Welcome to ResearchWorks at the University of Washington

First time adding to ResearchWorks? Please contact us.

ResearchWorks is the University of Washington's digital repository (also known as "institutional repository") for disseminating scholarly work.

Looking to deposit a dataset? UW has a membership with the generalist data repository Dryad. Use our guide to see if Dryad is the right place for you to deposit your data.

About ResearchWorks UW Bothell ResearchWorks

Communities in ResearchWorks

Select a community to browse its collections.

Now showing 1 - 7 of 7

Recently Added

Hanford Litigation Office digitized documents spreadsheets
(2003) Hanford Litigation Office
Spreadsheet keys for Department of Energy documents that were digitized as part of a legal action for plaintiffs affected by the radiation from the Hanford Nuclear site.
The Value of Resilience: Flood Risk, Information Disclosure, and Housing Markets in New York City
(2026-04-20) Chen, Chin-Wei; Berney, Rachel
Flood risk increasingly shapes where households live, how much they pay for housing, and how safety is traded off against affordability in U.S. urban housing markets. As climate change intensifies flooding hazards and expands exposure beyond traditionally recognized high-risk areas, many households face constrained choices between safer locations and affordable housing. Historically, housing markets have often failed to fully price flood risk, reflecting limited disclosure, uneven risk communication, and uncertainty about whether public mitigation investments meaningfully reduce risk. As flood risk information becomes more visible and as governments invest in resilience, understanding how households and markets navigate the trade-off between safety and housing costs is critical for equitable urban policy. This dissertation examines how flood risk, information disclosure, and mitigation investments interact to shape housing market outcomes, with a particular focus on New York City. The first study systematically reviews the empirical literature on climate-related hazards and housing prices, highlighting how research has evolved from disaster-focused analyses toward frameworks that examine ongoing risk exposure and household decision-making. The review emphasizes how flood risk introduces a persistent safety-affordability trade-off in housing markets and identifies gaps in how studies account for information disclosure and mitigation as mechanisms that may alter this balance. The second study examines how the evolving flood-risk information environment, including the release of risk data on real estate platforms and new disclosure requirements, affects housing prices over time using an interrupted time series approach. The findings show modest and uneven market responses, with disclosure events shifting housing price trends rather than causing abrupt repricing, suggesting gradual diffusion of flood-risk information in housing markets. The third study evaluates whether community-level flood mitigation activities influence the relationship between flood risk and housing prices using transaction-level hedonic models. The findings show that some mitigation investments can partially offset the negative capitalization of flood risk by signaling improved safety and resilience, although the magnitude of these effects varies across neighborhoods and types of intervention. Taken together, these studies demonstrate that flood risk is not simply capitalized into housing prices but negotiated through trade-offs among safety, affordability, and trust in public action. Risk disclosure can gradually reshape market behavior by increasing the visibility and salience of climate risk, while mitigation investments influence whether households view flood-prone locations as viable long-term housing options. By linking flood risk analytics, market behavior, and public investment, this dissertation provides policy-relevant insights into how cities can manage climate risk while addressing housing affordability and social equity.
New Institutionalisms and Mechanisms of Comparative Policy Analysis
(2026-04-20) Alqadhib, Abdulaziz M.; Whittington, Jan
This dissertation examines why renewable energy transitions unfold differently across political and economic institutional systems by comparing the structures shaping solar development in Texas and Saudi Arabia. Both regions possess strong solar resources and face decarbonization pressures; however, their trajectories diverge in terms of pace, coordination, and policy effectiveness. This study evaluates the institutional environments, governance arrangements, and historical pathways’ structural opportunities and constraints in renewable energy transitions.Using a comparative institutional framework integrating New Institutional Economics, Transaction Cost Economics, and Path Dependence, the research applies process tracing, archival research, and project-level comparison supported by regulatory filings, operational data, national planning documents, and reports from public energy institutions. The findings show that decentralized, contract-based institutional systems reduce uncertainty and coordination hazards, while hierarchical, layered systems elevate administrative transactions and reinforce carbon-centric routines, shaping transition outcomes.
Spatio-Temporal Statistical Inference for Human Mobility Using GPS Data
(2026-04-20) Wu, Haoyang; Dobra, Adrian; Chen, Yen-Chi
Understanding where individuals spend their time over space and time is a central question in the study of human mobility. The increasing availability of high-resolution GPS data provides unprecedented opportunities to address this question, but also poses substantial statistical challenges arising from measurement error, heterogeneous sampling frequencies, and complex temporal structure. This dissertation develops a unified spatio-temporal statistical framework for modeling and estimating interpretable summaries of long-term human mobility from GPS data. At the core of the framework is a stochastic representation of daily mobility patterns, in which GPS observations are viewed as noisy measurements of latent spatio-temporal movement processes. Within this data-generating view, key inferential targets are formulated as time-allocation functionals that quantify the proportion of time individuals spend in different spatial regions. Estimation procedures are constructed by combining time-weighted representations of observed locations with aggregation across days, yielding activity-related summaries with well-defined statistical properties. This approach shifts attention from trajectory reconstruction to the principled estimation of time allocation over space. The central inferential construct emerging from this modeling strategy is the activity space, defined as a time-weighted characterization of routine spatial behavior. Rather than treating movement paths as primary objects of analysis, activity spaces are derived as functionals of latent daily processes, allowing for coherent inference under realistic measurement conditions. The framework accommodates multiple spatial supports, including continuous domains, geometrically constrained environments, and aggregated regional contexts. The dissertation consists of three complementary main chapters. Chapter 2 establishes the foundational modeling and estimation framework for daily mobility processes and derives statistical properties for time-proportion estimators. Chapter 3 extends this framework to polygon-network representations, incorporating geometric constraints into both modeling and inference. Chapter 4 integrates the resulting mobility summaries into applied analysis, demonstrating how time-weighted activity measures can be combined with external spatial information to study contextual exposure in public health settings. Together, these contributions provide a coherent model-based approach to spatio-temporal inference on human mobility that links data generation, estimation, spatial representation, and scientific application within a unified statistical framework.
Asian Indian Families' Experiences with Autism, Neurodiversity, and Language Intervention in the United States
(2026-04-20) Ram, Poornima; Kover, Sara
Autism research in the field of communication science disorders (CSD) has insufficient representation of cultural perspectives. Research through an intersectionality lens can better inform training, practice and policies for CSD, specifically in the U.S., where families seeking SLP services represent a wide range of racial and cultural identities. Using a qualitative methodology, this dissertation explored the lived experiences of Asian Indian families with autistic children to gain insight into cultural and linguistic factors that may influence how these families approach and engage with speech-language therapy services, and how cultural contexts shape their understanding of the concepts of disability, autism, and neurodiversity-affirming care. Fourteen mothers and two fathers from six states across the U.S. participated in the study. Participant accounts were captured through semi-structured interviews. Three broad themes were identified: dissatisfaction with SLP services; the stigma of autism; and cultural factors can be both barriers and supports. Implications and recommendations for practice, training and research are provided.
Online and Network Sampling Methods for Survey Research: A Total Survey Error Framework
(2026-04-20) kahveci, ihsan; Almquist, Zack W
Survey research faces an existential crisis. Response rates for major surveys have declined steadily over several decades, and the costs associated with maintaining high-quality probability sampling panels have become prohibitive for all but the most well-resourced organizations. Populations of increasing scientific and policy relevance, such as individuals experiencing homelessness and those who use drugs, are systematically excluded from address-based sampling frames. These challenges necessitate the development of non-probability methods that support valid population inference. This dissertation extends the Total Survey Error (TSE) framework to evaluate two alternatives: population estimates derived from algorithmically targeted online samples and survey data collected through network sampling and aggregated relational data. Application of the TSE framework to these methods demonstrates that each introduces distinct error sources that require careful attention to design and statistical adjustment. Social media recruitment via advertising platforms can rapidly yield large, low-cost samples; however, algorithmic optimization for engagement introduces selection bias, leading to samples that overrepresent certain demographic groups, such as college graduates. Propensity score adjustment combined with calibration using a probability sampling reference survey can substantially correct these biases, but its effectiveness depends on the relationship between selection mechanisms and the outcome of interest. Adjustment performs well for time-invariant measures, such as chronic health conditions, but less effectively for time-variant measures, such as health behaviors that may correlate with social media use and information exposure. Survey mode, whether interviewer-administered or self-administered, shapes measurement error in population-specific ways. Interviewer presence improves response rates but can influence response content, and the direction of this influence depends on the social expectations associated with the population. Among individuals experiencing homelessness, social desirability bias appears to reverse: the socially expected role of demonstrating need may create pressure to report worse health when an interviewer is present. These findings support a hybrid approach in which interviewers administer questions where completeness is paramount, while respondents complete sensitive questions independently. Network-based data collection methods, particularly those using aggregated relational data, offer a cost-effective approach to characterizing the social networks of hidden populations. When combined with social media recruitment, these methods can produce diverse samples at a competitive cost, provided that researchers attend to design decisions and post-adjustment strategies. The resulting network data reveal that participants maintain broad acquaintance ties to other people who use drugs but report far fewer trusted contacts, a distinction with direct implications for how harm reduction resources might be disseminated. However, the overrepresentation of highly active users remains a limitation. As traditional probability sampling becomes increasingly difficult to sustain, online and network sampling methods are likely to continue to grow in adoption. The central question is no longer whether researchers will use these methods, but how to use them effectively and responsibly. This dissertation provides practical tools and conceptual clarity to support this effort, while recognizing that translating the TSE vocabulary into practice requires ongoing methodological development across survey methodology, demography, public health, and the social sciences.
Islamophobia: Towards an Empirical Understanding
(2026-04-20) Mabruk, Aminah; Harris, Alexes
Compelled by rising Islamophobia in the U.S. and the dearth of extant research examining Islamophobia as understood by Muslims, this research centers the following questions: How do Muslim adults conceive of Islamophobia and their experiences with it? How do they perceive and make sense of these experiences? How does Islamophobia impact their daily lives? A total of 51 interviews were conducted with self-identified Muslim adults (i.e. 18 years or older) living in the U.S. exploring these questions through a mix of verbal survey questions and free-response interview questions. While the broader study also examines the potential health effects of Islamophobia, this thesis focuses on how the study participants understand and experience Islamophobia empirically and centers their interview responses in this analysis. Developing an empirical understanding of Islamophobia represents a crucial first step before exploring the relationship between Islamophobia and health that will be examined in my future work.
Modeling population status and demographic rates of baleen whales using historical whaling data
(2026-04-20) Rand, Zoe; Branch, Trevor A
Commercial whaling in the 20th century decimated the populations of many baleen whale species. Blue whales (Balaenoptera musculus) were especially impacted, with multiple populations whaled to near extinction, resulting in their current listing globally as Endangered by the IUCN. During whaling, extensive biological data were collected that, when combined with contemporary statistical methods, can be used to answer long-standing questions about baleen whale demography and population status. In this dissertation, I use historical data from whaling and contemporary Bayesian statistical methods to model baleen whale population dynamics and demography, with a particular focus on the highly exploited blue whale. In Chapter 1, I used a multi-state mark-recovery model to investigate population structure in Antarctic blue whales using historical mark-recovery data and found that they move frequently in the Southern Ocean, suggesting they are a single well-mixed circumpolar population. In Chapter 2, using the extensive fetal sex ratio data collected during whaling, I investigated ecological theories about adaptive sex ratio behavior, finding that longer rorqual (family Balaenopteridae) whale mothers have more female offspring. This suggests there is an advantage to being large that larger mothers pass on their daughters, likely stemming from the high costs of gestation and lactation for female baleen whales. In Chapter 3, I combined historical catch data and contemporary abundance estimates to build a population assessment model for Antarctic blue whales, finding that at the end of whaling they were at just 0.2% of pre-whaling levels, although their population size is currently increasing. Despite increasing, they are currently at less than 2% of pre-whaling levels so they still have many decades before they recover from whaling. In Chapter 4, using a global compilation of blue whale aging and reproductive data, I modeled age-length relationships and estimated reproductive rates for Antarctic, pygmy, and eastern North Pacific blue whales, finding that asymptotic lengths were longer for females than males across subspecies, and that Antarctic blue whales were the largest while pygmy blue whales were the smallest. In addition, I estimated pregnancy rates and age of sexual maturity for female eastern North Pacific and pygmy blue whales and, in Chapter 5, estimated natural survival for pygmy blue whales. These projects provide a deeper understanding of blue whale population dynamics and demography and lay the groundwork for age-structured stock assessments of blue whales.
Behavioral and Cognitive Avoidance as Predictors of Session-by-Session Change During Prolonged Exposure Therapy
(2026-04-20) Pandey, Shivani; Zoellner, Lori A; King, Kevin M
Avoidance of trauma-related stimuli and feelings can provide short term relief. However, long-term avoidance can become pervasive, hindering new learning that is necessary for therapeutic change. This study investigated the role of both pre-treatment and in-session measures of behavioral avoidance and rumination, conceptualized as a cognitive avoidance strategy, on PTSD and depression symptom change during a course of prolonged exposure (PE) therapy. We hypothesized that higher pre-treatment and in-session avoidance would be predictive of flatter reductions in both PTSD and depression symptom change from pre-to-post treatment. Data from a randomized control trial (N = 149) were used to investigate study questions. Pre-treatment behavioral avoidance and rumination were measured with self-report measures. Avoidance and unproductive processing, a form of rumination, during PE sessions was measured using an observational coding system (CHANGE). Structural equation models were used to investigate study questions. Contrary to hypotheses, analyses revealed higher pre-treatment behavioral avoidance, but not rumination, was associated with steeper decreases in PTSD symptoms (β = -.373; p = .004) throughout treatment. Higher baseline rumination, but not behavioral avoidance, was associated with steeper decreases in depression symptoms (β = -.221; p = .032) from pre-to-post treatment. Higher in-session avoidance was associated with less declines in PTSD symptom trajectories (β = .327; p = .026). Taken together, individuals with high baseline rumination and avoidance can still make clinically meaningful shifts during PE. Mid-point of treatment seems to be an important time during which therapeutic stagnation may occur, indicating that clinicians should pay special attention to avoidance and emotional engagement at this time to ensure therapeutic gains.
Discovering plasticity rules for learning and resilience in neural circuits
(2026-04-20) Bell, David G; Fairhall, Adrienne L
While modern supervised and reinforcement learning algorithms can train neural networksto solve a wide range of tasks, the brain often operates in data-sparse regimes where such extensive supervision is unavailable. This thesis argues that the brain succeeds in these settings by leveraging inductive biases about the tasks it is likely to encounter. These biases are embedded in initial connectivity, cell-type structure, and critically, in synaptic plasticity rules. Here, we investigate how unsupervised synaptic plasticity can shape neural circuits prior to extensive behavioral experience. In the first part of this thesis, we study plasticity in the zebra finch song system. In collaboration with researchers at the California Institute of Technology, we examine the restoration of singing behavior following viral perturbation of nucleus HVC, a premotor region essential for song production. Adult male zebra finches transiently lose song after viral manipulation but recover within approximately two weeks. Strikingly, birds prevented from practicing during early recovery subsequently require less practice to regain song, suggesting that recovery is partially unsupervised. We model this process using several unsupervised plasticity mechanisms, including spike timing-dependent and homeostatic plasticity. While standard homeostatic rules restore regular spiking activity in a network model of HVC, they fail to reproduce experimentally observed synaptic reorganization. We therefore propose a local population-level homeostatic rule that recruits previously silent neurons, accounting for both activity recovery and synaptic changes. In the second chapter, we employ meta-learning, a technique by which biologically plausible learning rules are learned via a supervised procedure, to discover biologically plausible plasticity rules that organize robust sequential dynamics in HVC-like networks. In this framework, candidate unsupervised plasticity rules are optimized by a supervised outer loop to maximize a task objective. Starting from disordered connectivity, the learned rules reliably self-organize networks into sequence-generating circuits resembling those observed in vivo. Analysis of resulting rules reveals that plasticity on recurrent excitatory synapses generalizes Oja’s rule, replacing the classical Hebbian term with a spike timing-dependent component. We further show that learned plasticity rules can compensate for continual synaptic turnover and that learned inhibitory plasticity enhances the precision and robustness of sequential dynamics. In the final chapter, we apply meta-learning to the self-organization of neural inte- grators—circuits that generate long timescales via carefully tuned structure to maintain representations of sensory inputs. Such integrators underlie functions including head di- rection coding and oculomotor control. We hypothesize that unsupervised plasticity can shape these circuits from weak structural priors. Using meta-learning, we identify plasticity rules that reliably organize integration dynamics without requiring previously hypothesized anti-Hebbian mechanisms. Instead, the learned rules rely heavily on three-factor plasticity. In a simplified model, we demonstrate how such three-factor mechanisms can tune integrator circuitry and stabilize persistent dynamics.