Interpretation of Visible Labs: The Benefits and Drawbacks

Thesis (Master's)--University of Washington, 2025Natural history museums (NHMs) face a conundrum: they appeal broadly to audiences yet struggle to compete with other informal science venues such as science centers, aquariums, zoos, and botanical gardens (Steiner & Crowley, 2013). NHMs are unique in that they hold collections, and many are also sites of active research. More recently, some of these NHMs have begun to showcase their research to the public via visible lab spaces. These attributes can position NHMs as distinctive sites for public engagement. Despite their potential, relatively few studies have examined the unique role of visible labs in informal learning venues such as NHMS. This study investigated the interpretative strategies used within visible labs and examined how these strategies aligned with educational frameworks. A case study approach including interviews with museum professionals engaged in interpretive planning and interpretation for visible labs and supplemented with document analysis was employed. Findings show that the unique features of visible labs include the process of science as a learning outcome, the demystification of scientists and science, and personalization and connection between the public and scientists. However, to leverage these benefits, scientists in visible labs need more support and validation. These findings demonstrate that visible labs are a unique resource for NHMs, adding to their value and separating them from other informal science venues

Lymphatic chain gradients regulate the magnitude and heterogeneity of T cell responses to vaccination

Thesis (Ph.D.)--University of Washington, 2025Upon activation, T cells proliferate and differentiate into diverse populations, including highly differentiated effector and memory precursor subsets. Initial diversification is influenced by signals sensed during T cell priming within lymphoid tissues. However, the rules governing how cellular heterogeneity is spatially encoded in vivo remain unclear. Here, we show that immunization establishes concentration gradients of antigens and inflammation across interconnected chains of draining lymph nodes (IC-LNs). While T cells are activated at all sites, individual IC-LNs elicit divergent responses: proximal IC-LNs favor the generation of effector cells, whereas distal IC-LNs promote formation of central memory precursor cells. Although both proximal and distal sites contribute to anamnestic responses, T cells from proximal IC-LNs preferentially provide early effector responses at inflamed tissues. Conversely, T cells from distal IC-LNs demonstrate an enhanced capacity to generate long-lasting responses to chronic antigens in cancer settings, including after checkpoint blockade therapy. Therefore, formation of spatial gradients across lymphatic chains following vaccination regulates the magnitude, heterogeneity, and longevity of T cell responses

Developing non-invasive neuroimaging biomarkers for Alzheimer's disease using machine learning

Thesis (Master's)--University of Washington, 2025With the FDA’s recent approval of the anti-amyloid antibodies lecanemab and aducanumab, the prospect of disease-modifying therapies for Alzheimer’s disease has become a clinical reality. However, these treatments are only effective in the early stages of the disease which is why there is a need for accurate AD early detection methods. PET imaging has the ability to detect key biomarkers associated with AD up to 20 years before symptoms occur. However, PET is largely unavailable in many countries and even in the U.S. is limited and costly. Here, we present a deep learning framework for synthesizing Aβ, Tau, and FDG-PET from MR images, which can reduce reliance on standard PET imaging by reconstructing the same pathology from learned representations in the MR inputs. We find that our UNet is able to synthesize structurally similar and clinically relevant Aβ, Tau, and FDG-PET images, potentially introducing MRI as a more accessible biomarker detection modality. Early AD is also characterized by network-wide functional connectivity changes, which have been observed in the DMN and other regions including the salience and dorsal attention regions. Currently, functional connectivity evaluation methods do not incorporate spatial information into their analysis nor do they evaluate connectivity dynamically, which neglects important interactions that occur in the brain. In the second portion of this thesis, a previously developed Graph Diffusion Autoregressive (GDAR) Model is applied to fMRI data to analyze dynamic functional changes in physiologically distinct brain networks. We found notable differences in connectivity among AD versus control subjects, informing future analysis to develop features that can separate AD from control subjects with the ultimate goal of developing AD early detection functional biomarkers

Exploring the Intersection Between Persistent Poverty and Rurality Status for Advanced Stage Diagnosis of Breast Cancer using the Surveillance, Epidemiology, and End Results (SEER) Program

Thesis (Master's)--University of Washington, 2025Persistent poverty has been associated with disparities in cancer outcomes. Prior studies have described these disparities as most important for rural areas. This study seeks to provide insight into the roles of persistent poverty and rurality status, on receiving an advanced stage breast cancer diagnosis (stage II, III, and IV) relative to stage I. This cross-sectional study utilized the SEER database, including female individuals diagnosed from 2010 to 2020. Persistent poverty was defined as an area that has consistently maintained a poverty rate of 20% or more for a period of at least 30 years. Urban rural status was defined based on the percentage of people living in a non-urban area. Adjusted multinomial logistic regression models were used to estimate the likelihood of receiving an advanced stage diagnosis associated with persistent poverty and urban-rural status. Stratified analysis and a likelihood ratio test comparing models with and without interaction terms were used to assess effect modification by urban-rural status. A total of 492,050 female individuals were included in this study of which 6.9% lived in persistent poverty areas. Individuals residing in persistent poverty areas had higher risks of presenting with a more advanced stage at diagnosis. The risks were also higher for individuals residing in mostly rural and all rural areas. Stratified analysis by urban-rural classification revealed that the effect of persistent poverty was greater among all urban and mostly urban areas. Effect modification was found by urban-rural classification (p=0.002). This study found that persistent poverty and urban-rural status is associated with advanced breast cancer stage at diagnosis. Additionally, we found that the effect of persistent poverty is modified by urban-rural classifications

A study on the association between educational attainment and substance use disorder from ages 33 to 47, and the moderating role of perceived neighborhood social cohesion.

Thesis (Master's)--University of Washington, 2025AbstractA study on the association between educational attainment and substance use disorder from ages 33 to 47, and the moderating role of perceived neighborhood social cohesion. Shawna Hui Chair of the Supervisory Committee:Isaac Rhew Department of Epidemiology & Department of Psychiatry and Behavioral Sciences Purpose: Examine the association between educational attainment at age 27 and later substance use disorder (SUD), and if the hypothesized association is modified by perceived neighborhood social cohesion assessed at age 27.Methods: This study used data from the Seattle Social Development Project (SSDP), a prospective cohort study that recruited 5th graders in 1985 [N = 808] from 18 elementary schools in lower income neighborhoods in Seattle. The study collected data via interviews conducted from 5th grade through age 47. Participants were asked at age 30 about their educational attainment at age 27. A binary variable was created to reflect if the participant completed at least a 4-year college degree or not [0 = completed at least a 4-year college degree or more, 1 = completing less than 4-year college degree]. Participants were administered relevant SUD modules from the Diagnostic Interview Schedule to determine if they met DSM-IV criteria in the past year for abuse, dependence, or both for alcohol, cannabis, and other drugs. A binary variable was created to reflect if the participant met criteria for abuse and dependence (SUD) for any of those substances at any time at ages 33, 39, and 47 [1 = met SUD criteria at least once for at least once substance based on interviews from age 33, 39, and 47, 0 = did not met SUD criteria based on interviews at age 30, 33, 39, and 47]. Perceived neighborhood social cohesion was measured at age 27 using a subscale of the Collective Efficacy measure created by Sampson and Raudenbush. A dichotomous variable was created to indicate low vs. high perceived neighborhood social cohesion. Poisson regression was used to estimate prevalence ratios for the association between educational attainment and SUD. To assess effect modification, analyses were stratified by high and low perceived neighborhood social cohesion. Results: Participants missing data on the exposure or covariates were excluded, resulting in a final analytic sample size of 613 participants. Among this sample, about 75% of participants completed less than a 4-year college degree and about 30% of the total participants met criteria for SUD. Individuals with less than a 4-year college degree had 1.17 times (95% CI: 0.84, 1.65) higher prevalence of later SUD compared to individuals with college education or more after adjusting for previous substance use assessed at age 18, sex, and race and ethnicity. After stratifying by low (PR = 1.23, 95% CI: 0.72, 2.08) and high social cohesion (PR = 1.17, 95% CI: 0.75, 1.83), the prevalence ratios were slightly different. Conclusions: The study did not find evidence for an association between low educational attainment and SUD from ages 33 to 47 after adjusting for previous history of substance use, race and ethnicity, and sex. The results of the stratification analysis suggest that neighborhood social cohesion is not an effect modifier in the relationship between educational attainment and SUD from ages 33 to 47. Future research should continue to examine the role of neighborhood factors as a buffer against low SES to expand opportunities for interventions

Schoenberg's Tonalities in the Middle Period: Deterritorializing Pierrot Lunaire

Thesis (Master's)--University of Washington, 2025Arnold Schoenberg’s freely atonal period has been overshadowed in scholarship by his later serialist period. When the middle period is mentioned in literature, it is often viewed as existing only in service of the twelve-tone method. Rather than being an unorganized method of composition as some suggest, Schoenberg’s middle period presents music that sees the composer keenly aware of the concepts of tonality. The composer’s thoughts on the tonal system are elucidated in his theoretical writing in his Harmonielehre. These thoughts are also present in his 1912 Pierrot Lunaire.In this thesis, I argue that Pierrot Lunaire makes clear the connection between Schoenberg’s theoretical concepts and his compositional practice. Utilizing the Deleuzian concept of the refrain, I analyze Pierrot Lunaire as musically presenting Schoenberg’s deterritorializing process regarding the elements of tonality, which he then reterritorializes into an atonal framework. What this analysis reveals is a musical work that sees the composer actively working towards what he believed to be the inevitable usurpation of the tonal framework. Rather than being a chaotically explored dead-end on the path to twelve-tone music, I argue that Pierrot Lunaire evinces a complicated, conscious approach towards atonality that is reliant on the composer’s own theoretical writings

Exploring Quantum Machine Learning-Enhanced Models for EEG Data Classification

Thesis (Master's)--University of Washington, 2025Electroencephalography (EEG) records brain activity linked to both executed and imagined movements, but separating true motor signals from background noise in high-dimensional EEG data remains a challenge. Reliable classifiers are therefore vital for accurately tracking patient progress over time. This work is part of a larger initiative, the Smart NeuroRehab Ecosystem, which has two primary goals: (1) to propose innovative physical-rehabilitation strategies for neurologic conditions such as stroke using emerging technologies that make therapy more accessible, and (2) to collect and analyze EEG data using machine learning (ML) models that classify movement-related brain signals. EEG data are complex and often difficult to interpret. In this research, we explore the use of quantum machine learning as an alternative approach for EEG signal classification. Compared to classical ML strategies, quantum methods may offer a fundamentally different way of representing and processing data, potentially improving classification performance or computational efficiency. We implement and analyze a ten-qubit Variational Quantum Classifier (VQC), and compare its performance to a tuned Random Forest baseline using EEG data from a publicly available 64-channel dataset. The task involves classifying each EEG time-window as either a movement or rest condition. Across 40 preliminary runs, the VQC achieves a macro-F1 score of approximately 0.75, accuracy of 0.76, and AUROC of 0.83, outperforming the Random Forest (macro-F1 ≈ 0.71, AUROC ≈ 0.79). In addition to higher macro-F1 and AUROC scores, the VQC also demonstrated significantly better precision and recall on the movement class, based on paired statistical tests. Most experiments were conducted on a quantum simulator, with a subset tested on a cloud-based quantum processor. These findings suggest that hybrid quantum-classical models can match or exceed the performance of tuned classical pipelines without increasing computational complexity. Within the scope of the Smart NeuroRehab project, this work demonstrates that quantum approaches may offer a practical path to continuous monitoring of EEG in clinical settings. Future improvements in quantum hardware may expand the range of practical applications in biomedical signal analysis

DAESC-GPU: A GPU-powered Scalable Software for Single-cell Allele-Specific Expression Analysis

Thesis (Master's)--University of Washington, 2025Allele-specific expression (ASE) is a powerful signal to study cis-regulatory effects. We previously developed DAESC, a statistical method for single-cell differential ASE analysis across multiple individuals. Despite improved power, the lack of computational efficiency limits its utility on large-scale datasets. Here, we present DAESC-GPU, an accelerated version of DAESC powered by Graphics Processing Units (GPUs). DAESC-GPU is dozens of times faster than DAESC and scalable to datasets of over a million cells. Application of the software on single-cell ASE data from the OneK1K cohort identified novel genes with regulatory patterns specific to naïve and central memory CD4+ T cells

Solvation Meta Predictor

Thesis (Master's)--University of Washington, 2025Accurately predicting the aqueous solubility of organic molecules is essential in a wide range of scientific and industrial domains, including drug development, food, and energy storage. This study builds upon prior work by Panapitiya et al. by introducing a multi-stage ensemble learning framework to enhance the predictive performance of solubility models using the SOMAS dataset. The dataset comprises 11,696 molecules with diverse structural and physicochemical properties, including 2D, 3D, and quantum descriptors. Three base models, a Molecular Descriptor Model (MDM), a Graph Neural Network (GNN), and a SMILES model developed by Panapitiya et al. were utilized and evaluated using RMSE, MAE, R², and Spearman correlation. Among individual models, MDM achieved the strongest performance, but ensemble methods consistently outperformed standalone models. Simple averaging improved predictive accuracy, while Optuna-based ensemble weight optimization yielded the best overall results. Additionally, a Mixture of Experts (MoE) architecture was implemented to dynamically weight model outputs based on structural input features, demonstrating strong performance and scalability. This work highlights the value of combining diverse molecular representations and advanced ensemble techniques, providing a robust, adaptive framework for high-accuracy solubility prediction and future data-driven molecular design

Accelerating and enabling discovery in the decade of astronomical surveys

Thesis (Ph.D.)--University of Washington, 2025With the advent of a new generation of astronomical surveys such as the Legacy Survey of Space and Time (LSST) from the Rubin Observatory astronomers will have access to a wealth of data. If we are to fully exploit the data these surveys will generate, we will need to develop novel algorithmic approaches for analyzing astronomical images and tools to scale these algorithms to petabytes of data. In this thesis I focus on these two aspects scaling novel algorithms to extract more science from Rubin data than was previously possible, and developing a novel approach to the analysis and classification of lightcurves. The challenges in scaling to petabytes of data are multi-faceted. The Vera C. Rubin Science Pipelines are a collection of algorithms and a workflow management functionality intended to be used to process data taken by the Rubin Observatory's Legacy Survey of Space (LSST). In the first chapter of this thesis I describe how we implemented an Amazon Web Services and Google Computing Services compliant cloud service backend for the Rubin Middleware components that enable executing the Rubin Science Pipelines on cloud resources. I demonstrate how for short-term projects with a large in-going dataset and a small out-going data volume of results the cloud is almost always cost effective. Analysis results may be retrieved much sooner by allocating more resources, than they would when allocating less compute resources, at the same cost. In chapter 2 I demonstrate how new algorithms can be used to improve on the amount of science delivered by processing Rubin-like data on Rubin-like scales. Kernel Based Moving Object Detection package (KBMOD) is a tool developed to perform searches for moving objects on collections of images using a shift-and-stack method along linear trajectories. \citet{Smotherman2024} demonstrated it can detect objects below the SNR of a single exposure. In this chapter I discuss work required to improve the performance of KBMOD and execute it on all of DEEP's data (~200TB, equivalent to 10 nights of Rubin data), while simultaneously being able to increase the range of searched angles by a 100\% and the range of searched velocities by an additional 38\% compared to the previous search. Compared to Smotherman et al (2024), we achieve a 10\% higher peak detection efficiency but a 0.3 magnitude lower limiting magnitude at which 50\% of the objects were recovered. We identify the higher filtering threshold values, chosen due to a large number of estimated returned results, as the key culprit for the loss of limiting magnitude. In the final chapter of the thesis I develop a new approach to time series classification. By applying ideas inherited from differential geometry on Riemannian manifolds I demonstrate how it's possible to construct a measure of distance between two curves based on nothing more than their shape. I consider two distance measures Square Root Velocity and varifold fidelity measures. The latter is robust different light curve parameterizations. Multiple classification schemes are constructed based on these distances including agglomerative (hierachical clustering), fitting a sum of Gaussians, and a K-Means like algorithm to find the generalized means (Frechet means). Classification accuracy for a high SNR dataset was 96.58\%, 95.9\% and 98.93\% for the sums of Gaussians, agglomerative clustering and K-Means approach respectively. Validation of the approach on the PLAsTICC lightcurves dataset was less successful, achieving a top classification accuracy of 77.03\%. Two key reasons for the drop in classification accuracy are identified: heteroskedastic uncertainties in the data, and reparameterizations of curves