University Of Washington

NSF Awards · FY 2024 · 2024-09

Cities are loci of resource consumption, economic activity, and innovation. Given the increasing ability to collect, transmit, store, and analyze data, there is the opportunity to go beyond today’s understanding of cities to enable better operations, better planning, and better policies. While there are already troves of open data about cities, their potential remains underexplored because of unique challenges related to the diversity and scale of urban data and the complex computations required to obtain trustworthy insights. This project builds tools and infrastructure that meet the unique requirements of urban computing. The open-source cyberinfrastructure supports data-driven exploration and empowers a broad range of stakeholders to analyze and model urban data at scale. This cyberinfrastructure serves as a catalyst to create and sustain a cohesive community around urban computing. By enabling sharing and collaboration, this cyberinfrastructure also streamlines and advances urban research and democratizes urban computing. The project includes activities and mechanisms to engage the community and integrate the results to support education. This project addresses two critical obstacles in urban computing: (1) the lack of documented, robust, well-engineered tools and open computing platforms and (2) the dispersed community of cross-disciplinary researchers and developers, which limits knowledge sharing and collective solutions. A core component of the project is the development of a cyberinfrastructure that integrates methods and tools for the exploration of urban data that are scalable, reusable, and interoperable, and solutions to common challenges, including data discovery, cleaning, analytics, modeling, visualization, and reproducibility. The project deploys a cloud-based, open, collaborative environment that supports the use of this infrastructure over large and diverse urban data sets, allowing communities of users to quickly create analyses that are reproducible by design and that can be debugged, shared, and extended. The intellectual merit lies within the novelty of the tools and techniques it produces, as well as in the software engineering challenges involved in developing, maintaining, and supporting cyberinfrastructure that will be deployed and widely adopted. This Office of Advanced Cyberinfrastrucure project is jointly funded by the Cyberinfrastructure for Sustained Scientific Innovation (CSSI) program and the National Discovery Cloud for Climate (NDC-C). 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 · 2024-09

Floating, single-celled algae, or phytoplankton, form the base of marine food webs. When phytoplankton have sufficient nutrients to grow quickly and generate dense populations, known as blooms, they influence productivity of the entire food web, including rich coastal fisheries. The present research explores how the environment (nutrients) as well as physical and chemical interactions between individual cells in a phytoplankton community and their associated bacteria act to control the timing of bloom events in a dynamic coastal ecosystem. The work reveals key biomolecules within the base of the food web that can inform food web functioning (including fisheries) and be used in global computational models that forecast the impacts of phytoplankton activities on global carbon cycling. A unique set of samples and data collected in 2021 and 2022 that captured phytoplankton and bacterial communities before, during, and after phytoplankton blooms, is analyzed using genomic methods and the results are used to interrogate these communities for biomolecules associated with blooms stages. The team mentors undergraduates, graduate students, and postdoctoral researchers in the fields of biochemical oceanography, genome sciences, and time-series multivariate statistics. University of Washington organized hackathons develop publicly accessible portals for the simplified interrogation and visualization of ‘omics data by high schoolers and undergraduates and are implemented in investigator-led undergraduate teaching modules and the University of Rhode Island Ocean Classroom. The research team also returns to Orcas Island, WA, where the field sampling takes place, to host a series of annual Science Weekends to foster scientific engagement with the local community. Phytoplankton blooms, from initiation to decline, play vital roles in biogeochemical cycling by fueling primary production, influencing nutrient availability, impacting carbon sequestration in aquatic ecosystems, and supporting secondary production. In addition to environmental conditions, the physical and chemical interactions between individual phytoplankton can significantly modulate blooms, influencing the growth, maintenance, and senescence of phytoplankton. Recent work in steady-state open ocean ecosystems has shown that important chemicals are transferred amongst plankton on time-dependent metabolic schedules that are related to diel cycles. It is unknown how these metabolic schedules operate in dynamic coastal environments that experience perturbations, such as phytoplankton blooms. Here, the investigators are examining metabolic scheduling using long-term, diel sample sets to reveal how chemical and biological signals associated with the initiation, maintenance, and cessation of phytoplankton blooms are modulated on both short (hrs) and long (days-weeks) time scales. Findings are advancing the ability to predict and manage phytoplankton dynamics, providing crucial insights into ecological stability and future oceanographic sampling strategies. Additionally, outcomes of this study are providing a new foundational understanding of the succession of microbial communities and their chemical interactions across a range of timescales. In the long term, this research has the potential to identify predictors of the timing of phytoplankton blooms, optimize fisheries management, and guide future research on carbon sequestration. 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 · 2024-09

PROJECT SUMMARY / ABSTRACT HIV infection rates in the U.S. military have doubled since 2003, yet no interventions targeting HIV transmission in this population have been tested in over 15 years. Eighty percent of new infections occur among active-duty men who have sex with men (AD-MSM) – a population that is critically understudied due to a long history of policies that prevented researchers from asking service members about sexual identity or same-sex behavior. Permitted since 2012, research on behavioral health among AD-MSM remains scarce. Initial evidence suggests that HIV prevention for AD-MSM should target alcohol-related sexual risk behavior (AR-SRB). The causal link between alcohol use and risky sex is well established. Heavy drinking is prevalent among service members, highest among AD-MSM, and driven by highly pro-drinking military social norms. Alcohol misuse and risky sex are also both link to traumatic stress, particularly sexual assault, and AD-MSM have over 5 times greater odds of military sexual trauma (MST) relative to their straight male peers. Pre- exposure prophylaxis (PrEP), a common target of current civilian HIV prevention efforts, is less viable for AD- MSM due to significant social and structural barriers unique to the military – persistent stigma, absence of medical confidentiality, unavailability of PrEP at many installations, and prohibitions against its use by service members on deployment, shipboard, or in certain occupational specialties. Internet-based personalized feedback interventions (iPFI) are an ideal modality to address AR-SRB among AD-MSM. Delivered privately, iPFI has shown efficacy for reducing heavy drinking and related problems. The present 2-phase study seeks to develop a trauma-informed iPFI targeting both heavy drinking and sexual risk behavior among AD-MSM. Phase 1a: A survey of 160 AD-MSM will establish behavioral norms and provide data to test a novel behavioral framework describing traumatic stress and military culture as pathways to AR-SRB. Phase 1b: Qualitative interviews of 15 AD-MSM will contextualize AR-SRB and gather feedback on draft iPFI components. Phase 1c: We will finalize a novel iPFI based on findings and extant iPFI models. Phase 2: A randomized controlled trial (N=50) will provide data on intervention feasibility, acceptability, and preliminary efficacy. Pilot data will be used for an R01 application to NIAAA seeking to test the further-refined iPFI in a fully powered RCT. The training plan for this application will focus on growing expertise in intervention development, AR-SRB, behavioral responses to traumatic stress, AD-MSM health, and methodologies for research on stigmatized behaviors and hard-to-reach populations. A highly productive team of mentors is committed to Dr. Walton’s success and will each contribute unique expertise to his research and training plans. Support from this award is crucial to Dr. Walton’s development as an independent scientist, in the vanguard of scholarship on AD-MSM, who can conduct meaningful research on the prevention of both alcohol misuse and HIV.

NSF Awards · FY 2024 · 2024-09

This project centers on understanding the arithmetic of solutions to systems of polynomial equations, i.e., varieties. A key tool in the project is to use the limiting geometric structure of solutions of large complexity, thereby allowing the PI to study solutions of increasing complexity in a uniform manner. Understanding the arithmetic of varieties has many applications including to cryptography and to coding theory. This project also funds mentoring and training of early career mathematicians, particularly those from groups who have been historically excluded from mathematics. In addition to training Ph.D. students at their own institution, the PI also co-organizes the Roots of Unity workshop series and the Women in Numbers conference series. More specifically, the main research focus of the proposal is to organize and, in the case of a rank 0 curve, even describe all algebraic points on a curve. This includes characterizing the local splitting behavior of the residue fields of points that appear in a fixed linear system. In addition, the PI will use the Abel-Jacobi map to package all algebraic points on a curve with rank 0 Jacobian in terms of a finite set of complete linear systems. This project builds on the PI's prior work on isolated and parameterized points and on degree sets over Henselian fields. The proposal also includes complementary projects that explore the behavior of algebraic points on surfaces. These complementary projects focus on particular classes of surfaces of negative Kodaira dimension and surfaces of Kodaira dimension 0 with a view to understanding the different phenomena that can arise for higher dimensional varieties. 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 · 2024-09

The upper ocean boundary layer (OSBL) controls the vertical transfer of heat, mass, and momentum from the atmosphere to ocean interior, affecting water properties and influencing climate. Turbulent OSBL flows, unresolved by ocean circulation models, must be parameterized. Over recent decades, significant developments in OSBL parameterization schemes (i.e., boundary layer models), have occurred primarily through comparisons with Large Eddy Simulations (LES). Nevertheless, multiple competing schemes with different physical assumptions are still in common use. Recent systematic comparison of predictions finds differences averaging 15% in mixed layer depth, larger under stabilizing, wind-wave forced conditions. It is unclear which of these schemes is best since LES may not represent the ocean accurately and comparisons with oceanic data are limited. This project will try correct both of these deficiencies by developing a new model for the OSBL which includes both local and non-local transports and is tuned to both observational and LES results. A database of OSBL turbulence properties from existing Lagrangian float data will also be compiled both to tune and validate this model and to serve as a reference for other future efforts. This development has potential to significantly improve physical and biogeochemical model predictions on a wide range of space and time scales directly through our modelling efforts, and by providing a database of unique and directly relevant measurements and analysis of the turbulence dynamics and covariances responsible for upper ocean vertical fluxes to the model development community. The project will support the multi-institutional collaboration of an early-career PI, a graduate student at UW, and the individual outreach efforts of the PIs in local public schools. Parameterization of OSBL has been hampered by two current obstacles. First, recent measurements and LES comparisons suggest there are significant fundamental deficiencies in the physical assumptions of many schemes. Specifically, observations suggest that (1) there are strong deviations from standard surface layer “Monin- Obukhov” similarity scaling due to surface wave impacts; (2) overturning scales near the entrainment zone of mixed layers are much smaller than can be accurately represented in LES; and (3) vertical transport of turbulence has substantial non-local components not well represented by a diffusive cascade across discrete OSBL depths. Second, recent decades of detailed OSBL observations have had little impact on model formulation, presumably because the data and the dynamic scaling behavior it supports have not been presented to the modeling community in useful ways. This project has two main task to address both of these pressing issues. Task 1: Develop a new model for the OSBL which includes both local and non-local transports and is tuned to both observational and LES results. This will build on an existing local second moment closure (SMC; from APL/UW) that includes surface wave effects, and a newly developed non-local, plume-based, assumed distribution higher order closure (ADC; from OSU and collaborators) that vertically exchanges turbulence properties (e.g. Reynolds covariances) across the boundary layer, evolving in reference to production length scales. The investigtors will develop a nonlocal SMC comparable in computational expense to traditional two-equation ‘single-point’ local SMCs by combining a bottom-up approach of adding nonlocal closures based on Lagrangian float measurements to an SMC’s Algebraic Reynolds Stress Model (ARSM), and a top-down approach reducing the large number of dynamic equations for Reynolds covariances in the higher order ADC model to nonlocal linearized ARSM closure expressions. Task 2: Develop a database of OSBL turbulence properties based on ~25 years of Lagrangian float data to tune and validate this model and provide reference ground truth to guide development and validation of other OSBL mixing schemes. A recent APL/UW Ph.D. computed profiles of vertical velocity variance and skewness for both wind/wave forced and convective cases. This project will expand this to include dissipation rate by an inertial subrange method, turbulence length scales computed directly from Lagrangian trajectories and indirectly from dissipation and kinetic energy. Air-sea fluxes and mean profiles of velocity and density will be available for all cases. The turbulent quantities will be scaled on the forcing, further complemented by dimensional scalings generated from Task 1. This database will be distributed within the framework of the widely used community-driven General Ocean Turbulence Model (GOTM) effort. 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 · 2024-09

The advent of computationally intensive applications such as generative artificial intelligence (AI) has created a vital demand for dense and ultra-low power multi-TB/s inter/intra-rack optical communications (with <100m reach) as well as low-latency chip-to-chip interconnects. Similar needs will be soon required for edge-to-cloud connectivity and 5G/6G front/back-haul networks. Silicon photonic (SiPho) Transceivers have shown a great promise to address this challenge by ultimately co-packaging optical transceivers with high performance GPU/FPGA/SoCs in a same package (“co-packaged optics” or CPO). Among various realization approaches, micro-ring modulators (MRM) have significantly improved the energy-efficiency and shoreline (or edge) bandwidth (BW) densities in terms of Tb/s/mm compared with conventional Mach-Zehnder modulators (MZM) and VCSELs. State-of-the-art demonstration could achieve 100Gb/s data-rates per wavelengths with ~5pJ/b energy-efficiencies and less than 0.5Tb/s/mm BW densities using multi-level amplitude modulation. These numbers are still an order of magnitude behind what future AI processing demands. Since there is a large energy penalty in scaling up baud-rates, vast parallelization degrees should be utilized to address multi-TB/s aggregate off-package data-rate demands. So far Wavelength division multiplexing (WDM) have been proposed and demonstrated to do so. While multiplexing can be a near future solutions, it is evident that advanced coherent modulations like QAM can be an ultimate solution to increase spectral-efficiency and overall aggregated bandwidths per fiber for CPO. However, today’s coherent optical transceivers are not yet suitable for CPO applications in AI datacenters. In this proposal, we are introducing Coherent CPO (C2PO) to enable compact and low-power QAM modulation using MRMs. Unlike today’s datacenter connectivity, future of interconnects will rely on massively parallelized multi-channel energy/area efficient coherent optical links that should be co-packaged with processor and accelerators. Key technologies such as generative AI, autonomous vehicles, AR/VR and 6G all require such a critical backbone technology paradigm shift. Our proposed work enhances key metrics by 10x-100x compared with today’s commercial and R&Ds solutions. Proposed method can be extended in future to higher QAM modulations such as QAM-64 to support data-rates of +1Tb/s per wavelength in each fiber. Education, workforce development, and outreach activities within this project focus on the semiconductor industry's future needs. These activities encompass: Developing course materials for the co-design of RF/High-speed circuits with emerging devices and creating analog layout and design automation (in Python) for high-school internship/outreach programs. PI will leverage various mechanisms to achieve broader impacts in diversity, education, and outreach through this project. 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 · 2024-09

PROJECT SUMMARY In deadly and common familial hypertrophic and dilated cardiomyopathies, structural variation at the single protein level leads to adverse ventricular remodeling, systolic dysfunction, and diastolic dysfunction. Muscle contraction is driven by interactions among motor proteins, structural filaments, and regulatory proteins within sarcomeres. Structural perturbations to the contractile machinery disrupt the kinetics of these interactions and give rise to systems-level dysfunction in cardiac tissue. This project will investigate the mechanisms of cardiac contractile dysfunction across multiple biologically relevant spatial and temporal scales using a combined computational and experimental platform. The proposed work focuses specifically on structural perturbations that impact the essential interaction between actin and myosin. The overarching hypothesis of this work is that structural perturbations along a structural communication pathway within the upper and lower 50 kDa domains of myosin modulate the electrostatic potential and surface area of myosin’s actin binding surface and modulate the association of myosin heads onto thin filaments. Recent stopped flow kinetics and x-ray diffraction-based measurements have shown that cardiomyopathy mutations and the small molecule 2’-deoxy-ATP modulate actomyosin affinity. I have used computational simulations to show that these mutations and small molecules alter the structure and dynamics of the upper 50 kDa domain of myosin. However, a general description of the ‘rules’ by which mutations and/or small molecules modulate actomyosin interaction requires further study. These computational predictions also require rigorous testing using in vitro methods. The goals of this work are to establish a mechanistic framework that explains how structural perturbations to myosin affect its interaction with actin and to modify actin-myosin interactions with small molecules designed to modulate myosin structure. These goals will be accomplished by simulating the impact of mutations on myosin structure, myosin recruitment, and actomyosin interaction (Aim 1), testing computational predictions in single molecules and contractile organelles from stem cell-derived cardiomyocytes (Aim 2), and developing small molecules designed to modulate actin-myosin interactions by targeting structural communication pathways in myosin (Aim 3). The project will utilize machine-learning infused computational workflows and state-of-the-art stem cell technologies to accelerate translational cardiomyopathy research with a combined computational/experimental platform. The proposed training program will provide me with new skills in stem cell biology, protein biochemistry, and muscle mechanics that increase the scope of my research. I will be mentored by a diverse team led by Dr. Michael Regnier, an accomplished researcher in muscle biology who has significant experience in leading collaborative, multiscale, and interdisciplinary research projects. Research and career development will leverage resources provided by the Institute for Stem Cell and Regenerative Medicine and Center for Translational Muscle Research at the University of Washington.

NIH Research Projects · FY 2025 · 2024-09

Project Summary Emerging and rapidly progressing technologies can now measure the molecular phenotypes of genes, transcripts, proteins, metabolites, and gut microbiota. These omics data provide an unprecedented level of granularity into both clinical and biological measurements, showing great promise to understand biological mechanisms governing human health and disease, and to uncover the underlying hetero- geneities that contribute to disease manifestations. However, many statistical methods used for analysis of omics data only establish associations. These associations may merely represent correlates or con- sequences of disease processes, and thus may not reveal disease mechanisms or guide therapeutics and clinical care. On the other hand, existing causal inference methods are not adequately equipped to handle the high dimensionality, correlation, and complexity of omics data. The goal of this project is to develop new statistical methods for causal inference that integrate large-scale omics data and im- plement them in user-friendly open-source software. We will develop a new framework that broadens the scope of mediation analysis to jointly analyze high-dimensional omics mediators, through novel ap- plications of two powerful ideas in statistics and machine learning: sufficient dimension reduction and variational autoencoders. The proposed framework can identify a disentangled representation of key mediation pathways, effectively distilling vital signals from large-scale omics mediators. Moreover, we will develop robust and scalable multivariable Mendelian randomization methods for large-scale omics measures, and then extend these methods to identify shared risk pathways across multiple outcomes. Lastly, we will introduce a novel framework for testing the pairwise causal directions between two omics modalities (e.g., microbiome and metabolites) by leveraging the asymmetry in temporally-ordered data. To maximize the impact of the proposed methods, we will develop and maintain open-source software for our methods, and integrate our proposed Mendelian randomization methods into two state-of-the-art platforms (MR-Base and MendelianRandomization). This project aims to address the need for robust, rigorous, and computationally efficient causal inference in large-scale omics data, and ultimately trans- form the potential of massive biomedical data into trustworthy, actionable, and generalizable knowledge to solve public health challenges.

NSF Awards · FY 2024 · 2024-09

Human activities have drastically changed the environment, including the introduction of noise, light, and chemicals – termed sensory pollutants – that can be detected and processed by an animals’ sensory systems. Over the last twenty years, studies have repeatedly demonstrated the adverse effects of noise and light pollution on animal behavior, and there is growing evidence that chemical pollutants, like nitrate radicals (NO3) and ozone, can have similar effects. However, few studies have examined how pollutant-enhanced degradation of scents affects the olfactory processing in the brain. Pollutants in the atmosphere, such as NO3, are thought to eliminate the ability of an insect, such as a pollinating bee or moth, to recognize the smell of a flower. Three-fourths of the world's flowering plants and about one-third of the world's food crops depend on animal pollinators for producing fruits, grains, and other crops. Unfortunately, little is known about how different chemical pollutants, such as ozone, NO3, or hydroxy radicals (OH), may degrade certain chemicals in the scent, and how that, in turn, may influence behavior in different insect pollinators. Furthermore, how the degraded scent is processed in the pollinator’s olfactory system to suppress behavior is unknown. Plant-pollinator systems are critical for ecosystem functioning and food security, and atmospheric pollutants give rise to smog and haze that severely impact human health. Using an interdisciplinary approach, this research will shed light on how these atmospheric processes affect olfactory and behavioral functions. The project will also introduce students to interdisciplinary research. High School students in the Upward Bound Program will participate in the project through summer seminars and lab experiences. Finally, the project includes training undergraduates, graduate students, and postdoctoral associates and helps prepare them for independent scientific careers. In the project, two pollinators, the nocturnal moth (Manduca sexta) and diurnal honeybee (Apis mellifera) will be used to examine the effects of daytime (ozone, OH) and nighttime (NO3) pollutants on diverse floral scents and determine how scent degradation affects neural processing in the primary olfactory system, the antennal lobe (AL). Behavioral assays, multichannel recordings, and state-of-the-art mass spectrometric approaches will be used to study the processing of degraded scents in antennal lobe circuits. This will be accomplished with three Objectives: (1) Testing the hypothesis that daytime (OH, ozone) and night (NO3) atmospheric pollutants degrade diverse floral scents, and this chemistry affects specific compounds in the scents more so than others. (2) To test whether degraded scents influence the processing and balance of excitation and inhibition in the pollinator’s primary olfactory center, the antennal lobe (AL), and (3) using wind tunnel and common garden experiments with focal plant species, the hypothesis is that the degraded floral scents eliminate the pollinator’s ability to locate the flowers and reduce pollination. Together, these experiments may provide a basic framework for understanding the effects of scent oxidation and its processing in early olfactory circuits, and its effect on plant-pollinator interactions. This project is supported jointly by Division of Integrative Organismal Systems in the Directorate for Biological Sciences of NSF and the Kavli Foundation. 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 · 2024-09

Project Summary Natural environments present numerous challenges to the visual system, including the presence of large and frequent changes in light intensity. Such changes occur when an animal moves from sunlight into shadow, or when it shifts its gaze from a bright to a darker area in a scene (e.g., sky versus shaded ground). These changes can occur frequently, e.g. many times per minute, and the intensity changes can be as large as 50-100-fold. These sudden, frequent, and large changes in light intensity present a challenge to the retina, which must transmit a reliable visual signal as it dynamically adapts to the new intensity: this adaptation is likely to be only partial, because the intensity is likely to change again within the next few seconds. Furthermore, adaptation mechanisms triggered by these changes in light intensity alter the spatial and temporal integration of retinal ganglion cell (RGC) receptive fields, which is tantamount to changing the neural code sent to downstream brain circuits. How can reliable visual signaling be achieved when the code is in perpetual flux? These dynamic lighting conditions differ substantially from those typically probed in laboratory experiments, where the mean and contrast of stimuli are often held approximately constant. The overarching goal of this proposal is to understand how naturalistic dynamic intensity conditions impact retinal function and visual signaling. We hypothesize that by examining retinal coding under these dynamic intensity conditions, we will learn how diverse adaptation mechanisms work in concert across multiple cell types to provide a reliable signal to downstream brain areas in natural environments. This work is significant because it will advance our understanding of how the visual system copes with rapid and naturalistic changes in light intensity. Aim 1 will determine the role of adaptation in phototransduction in shaping RGC responses. Aim 2 will probe the contribution of RGC spike generation. And Aim 3 will determine the impact of adaptation in intermediate circuitry, with a focus on the role of AII amacrine cells. These mechanisms will be linked using a CNN-based framework that permits study not only of how the individual mechanisms work, but how they interact and collectively shape RGC responses. These aims will reveal how adaptation in distinct loci of retinal processing (photoreceptors, interneurons and RGCs) shape the encoding of visual features in dynamic environments. Furthermore, they will reveal how key RGC types in the primate and rodent visual systems deal with naturalistic fluctuations in light intensity as they visually scan and move through the environment.

NIH Research Projects · FY 2025 · 2024-09

ABSTRACT Cervical cancer is highly preventable with early detection and treatment, but due to lack of accessible early screening options, incidence and mortality are still very high in low-resource settings (LRS). High-risk human papillomavirus (hrHPV) is responsible for most cervical cancer cases, and the WHO recommends testing for hrHPV and preemptively treating all patients who are positive as the primary prevention strategy in LRS. Nearly all available tests look for hrHPV DNA, which can result in overtreatment because it cannot differentiate infections that will clear naturally. Detectable hrHPV mRNA, however, is strongly associated with higher grade cervical precancers, making it a more specific biomarker for cervical cancer risk. This proposal aims to address the need for more specific early cervical cancer detection technology by developing novel in vitro and in vivo methods for detecting hrHPV mRNA. In the F99 phase of this proposal, I will develop and pilot a sample-to-answer mRNA test for HPV16 and HPV18. I will first develop a minimally instrumented method for preparing mRNA for detection from both provider and self-collected cervical samples. I will then amplify this mRNA using an isothermal assay that produces a real-time fluorescent signal, which I will read using a low-cost fluorimeter. I will integrate the individual assay components into a workflow with minimal user steps, making a test that is deployable to LRS. I will work with my sponsor, Dr. Rebecca Richards-Kortum, an established expert in the field of point-of-care cancer detection technologies to develop this test. I will then work with my co-sponsor, Dr. Kathleen Schmeler, the VP of Global Oncology at the UT MD Anderson Cancer Center with extensive experience translating cancer detection technologies to LRS to pilot this test with clinical samples in both Houston and Brazil. This training plan will help me gain experience with technology development, scientific communication, and clinical collaborations both locally and globally, and is enhanced by the location of my training at Rice University in the Texas Medical Center, where I will have access to world-class equipment, resources, and clinical collaborators. In the K00 phase of this proposal, I will develop a technique for detecting hrHPV mRNA in vivo. This will permit real-time early cervical cancer detection and monitoring, which can help assess disease progression and inform treatment faster than repeated in vitro sampling and testing. In the proposed project, I will use a combination of sequence- specific fluorescent mRNA labeling, aptamer-mediated label delivery, and high-resolution fluorescence imaging to detect hrHPV mRNA sensitively and specifically in vivo. I will seek a mentor with experience in biomarker label design and delivery at an institution with robust imaging resources and clinical collaborations. The proposed work will prepare me for an academic career as a cancer researcher dedicated to improving global access to early cancer detection. In addition to technical experience, I will develop my scientific communication skills, build a network of collaborators, and mentor the next generation of cancer researchers, laying the foundation for a career developing and deploying novel cancer prevention technologies with clinical impact around the world.

NIH Research Projects · FY 2025 · 2024-09

Access to mental health care should be a critical performance metric for all governments and healthcare systems. However, there are few psychometrically evaluated measures of access. Theoretically informed and psychometrically sound access metrics are needed to hold governments and healthcare systems accountable for making mental health services accessible to all people, and to help design and evaluate new programs and policies focused on improving access. This proposed project is a collaboration between investigators at the University of Ibadan in Nigeria and the University of Washington in the United States. The project has two major objectives. The first objective is to develop and psychometrically evaluate a metric of perceived access to mental health care for Nigeria. The second objective is to enhance the research capacity of early-state global mental health researchers in Nigeria. The proposed research activities include three specific aims. For Specific Aim #1, we will conduct in-depth qualitative interviews with patients with depression and/or anxiety living in Ibadan, Nigeria, along with their caregivers and clinicians, to identify common impactful barriers that prevent or delay people from receiving needed care. Qualitative research with the intended population is needed to ensure that the perceived access metric contains items about barriers that are relevant for the population. However, because not all barriers to mental healthcare are modifiable, we will need to divide barriers into two groups: 1) access barriers and 2) non-access barriers (i.e., attitudinal and need barriers). For Specific Aim #2, we will obtain consensus from health providers and policy makers regarding the modifiability of the identified barriers by healthcare systems and/or ministries of health. Once the access barriers are identified, we will generate items to include in the perceived access metric and then revise them using cognitive interviews with patients. Working with patients to first identify barriers and then providers and policy makers to determine which barriers are modifiable will optimize the content validity of the access metric. For Specific Aim #3, we will conduct a survey with 250 patients with depression and/or anxiety to assess the critical psychometric properties of reliability, construct validity and cross-population validity. At the conclusion of the proposed research, we will have a patient-centered metric of perceived access that is ready to use for policy evaluations, quality improvement, and research. With valid and reliable metrics of access, gaps in access can be detected and the impact of policies and programs designed to enhance access can be meaningfully evaluated. The proposed research is closely aligned with NIMH’s Strategic Plan Goal 4 (Objectives 4.1 and 4.3). Specifically, the access metric can be used to test the mechanism of actions for interventions designed to engage hard to reach patients in effective mental health treatments. The proposed research is also responsive to World Health Organization’s Mental Health Action Plan which has the following objective: “Proactively identify and provide appropriate support for groups at particular risk of mental illness who have poor access to services.”

NIH Research Projects · FY 2026 · 2024-09

ABSTRACT Enzymes do not drift aimlessly within a cytoplasmic soup. Rather cell regulatory events occur within the confines of highly organized intracellular signaling nanodomains. Protein kinase compartmentalization enhances the fidelity of signal transduction and permits the parallel processing of chemical signals within the same cell. Our lab discovered and defined A-Kinase Anchoring Proteins (AKAPs), a prototypic class of signal organizing proteins. The physiological significance of this mechanism is validated in several diseases. This proposal explores new PKAc mutants that locally drive cortisol secretion in adrenal Cushing’s syndrome. This is an endocrine disorder where adrenal cells release excess stress hormone cortisol. This disease afflicts about fifteen per million people annually and is 3-5 times more prevalent in women than men. Symptoms include adrenal hyperplasia, midsection weight gain and comorbidities such as hypertension, hyperglycemia, and psychiatric disorders. Adrenal (also called non-ACTH) Cushing’s is driven by somatic mutations in the catalytic subunit of protein kinase A (PKAc). Most mutations occur within regions of PKAc that bind regulatory subunits. This protein- protein interaction is not only necessary for autoinhibition of kinase activity but directs compartmentalization of PKA holoenzymes through association with AKAPs. The most prevalent mutant PKAc-L205R is found in ~45% of Cushing’s patients. Other PKAc mutations occur at frequencies of less than 1%. Using an innovative personalized medicine workflow to screen clinical samples we have discovered a new PKAc-W196G variant in ~20% of adrenal Cushing's patients. This single amino acid change promotes structural perturbations in PKAc. Preliminary analyses of patient tissues harboring PKAcW196G detect elevated levels of type I regulatory subunit (RIa). Proximity proteomic screening of adrenal cell lines reveals that PKAcW196G interfaces with RI, dual-function AKAPs, and the steroidogenic acute regulatory protein. StAR is a PKA substrate that controls the rate-limiting step in cortisol biosynthesis. CRISPR/Cas knockout of dual-function AKAP220 in adrenal cells attenuates stress hormone release. This exciting new data has forged an experimental plan of two specific aims. AIM 1: EVALUATES THE MUTATIONAL FREQUENCY AND IMPACT OF PKACW196G? Genetic, biochemical, and cellular approaches will monitor a) mutational frequencies of PKAcW196G and its localization in patient tissues, b) the physiochemical and activity profile of PKAcW196G and c) how this Cushing's kinase alters endocrine responses. AIM 2: TESTS IF ANCHORED TYPE I PKA SIGNALING PREDOMINATES IN ADRENAL CUSHING’S SYNDROME? PKAcW196G and the less common PKAcW196R analog are incorporated into AKAP220 signaling islands via association with RIa. Whole animal physiology studies and a novel precision pharmacology strategy will establish if a) PKAcW196G (& W196R) signal through AKAP220 and if b) organellar targeting enhances the efficacy of PKA drugs to reduce hypersecretion or biosynthesis of stress hormone.

NIH Research Projects · FY 2025 · 2024-08

Project Summary Newborn screening (NBS) provides early diagnosis of treatable disorders. Most NBS is based on measurement of biomarkers in dried blood spots (DBS). However, there are hundreds of pediatric and actionable diseases for which a biomarker is difficult to imagine. This has led to consideration of whole genome sequencing as a new paradigm in NBS (newborn sequencing, NBSeq). NBSeq has significant challenges one of which is the false negative problem expected when DNA variants of unknown significance (VOUS) are ignored. Ignoring VOUS is a necessary element of NBSeq because downstream biochemical analysis of all newborns with VOUS is not feasible because of the commonality of these DNA variants. The goal of this grant is to develop and explore new technology for massively parallel, rapid, and inexpensive biochemical annotation of VOUS. We have developed a new technique we call VOUSDO where we prepare a library of DNAs that encode every possible amino acid substitution in a protein at one change per protein. Human cells are then transfected with this library, and the cells are engineered to express only a single protein variant per cell. The cells are also engineered to express an RNA barcode whose sequence serves to identify the protein variant. After protein variant expression, the protein is engineered to capture its respective barcode. The degree of barcode capture depends on the abundance of the protein. Thus, any amino acid substitution that causes the protein to misfold and become degraded will lead to a protein variant that under-captures its barcodes. Barcodes are converted to DNA at the end of the analysis and these are subjected to next generation sequencing to reveal their sequences. The DNA sequence read frequency of each protein variant is compared to that of the wild type protein to provide the fraction of variant that folds in cells relative to wild type. The above technique works for cytosolic proteins. For proteins that target cellular organelles or are secreted, we will employ a second technique called VAMP-seq. Here, the protein is fused to a fluorescent protein, and the degree of fluorescence depends on the level of folded protein expressed in cells. Again, cells are engineered so that each cell expresses a single protein variant. RNA barcodes are used as well to identify the protein variant. Fluorescence-activated cell sorting is used to collect cells depending on the extent of fluorescence. Barcodes in each collection bin are read as for VOUSDO to give the relative extent of folding of each variant relative to that of wild type protein. These techniques have the potential to provide biochemical annotation of every single site amino acid substitution in the full list of proteins being included in NBSeq pilot studies worldwide. Knowing which VOUS greatly reduce the extent of protein folding is expected to massively reduce the false negative problem associated with ignoring VOUS in NBSeq programs.

NIH Research Projects · FY 2025 · 2024-08

PROJECT ABSTRACT Childhood exposure to violence (CEV) is a significant and preventable public health problem in the United States. Children of all ages are at risk for CEV, but children ages five and under are particularly vulnerable. The traumatic stress caused by early CEV has a significant, deleterious effect on developmental outcomes. Young children consistently interact with the healthcare system for well-child visits, and this system can play a critical role in identifying and responding to CEV. Yet, research to date indicates that clinicians in pediatric healthcare settings do not consistently assess for CEV, particularly recent or current CEV. The Joint Commission’s 2023 mandate to assess social risks (e.g., CEV) across healthcare settings provides a unique opportunity to routinely assess CEV. The objectives of the proposed research in this Pathway to Independence Award are to examine the implementation of CEV assessment in pediatric primary care settings, assess patterns of early CEV assessment across one health system, and investigate whether these assessments are associated with healthcare and health outcomes. The specific aims are to: (1) identify existing practices for CEV assessment and follow-up in pediatric primary care settings; (2) delineate facilitators and barriers to CEV assessment and follow-up; (3) characterize patterns of early CEV assessment patterns by patient, clinician, and site; and (4) elucidate how early CEV assessment is associated with healthcare referral, healthcare receipt, and child health outcomes. This study directly attends to the Joint Commission mandate, and findings will inform related measurement development and implementation efforts. To acquire the necessary skills to accomplish this project, I will receive training in four critical areas: (1) psychometrics; (2) implementation science; (3) social determinants of health research methodology; and (4) big data analysis techniques. I will also engage in professional development, extend my professional networks, and further develop my grant writing and oral presentation skills. I have assembled an interdisciplinary mentorship team with expertise in pediatrics, violence prevention, implementation science, social determinants of health research, and psychometrics to ensure completion of the proposed research and training plan. This Pathway to Independence Award will enable me to develop into an interdisciplinary, independent investigator who examines strategies to prevent CEV and reduce its negative consequences across the life course.

NIH Research Projects · FY 2025 · 2024-08

PROJECT SUMMARY/ABSTRACT Vaccine-mediated protection against viral pathogens frequently depends on neutralizing antibodies. However, the HIV-1 envelope (Env) glycoprotein gp120 is heavily and variably glycosylated, resulting in a dynamic shield that occludes conserved neutralization epitopes, impeding development of vaccination strategies to induce neutralization of diverse viral strains. Broadly neutralizing antibodies (bnAbs) sometimes develop during chronic HIV-1 infection, but limited information exists about how to recapitulate this process with vaccination. However, evidence points to specific characteristics of the virus that establishes a new infection, the transmitted/founder (T/F), and subsequent diversification of the Env viral quasispecies as important modulators for breadth development. In our previous study, a vaccine expressing a novel clade C T/F Env (identified as Z1800M) from a person living with HIV (PLWH) who developed elite plasma neutralization breadth, elicited the same tier 2 autologous neutralization specificity in immunized rhesus macaques (RM) as in early infection. The targeted epitope comprised the V5 loop and portions of the CD4 binding site (CD4bs) on the Z1800M T/F Env gp120, both highly exposed due to a pronounced lack of glycan coverage. Interestingly, exposure of CD4 contact residues was modulated by naturally occurring changes in V5 in early escape variants. These results support that this Env could potentially be exploited to maximize exposure of the CD4bs. To complement these results, we acquired an extensive set of longitudinal Env sequences from PLWH Z1800M that supports strong selective pressure during infection on two regions comprising bnAb epitopes: the CD4bs and V2 apex. Leveraging on this wealth of data, we will use an innovative combination of sequence-based bioinformatic tools, artificial intelligence and machine learning based in silico structural prediction approaches, and in vitro biochemical and immunologic studies to select HIV-1 envelope vaccine immunogens that maximize exposure of epitopes recognized by broadly neutralizing antibodies, including the CD4bs. We proposed to immunize nonhuman primates with the Z1800M T/F Env to expose the CD4bs and sequentially boost with three longitudinal Envs down selected from hundreds of sequences using our in silico and in vitro methods to maximize epitope exposure during vaccination. We predict that this strategy will transition antibodies from autologous to heterologous neutralization, leading to a translatable vaccine approach to advance into clinical studies.

NIH Research Projects · FY 2025 · 2024-08

PROJECT SUMMARY Phosphofructokinase (PFK) is an essential node governing the flux of glucose through glycolysis. It catalyzes the rate limiting step of glycolysis, converting fructose-6-phosphate to fructose-1,6-bisphosphate. Dysfunction of PFK is associated with many diseases including cancer, glycogen storage diseases, heart disease, and many others. As befitting of its role as the gatekeeper of glycolysis, the activity of PFK is tightly regulated. In humans, there are three PFK isoforms: liver (PFKL), platelet (PFKP), and muscle (PFKM), each with unique regulatory properties. Our understanding of these regulatory properties is not complete, mainly because isoform-specific structural information is lacking. Understanding PFK regulation is useful in the design of therapeutics and tools for research. The proposed research will use cryo-EM, mutagenesis, in vitro kinetic assays, biophysical assays, and cell-based assays to better define three aspects of isoform-specific PFK regulation. Firstly, PFKM is unique due to its non-canonical response to pH and ATP. The biophysical basis for the regulation by ATP and pH in PFKM will be assessed using cryo-EM, mutagenesis, and in vitro kinetics assays. Secondly, as structural infor- mation on eukaryotic PFK emerges, differences in the active and inactive conformations have been identified. Specifically, in PFK, the flexible C-terminal tail has been shown to become ordered upon PFK inactivation and loss of the C-terminal tail in PFKL results in altered response to regulation by PFK. The proposed research will define the role of the flexible C-terminal tail in the regulation of each PFK isoform using mutagenesis and kinetic assays. Finally, PFKL has been demonstrated to oligomerize into filaments. It is hypothesized that PFKM can also oligomerize into filaments. The proposed research will identify PFKM filaments in cells, structurally charac- terize the filament, and define the role of filaments in PFKM regulation. The goal of this research is to better define mechanisms of PFK regulation. The applicant’s long-term goals involve using cryo-EM and cell-based assays as an independent researcher. This fellowship will help to prepare the applicant for a productive career as an independent researcher using cryo-EM and cell-based assays to study important biological questions.

NIH Research Projects · FY 2024 · 2024-08

PROJECT SUMMARY The global burden of sexually transmitted infections has increased over the last several decades. In 2020 alone, the World Health Organization estimated that 128.5 million incident cases of Chlamydia trachomatis (CT) occurred among individuals of reproductive age. Although risk of infection persists across the general population, young women aged 15–24 years, as well as gay, bisexual, and other men who have sex with men (MSM), have a significantly greater risk for acquiring CT. Successful interruption of CT infections and subsequent sequalae requires the implementation of wide-scale public health prevention measures. However, effective primary- and secondary-level prevention strategies, including prophylactic antibiotic use and vaccines, are underutilized or unavailable. Interest in the use of doxycycline prophylaxis has grown in recent years but concerns of antibiotic resistance in Neisseria gonorrhoea and future resistance in CT limit its feasibility and warrant additional research on considerations for successful implementation. Given these concerns, an effective vaccine is likely required for enduring population-level declines in CT infections. Testing of vaccines to protect against CT infections has thus far been limited to Phase I trials and further testing is needed to understand the efficacy and safety among broader populations. The vaccine development process could be strengthened by parallel investigations using mathematical models and health economic evaluations to assess the impact of a CT vaccine. This proposal addresses the following questions: Among individuals potentially eligible for antibiotic prophylaxis, what is the frequency of, and characteristics associated with, antibiotic use that can inform the utility of doxycycline as a preventative measure? What are the potential impacts of a proposed CT vaccine on disease burden? Is CT vaccination a cost-effective prevention strategy compared to current screen-and-treat practices in the United States? To answer these questions, the project will characterize the frequency and factors associated with antibiotic use among MSM in Seattle, WA (Aim 1) using longitudinal data from the ExGen Study, a prospective cohort study of MSM. Second, a mathematical model will be developed to predict the 50-year impact of national rollout of a theoretical vaccine on the burden of urogenital and rectal CT infections in the United States (Aim 2). Model structure and parameters will be sourced from peer-reviewed published literature, publicly available surveillance systems, and vaccine target product profiles. Finally, the cost-effectiveness of CT vaccination strategies, in combination with screen-and-treat interventions, will be compared to screen-and-treat alone in the United States (Aim 3) using the mathematical model developed in Aim 2, alongside cost data from peer- reviewed and public sources. This research addresses critical knowledge gaps about CT prevention strategies and serves as a training opportunity for the applicant to develop into an independent, academic researcher.

NSF Awards · FY 2024 · 2024-08

The ability to determine the internal properties of a medium by making measurements at the boundary of the medium provides important insight in a wide range of scientific applications. The question is whether one can one "see" what is inside the medium by making measurements on the outside. This project involves establishing a deeper mathematical understanding of the inverse imaging technique called electrical impedance tomography (EIT), which arises both in medical imaging and geophysics. EIT attempts to determine the electrical properties of an object by making voltage and current measurements from electrodes located at the boundary of the object. This project will also investigate the question of determining the inner structure of the Earth by measuring the travel times of earthquakes measured at different seismic stations located throughout the Earth. Graduate students will be trained and contribute to these projects. This project will address the mathematical theory of several fundamental inverse problems arising in many areas of science and technology including medical imaging, geophysics, astrophysics and nondestructive testing, to name a few. Three topics of research will be addressed. The first one is Electrical Impedance Tomography (EIT), also called Calderon’s problem. The second topic is travel time tomography in anisotropic media. The third topic is inverse problems for non-linear hyperbolic equations. EIT is an inverse method used to determine the conductivity of a medium by making voltage and current measurements at the boundary. Specific projects will address mathematical challenges in developing and understanding the frameworks that address the case of partial data, anisotropic conductors, the recovery of discontinuities of a medium from boundary information, quasilinear model equations, and high frequencies for anisotropic media. An understanding of travel time tomography involves the determination of a Riemannian metric (anisotropic sound speed) in the interior of a domain from the lengths of geodesics joining points of the boundary (travel times) and from other kinematic information. This project will address the two dimensional scenario, the range characterization and boundary rigidity for simple manifolds, and a novel metric from the area of minimal surfaces bounded by closed curves on the boundary. The investigator will also develop a framework for using the interaction of waves to create new waves that will give information about the object being probed. Specific topics include the study of an inverse problem for the non-linear Klein Gordon equation and inverse problems arising in 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.

NIH Research Projects · FY 2024 · 2024-08

Project Summary Alzheimer’s disease (AD) lacks effective treatment, primarily due to our limited scientific understanding of the early cellular pathways leading to end-stage pathologies like amyloid-β (Aβ) and tau. To bridge this knowledge gap, we propose significant advancements in XAI that can expedite data-driven discoveries in AD. The growing availability of multimodal single-cell data from donor cohorts, covering the entire spectrum of AD pathology, underscores the need for machine learning (ML) models to learn low-dimensional embeddings, a crucial step in interpreting these datasets. Current XAI technology addresses the opacity of supervised ML models by calculating feature attributions, indicating the importance of individual features like gene expression levels to the model’s output, such as a Aβ level, for specific samples such as cells. However, a significant gap exists between model explanations and biological insights: (1) Current XAI techniques face limitations in the context of unsupervised embedding learning and the integration of prior knowledge. (2) The computation of accurate feature attributions poses challenges due to its exponential complexity. (3) Validating hypothesized causal factors requires interventional experiment. IDEAL-XAI addresses these limitations by focusing on the following objectives: Aim 1: Generate biologically informed explanations of AD progression. To bridge the gap between gene-level attributions and systems-level explanations, our innovative XAI methods operate within a unified latent embedding space, incorporating diverse biological concepts from prior knowledge. These methods interpret patterns within the embedding space, including disease progression, to identify potential driver genes. Aim 2: Compute accurate feature attributions to identify putative AD drivers. We propose theoretically grounded techniques to rigorously compute Shapley values for modern large deep models such as transformers and graph neural networks, handle feature correlations and multimodality, and evaluate feature attribution methods in a principled manner to assist investigators discern the most effective techniques for their applications. Aim 3: Validate computational hypotheses in human neurons and microglia. To validate our computational hypotheses regarding AD drivers, we will perform experiments in human neurons and microglia, two critical cell types in AD, across various stages. By modulating the expression of potential drivers in these cells, we aim to measure their impact on various AD-related phenotypes, providing essential insights into therapeutic targets. The successful IDEAL-XAI project will significantly advance XAI principles and techniques, with broader applications in biomedical research areas. Moreover, it will enhance our understanding of AD and expedite the discovery of potential therapeutic targets. IDEAL-XAI represents a crucial step toward unraveling the mysteries of AD and translating complex ML models into actionable biological insights.

NIH Research Projects · FY 2025 · 2024-08

Project Summary/ Abstract Social justice is the primary ethical value underlying health equity. The public health literature, due to its disciplinary limitations, does not “secure” conceptions of health equity to an in-depth theory of social justice. Political philosophy offers several theories of social justice. Which of these are best suited to health equity? The objective of this solo-authored book project is to answer this question. The book has three specific aims: To identify and critically assess (1) the implicit ethical goals that underlie much public health research, education, and policy on health equity; (2) the dominant theories of social justice that are frequently applied to public health and healthcare ethics; and (3) alternative theories of social justice that could be applied to health and healthcare. As the book will be a work in normative bioethics and its primary methodology is philosophical analysis and critical assessment of concepts and arguments, it does not have a hypothesis per se but rather a central claim to be explained and defended. The central claim is that we need to develop and apply the political philosophical theory of “relational egalitarianism” to health equity. Relational egalitarianism is a theory committed to elucidating and defending relational equality. According to this form of equality, foremost, social justice means that people must be able to stand in front of each other as equals. Health inequity, according to this theory, occurs primarily when health disparities are caused by or lead to relational inequalities, or both. The innovation of the project is that it will (1) develop relational egalitarianism to apply to health equity as an alternative to dominant analytic theories of social justice, and (2) it will engage in literature from analytic political philosophy along with theories of structural and interactional racism, sexism, transoppression and ableism from the critical theory literature. Its significance is that it will demonstrate how relational egalitarianism can help guide choices about how health equity should be understood, measured, and represented in the health sciences, policy, and education. As the reduction of health inequities, which will improve the health of marginalized groups, is frequently cited as a primary goal of public health, the book is of clear

NSF Awards · FY 2024 · 2024-08

Spheres are among the simplest geometric objects, serving as building blocks for more complicated topological spaces. The homotopy groups of spheres (collections of continuous functions between spheres, considered up to certain deformations groups) hold fundamental information about maps between topological spaces and have deep connections to number theory, algebraic geometry, differential topology, and geometric topology. Despite their ease of definition, there are few effective methods to compute homotopy groups of spheres. Using equivariant technology the PI will explore the rich connections between equivariant homotopy theory and chromatic homotopy theory and use them to develop powerful new computational techniques. This research will be integrated with conference organization, and graduate students and postdocs mentoring and training. The PI will also be engaged in outreach to local middle school teachers and students. The research involves a range of projects that will leverage recent discoveries in equivariant homotopy theory to advance computations in chromatic homotopy theory. The study of Lubin-Tate theories is one of the most important areas of research in chromatic homotopy theory. In 2009, Hill—Hopkins—Ravenel's resolution of the Kervaire invariant problem elevated equivariant homotopy theory as a potent tool to drive significant progress in chromatic homotopy theory and address classical problems in geometry and topology. The projects involve exploring computational structures in the equivariant slice spectral sequences of Real bordism theories and Lubin-Tate theories at the prime 2. This endeavor extends to achieving analogous results at odd primes. To achieve these goals, the PI plans to employ new equivariant techniques, including transchromatic isomorphisms, stratification results, and the generalized Tate diagram of spectral sequences. These methods will enable extensive equivariant chromatic computations, establish general differential patterns, and reveal a broader range of transchromatic phenomena in the equivariant slice spectral sequences of norms of Real bordism theories and Lubin-Tate theories across various groups and heights. 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 · 2024-08

This research project will develop new theory and methods for assessing the sensitivity of causal inferences to violations of underlying assumptions. Applied causal inference work in the social, behavioral, and economic sciences often use a handful of methods commonly known as "quasi-experimental" designs. However, the validity of these methods requires strong assumptions about the data-generating process, many of which are difficult to defend in practice. What if these assumptions are false? In such cases, sensitivity analyses play an essential role by allowing researchers to quantify how strong violations of key assumptions need to be in order to substantially change a research conclusion. This project will develop a unified theoretical framework that allows researchers to easily quantify how violations of key assumptions affect causal effect estimates using such methods. These results will allow applied scientists and decision makers to draw robust and trustworthy conclusions using valid, but imperfect scientific evidence. Graduate and undergraduate students will be trained and mentored. Dissemination activities for the new methods will include redesigned academic courses, workshops on sensitivity analysis for practitioners, and the development of open-source software. This research project will develop a comprehensive suite of sensitivity analysis tools for popular identification strategies in causal inference, such as instrumental variables, difference-in-differences, and regression discontinuity designs. The investigator will bound the bias due to violations of common assumptions utilized in these settings, such as violations of the exclusion and independence restrictions in instrumental variable estimation; violations of the parallel trends assumption in differences-in-differences designs; or violations of the continuity assumption at the cutoff point in regression discontinuity designs. Analytical formulas will be derived for the bias due to such violations, as well as easily interpretable sensitivity statistics for routine reporting that can be used to quickly communicate the robustness of a result. Other innovations will include allowing for multiple, simultaneous violations of assumptions, incorporating expert knowledge on the relative importance of variables to bound the bias, and enabling valid statistical inference using both classical methods as well as leveraging modern machine learning algorithms. 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 · 2024-08

This project brings together U.S.-based and Bhutan-based scientists to investigate key questions on high mountain glaciers and their impact on landscape change in the Bhutanese Himalaya using geophysics. High mountain glaciers serve as important agents of erosion as well as sediment and water sources that contribute to many of the major rivers in Asia. Scientists have observed significant changes to Himalayan glaciers in response to a changing climate, which could severely impact downstream rivers and the populations that rely on these rivers for survival. Furthermore, melting glaciers can lead to glacial outburst floods, which occur when a glacial lake breaches its natural dam, resulting in a sudden release of flood water. This project will use geophysical methods including radar and seismic experiments to study the Lunana glaciers region of Bhutan, to better understand landscape change, glacier dynamics, and hazards. This project will improve our understanding of sediment transport, landscape change, glacier movements, and glacial outburst floods in the Bhutanese Himalaya. It will also test the potential of seismology to monitor sediment transport and river levels downstream of the Lunana glaciers and the Pho Chu River Valley, including the possibility for flood early warning. Seismic and radar surveys will reveal ice thickness, hydrologic processes, glacial moraine dam stability, the abundance and state of permafrost and debris-covered ice in the region, and subglacial sediments or materials. The project also aims to build geoscience, research, and field skills capacity in Bhutan and the Himalayan region. This project is jointly funded by three EAR Programs: Geomorphology and Land Use Dynamics (GLD), Hydrologic Sciences (HS), and Geophysics (PH). 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 · 2024-08

Partial differential equations (PDE) describe a wide range of phenomena across science and engineering. Of particular interest in this project is a suite of geophysical problems, including (1) rainwater runoff over land surfaces, (2) meltwater flow through snowpack, and (3) the flow of glaciers. The complexity of these problems is far beyond the ability of classical techniques to give exact solutions, and computer simulations are required to understand the models. However, existing numerical methods fail to capture certain essential features present in the physical and mathematical systems. Frequently, these features take the form of inequality constraints -- glacier thickness or water depths cannot be negative. This project will develop new methods that provide accurate approximations to such models while fully respecting physical bounds constraints. Through the co-investigator's involvement in the Juneau Icefield Research Program, findings of this project will directly inform field research. The mathematical and computational techniques developed in this project will be incorporated in open source software projects that are widely used in research and educational endeavors, pushing forward the state of the art in scientific simulation. This project will train a doctoral student in mathematics and provide for undergraduate research experiences through the McNair Scholars program at Baylor University. Many common variational problems obey a maximum principle, but only a restricted class of numerical methods preserves this important feature. An alternative is to explicitly enforce the maximum principle by recasting the problem as a variational inequality subject to physical bounds constraints. Additionally, many important problems inherently arise as variational inequalities. This project develops techniques for bounds-constrained solution of partial differential equations and variational inequalities. These techniques will be developed with suite of challenging geophysical problems in mind. These target applications include meltwater flow through snow, rainwater runoff over land surfaces, and glacier dynamics. These models are nonlinear, time-dependent, coupled partial differential equations with positivity constraints on a thickness variable. At the discrete level, bounds constraints are applied on the Bernstein control net of the finite element space. Since this allows for high-order spatial accuracy, it is also important to obtain high temporal accuracy for dynamic problems. This project will adapt Runge-Kutta methods to ensure bounds constraints hold over time. For single-stage methods, one can advance the solution by posing a variational inequality that minimizes a defect subject to bounds constraints. For higher-order time-stepping methods based on polynomial approximation, such as Galerkin-in-time or collocation-type Runge-Kutta methods, one can force the collocating polynomial to satisfy the bounds constraints uniformly in time, which may be especially important for tightly coupled processes. In addition to method development and computational applications, research will address how to adapt stability and convergence theory for Runge-Kutta methods to the bounds-constrained setting. To facilitate broader adoption by the computational mathematics and geophysics communities, newly-developed methodology will be included in the Firedrake project and the icepack library for glacier modeling built on top of it. Aspects of this research will be included in applied mathematics graduate classes at Baylor University and in earth and space sciences at the University of Washington. 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.