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Designing for Entangled Speculation: A Research Through Design Approach for Exploring Wicked Problems
Full text linkThesis (Ph.D.)--University of Washington, 2026This dissertation explores entanglement as a structuring logic for speculative engagement, aiming to unsettle dominant ontologies and epistemologies to and expand design practice toward the ethical demands of an entangled world. This approach matters because the wicked problems we face in the 21st century, such as climate crisis, social inequity, global health crises, and extractive infrastructures, resist tidy solutions and expose the limits of conventional design frameworks that rely on separability, linear causality, or predictable outcomes. Grasping the magnitude, scale, and complexity of these challenges remains to be understood and will require new methods, approaches, and practices that can hold contradiction, that can embrace the unknown, that can imagine pathways of transformation. Many fields, disciplines, and communities develop their own knowledge practices for creating and maintaining contributions of meaning and understanding of how our world is entangled, from social and ecological entanglements found in HCI, social science, anthropology, animal studies, multispecies justice, critical geographies, law, art, and design, to the scale of entangled particles in quantum physics. By integrating insights of quantum mechanics with design speculation, this research acknowledges the world as it actually is: interconnected, uncertain, and deeply entangled. This dissertation presents Designing for Entangled Speculation (DES), a framework that integrates conceptual resources from quantum entanglement—superposition, observer effect, interconnectedness, and nonlocality & nonlinearity—to expand the theoretical and methodological capacity of design speculation. Using a modified Research through Design (RtD) approach, I conducted three investigations representing varied configurations of abstraction, situatedness, and stakeholder expertise. Post(-)human Hazmat explored speculative narrative as a means of interrogating multispecies relationality. Speculative F/Actors: Climate Futures operationalized collaborative material speculation to model climate-related system interactions. Entangled Justice convened practitioners and researchers with diverse expertise from domains including circular economy, marine energy, and climate migration to generate shared knowledge and ignorance maps through structured collaborative inquiry. A diffractive, cross-case analysis demonstrates how quantum entanglement concepts shape the dynamics of speculative engagement, influence participants’ reasoning about complex systems, and foreground ethical and epistemic considerations within design processes. Together, these contributions position DES as an ethically attuned framework for opening design to more speculative and accountable engagements with the complexities of an entangled world
Evaluating the Role of Saliency and Beliefs in the Recall of Corrections
No full textThesis (Ph.D.)--University of Washington, 2026In today’s rapidly evolving online social network (OSN) landscape, health misinformation has emerged as a significant concern, especially in the aftermath of the pandemic. One effective educational strategy against health misinformation involves providing users with corrections that offer accurate, relevant facts. Although a large body of research has evaluated the effectiveness of corrections, there is still much uncertainty around the precise effects that correction messages have on reducing misinformation beliefs. Existing research acknowledges the importance of the underlying beliefs of users when it comes to the effectiveness of corrections. Several factors are found to affect the belief reinforcement and change processes such as the continued influence of misinformation, inattention, and use of intuition when evaluating facts. On the other hand, the aspects of corrections that are salient to a reader's memory are relatively understudied. In this dissertation, I investigate mechanisms grounded in the memory and decision-making literature, such as frequency and valence, that have been theorized to increase the salience of experience, and evaluate whether these mechanisms enhance the salience of corrections to misinformation in memory and how strongly they affect the recall of those corrections during misinformation judgments. Drawing inspiration from studies on human memory and learning, my initial experiments tested the hypothesis that exposing people to more frequent corrections would increase their availability and improve their ability to identify misinformation by making the corrections easier to recall during judgment. I conducted two laboratory experiments to test whether experiencing frequent corrections to misinformation improved participants' ability to discriminate between true and false news claims during extended extreme events like the COVID-19 pandemic. Results from both experiments indicate that increasing frequency of corrections may not improve the ability of participants to identify misinformation. The results also suggest that prior beliefs better determined the likelihood that individuals were likely to accept corrections. In subsequent experiments, I reoriented my study to another crucial aspect of memory salience: emotional salience. This line of inquiry sought to understand how individual preferences for content with varying degrees of emotional valence could improve the salience of corrections in memory. The goal was to design targeted corrections for health misinformation and study the effect of such corrections on recall of their content by human subjects. Finally, the results of the experiment were used to study the specific features of correction text that are likely to increase cognitive load on the user perusing the content. Through multiple experiments, this dissertation makes significant and original contributions in: experimental methodology and design, empirical findings in effectiveness of corrections, and insights into cognitive effects of textual features of corrections. Initial experiments demonstrated that merely increasing correction frequency was insufficient to increase the salience of corrections in memory. Results revealed that pre-existing individual beliefs were stronger predictors of correction effectiveness than intervention quantity. Subsequent experiments, focused on message quality and personalized design. The results showed that overall emotional targeting failed, but successful correction recall was significantly driven by individual working memory capacity. The findings from this research enables evaluation and application of correction strategies in realistic OSN environments. This research concludes by underscoring the need to move beyond statistical correlation to develop a mechanistic, individualized model based on a cognitive architecture to accurately design effective corrections
AI and The Future of Holocaust Research & Memory
No full textHow will the advent of AI impact the future of Holocaust studies? Will it provide new methods for analyzing data and displaying information for research and education that will benefit the field, or will the reduction of victim data to datasets and the problems of accuracy, distortion, and the stochasticity yet again strip people of their humanity? This paper arises from a May, 2025, workshop and public symposium at the University of Washington convened to address precisely these questions. Recognizing the intrinsically multidisciplinary nature of the issues and challenges before the field, the authors represent a range of expertise, including computer science, information science, history, sociology, anthropology, Jewish studies, museology, material culture, media and communication studies, literary studies, and art history. Over the course of two full days, we investigated the following themes: (1), AI and Holocaust Studies research, (2), AI and libraries, archives, and museums, (3) the limits of representation and reception, and (4) AI and computational sciences. The contributions included in this paper have been written by the participants and organizers in the months following the event. They include reflections, provocations, and refusals
Profiling the Heterogeneous Outcomes of Blast Trauma and Substance Use in Translational Mouse Models
No full textThesis (Ph.D.)--University of Washington, 2026There is a complicated and bidirectional relationship between stress and substance use. Individuals who are diagnosed with a psychiatric disorder and a substance use disorder (SUD) tend to have a higher number of symptoms, more severe symptoms, decreased quality of life, and less responsiveness to treatment. Traumatic brain injury (TBI) is a unique form of stress in that it has both physical and psychological components. The most common type of TBI is mild TBI (mTBI), representing nearly 75% of all TBI diagnoses. Despite the label of “mild,” mTBI can result in behavioral and physiological symptoms that develop acutely and persist for years after the initial injury. Individuals who have a history of TBI show elevated rates of various substance use disorders, including alcohol, opioids, nicotine, cannabis, and psychostimulants. The Veteran population has a higher risk of psychiatric disorders and hazardous substance use, particularly alcohol, due to the frequency of exposure to traumatic events. Exposure to blast overpressure waves is the primary source of mTBI in military service members, and also commonly results in PTSD and chronic pain. Effective treatments and personalized clinical guidance is critical to improving outcomes for individuals experiencing comorbid stress and substance use disorders. Preclinical animal models are a valuable method for improving our understanding of the relevant mechanisms underlying the pathophysiological progression; however, there is currently a translatability crisis, in which potential treatments that have been effective in animal models are not effective in clinical trials. Therefore, it is critical to utilize translationally-relevant preclinical models and embrace the heterogeneity that they capture. The aim of this dissertation was to understand substance use patterns, stress and anxiety-like behavior, blast trauma, and the relationship between them in translational mouse models. First, I demonstrated the value of our novel Socially Integrated Polysubstance (SIP) system and the insights that it provides into individual intake patterns in group-house male and female mice. Again using the SIP system, I next quantified alcohol and opioid polysubstance use patterns in group-house male and female mice. Based on differences in behavioral phenotypes, I identified three clusters of mice with distinct alcohol and opioid dose preference and polysubstance use patterns. Building upon this, I used the SIP system to investigate how repetitive blast trauma influences various alcohol drinking patterns, as well as the chronic outcomes after blast trauma and alcohol use. This experiment revealed that only certain alcohol drinking patterns are predictive of biological outcomes and there are differential effects of repetitive blast exposure and chronic alcohol intake on glymphatic function and brain glucose metabolism. In the discussion, I consider various experimental parameters that are critical to designing translationally-relevant preclinical animal models. Finally, I cogitate on a number of future experiments that are still needed to help refine treatment options and identify therapeutic or lifestyle interventions at multiple stages of progression for individuals with comorbid stress and hazardous substance use
Biogenic Carbon Accounting in Wood Environmental Product Declarations: A comparison of methodologies in European and North American Wood Product EPDs
Full text linkEnvironmental product declarations (EPDs) are a key mechanism for reporting the environmental impacts of construction materials – including wood products. EPDs are standardized, third-party-verified documents that aim to clearly and transparently report the environmental impacts associated with production, use, and disposal of a product calculated according to standard life cycle assessment (LCA) accounting rules. Structural wood products are touted globally for their low environmental impacts relative to other fossil-intensive construction materials. EPDs are one mechanism – often used in combination with certifications, owners sourcing requirements, and others – used by the building industry to understand the relative impact of wood products. However, the environmental benefit of biogenic carbon sequestration and storage associated with forest growth and production and use of structural wood products is currently reported differently across regions according to varying accounting mechanisms for wood EPDs in different regions. As EPDs grow increasingly important in the context of global policy and trade, these differences in rules and accounting methods applied to structural wood product EPDs merit greater attention. This report dives deep into the accounting and reporting standards for structural and architectural wood product EPDs in Europe and North America with the goal of (1) identifying key challenges in comparability based on current standards and (2) highlighting the largest opportunities to increase the comparability of product category rules (PCRs) and related international LCA standards moving forward. The greatest differences between European and North American wood product EPDs stem from differences between these regional parent LCA standards for construction products: EN 15804:2012+A2:2019 (CEN, 2019) and ISO 21930 (ISO, 2017). Despite global agreement on the general framework of EPDs, inherent differences in the emissions and removals included in European and North American wood EPDs prevent direct comparison of products across markets due to their different approaches to quantification and reporting of biogenic carbon removals and emissions
Spatial Dynamics of Environmental Health: The Impact of Vegetation Greenness and Heat Exposure on Mental Health Outcomes Across California Census Tracts
No full textThesis (Master's)--University of Washington, 2025This study examines spatial relationships between environmental factors, socioeconomicconditions, and mental health across California census tracts using a Spatial Durbin Error Model.
Analysis of 7,963 tracts revealed significant spatial autocorrelation in mental health distress
(Moran's I = 0.4713, p < 0.001). Median household income was the strongest predictor of mental
health distress (direct effect: β = -0.0000489, p < 0.001; indirect effect: β = -0.0000193, p <
0.001). Vegetation greenness showed a significant protective direct effect (β = -3.8818, p <
0.001) without significant spillover effects, indicating localized benefits. Conversely, maximum
temperature demonstrated no significant direct effect but had significant positive indirect effects
(β = 0.1022, p = 0.0016), suggesting regional rather than local influence. The substantial spatial
error parameter (λ = 0.73511) and strong spatial autocorrelation in both vegetation (r = 0.820)
and temperature (r = 0.992) validate the spatial modeling approach. These findings enhance
understanding of how environmental factors influence mental health through different spatial
mechanisms and inform targeted intervention strategies addressing both socioeconomic and
environmental determinants of health
Supporting bioinformatics analysis using a hybrid cloud and HPC architecture
No full textThesis (Master's)--University of Washington, 2025The exponential growth of next-generation sequencing data requires novel strategies for storage, transfer, and processing of said data. We present a scheduler a based on the Temporal.io workflow framework which enables two key optimizations of bioinformatics workflows. Firstly, we enable users to transparently map workflow steps to diverse execution environments, including high-performance computing (HPC) resources managed by the SLURM resource manager. When tested on a Bulk RNA sequencing workflow, this feature allows a 26% reduction in credit consumption on the NSF Bridges 2 supercomputer by performing adapter trimming locally and all other steps on the supercomputer. Secondly, we enable asynchronous execution of workflows, a feature which guarantees that workflows will achieve reasonable resource utilization even when the scheduler cannot make use of a system's full RAM and CPU resources. When benchmarked on the same Bulk RNA sequencing workflow, this optimization facilitates a reduction in workflow makespan of between 13% and 23%, depending on the exact workflow configuration. Taken together, these features will enable reductions in the cost and time requirements of bioinformatics pipelines for researchers
Structure based stabilization of native-like antigens with deep learning
No full textThesis (Ph.D.)--University of Washington, 2025Effective vaccines prevent illness and death by stimulating protective immune responses against infectious pathogens. Protective immune responses can recognize and neutralize antigens that are important proteins used by pathogens to infect and replicate. However, these key proteins often evolve multiple conformations or instability to infect and evade the host immune system. Structure-guided design has advanced vaccine development by introducing mutations that stabilize these proteins to elicit stronger immune responses. Deep learning based methods have transformed our ability to predict and design protein structures. In this work, I investigated how to best apply these novel protein design methods to the stabilization of native-like antigens. I focused on four pathogens that cause significant morbidity and mortality: Human Rotavirus, Group A Streptococcus, Mycobacterium tuberculosis, and Rabies virus. For each pathogen, I chose key proteins that are compelling vaccine targets and present distinct challenges for antigen stabilization. By pairing existing methods with novel deep learning based tools, I identified mutations that improve antigen stability and immunogenicity. In the process, I have identified principles which may be applicable for structure guided design across a wide range of antigens
Evaluating Uncrewed Vehicle Systems (UVS) for Regulatory Monitoring of Compensatory Mitigation Sites
No full textThesis (Master's)--University of Washington, 2025In wetland resource management, drones, also known as uncrewed vehicle systems, offer unique advantages for monitoring and ensuring regulatory compliance at wetland mitigation sites. However, before introducing drone monitoring to evaluate performance standards on these sites, agencies need to understand the fundamental processes of working with drone-derived data, how these data compare to that collected via traditional field methods, and the respective advantages and disadvantages of drone-based monitoring. This study evaluated two critical components of the classification process: classification method and machine learning algorithm. I assessed overall accuracy and woody vegetation class accuracy at six mitigation sites in western Washington. These sites encompassed wetland, buffer, and riparian zones, and ranged in age from 3 to 10 years. Results from four classification trials indicated that object-based classifications consistently outperformed pixel-based approaches. The choice of machine learning algorithms, whether support vector machines or random trees, had no significant impact on accuracy metrics. I then compared woody cover measurements from drone- and field-derived data and analyzed results at the mitigation zone and sample plot scales. At the zone scale, I represented drone cover as the percentage of woody cover across the entire zone and field cover as the average woody cover of all plots. At the plot scale, I expressed drone cover as the percentage of woody cover within each plot, and field cover as the raw data collected in each plot. Differences in mean woody cover estimates between methods ranged from marginally significant to significant at the zone and plot scales. While correlations were significantly positive at the zone scale, they were weaker and more variable at the plot scale. These findings suggest that drone methods underestimate woody cover, and further refinement is needed to improve agreement between methods at the plot level. This study highlights the potential for drones to help site managers meet monitoring demands, particularly when resources are limited. A key drawback is the upfront time investment required to learn processing and analysis techniques. Overall, these findings contribute to developing guidelines for government agencies to adopt drone-based monitoring using off-the-shelf equipment and user-friendly methods that prioritize practical implementation, affordability, and accessibility