University Of Washington

NSF Awards · FY 2025 · 2025-01

This project provides funding for the Research Vessel Rachel Carson to conduct oceanographic research missions supported by the National Science Foundation. The oceanographic research vessels of the Academic Research Fleet (ARF), operated by the academic institutions within the University-National Oceanographic Laboratory System (UNOLS) framework are multi-use facilities used to expand knowledge of the ocean environment. The surface work of these ships is complemented by human-occupied, remotely operated, and autonomous undersea vehicles and sensors that provide vital tools to understand the oceans and their resources. These seagoing research and educational facilities enable scientists and students to study natural phenomena and train future scientists while on board state-of-the-art oceanographic research vessels utilizing high-quality instrumentation. The ship operators will also conduct learning activities for students and the general public including hands-on demonstrations of marine science research guided by faculty, students and ship crew members. 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 · 2025-01

Candidate: Dr. Cody McDonald is an Assistant Professor in the Department of Rehabilitation Medicine at the University of Washington (UW). She completed her MPH and PhD at UW and has been engaged in rehabilitation-related research since 2014. Dr. McDonald has worked on several federally-funded research grants, including a randomized crossover trial and multiple outcome measure development studies. She also collaborates with colleagues at the federally-funded UW Center for Outcomes on Rehabilitation Research. Dr. McDonald’s research experience includes multiple phases of the translational research process: collecting and analyzing qualitative and quantitative data; development and application of new outcome measures; preparing for and implementing randomized crossover trials. Dr. McDonald’s long-term career objectives are to elucidate grief and emotional health after amputation and understand how grief may impact health outcomes; to develop measures centered in patient priorities to assess constructs of importance for individuals with impairments; and to develop and employ strategies for measure development and implementation to improve health equity. Environment: The UW School of Medicine consistently ranks among the top Medical Schools in the nation in the U.S. News and World Report for its excellence in research, clinical training programs, and patient care. The School of Medicine is an international leader in biomedical research, receiving over $500 million in research funding annually. The Department of Rehabilitation Medicine at the University of Washington has had a strong commitment to research since its inception. Research, both basic and clinical, is a major component of the departmental mission. The Department is consistently ranked in the top two in NIH funding and the top three in overall research funding for all rehabilitation departments. Research: In the proposed research, I will develop and calibrate a new patient-driven measure to assess grief after amputation using a health equity approach. This project includes two aims: first to develop a new self- report measure to assess grief using a health equity approach, and second, to conduct initial calibration of this new tool. Community-based participatory research methods and principles will be used throughout the proposed project. A community advisory board will be developed and engaged to inform project decisions. To develop the measure, I will conduct a literature review of existing grief measures. Next, I will use focus group and interview discussions with individuals with amputation (with a focus on minoritized groups) and guided by the diagnostic criteria for prolonged grief disorder and the socioecological model of health. Using existing measures and qualitative findings, I will develop new items and test them through cognitive interviews. Based on interview results, I will narrow items to a final item bank. The final item bank will be used to conduct a national survey of adults with limb amputation. This survey will yield data on the psychometric properties of the grief measure including initial calibration data and information about how items function within the measure.

NIH Research Projects · FY 2026 · 2025-01

PROJECT SUMMARY The overall objective of the proposed research is to estimate the present and future burden of extreme temperature in a geographically detailed manner for the US. Extreme temperature risk will be assessed on a 4x4 km grid for the contiguous US and roll into socio-demographic and -economic information available on the census tract and county level. The main emphasis lies in understanding how risk increases across the lifespan and quantifying the expected impact in aging populations. Understanding how extreme temperature effects are modified by other factors such as socio-demographic or socio-economic characteristics, pre-existing health conditions, and air pollution etc. is another key aim of the proposed work. In a previous study, the PI estimated that approximately 124,000 (95% UI: 105,000-145,000) deaths in the US in 2019 occurred due to exposure to non-optimal temperature. This renders temperature the leading environmental risk factor in North America, currently contributing to more deaths than air pollution. Cold exposure, in particular, adds to this high burden, but substantial heat-related effects occur in states such as Arizona, Texas, or Louisiana. With temperatures rising, the significance of heat-related deaths is likely to increase. In addition, population aging in the US, with the population aged 65 years and above expected to double by 2060, is potentially aggravating the impact of extreme temperature. While several studies have assessed temperature impacts on all-cause or broad categories of disease, there is no systematic assessment of the impact of extreme temperature on cause- specific mortality or of the role of risk modification, especially by age. Furthermore, most related studies have conducted risk assessment in select locations, mostly individual cities, but spatially detailed continuous assessments across regions are lacking. Here, we suggest research that is driven by three hypotheses and centered around three aligning research aims. In Aim 1, we will derive all-cause and cause-specific risk functions for extreme high and low temperature across the lifespan and estimate county-specific burden. In Aim 2, we will project future impacts of extreme temperature-related disease using climate and population change scenarios. Finally, in Aim 3 we will assess the risk reduction potential associated with interventions to reduce temperature- health risk and provide regional risk reduction guidance. Detailed estimates allow identifying priority areas and target specific population and patient groups. The proposed work draws from the extensive data and infrastructure available at IHME and builds upon work previously conducted within the Global Burden of Disease study. Results will be published in scientific journals and via an online visualization tool, allowing the scientific community, as well as non-technical audiences, the media, and policy- and decision-makers to view and utilize the results. To ensure effective communication and policy translation of results, the research team is supported by a policy advisory panel and the IHME Global Impact group.

NIH Research Projects · FY 2026 · 2025-01

Myelin is a major structural component of the brain playing a vital role for its proper function. Demyelination is commonly observed in numerous neurological conditions where it can be either a primary pathological substrate or a direct consequence of damage to axons, neurons, or oligodendroglia caused by various injuries. Recent animal studies indicate that demyelination is an important component of the sequence of pathological events in ischemic stroke. After initial myelin damage, infarcted brain tissue undergoes either progressive demyelination and necrosis or remyelination accompanied by axonal remodeling. Current knowledge about myelin damage and repair in stroke is exclusively based on animal models. We propose a clinical study that aims to a) demonstrate the feasibility of quantifying myelin loss and recovery in stroke patients; b) establish a relationship of demyelination and remyelination with various clinical factors; and c) identify associations of demyelination and subsequent remyelination with functional recovery after stroke. The study is driven by the overarching hypothesis that demyelination in stroke provides an indicator of the extent of overall tissue injury, and, vice versa, brain tissue repair after stroke can be monitored by quantifying remyelination. Demyelination and remyelination in stroke can be assessed with high specificity and sensitivity using a novel quantitative magnetic resonance imaging (MRI) technique named single-point macromolecular proton fraction (MPF) mapping. This method has been extensively validated by histology in animal models and demonstrated promising results in human studies of neurological diseases and brain development. As a clinically-targeted quantitative myelin imaging modality, MPF mapping offers a number of advantages including fast acquisition, independence of magnetic field strength, insensitivity to changes in magnetic relaxation caused by iron deposition, high reproducibility, and straightforward implementation in a clinical setting. In the proposed study, we will further improve the MPF mapping method by accelerating acquisition and introducing a novel reconstruction algorithm that computes absolute macromolecular proton concentration (MPC) to eliminate the confounding effect of edema on MPF. Whole-brain MPF mapping protocols with less than 3 minutes total scan time will be implemented for the most widely used clinical MRI platforms based on unmodified manufactures’ pulse sequences. We will prospectively recruit acute and sub-acute ischemic stroke patients during inpatient treatment and conduct follow-up examinations for six months. Based on longitudinal quantitative myelin assessment using MPF/MPC and a series of clinical outcome measures in several functional domains, the following hypotheses will be tested: 1) MPF/MPC is capable of detecting remyelination during post-stroke brain tissue recovery; 2) an extent of demyelination at baseline and/or subsequent remyelination observed longitudinally is associated with outcomes of rehabilitation; and 3) inclusion of quantitative myelin measures into predictive statistical models improves prognosis of post-stroke rehabilitation.

NSF Awards · FY 2025 · 2025-01

The goals of AccessComputingWorkforce are to educate the next generation of computing innovators about accessibility and to make computing education itself accessible. Accessible computing technology benefits a wider range of users and offers economic advantages. Most U.S. computing curricula lack accessibility training, which creates a skills gap between industry needs and the workforce. The absence of accessibility in computing education further detracts from workforce potential by limiting who can succeed in the field and restricting student exposure to advanced computing tools and technologies. Expanding accessibility training helps close the skill and knowledge gap in the computing workforce, which in turn ensures all Americans can benefit from new computing technologies. Making computing education accessible also enables more Americans to enter the computing workforce prepared to meet industry needs. AccessComputingWorkforce is a national resource available to students, educators, and organizations across the U.S., and it promotes the long-term impact of its efforts through the creation of durable resources that are available to anyone. This project engages students, educators, and organizations across the nation to develop accessibility-focused courses, modules, and innovative teaching practices, as well as techniques and best practices for making computing courses accessible. The project fosters a community of computing students to develop their interest, skills, and knowledge in accessibility, and to provide learning opportunities and mentorship in preparation for accessibility-focused careers and research. Additionally, the project partners with computing organizations to promote the inclusion of accessibility in curricula; work with industry to understand skill gaps and inform curriculum development; and collaborate with companies and open-source communities to ensure educational and research tools are accessible. We also work with partners to conduct research, disseminate findings and resources through papers and publications tailored for different stakeholders, and share knowledge with relevant communities. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NIH Research Projects · FY 2026 · 2025-01

Alzheimer’s disease (AD), a severe progressive neurodegenerative disease of aging and the most common form of dementia, affects an estimated 30 million people worldwide. The presence of both amyloid plaques and neurofibrillary tangles pathologically define AD, however, TDP-43 pathology often occurs comorbidly with AD and correlates with more rapid cognitive decline and faster rates of hippocampal atrophy. We have previously shown that there is a synergistic relationship between tau and TDP-43 in a novel C. elegans model of combined tau and TDP-43 proteotoxicity, however the mechanisms that drive this interaction are unknown. In our C. elegans models we have performed bulk RNA sequencing to compare gene expression in transgenic animals expressing tau and TDP-43 alone and in combination. Preliminary results show that while tau pathology alone results in an upregulation of genes encoding lysosomal enzymes, these same genes show decreased expression in the combined tau and TDP-43 animals. We hypothesize that tau promotes a compensatory upregulation of the lysosomal autophagy pathway, as has been observed in human AD sequencing studies, but the combination of tau+TDP-43 impairs this response. In Aim 1 we will further interrogate this potential mechanistic pathway of tau and TDP-43 synergy by targeting specific genes involved in the lysosomal autophagy pathway in our C. elegans models of proteotoxicity. We will pair these functional assessments with a highly quantitative and spatially preserved interrogation of well-characterized human post- mortem samples to establish relevance to human disease and identify additional mechanisms of tau+TDP-43 synergy. One reason these pathways have remained elusive may be due to the heterogeneity of pTPD-43 pathology observed in AD. Distinct subtypes of pTDP-43 pathology have been described and our preliminary data suggest different relationships exist between tau and TDP-43 depending on the subtype present. In Aims 2 and 3 we will use NanoString technology to measure both protein and gene expression in key brain regions along the trajectory of pTDP-43 pathologic progression, taking into account the pTDP-43 morphologic subtype. Together, these studies will identify potential mechanisms that underly the observed synergistic proteotoxicty of tau and TDP-43, significantly advancing our understanding of the role of TDP-43 in the pathophysiology of AD and ultimately contributing to novel treatment and diagnostic strategies for AD with comorbid TDP-43 pathology in future work.

NSF Awards · FY 2025 · 2025-01

Sea ice in the Southern Ocean around Antarctica is intricately linked to ecosystems and the global climate, and it appears to be on the brink of rapid change. Recent unexpected decreases in Antarctic sea ice have been partly attributed to ocean heat release, which may be influenced by variations in freshwater input, turbulent ocean mixing, and internal system feedbacks. Studying how these factors modulate the growth and melt of sea ice has been challenging due to the remoteness of the Southern Ocean and its blanket of winter sea ice. This research will combine observations and modeling to quantify the impacts of episodic snowfall and storms on the Antarctic snow-ice-ocean system. The results will contribute to a clearer understanding of ongoing and future changes in Antarctic sea ice, enabling better projections of global ocean circulation, carbon uptake, and ecosystem health. The primary goal of this work is to characterize the time evolution of the Antarctic sea ice mass budget and upper ocean properties following perturbations from snowfall and storms. Composite analyses of autonomous Argo profiling float measurements, atmospheric reanalysis data, and satellite remote sensing will be used to illuminate the competing processes these events set in motion, which can include snow-ice conversion, basal sea ice melt, and trapping of wind-blown snow in sea ice cracks, known as leads. A new one-dimensional coupled ice-ocean model configuration will be developed and used for sensitivity experiments that isolate impacts and feedbacks. These findings will be applied to trends in key variables diagnosed in global climate models to explore the consequences of changes in Southern Hemisphere storm tracks and snow accumulation expected in a warming climate. Broader impacts of this project include mentoring and teaching in educational programs that expand participation in the earth sciences and hosting a workshop to enhance writing skills for local climate advocacy. 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 · 2024-12

PROJECT SUMMARY / ABSTRACT Hypertrophic cardiomyopathy (HCM), characterized by unexplained left ventricular hypertrophy, affects 1:200- 500 people. HCM can cause heart failure and sudden cardiac death. Pathogenic single nucleotide variants (SNVs) causing missense substitutions in myosin heavy chain 7 (MYH7; encoding β-MHC) account for 35% of inherited HCM cases. Pathogenic MYH7 variants are clinically actionable because they identify patients at risk of developing HCM, prompting early interventions that can prevent sudden cardiac death. However, 75% of MYH7 missense variants are of unknown significance (VUS) because there is insufficient evidence to determine pathogenicity. Furthermore, variable expressivity, or symptomatic severity, in patients with pathogenic MYH7 variants limits their prognostic value. Identification of HCM-modifying factors would enable clinicians to personalize patient counselling. The goal of this proposal is to establish a new system to accelerate MYH7 VUS reclassification and determine the role of putative HCM-modifiers using high-throughput in vitro models to provide clinically actionable information for patients with MYH7 variants. Human induced pluripotent stem cells (hiPSCs) retain their donor’s genetic background, can be differentiated to cardiomyocytes (hiPSC- CMs), and are an ideal model for MYH7 variant effect studies. hiPSC-CMs with pathogenic MYH7 variants have shown decreased survival, decreased variant-to-wildtype β-MHC protein, and increased atrial natriuretic peptide (ANP) expression, which correlate with measures of HCM severity. During my postdoctoral training, I developed a novel gene-editing tool to enable pooled generation of MYH7 variant hiPSC libraries. High-throughput assessments of a pilot MYH7 variant library in hiPSC-CMs correctly segregated all pathogenic and benign variants, showing that my approach can be scaled to determine the functional effect of thousands of MYH7 SNVs in hiPSC-CMs (Aim 1). These functional data will aid in the reclassification of up to 140 VUS, yet HCM expressivity is variable and the roles of putative HCM-modifiers remain largely untested. Genome-wide association studies (GWAS) have linked many single nucleotide polymorphisms (SNPs) with HCM. I hypothesize that these GWAS SNPs act as genetic modifiers that drive the expressivity of pathogenic MYH7 HCM variants. To test this hypothesis, I propose to measure the phenotypic variability of pathogenic MYH7 variants in hiPSC-CMs generated in an isogenic hiPSC background with three different HCM-associated SNPs separately introduced (Aim 2). Moreover, males manifest HCM earlier than females and have increased expressivity after puberty, however, the source of this sex difference remains unknown. I hypothesize that both sex chromosomes and sex hormones contribute to HCM sexual dimorphism. To test this hypothesis, I propose to establish a co-culture ‘village’ of pooled MYH7 variant hiPSC-CMs generated on healthy XX and XY hiPSC backgrounds and examine phenotypic variability with and without sex hormones (Aim 3).

NIH Research Projects · FY 2026 · 2024-12

PROJECT SUMMARY/ABSTRACT Organs have specific three-dimensional (3D) architectures that are essential for their physiological functions. By integrating chemical and physical inputs, cells build and sculpt complex 3D tissue shapes. Understanding the molecular and physical architectural rules governing the formation of our organs could offer insights into how deviations from these rules contribute to human diseases. Despite the prevalence of looped epithelial tubular structures throughout our organs we lack fundamental knowledge of the strategies cells use to build these complex tissue structures. The goal of my research program is to understand the emergent properties cells use to sculpt organ shapes. To achieve this goal, we use the developing Drosophila renal system which involves the generation and extension of epithelial tubes that bend and loop in 3D in a stereotypic manner. This experimentally tractable system is ideal to identify the parameters that generate complex tissue forms while keeping the cells in an in vivo context. Here we will couple our ability to both visualize and perturb key drivers of tissue morphogenesis to determine how these drivers influence cell shape, collective cell behavior, and ultimately organ form. In this project, we will focus on the following questions: (1) How is contractile machinery in cells organized to coordinate tissue-scale behavior? and (2) How do cells coordinate to dynamically pack (and repack) in a tissue that is changing shape? To address these questions, we are developing techniques to visualize, track, and quantify both the 3D cell behaviors and machineries that drive these tissue movements in vivo. We will build on work by myself and others to understand the molecular and physical principles cells use to bend and extend epithelial sheets to reveal how cells drive the formation of looped epithelial tubes. Identifying these general principles of tissue morphogenesis will provide insight into how dysregulation of these rules leads to human disease and provide opportunities to develop novel therapeutic strategies to treat them.

NIH Research Projects · FY 2026 · 2024-12

PROJECT SUMMARY/ABSTRACT Developmental and epileptic encephalopathies (DEEs) are severe pediatric neurological disorders with a strong genetic link characterized by drug-resistant seizures and developmental delays. To develop precision therapies in the era of genomic medicine, one must first obtain a precise molecular diagnosis. Sequencing technologies can now identify genetic etiologies of DEEs in about 50% of patients, but the remaining 50% have genetically “unsolved” DEEs. Furthermore, understanding how these genetic alterations lead to disease underlies important aspects of basic biology and is paramount for moving effective therapies into the clinic. We have recently established the diagnostic utility of genome-wide DNA methylation analysis to uncover causal and candidate etiologies in DEEs. By investigating DNA methylation arrays for >500 individuals with unsolved DEEs, we determined that rare differentially methylated regions are accompanied by underlying rare CG-rich tandem repeat expansions (TREs) and other structural variants. We also diagnosed a subset of individuals by leveraging DNA methylation signatures (“episignatures”), which are clinically validated biomarkers of >100 neurodevelopmental disorders, including epilepsies. Our work represents a significant advance towards solving the unsolved and opens further questions regarding genomic discoveries in DEEs and functional investigations of episigantures, each of which is highly applicable to hundreds of genetic disorders. In the F99 phase of this proposed research, I will utilize our growing cohorts of short- and long-read genome sequencing data from individuals with unsolved DEEs and their parents to investigate rare TREs more broadly as potential novel etiologies of DEEs and RNA-seq data to probe TRE function. I will harness overlapping epigenomic and transcriptomic datasets to investigate episignatures in brain-relevant cell types using CHD2- encephalopathy as a model. Individuals with loss-of-function variants in CHD2 harbor a robust episignature in the blood, but no work has been done to characterize these episignatures in brain-relevant cell types, such as neuronal progenitor cells and 2D neurons. I will characterize episignatures and correlations with gene expression for CHD2 in these cell types and determine if restoration of CHD2 is sufficient to alter episignatures back to normal. This will determine whether episignatures can serve as biomarkers for therapeutic development. In the K00 phase of this proposed research, I will continue to use cutting-edge sequencing technologies to understand human genetic variation and how alterations lead to disease phenotypes. I will expand on my bioinformatics skills by developing software solutions to address new technologies and growing datasets. I will use these tools on large, diverse datasets to understand disease biology and translate my findings into the clinic. The proposed work will yield novel genomic, epigenomic, and transcriptomic discoveries that will increase our understanding of pediatric epilepsies, their etiologies and how those etiologies lead to disease. Ultimately, this work has diagnostic, prognostic, and therapeutic implications relevant for hundreds of genetic disorders.

NIH Research Projects · FY 2024 · 2024-12

ABSTRACT The primary goal of this F31 proposal is to characterize the social and behavioral determinants of non-retention, including the influence of caregiver support, and to determine trajectories of depression and adherence self- efficacy among youth living with HIV (YLH)in Kenya. There are an estimated 3.4 million adolescents and young adults aged 10-24 years living with HIV globally. Compared to other age-groups, adolescents are less likely to be linked to care, retained, and achieve viral suppression, with many disengaging in care leading to poor clinical outcomes and onward transmission of HIV at the community-level. There are distinct subgroups of YLH that likely have distinct retention determinants. Younger YLH (10-14) often have parental support in HIV management while older YLH (15-19) desire increased autonomy in health decision-making; young adults (20-24) may have long-term partners who influence retention. The proposal will leverage data from a large 24-site cluster randomized trial in Western Kenya (DiSC; NCT#05007717; MPIs: Drs. Grace John-Stewart and Pamela Kohler) among 1904 YLH and 746 caregivers. Aim 1 will explore age-specific demographic, social, behavioral, and clinical cofactors of non-retention and loss-to-follow-up (LTFU) among YLH during 12-month follow-up, including adherence self-efficacy, social support, substance use, depression, and anxiety using generalized linear mixed effects models. Cofactors of LTFU will be estimated using Cox proportional hazards regression. Aim 2 will determine trajectory patterns of depression and adherence self-efficacy and predictors of trajectory groups using group-based trajectory modeling. Aim 3a will explore the relationship between caregivers’ depressive and anxiety symptoms and adolescent HIV outcomes including non-retention, ART adherence, and viral non- suppression. These associations will be modeled using generalized linear mixed effects models. Aim 3b will examine how caregivers’ mental health impacts caregiver motivation and behaviors to support adolescents in staying adherent to ART and retained in care. Findings from this project will advance our understanding of non- retention among YLH and contribute to interventions to optimize retention among YLH. This proposal will provide the F31 candidate with rigorous predoctoral training, including 1) advanced epidemiologic statistical methods including longitudinal data analyses, 2) experience with developing and leading a mixed methods study, 3) content expertise in retention and mental health among youth living with HIV, and 4) understanding of caregivers’ roles in supporting YLH.

NIH Research Projects · FY 2026 · 2024-12

PROJECT SUMMARY/ABSTRACT Opioids are the most effective pharmacological treatment for pain but feature severe negative side effects including tolerance and abuse liability, highlighting the need for strategies to make opioid drugs safer. The key molecular target modulating opioid analgesia and problematic side effects is the mu-opioid receptor (MOR), a G protein-coupled receptor (GPCR) found in the nervous system. To investigate MOR signaling, our lab pioneered a translationally relevant, cross-species transgenic model where mammalian MOR is expressed pan-neuronally in C. elegans (tgMOR). This yields opioid-induced behavior as a primary readout where addition of opioid drugs induces behavioral paralysis in a dose-dependent manner. Utilizing this innovative platform, my proposal aims to identify and characterize genetic regulators of opioid drug action in the context of behavioral sensitivity and tolerance. This project directly addresses NIDA’s Priority Scientific Area 1 that aims to further understand the genetic, neuropharmacological, and behavioral changes induced by opioid use. At present, we do not fully understand how tgMOR utilizes endogenous signaling components to generate opioid-induced behavioral effects. To address this, Aim 1 will investigate how G-protein signaling influences behavioral outcomes in tgMOR C. elegans. Aim 1 will use traditional genetics and CRISPR engineering to insert stop cassettes in genes of four selected mediators of MOR signaling. These four tgMOR mutants will undergo automated behavioral assays to evaluate changes in opioid sensitivity. Additional experiments will evaluate behavioral responses in a transgenic rescue model using Mos1-mediated single copy insertion (MosSCI) to express human and C. elegans genetic constructs of two selected MOR mediators in tgMOR mutants. Aim 2 will test whether genetic regulators of MOR signaling alter behavioral tolerance responses. The speed and genetic tractability of tgMOR C. elegans will allow comprehensive testing of two selected tgMOR mutants as well as multiple opioid drugs. Experiments will utilize automated behavioral assays to evaluate opioid sensitivity and tolerance in two tgMOR mutants. These results will be confirmed by performing transgenic rescue experiments with human and C. elegans constructs of two regulators of MOR signaling using MosSCI technology. Thus, our tgMOR C. elegans model provides an innovative, unbiased behavioral platform to genetically investigate MOR signaling with the potential to open up new avenues to manage opioid substance use disorder.

NSF Awards · FY 2024 · 2024-12

Quantum computers offer the potential for substantial speed-ups over classical computers. This project focusses on the extent to which such speed-ups are limited by constraints on the amount of quantum memory available. These constraints are likely to be considerable, particularly in the near term. Considering the resources of time and quantum memory together is critical for understanding which applications will be able to make best use of quantum computers. This project will build on methods developed by the investigator to analyze the combinations of time and quantum memory necessary for computational tasks, extending these methods to understand the trade-offs between these two resources required for a wide variety of computational tasks. This project also focuses on improving and extending methods, known as lifting theorems, that allow one to translate the understanding of computation by individual computing devices to computations involving interacting devices. Such theorems have been at the heart of many results in the last decade that have resolved longstanding open questions and have the potential to achieve much more. Thus there will be positive impacts on the field of complexity theory as well as development of near-term quantum computing devices. In addition, there will be training and mentoring of both undergraduate and graduate students, who will have the benefit of working in interdisciplinary research. The space used by a quantum algorithm is the maximum number of qubits that need to be maintained in simultaneous superposition during the algorithm's execution. Maintaining a large number of qubits in simultaneous superposition is a particularly difficult challenge, making space an even more important resource for quantum algorithms than space is for classical algorithms. The investigator recently introduced a technique for producing strong time lower bounds for space-bounded quantum algorithms and applied it to a number of computational problems in linear algebra and Boolean algebra, matching or improving the best classical lower bounds and yielding optimal results in many cases. This project will focus on extending this technique, and related analyses in quantum computation, to a wide variety of other computational problems for which classical time-space trade-off lower bounds are currently known. It will also focus on developing new methods for obtaining quantum time-space trade-off lower bounds for problems with sub-linear-time quantum algorithms. This project also aims to improve the parameters and extend the range of communication models in current lifting theorems that derive communication complexity lower bounds from query complexity lower bounds. 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 2024 · 2024-12

The broader impact of this I-Corps project is the development, validation, and introduction to market of a correction technology to address gaps in Global Positioning System (GPS) performance in urban areas. This technology has the potential to enhance the accuracy and reliability of autonomous systems such as drones, urban air mobility vehicles, and delivery robots, The solution will help them navigate more safely and efficiently in high-density environments where existing GPS signals are error-prone and offer degraded performance. The societal impact of this project includes safer urban transportation, streamlined emergency response and healthcare logistics, and environmental sustainability through optimized route planning and reduced congestion. Commercially, this technology has the potential to meet the critical need for affordable, modular, and lightweight GPS systems, overcoming the cost, size, and power limitations of the current technologies in critical industries. Low power consumption and flexible design make the solution ideal for seamless integration into various autonomous platforms without requiring additional infrastructure. This adaptability positions this technology to impact a wide range of sectors from logistics to aerospace. This I-Corps project utilizes experiential learning coupled with a first-hand investigation of the industry ecosystem to assess the translation potential of the technology. This solution is based on the development of a machine learning-powered sensor fusion system that integrates multiple sensing modalities, including inertial measurement units, barometric pressure, and time-of-flight sensors, to enhance real-time GPS accuracy. The system employs deep neural network models, specifically recurrent architectures, to adaptively correct GPS data, especially in challenging environments like dense urban landscapes where GPS signals are often obstructed or reflected. By leveraging cost-effective, low-power sensors, this project introduces a scalable method to provide accurate positioning without relying on extensive infrastructure resources. The research supporting this solution demonstrates significant improvements in localization precision, with a substantial reduction in positioning errors, improving precision and improved adaptability to varying noisy environmental conditions. The technical results show a 30% improvement in positioning accuracy and a fivefold improvement in precision compared to conventional GPS solutions in urban conditions at high speeds (i.e., exceeding 10Hz). The approach, built on foundational advances in sensor fusion and machine learning, offers a robust framework that can dynamically integrate real-time data streams enabling high-precision positioning across multiple fields, including navigation, transportation, and autonomous 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.

NIH Research Projects · FY 2026 · 2024-12

PROJECT SUMMARY/ABSTRACT Aneuploidy and chromosomal mosaicism in human embryos complicate predicting pregnancy outcomes already plagued with frequent pregnancy losses. Given the rapid increase of fertility treatments such as assisted reproductive technology (ART) treatments, there is a critical need to better assess the developmental potential of embryo candidates, especially in patients with poor prognoses. Both recent clinical and in vitro studies suggest that human mosaic embryos can selectively eliminate aneuploid cells for healthy development; however, the mechanisms mediating this embryonic self-correction have yet to be systematically studied in humans. Here, I propose to investigate the role of autophagy, which regulates both cell survival and death, in aneuploidy depletion. Although previous work in mouse and human preimplantation models demonstrated autophagy upregulation in eliminated cell types, our preliminary data in a human gastrulation model revealed that autophagy instead aids in cell survival. Because both technical and ethical limitations restrict studying cellular processes during early human embryogenesis, I will develop a novel platform to quantitatively screen and sort mosaic gastruloids—an in vitro multicellular model recapitulating cell fate and signaling during gastrulation—comprised of euploid and aneuploid human embryonic stem cells (hESCs). Analyzing single gastruloids is challenging by most current technologies like gene expression assays, which only allow for low-throughput sorting or bulk analyses. Thus, new tools are required to systematically study the heterogeneity among gastruloids undergoing dramatic changes during self-organization. The project is divided into two main Aims. I will use an automated microscope system that I have developed to perform image-based phenotypic screens of single gastruloids using live reporters and a custom computational pipeline. Firstly, I will generate a mixed population of mosaic gastruloids and identify distinct phenotypes based on patterning behavior via clustering. Secondly, I will investigate autophagy-dependent aneuploidy depletion within the mosaic gastruloids by measuring autophagy markers (LC3B and p62) and p53- responsive genes (p53, p21, cyclin G1, bcl-2). The gastruloids will be treated with bafilomycin A1 or rapamycin to inhibit or enhance autophagy, respectively. I hypothesize that phenotypic abnormalities in mosaic human embryos are dependent on the degree of aneuploidy present, and that autophagy can prevent the elimination of aneuploid cells. The proposed project will enable future research to delineate other embryo self-correction mechanisms critical in overcoming error-prone development and to improve fertility treatments for an increasing number of patients.

NSF Awards · FY 2024 · 2024-12

Cloud service providers offer Field Programmable Gate Arrays (FPGAs) as a time-shared service for efficiently accelerating high-value workloads such as machine learning, genome sequencing, databases, encryption, and other applications with strict security requirements. While the hardware is time-shared between multiple tenants, there is generally believed to be no information leakage between subsequent users since the FPGA bitstream and memories are digitally erased after each tenant’s use. The project studies “FPGA pentimenti” data that leaks between subsequent users through analog effects. The project’s broader significance and importance are developing techniques for securing cloud infrastructure and promoting education and research in FPGA security. This project studies, characterizes, and develops mitigations for FPGA pentimenti. Specifically, this project investigates how data from previous users is leaked via an analog side channel due to bias temperature instability effects. This project establishes bounds of data-recovery capabilities within the cloud FPGA model and identifies effective defenses for all stakeholders. This project also characterizes techniques for extracting FPGA pentimenti. With this knowledge, this project develops mitigations to reduce or eliminate these analog side-channel attacks from the perspective of the manufacturer, cloud provider, and end-user. 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 · 2024-12

PROJECT SUMMARY The ability of an organism to rapidly and accurately assess whether an environmental stimulus is worth approaching or avoiding, also known as valence, is critical to survival. One brain region necessary for this computation is the basolateral amygdala (BLA), which contains discrete ensembles of anatomically intermixed excitatory principal neurons that respond selectively to either positive- and negative-valence stimuli. Recently, I demonstrated that multiphoton activation of these activity defined valence ensembles was sufficient to bias valence-specific behavioral output via mutual ensemble inhibition. However, how this inhibition is generated and its relevance to avoidance behavior is not known. In this proposal, I first aim to determine how functional antagonism of valence-selective ensembles is generated (Aim 1: K99) using chronically implanted microprisms to record the simultaneous activity of both GABAergic and glutamatergic principal neurons during valence stimulus testing. After mapping activity, I will perform microcircuit mapping using multiphoton activation of individual parvalbumin-expressing (PV) interneurons to establish the rules that govern their valence ensemble connectivity and relevance to valence-specific behavioral output. Next, I will identify how antagonistic valence ensemble activity is modulated during avoidance (Aim 2: K99). Using two-photon GRIN lens imaging and a novel specialized conversion adapter allowing for image registration between both freely moving 1-photon calcium and 2-photon imaging, I will determine how valence ensembles in the BLA are modulated avoidance. I will then generate computational models to identify specific ensembles responsible for encoding avoidance behavior. I will then target these ensembles for multiphoton inhibition to assess their role in producing avoidance behavior. Finally, I will isolate how norepinephrine (NE) release from the locus coeruleus (LC) alters BLA ensemble activity through gain control to promote avoidance behavior (Aim 3: R00) to identify the endogenous signal that is governing BLA ensemble recruitment to promote avoidance behavior. Using combined optogenetics and fiber photometry I will first measure NE release in the BLA following an acute stressor, and compare this to release evoked by optogenetic activation of LC terminals in BLA. I will then conduct 1-photon calcium imaging, avoidance behavioral assays, pharmacology, and graph theory analyses, I will evaluate whether activation of LC terminals in BLA alters ensemble activity via β2-adrenergic receptors (ARs). The training received following the completion of this proposal will facilitate my transition to running an independent laboratory focused on neuromodulation of avoidance circuits.

NSF Awards · FY 2024 · 2024-10

Antarctic Ice Sheet (AIS) melt is a leading contributor to global sea level rise, which affects coastal communities around the world. Therefore, understanding the mechanisms that control the location and magnitude of melting is crucial to plan for the effects of climate change. The primary heat source causing AIS melt is the ocean, since air temperatures in the Antarctic currently are currently often too cold to melt the glaciers from above. Therefore, ocean currents, which carry heat from the open ocean to the ice sheet, play a key role in determining trends and variability of ice melt in space and time. Previous work on ocean-driven melting of Antarctica have focused on the ocean circulation close to the ice sheets. However, recent modeling studies suggest that changes in ocean currents hundreds of kilometers offshore could have cascading effects on the heat supply to the ice sheet. This project will use observations collected by robotic floats and a climate model to investigate the mechanisms that relate ocean circulation far offshore to ice sheet melt. Characterizing these processes is necessary to improve sea level rise projections. The water mass responsible for driving much of the AIS melt is called Circumpolar Deep Water (CDW) and originates in the open ocean within the Antarctic Circumpolar Current (ACC). While many previous studies have examined cross-shelf heat fluxes, few have analyzed the circumpolar pathways of CDW from the ACC to the continental slope and their influence on AIS mass loss. Importantly, these remote processes control the offshore reservoir of CDW that precedes on-shelf heat transport. This project will quantify the pathways of CDW from its origin in the ACC to the Antarctic continental slope and determine the physical mechanisms that govern the variability of these pathways. Researchers will conduct a series of Lagrangian particle release experiments in a data-assimilating state estimate of the Southern Ocean. Analyzing the trajectories will allow them to statistically constrain regional, seasonal, and interannual variability in the remote pathways of ocean heat transport toward the AIS. The Lagrangian experiments will be complemented by autonomous profiling float data from the West Antarctic. These observations will enable the validation of the model and further probe the physical mechanisms of onshore heat transport. Together, these results will help discern the controls on AIS melting, which has implications for numerous climate feedbacks. 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 2024 · 2024-10

A long-standing goal of computing research is to create "tools for thought", in which computers extend our abilities to think and communicate in both work and social contexts. Used carefully, large language models (LLMs) -- and their remarkable ability to process and generate text -- can contribute to this goal. Already, people use LLMs to generate and organize ideas, summarize documents, support writing, plan events or meals, generate computer programs, and analyze data. However, current LLM usage prioritizes conversational "chat" interactions involving a single person and one-at-a-time responses, whereas creative work requires considering a variety of possibilities and may include multiple collaborators. The goal of this project is to leverage and evaluate LLMs as "tools for thought" that support creative, open-ended, and collaborative work. The main aims are to (1) integrate LLMs into larger, interactive systems while safeguarding LLM output quality, (2) help people generate and consider diverse, relevant ideas, and (3) support collaborative work involving multiple people and LLMs interacting together. This project looks beyond current chat-based interactions to leverage LLMs to support people's everyday work in a reliable and effective manner. More specifically, this project develops novel methods, evaluations, and applications to better leverage LLMs as tools for thought in both single-user and cooperative scenarios. The main approach is to scaffold LLM-powered systems to provide higher control and reliability, while focusing on a key step of open-ended information work: "divergent" phases of generating diverse yet relevant candidate ideas, followed by "convergent" phases in which one navigates, selects, and synthesizes the most promising ideas. The first objective of this project is to develop a design space and guidance for building more reliable and controllable LLM workflows, drawing upon over a decade of crowdsourcing research and documenting the adaptations necessary to build effective workflows and evaluate LLM capabilities. The second objective is to enable cycles of divergent and convergent work: developing robust operations for generating diverse yet relevant candidates -- whether they be writing suggestions, brainstorming ideas, or salient quotes to extract from a text -- alongside methods for choosing among and combining responses. The third objective expands this focus to cooperative projects, enabling hybrid multi-user/LLM workflows and investigating how LLMs could improve awareness and coordination among collaborators. In support of these objectives, the project will develop and evaluate user-facing applications for tasks such as scientific writing, text analysis, and design ideation, providing practical examples of LLM-supported "tools for thought". 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 2024 · 2024-10

This project aims to serve the national interest by producing instructional activities to optimize physics quantitative literacy (PQL) development that are grounded in validated ways that students develop reasoning. Introductory physics is required for many STEM majors, in large part, because developing a strong foundation in quantitative reasoning is recognized as being important for their subsequent studies and careers. This project will center on two broad instructional aims: improving quantitative literacy for all STEM majors through physics course-taking, and helping reduce barriers that prevent students from economically disadvantaged communities from entering STEM majors. Instructors can help all of their students improve their essential quantitative reasoning by making PQL an explicit learning objective. In order for that to happen widely, instructors need effective materials and methods they can adopt and adapt in a variety of contexts, as well as validated assessments to measure PQL and models for analyzing and interpreting the results. The significance of this project is the development and dissemination of these instructional materials. This project aims to accomplish two goals. The first goal is to create an emergent model of PQL development based on student resources. Using methods related to item response theory and knowledge space theory, this project plans to augment analysis of existing multiple-choice tests by defining a partial-credit scoring model that recognizes the value of students’ responses to test items that are partially correct. By analyzing data from students in introductory, middle-division, and upper-division physics courses, the project aims to produce an emergent longitudinal model of PQL development based on the landscape of student responses to test items across multiple courses. The second project goal is to develop, implement and disseminate instructional materials and methods that will be founded on the emergent model of PQL development. These materials will help students conceptualize the algebra and calculus quantitative reasoning that underpins STEM quantitative literacy and will be disseminated widely across a variety of learning environments to broaden the impact for a diverse group of learners. The model of PQL development produced will be used as a framework to guide the production and refinement of: 1) modular, cooperative activities that can be used in small group settings, think-pair-share lecture settings, or as homework, and 2) formative assessment questions that can be used on tests and quizzes. The NSF IUSE: EDU Program supports research and development projects to improve the effectiveness of STEM education for all students. Through its Engaged Student Learning track, the program supports the creation, exploration, and implementation of promising practices and tools. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2024 · 2024-10

This project aims to serve the national interest by developing an empirically validated tool to assess a student’s knowledge of computing concepts. Such well-designed knowledge assessments, vetted by rigorous design and psychometric processes, are still relatively uncommon in the computer science education community. Moreover, students learn computing concepts in a variety of increasingly different educational contexts: at home, on the job, in a course offered online, or in a traditional classroom. Thus, there is a pressing need for an empirically validated assessment of computing knowledge that works in a variety of educational contexts and with different programming languages. The goal of this project is to design and pilot such a multi-contextual and multi-language assessment by enhancing the existing Second Computer Science 1 (SCS1) course assessment. The new assessment, referred to as SCS1++, will be designed by addressing known issues with the existing SCS1 assessment while also increasing the number of questions on the assessment. Specifically, the project will (1) construct a coherent argument for validity claims of SCS1++ as a whole; and (2) create subscales aligned with the concepts on the assessment to improve the assessment’s formative values. The project will directly inform the research on equitable CS assessment by updating the current SCS1 with improved questions and rigorous validation using advanced psychometric tools. A design-based research approach will be used, based on theory, and the system will be evaluated in real educational settings. The NSF IUSE: EDU Program supports research and development projects to improve the effectiveness of STEM education for all students. Through its Engaged Student Learning track, the program supports the creation, exploration, and implementation of promising practices and tools. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2024 · 2024-10

Understanding the dynamics of snow water equivalent (SWE) is vital for effective water resource management, especially in the Western United States where snowpack serves as a major water source. Accurate SWE forecasting is a significant challenge due to the complex interactions among snow physics, atmospheric conditions, and varied terrains. This project aims to revolutionize SWE prediction by integrating cutting-edge artificial intelligence (AI) techniques, specifically physics-informed neural networks (PINNs). In addition to advancing scientific knowledge about snow water processes, this project is expected to have positive societal impacts, such as improved water resource management and informed decision-making in response to climate change. The project will also enable inclusivity and education by involving graduate students and underrepresented groups in AI research, fostering a diverse community of future experts in SWE forecasting research. This project will employ an innovative approach that combines graph neural network models with physics-based constraints and partial differential equations. This integration will enable the creation of more accurate and reliable SWE forecasts by capturing the detailed processes of snow accumulation and melt. The GeoWeaver workflow management platform will be utilized for making advanced AI tools accessible to researchers and practitioners. The project also includes a series of hackathon-style workshops providing students and snow researchers with hands-on experience in AI and SWE forecasting. Overall, the project seeks to democratize access to AI research workflows and tools for snow researchers, foster interdisciplinary collaboration, and support sustainable resource management, thereby enhancing our understanding of water resources and contributing to the broader discourse on climate change and water sustainability. 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 2024 · 2024-10

Summer streamflows are critically low in numerous Pacific Northwest watersheds and are projected to decline further as temperatures rise and snowpack diminishes. These flow conditions result in poor quality aquatic habitats that are detrimental to salmon and other fish. The Nooksack Tribe, along with other Tribes in the region, are looking closely at management options that could help to sustain the survival of salmon, which are critical for cultural, spiritual, environmental, and economic uses. Forests are a key influence on the amount and timing of streamflow in a watershed, and forest management approaches such as thinning in lieu of clearcut harvest may drive increased streamflow during the dry summer season. A few previous studies support this concept based on observational data collection and numerical modeling, but there is limited confidence in these effects for western Washington due to a lack of regionally relevant observations and modeling. This project will assemble a regional coalition of scientists, Tribal representatives, and resource managers to collect relevant data, implement modeling, and provide actionable results that can inform strategies, decisions, and policy. In the Pacific Northwest region of the United States, threatened and endangered salmon species sustain continued losses due to low summer flows and elevated stream temperatures. These critical streams are fed by watersheds that have experienced over a century of clear-cut timber harvest rotations, which have resulted in a mosaic of young, regenerating forest stands. Preliminary investigations of the effect of forest age and regeneration on summer low flows indicate that the legacy of even-age management may have contributed to declines in summer flows relative to mature old growth stands, but the issue is still understudied. For example, model representations of forest transpiration as a function of stand age is based on two studies located in the coastal range of Oregon, rather than in upland forest plantations of the western Cascades. This project aims to build a community of Tribal representatives, scientists, and water managers to guide the development of a targeted, decision-relevant research plan. Stage 1 will include workshops, field reconnaissance, and instrumentation testing, and Stage 2 aims to collect sap flux, soil moisture, and snow data across forest types to support testing and implementation of two hydrological models. Together, the field and modeling approaches will build actionable knowledge of the hydrologic linkages between the upper watershed, where forest management is occurring, and the stream channel, where salmon are spawning and rearing. This project is in response to the Civic Innovation Challenge program’s Track A. Climate and Environmental Instability - Building Resilient Communities through Co-Design, Adaption, and Mitigation and is a collaboration between NSF, the Department of Homeland Security, and the Department of Energy. 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 2024 · 2024-10

Quantitative study of morphology is central to many fields within the biological sciences, as anatomical systems hold important clues on how organism move, feed, reproduce and interact with the environment in which they live in. Through these studies, biologists can understand what changes have happened through time; get a good glimpse of the variability within a species, and even predict whether they are likely to survive with global environmental challenges. Morphological data is best acquired using 3D imaging technologies. These technologies can range from surface scanners to high-resolution 3D microscopes that use UV or X-ray to collect data. Biologists then process and visualize these datasets in computers to either collect data for research or use them as visual aids in the classroom. However, some of these imaging modalities produces datasets that are much larger than typical personal computers can handle; or may require specialized software that may not be available to everyone due to licensing or cost issues. Both can have the potential to create sharing and usage obstacles for publicly funded data, impeding teaching, scientific exploration and collaboration. This project provides equitable and convenient access to publicly funded cyberinfrastructure (JetStream2) and data resources (MorphoSource) for biologists, who otherwise may not benefit from their availability; either because they lack the computational resources or the technical expertise to use them. The project team will also train hundreds of early career scientists, whose research and teaching will benefit from the easy manipulation and use of digital biological specimens. Additionally, bootcamp opportunities will transform motivated grad students and post-docs into future inventors of new analytical tools and functionalities, which will grow the community of morphologists working in digital environments. MorphoCloud will make working with digital biological specimen data as routine as working with genomics data, so that biologists can focus on their scientific questions rather than get bogged down with complex workflows, technicalities, and limited resources. Proposed cloud services and analytical tools will also make wider-scale, multi-lab collaborations friction-free, via derived data exchange of 3D phenotypes. 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 2024 · 2024-10

Cloud computing is used pervasively in business, government, healthcare, education and security, as well as for entertainment and social interaction. Indeed, it has become one of the most critical pieces of infrastructure in the United States. Cloud computing continues to evolve to serve the ever-growing needs of these applications. Modern cloud applications are structured as a set of interacting microservices. These microservices need rich communication functionality, beyond what networks have traditionally offered, including load balancing, access control, performance monitoring and debugging, encryption, compression, and fault injection. Developers use service meshes to achieve this functionality, but service meshes today have notoriously low performance and high resource consumption due to repeated traversal of the host network stack. The vision of the project is to enable high-performance and efficient communication between microservices via application-defined networking (ADN), where developers specify needed communication functionality at a high level and a compiler automatically generates an optimized implementation. ADN has the potential to significantly improve cloud services by reducing the overheads of microservice applications, improving cloud application performance and reducing waste of resources like CPU and energy. ADN is a significant departure from current service meshes and traditional networking. In ADN, application developers specify microservices' communication needs using an SQL-like high-level language. From this specification, the ADN controller automatically generates a running implementation that is specific to the application and spreads the desired functionality across available software and hardware platforms (e.g., the kernel, SmartNICs), and it adapts the implementation to the workload. By specializing to application needs (e.g., even message headers are custom) and to the deployment environment, ADN implementations can be highly streamlined. ADN is a new design point in engineering network functionality, compared to the current paradigm of generality adopted by existing network stacks and service meshes. It also creates new opportunities are difficult to realize today. For example, fine-grained load balancing decisions can be made based on fields specific to the application's RPCs (Remote Procedure Calls), and the load balancer can be automatically scaled when the workload increases. Realizing the ADN vision requires innovations across the stack. The proposed work will be carried out through three complementary building blocks: (1) a declarative language with reusable abstractions to specify the desired network functionality; (2) a compiler that translates the specified network behavior into configurations for distributed hardware and software processing platforms; (3) a runtime system that dynamically adjusts these configurations to optimize application performance. Investigating these building blocks will address fundamental research questions regarding the concise specification of application-level (layer 7) network behavior, efficient execution of network policies on hardware, and disruption-free application-level network upgrades. 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.