The magnetic controller can direct swarms to propel through fluids, maneuver over surfaces and around obstacles, detect and respond to changes in their surroundings, and carry passive cargo. This project aims to advance the field of magnetic swarms by integrating large computer simulations, theoretical modeling, and experimental approaches within a cohesive framework. Mastering life-like swarm behavior could enable miniature ARMS robots that deliver medicine inside the body, inspect subsurface pipelines, or remove contaminants from water supplies. By opening new frontiers in materials science and programmable matter, this project advances the nation’s health, prosperity, and security while strengthening technological leadership. This project will also provide K-12, undergraduate, and graduate students with interdisciplinary training in computational and experimental techniques for materials science, physics, and engineering to develop our domestic workforce, improve public scientific literacy, and stimulate engagement with science and technology. Technical description: While magnetic swarms capable of dynamic reorganization have been demonstrated, a systematic approach to designing swarms with increasingly sophisticated functions in porous environments and unbounded 3D fluids remains a challenge. Large-scale simulations will capture the coupled magnetic, hydrodynamic, and contact interactions that drive collective motion across multiple length and time scales. Analytical theory will translate these data into design rules, while inverse-design algorithms will search efficiently for particle shapes, magnetic moments, and field protocols that enable adaptive aggregation to move through complex structures. Lithographically fabricated and chemically synthesized particles will test these predictions; high-speed imaging, particle tracking, and force mapping experiments will measure swarm structure, flow fields, and cargo transport efficiency. By combining computational analysis with experimental methods, swarm functionalities for advanced applications, such as adaptive organization, precise navigation, and targeted cargo transport in complex environments will be expanded. These advancements will create a foundation for future applications of colloidal swarms in sensing and delivery, turning theoretical insights into practical outcomes. More broadly, the proposed methods to accelerate swarm design will benefit other active material systems where flows of energy, matter, and information animate material structures to enable life-like capabilities. 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

Modern transportation systems generate massive amounts of data, including where and how vehicles and people move, traffic conditions, road conditions, and videos captured during actual trips. This includes detailed information about everyday driving behavior collected by cameras and sensors in cars and on roads. These datasets are essential for improving traffic safety, reducing congestion, and supporting the development of advanced technologies such as self-driving cars. However, they often contain sensitive personal details about individuals, making it difficult to share among traffic authorities, companies, and research institutions. This project addresses this challenge by developing secure methods for sharing transportation data while protecting individual privacy, serving the national interest by advancing transportation safety, supporting economic competitiveness in autonomous vehicle technologies, and strengthening infrastructure resilience through improved data-driven decision making. This project develops a comprehensive privacy-preserving platform for sharing diverse intelligent transportation systems data across different entities. The research targets multiple data types, including vehicle and road user information such as speed, travel times, and trajectories, as well as infrastructure data including traffic flow, control states, and videos. The project focuses particularly on naturalistic driving data collected by in-vehicle sensors and mobile devices. The research team will adapt and scale privacy-preserving techniques to support both centralized and distributed data-sharing models, ensuring secure data exchange without compromising individual privacy. The project will develop a web-based recommendation system to assist stakeholders in selecting appropriate privacy-preserving techniques for their specific datasets. Additionally, the team will create audit and compliance tools based on formal privacy guarantees and conduct user studies to ensure practical relevance. Secure cyberinfrastructure will be designed and deployed through collaboration with public and private partners. The platform will be evaluated using real-world transportation datasets to demonstrate effectiveness in enabling privacy-preserving data sharing that supports transportation research, improves traffic management, and accelerates development of data-driven mobility technologies. 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

Nontechnical Abstract This project aims to design a new class of engineered artificial materials, commonly known as metamaterials, that exhibit a high refractive index in ways not possible with naturally occurring substances. Refractive index determines the velocity of light, with a higher index resulting in a lower velocity. A high index helps guide light better and also bends it by a larger angle. This is crucial to get better optical components, such as lenses, which need to bend light to focus. A high nonlinear index also allows one light beam to control another light beam. These capabilities can help create faster computers, build better cameras, enhance augmented reality displays, and enable high-speed communication. These materials will be created by arranging nanometer length scale artificial materials (“nanocrystals”) in a periodic structure. Additional patterning at longer length scales will enable the development of new optical hardware. While the concept of creating such artificial materials is compelling, realizing it in practice is extremely challenging. This project addresses these challenges through a unique, multi-scale inverse design approach, driven by advanced computational modeling and machine learning. The project will also empirically validate the designed material properties, creating two testbeds: thermal imaging and nonlinear optical activation for optical information processing. Along with advancing the frontiers of optical imaging and computing, the program will train a new generation of scientists and engineers through hands-on interdisciplinary research experiences that span physics, chemistry, computation, artificial intelligence (AI), and materials science. By engaging high school, undergraduate, and graduate students, the project will broaden participation in cutting-edge science. Technical Abstract Designing materials with high linear and nonlinear susceptibilities can unlock a vast range of applications in photonics. Metamaterials present a unique opportunity to realize a high index, beyond what is available in naturally occurring materials. For instance, by combining nanocrystals appropriately, it may be possible to design a composite material with record high susceptibilities. The effective susceptibility of this composite material can be further enhanced via wavelength-scale patterning. Such a multi-scale metamaterial would be the first of its kind, where the constituent meta-molecules also comprise a metamaterial. While the multi-scale design of metamaterials is conceptually simple, it is extremely challenging in practice to design the exact combination of materials to achieve a desired property, while ensuring that the designs can be synthesized or fabricated. Guided by fundamental bounds based on the causality and passivity constraints of physical materials, this project will identify new design rules. Using a multi-scale inverse design approach, including a physics-inspired artificial neural network, the optimal combination of nanocrystals and meta-molecule structures will be identified. While the design techniques will be applicable to many material systems, a few promising ones will be downselected for experimental realization. These composite nanocrystals will be chemically synthesized and subsequently patterned to create the metamaterial. Ellipsometry and nonlinear pump-probe spectroscopy will be used to validate the design. The experimental data will help refine the design assumptions and provide new insight. Combining computational electromagnetics, optimization theory, machine learning, chemical synthesis, nanofabrication, and optical characterization, three research thrusts will be pursued: (i) create high linear susceptibility composite materials; (ii) create high nonlinear susceptibility composite materials; and (iii) demonstrate metamaterials made of the composite materials for nonlinear optical activation in optical neural network accelerators and high-efficiency thermal imaging. 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

Modern computer programs often use a large amount of resources and energy. Before running a complex program it is important to anticipate how many resources the program needs, to best provision these resources in order to avoid any waste, while at the same time ensuring that the program executes successfully. Database management systems often have multiple alternatives ways available on how to run a program, and the system needs to choose that alternative that uses the small amount of resources. This project develops novel techniques for estimating the amount of data produced by a program, which in turn can be used either to provision how many resources the program needs, or to chose between alternative ways to execute the program. The project develops a cardinality estimation system, which is "pessimistic", in the sense that it offers a one-sided theoretical guarantee that output cardinality of a query will never exceed that estimate. To compute this estimate, the project builds on information theory and computes a tight upper bound on the output cardinality. The estimate is computed from simple statistics on the input data, which can be collected offline, and which are already available in many current systems, such as cardinalities of base tables, number of distinct values in various columns, maximum degrees, or Lp-norms of degree sequences for various values of p. During the offline phase, the system can further refine these statistics by dividing the input data into buckets, then computing these statistics separately in each bucket, similar to how histograms are computed today by database management systems. What is novel about this project is that it uses all available statistics and computes an upper bound on the output size of a query, which is guaranteed to be the tightest upper bound possible given these statistics. The project studies new efficient ways to compute this bound, explores the potential use of Lp-sketches for computing the statistics offline and maintaining them incrementally, and extends the framework to group-by queries, for which traditional cardinality estimation methods are known to perform very poorly. 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.