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Bone Functional Adaptation: Life History Constraints and Implications for Aging Research
No full textThesis (Ph.D.)--University of Washington, 2025This dissertation examines skeletal adaptation across human life history, emphasizing how reproductive investment and aging influence bone mineral density (BMD), a crucial determinant of bone health and resilience. Specifically, it investigates how parity, habitual mechanical loading, and cellular aging–proxied by leukocyte telomere length (TL)--interact to shape regional variation in BMD. The first study analyzed associations between parity and regional BMD using anthropometric, dual-energy X-ray absorptiometry, health biomarker, and questionnaire data from the National Health and Nutrition Examination Survey (NHANES cohorts 2007–2018). Results showed that higher parity was linked with lower BMD, particularly in metabolically active skeletal sites such as the lumbar spine, highlighting the metabolic demands of reproduction on skeletal maintenance. The second study assessed whether habitual mechanical loading could buffer age-related BMD loss by comparing weight-bearing and non-weight-bearing skeletal regions. Findings demonstrated similar age-related declines between weight-bearing and non-weight-bearing regions, though gendered differences emerged; women experienced steeper declines in non-weight-bearing regions compared to weight-bearing regions, indicating that loading alone is insufficient to protect against aging-related skeletal deterioration. The third study investigated whether telomere length, a biomarker of cellular aging, is associated with regional BMD variation and parity (NHANES 1999–2002). Results showed that shorter TL correlated with lower BMD selectively in women but did not mediate the association between parity and BMD. Together, these findings demonstrate that reproductive history and cellular aging independently influence skeletal health across the lifespan. This dissertation emphasizes the complexity of skeletal aging and the importance of integrating life history theory, biomechanical analysis, and cellular biology to understand bone health in evolutionary, clinical, and public health contexts
Decentralized Multiagent Trajectory Planning
No full textThesis (Master's)--University of Washington, 2025In this work, we present novel approaches to solve the decentralized trajectory planning for networked multiagent systems. We first provide the new reformulation of the safety constraint for faster convergence with guaranteed safety, with the iteration to converge being a fraction of traditional linearization approaches. This newly proposed constraint reformulation is based on the combination of the dual problem to the minimum distance between convex sets and biconvex optimization. The second important result obtained is the fully decentralized, scalable trajectory optimization algorithm based on the reformulated biconvex optimization, along with the successive convexification (SCVX) for handling the nonlinear dynamics. This algorithm features a minimum sharing data in comparison to the traditional distributed gradient method or dual decomposition-based algorithm like alternating direction method of multipliers (ADMM). The number of variables shared is less than half that of ADMM-SCP in our setups. We also showed the strong convergence of our biconvex method to the partial minimum under mild assumptions. Numerical results with up to ten agents and hardware experiments were performed to validate our claims. This work will enable efficient coordination of large-scale cooperating unmanned vehicles navigating in complex environments
Paleoclimate and Historical Perspectives on Modern Climate Sensitivity
No full textThesis (Ph.D.)--University of Washington, 2025Determining modern climate sensitivity, i.e., the global surface warming from doubling prein-dustrial concentrations of CO2, is an urgent task as it controls how much the planet will warm from
greenhouse-gas emissions. The upper bound on estimates of climate sensitivity has been highly
uncertain for decades, but paleoclimates now provide a strong constraint. In this dissertation, we
combine proxy data from paleoclimate data assimilation with atmospheric general circulation mod-
els to show that the climate sensitivity inferred from paleoclimates is systematically higher than the
climate sensitivity that applies to modern warming from CO2. This difference in climate sensitiv-
ity arises because (a) ice sheets, topography, and vegetation changes drive atmospheric stationary
waves that alter the spatial patterns of sea-surface temperature (SST) over distant oceans during
both the cold Last Glacial Maximum and the warm Pliocene; and (b) these paleoclimate SST pat-
terns are associated with amplifying cloud feedbacks that make past climates more sensitive than
the modern climate. Accounting for these differences between climates leads to a substantial re-
duction (∼1.0°C) in the upper bound on modern climate sensitivity compared to recent community
assessments, such as IPCC AR6 (Forster et al., 2021). The leading role of spatial patterns of temperature change in determining climate sensitivity alsocompels a re-evaluation of the historical climate record (c. 1850–present). Previous studies have
identified major discrepancies in radiative feedbacks due to differences in the patterns of sea-surface
temperature across instrumental datasets. These discrepancies result from statistical infilling of the
expansive gaps between sparse SST observations over the global oceans. In this dissertation, we use
coupled data assimilation, which optimally combines observational and dynamical constraints from
all climate fields simultaneously, to reconstruct monthly and globally resolved SST, near-surface
air temperature, sea ice, and sea-level pressure over 1850–2023. The reconstruction provides a
novel and internally consistent perspective on coupled climate variability and recent trends, which
informs investigation of radiative feedbacks in the historical record. Chapter 1 introduces the research topics addressed in this dissertation. Chapter 2 quantifiesLast Glacial Maximum pattern effects and their impacts on modern climate sensitivity. Chapter
3 quantifies Pliocene pattern effects and provides stronger constraints on both modern climate
sensitivity and 21st-century warming. Chapter 4 presents a reconstruction of the historical climate
record (1850–2023) using linear inverse models and coupled data assimilation. Chapter 5 reviews
the conclusions of the dissertation
Interactivity and Illusions of Ability: How Using Generative AI Affects Investor Judgments
No full textThesis (Ph.D.)--University of Washington, 2025In this study, I use the setting of Generative AI (GenAI) to examine how processing tool interactivity affects investors’ self-assessments of ability and willingness to invest. Although GenAI can help investors process financial information, I theorize that the interactive nature of GenAI blurs the boundaries between investors’ own abilities and those of GenAI, prompting investors to discount their reliance on GenAI and misattribute its abilities to themselves. I rely on the advantages of a laboratory setting to disentangle the interactive element of GenAI from the mere presence of GenAI assistance. Across three experiments, I find that the interactivity underpinning GenAI heightens investors’ self-assessments of their own abilities and increases their willingness to invest, despite this interactivity not improving, and in fact hindering, their actual processing of information provided by GenAI. My study thus highlights one potential cost of using GenAI and other highly interactive processing tools
Understanding parent/caregiver support needs during genome sequencing in a pediatric research setting
Get PDFThesis (Ph.D.)--University of Washington, 2025Pediatric patients benefit from genome sequencing (GS) for disease diagnosis, treatment guidance, and reducing diagnostic delays. However, parents and caregivers navigating this complex system face unique challenges, including informed consent, understanding results, and managing expectations. The variability in genetic service delivery in different clinical contexts can impact the parent/caregiver experience. Existing research often examines implementation factors like clinical utility, provider perspectives, and ethical/social concerns but tends to focus on specific aspects of genetic testing or lacks a comprehensive look at parental needs during GS specifically. This dissertation aims to address this gap by examining parent/caregiver needs throughout the entire pediatric GS process, from pre-test counseling to post-test follow-up as well as in different clinical settings. It integrates findings from a scoping review of existing literature examining parental needs during both pediatric whole exome and genome sequencing and qualitative interviews conducted within the SeqFirst project, which investigates GS as a first-line diagnostic tool in children with atypical development and infants admitted to the neonatal intensive care unit (NICU). My results identify key themes: parents need clear, empathetic communication and tailored information, emotional support, and logistical guidance at every stage of GS. Findings highlight the interconnected nature of informational, emotional, and logistical needs, underscoring the importance of addressing them comprehensively. Recognizing and responding to these needs can inform patient-centered implementation practices, ultimately improving healthcare experiences and outcomes. Addressing gaps in current support strategies, especially in the NICU and developmental disorder settings, is crucial as GS becomes a mainstay in pediatric care. This study advocates for tailored interventions to support parents, ensuring effective communication, emotional well-being, and navigational assistance through complex healthcare landscapes
Musician Mental Health: Intersections of Poor Mental Health, Peer Support, and Instrumental Conducting
No full textThesis (D.M.A.)--University of Washington, 2025Introduction: The purpose of this dissertation is to contribute to emerging research and work in musician healthcare, and specifically musician mental health. It will review existing research on the mental health conditions that musicians face as a unique population, review existing research on the efficacy of peer recovery work as non-clinical mental health resource, and provide research and interview findings on existing peer support resources for classical musicians. Pedagogical and gestural implications for conductors will be introduced, as well as necessary further research study. Summaries of each topic listed above are provided below. Parts are not organized by any basis of importance or significance. Part One: This literature review aims to aggregate major studies on the mental health of classical musicians as a unique population, relative to existing data from general population studies. The goal is to contextualize the field of musician mental health, demonstrating how musicians on a broad level are an at-risk population relative to conditions such as anxiety, depression, stress, and life expectancy. The majority of the demographic focus is on student and professional classical musicians, though some relevant and important data on non-classical performing artists is also included. The paper will also provide implications for understanding the performance effects of poor mental health for musicians, as well as future research directions for overall well-being. Part Two: The purpose of this second literature review is to introduce peer recovery work as a developing mental health resource for individuals experiencing poor mental health. It will define and specify foundational qualities of peer recovery work, and provide existing organizations, services, and resources as models of its use for varying populations and disciplines, such as undergraduate university students, medical students, and individuals experiencing substance abuse issues. The review will also present emerging evidence and research on the efficacy of peer recovery work on various metrics measuring mental health wellbeing, showing instances of its significant positive impact and improvement of mental health conditions. Lastly, I emphasize the need to explore and develop more mental health resources like peer recovery work to supplement existing clinical interventions, in the context of overall worsening mental health across general populations. Part Three: In this final section, I will discuss existing wellness programs offering peer support services by trained peer support workers. The section will contain information gathered from personal research and various interviews with program directors, to present existing mental health resources for classical musicians. Implications of peer support principles for the conductor will be covered, with a focus on the pedagogical and gestural elements of the conductor/director role. Further directions for research and development for peer support and classical musicians will also be discussed
Alveolar Bone Height Preservation in Growing Patients After Decoronation, Extraction, or Retention of Ankylosed Primary Teeth: A Retrospective Cohort Study
No full textThesis (Master's)--University of Washington, 2025Introduction: Mandibular second premolars are frequently congenitally missing. Preservation of the alveolar ridge to facilitate future implant placement is critically important for favorable outcomes. Maintaining the retained primary tooth is desired unless the primary tooth becomes ankylosed during the growth period. This study examined three approaches to preserving alveolar bone height in growing patients when the retained primary tooth becomes ankylosed: decoronation, extraction, or retention of the tooth. We hypothesized that decoronation would show the greatest preservation of bone height, while retention would show the least. Methods: This retrospective cohort study used de-identified serial radiographs collected from practitioners in the greater Seattle-Tacoma area. Ankylosed primary mandibular second molars with congenitally missing mandibular second premolars were separated into three groups based on management method: decoronation, extraction, or retention. Alveolar bone height was measured at two time points and the change over time was calculated. Results: The mean age of each sample in years at T0 was 13.1, 13.6, and 12.6 for decoronation, extraction, and retention, respectively. The mean follow-up time in months for each sample was 22.9, 29.1, and 23.9 for decoronation, extraction, and retention, respectively. Decoronation of ankylosed mandibular primary second molars showed the most favorable change in alveolar bone height over time, with the bone height in the region either increasing or remaining stable. Retention showed the least favorable effect, with alveolar bone height decreasing as the patient matured. Extraction displayed an intermediate response. Conclusions: The alveolar ridge responds more positively during growth after decoronation in patients with ankylosed primary second molars. Growth may influence the extent of change observed in each group
Essay on structural estimation of entry games in oligopoly
No full textThesis (Ph.D.)--University of Washington, 2025This dissertation introduces a novel approach for estimating the structural parameters of demand, cost,and entry costs in a differentiated products model where product characteristics and input cost data
are not observed for non-entrants. Traditional methods for entry game estimation rely on the product
characteristics that are used as instruments to be observable for both entrants and non-entrants — a
scenario that is uncommon in practice. I first provide an extension of the standard identification strategy
that does not require such observability condition, but also demonstrate based on identification analysis
as well as Monte-Carlo study that such an approach requires impractically large sample size.
To overcome this limitation, I use the instrument-free methods proposed by Byrne et al. (2022) and
Imai et al. (2024), which allow estimation of the demand and cost function by addressing the endogeneity
of price using entrants' cost data. Building upon this foundation, I extend their framework to incorporate
entry-exit decisions. My findings indicate that using both demand and cost data offers a more practical
and effective estimation approach. I propose a data-augmented Markov Chain Monte Carlo (MCMC)
estimationmethodanddemonstratethroughMonteCarlosimulationsthatthisapproachyieldsconsistent
estimates.
Furthermore, I apply the estimation techniques developed in this research to estimate the structural
parameters of the Wisconsin nursing home market and discuss the social welfare implications of the
Certificate of Need (CON) law. Counterfactual simulations reveal that abolishing the CON law would
increase consumer and producer surplus by 165 million, respectively, while government
spending would rise by $700 million. I also estimate important market structures, such as labor/capital
elasticities, entry costs, and the difference in the distribution of service quality between entrants and
non-entrants
From Surface to Stratosphere: Understanding Interannual Climate Variability and Decadal Changes
No full textThesis (Ph.D.)--University of Washington, 2025This thesis investigates key processes governing interannual and decadal variability in the stratosphere and troposphere and emphasizes their implications for climate projections. Arctic Amplification (AA), the disproportionate warming of the Arctic relative to global mean temperatures, is a robust feature of climate change. Using machine learning and CMIP6 models, this work demonstrates that internal variability has amplified AA by 38% since 1980, reconciling discrepancies between observed and simulated AA. These inflated values of AA are made possible by a unique pattern of multi-decadal internal variability which warms the Barents and Kara Sea, while cooling the Tropical Eastern Pacific and Southern Ocean. These results highlight the critical role of internal variability in shaping observed climate trends. Focusing on the tropical tropopause layer (TTL), this thesis quantifies the drivers of interannual variability in temperature, water vapor, and cirrus clouds, revealing the central role of stratospheric processes in determining variability of this region. The QBO's impact on tropical clouds is further explored, identifying a seasonally synchronized response in cloud fraction, temperature, and even the radiative budget of the tropics. Finally, this work addresses the rapid warming of the Southern Hemisphere subtropical lower stratosphere, linked to changes in the SH BDC. These circulation changes reconcile observed temperature and ozone trends with simulations, shedding light on the dynamical processes influencing stratospheric variability and ozone recovery. Together, these studies advance understanding of how internal variability contributes to observed changes from the surface to the stratosphere, informing model validation and projections of future climate
Leveraging Multimodal Models to Detect Osteoporotic Compression Fractures
No full textThesis (Ph.D.)--University of Washington, 2025Osteoporosis is a chronic disease of low bone mineral density that affects older patients, predisposing them to fractures. While osteoporosis screening is evidence-based, it remains grossly under-utilized. Osteoporotic compression fractures (OCFs) are an early biomarker for osteoporosis but are often misclassified and under-reported on review by radiologists. Opportunistic screening, or leveraging pre-existing data, to detect OCFs to augment the current standard of osteoporosis screening could prompt appropriate diagnostic studies, treatment, and risk management. Current fracture detection tools show promise but are limited by key factors, namely manual curation of data inputs and lack of external validation and generalizability, limiting their potential clinical utility. They are also based on unimodal models, or those that leverage a single data modality. Multimodal models that leverage more than one modality have shown improved performance in clinical tasks and also better reflect real-world clinical workflows.In this dissertation, I focus on developing and evaluating multimodal models to detect OCFs, leveraging unstructured clinical notes, radiographs, and structured electronic health record (EHR) data. To achieve this, a spine radiograph dataset from previous work in our group was used. Matching patient IDs from this dataset, I obtained clinical notes from a quaternary healthcare enterprise database to annotate fracture events. With these datasets, I implemented and evaluated unimodal models for each of the modalities above (images only and notes only) to produce outputs for the multimodal models, described in the following aims in this research:
1) Aim 1: Implement and evaluate transformer models to extract fracture events from clinical notes. An ensemble algorithm to consolidate fracture events at the note- and patient-level was also developed to produce both structured data representing a patient history of fractures and feature representations for downstream input separately to multimodal models (Aim 3). Evaluation metrics demonstrated that fine-tuned transformer models are able to extract fracture events from clinical notes with good performance, albeit limited by the small training corpus.
2) Aim 2: Develop and assess an imaging analysis pipeline for detecting OCFs. An imaging analysis pipeline consisting of independently developed machine learning models were chained in a fully automated framework. Evaluation of the pipeline was performed with a dataset of radiographs acquired in various clinical settings to measure real-world performance. While we were able to develop a performant fully automated pipeline, the evaluation demonstrated subpar performance in detecting positive cases for OCFs at the image-level.
3) Aim 3: Develop and assess whether multimodal models combining NLP, imaging analysis, and structured EHR data perform better than imaging analysis alone in detecting OCFs. With the structured data and feature representations from Aim 1, the imaging analysis pipeline predictions from Aim 2, and other structured EHR data, numerous multimodal model architectures were trained and evaluated in detecting OCFs at the patient-level. The evaluation of these models demonstrated better performance than the unimodal models (images and notes only) in detecting OCFs even with a small training corpus, reaching an acceptable absolute performance