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Try Everything: Coupling Phytolith and Isotope Records to Reconstruct South American Landscapes During Global Warming and Cooling Events of the Cenozoic
No full textThesis (Ph.D.)--University of Washington, 2025The Intergovernmental Panel on Climate Change (IPCC, 2024) predicts that, regardless of policy changes, atmospheric CO₂ levels, and consequently, global temperatures, will rise over the next century to levels unprecedented in human history. However, these levels are not unprecedented in Earth's geologic past. Over the Cenozoic (the last 66 million years), global climate has undergone repeated warming and cooling episodes driven by fluctuations in atmospheric CO₂, superimposed on a long-term trend of cooling and declining pCO₂. To better predict how future climate change may affect global ecosystems, we can turn to the fossil record to examine how climate and biospheres responded to similar variability in the past.In this dissertation, I combine phytolith (opaline silica particles deposited in or around plant cells) and isotope records to reconstruct landscape evolution in South America during periods of global climate warming and cooling in the early and late Cenozoic. I also conduct a modern reference study to test and compare the spatial resolution of phytolith assemblages and isotope signatures across a spatially complex landscape.In Chapter 1, I explore the evolutionary history of lowland vegetation in southern South America during the Paleocene and Eocene using phytolith records. Previous reconstructions, including some paleobotanical data, proposed that grass-dominated ecosystems may have existed in the region at this time, contradicting global patterns of grassland evolution and other fossil evidence. I present a basin-wide phytolith analysis of Paleocene and Eocene terrestrial deposits from the San Jorge Basin, Argentine Patagonia. This expands on earlier phytolith studies and integrates new radiometric dates to provide a temporally resolved reconstruction of early Cenozoic vegetation. My results show that forests dominated lowland ecosystems from the Paleocene through the middle Eocene. Palm abundance increased from the middle to late Eocene as these forests began transitioning to cooler, drier, and more open vegetation types. These findings are supported by paleobotanical, geochemical, and faunal records. Grasses remained rare and were likely restricted to forest understories until at least the Early or Middle Miocene, challenging hypotheses that propose extensive early Cenozoic grassy habitats in South America.
In Chapter 2, I test the spatial resolution of phytolith assemblages across a heterogeneous landscape and compare them to the carbon isotope composition of the soil organic matter from which they were extracted. This study addresses a key uncertainty in fossil phytolith interpretation: to what extent do phytoliths reflect local versus regional vegetation? This is especially important for fossil reconstructions, where sample coverage is often sparse. Can we detect vegetation heterogeneity from just a few assemblages? How do phytolith signals compare to carbon isotopes, a widely used proxy for vegetation structure? To explore this, I analyzed samples from three transects in the piedmont of the Eastern Colombian Andes, a region that includes grasslands, forests, and palm swamps, each sharply demarcated. My results show that phytolith assemblages reflect both the vegetation at the collection site and surrounding plant communities. Phytoliths and carbon isotopes capture overlapping but distinct aspects of vegetation structure and are most powerful when used together. Additionally, phytoliths can offer insight into fire regimes, potentially indicating where and what kinds of vegetation were burning. These results underscore the value of phytoliths for reconstructing vegetation heterogeneity and fire history in both modern and ancient ecosystems.
In Chapter 3, I focus on a cooling period that followed the last major Cenozoic warming event, the Miocene Climatic Optimum (17 to 15 million years ago). This subsequent cooling, is recorded in the fossil-rich deposits of La Venta, a semi-arid region in Colombia. Despite its significance as one of the most important vertebrate fossil sites in the Neotropics, little is known about the vegetation that supported its fauna or how tropical climate changed during this time. To address these gaps, I use phytoliths and clumped isotope paleothermometry to reconstruct vegetation and climate between 14 and 7 million years ago. The results suggest that the region remained forested throughout the interval and that vegetation density increased as local temperatures cooled and seasonal rainfall may have intensified, creating more humid conditions overall. These findings contrast sharply with climate models that predict drying across the tropics during the Miocene Climatic Transition and provide the first terrestrial temperature record for tropical South America during this time. This discrepancy highlights the importance of regional proxy records in validating and improving global climate models
Understanding Aging at Multi-scale Using Explainable AI
No full textThesis (Ph.D.)--University of Washington, 2025As human lifespans increase, understanding the biological and clinical mechanisms that shape aging has become increasingly important. This dissertation presents a set of explainable AI (XAI) frameworks that illuminate aging at multiple scales, ranging from population-level health data to bulk transcriptomics and single-cell gene expression. I begin with IMPACT, an XAI framework for all-cause mortality prediction in NHANES dataset. IMPACT improves prediction accuracy over traditional models and uses XAI methods to reveal previously underappreciated risk factors and clinically meaningful feature interactions. Building on this foundation, ENABL Age extends the IMPACT framework to model biological age. ENABL Age combines machine learning with XAI to estimate biological age and to quantify how specific lifestyle, clinical, and biochemical factors contribute to accelerated or slowed aging. This framework provides individualized insights into modifiable components of aging and supports the development of interpretable precision aging tools. At the molecular scale, DeepProfile learns biologically meaningful latent representations from 50,211 cancer transcriptomes across 18 tumor types. It identifies universal immune activation signals, cancer-type specific subtype structure, and mechanistic links among mutation burden, cell-cycle activity, antigen presentation, and patient survival. By studying cancer across many organs, DeepProfile also offers insight into organ health and organ aging, illustrating how unsupervised learning can uncover clinically relevant biology from large transcriptomic datasets. Finally, ACE is an explainable deep generative model for single-cell RNA sequencing data that isolates aging-related gene expression changes from dominant background variation, enabling the study of cellular aging. Applied to mouse, fly, and human datasets, ACE recovers tissue and cell-type specific aging signatures, identifies conserved aging pathways across species, predicts biological age at cellular resolution, and prioritizes novel regulators such as Uba52, whose relevance is validated through lifespan-shortening RNAi experiments in C. elegans. Together, these contributions form an integrated XAI-driven framework for understanding aging at multi-scale and advance both mechanistic aging biology and transparent approaches for improving human healthspan
New Ways to Garble Circuits
No full textThesis (Ph.D.)--University of Washington, 2025A garbling scheme transforms a circuit C into a garbled circuit C-hat, along with a pair of short keys (k^(i)_0 , k^(i)_1) for each input bit x[i], such that the program, garbled program and input keys (C, C-hat, {k^(i)_x[i]}) can be used to recover the output z = C(x) while revealing nothing else about the input x. A main objective in the research of garbling schemes is reducing the size of the garbling material (C-hat, {k^(i)_x[i]}). On the one hand, theoretical schemes using the heavy tools of attribute-based encryption (ABE) and fully homomorphic encryption (FHE), or indistinguishable obfuscation (iO) can achieve constant size, independent of |C|. On the other hand, practically oriented schemes using only symmetric key cryptography all have sizes Ω(λ · |C|). Motivated by the gap in between, this thesis explores new ways of leveraging light-weight techniques from public-key cryptography to construct communication efficient garbling schemes. In particular, our explorations are centered around two primitives, linearly homomorphic encryption (LHE) and homomorphic secret sharing (HSS). In Part I, we apply LHE techniques to construct communication efficient garbling schemes that specialize for arithmetic operation gates over a modulus R or bounded integers. We define the (succinctness) rate of such schemes to be the per-gate garbling size normalized by log R or the range of bounded integers. Our results include:• rate-O(1) arithmetic garbling over bounded integers, and
• rate-O(λ_DCR) mixed garbling over Z_R and Boolean gates for any modulus R.
In Part II, we apply HSS techniques to construct communication efficient Boolean garbling schemes. Our results lead to a unified framework for garbling arbitrary Boolean gates (as truth tables) with 1-bit per output wire in garbling size. Consequences of this framework include:
• standard Boolean garbling with 1-bit per gate;
• rate-O(1) arithmetic garbling over Z_R for any modulus R.
All of the mentioned results were achieved for the first time without using FHE or iO
Mt. Bachelor Observatory final atmospheric data for YEAR 2023
No full textThe Mt. Bachelor Observatory (MBO) is a high altitude atmospheric research station that is located at the Mt. Bachelor ski area in Central Oregon. The observatory was started by Prof. Dan Jaffe in 2004. Over this time, his time at UW has made observations of ozone, carbon monoxide, mercury, nitrogen oxides, particulate matter and other atmospheric constituents. Data for 2004-current are available via the UW ResearchWorks archive. The coordinates for the summit observatory building are: Latitude: 43.9775 N, Longitude: 121.6861 W Elevation: 2.74 km asl. If multiple datasets are listed, please use data from the most recent version and/or highest version number.Mt. Bachelor Observatory final atmospheric data for YEAR 2023National Science Foundation, National Oceanic and Atmospheric Administratio
Within the Fence Line: Spatial Analysis of Naval Station Bremerton
No full textThesis (Master's)--University of Washington, 2025This thesis compares contemporary military planning codes with widely accepted civilian land use planning practices using Naval Station Bremerton as a case study. Military installations prioritize security, efficiency, and operational effectiveness, which often contrast with urban planning principles that emphasize mixed-use development, walkability, and community cohesion. This study addresses two primary research questions: 1) How do small urban space design practices compare to the planning codes and regulations of Naval Station Bremerton? 2) Where do these frameworks align, and where do they diverge? Through a systematic literature review and comparative analysis of base master plans, Department of Defense land-use policies, and Unified Facilities Criteria (UFC), the research explores the intersection of military and urban planning principles, particularly in areas such as land-use mix, density, pedestrian accessibility, and public space integration. The findings highlight areas where flexibility exists within military planning regulations and offer insights into improving military base land use and design, balancing operational effectiveness with enhanced livability and functionality
Application of Moringa leaves (Moringa oleifera), acidifiers, and probiotics as natural growth promoters to improve broiler chicken growth performance: A review
Full text linkBackground: The use of AGP as a feed additive is 96.97% utilized to stimulate growth and suppress infections by pathogenic microorganisms in the digestive tract. Long-term use of AGP can cause genetic mutations by pathogenic agents, resulting in decreased effectiveness of antibiotic therapy. Moringa is a plant that grows well in tropical areas and is widely known as a vegetable and traditional medicine containing various active compounds such as alkaloids, flavonoids, steroids, triterpenoids, and tannins. Methods: The research method is a literature study by analyzing secondary data based on reviews from several research journals related to the potential of moringa leaves, acidifiers, and probiotics in improving the growth performance of broiler chickens. Findings: These compounds act as antioxidants, antibacterials, and hepatoprotective agents, improving broiler chickens' carcass quality. This composition can be supported by the administration of acidifiers in the form of organic acids to inhibit the growth of pathogenic bacteria in the digestive tract, thereby optimizing the growth process of broiler chickens. Digestive bacterial balance can be achieved by administering probiotics, which play a role in enhancing immunity, health, and growth at all ages and classes of poultry, improving the balance of healthy bacteria in the digestive tract, promoting intestinal integrity and maturation, preventing inflammation, increasing feed intake and digestion by enhancing digestive enzyme activity, reducing bacterial enzyme activity, lowering ammonia production, neutralizing enterotoxins, and stimulating immune function. Conclusion: The combination of these three compositions is expected to provide optimal results for broiler chicken performance. Novelty/Originality of this Article: This article highlights a combined approach using moringa leaf compounds, acidifiers, and probiotics as an alternative to AGP in broiler feed, aiming to achieve optimal growth performance while avoiding the risks associated with antibiotic resistance
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
Improving synthetic aperture radar measurements of surface movement and snow depth in mountain environments
No full textThesis (Ph.D.)--University of Washington, 2025Mountainous regions store critical water resources and produce devastating natural hazards. As climate change disproportionately impacts mountainous regions, accurate and timely observations are needed for adaptive resource and hazard management and to understand changing cryospheric and geomorphic processes. Synthetic aperture radar (SAR) can provide these observations, but SAR-based measurements are subject to noise and errors that reduce their reliability. Relying on the extensive SAR archive, this dissertation develops workflows that integrate emerging data science approaches, including deep learning, with established geophysical methods to improve SAR-based measurements of surface movement and snow depth in mountainous terrain.In Chapter 1, I used a convolutional neural network (CNN) to remove atmospheric noise from interferometric synthetic aperture radar (InSAR) interferograms. The CNN was trained using thousands of Sentinel-1 interferograms and exploits differences in the spatial and topographic structure of atmospheric noise and deformation signals, without relying on external atmospheric data. This approach outperforms commonly used atmospheric correction methods and reveals previously obscured centimeter-scale deformation of rock glaciers and landslides in the Rocky Mountains. These improvements enable more reliable interpretation of subtle surface kinematics in high-relief terrain.
In Chapter 2, I developed a fused InSAR and SAR feature tracking approach to quantify surface displacement of moraines damming glacial lakes. Combining InSAR and feature tracking results in improved displacement time series that are more accurate than those produced using either method alone. Application to the Imja Lake moraine dam in Nepal reveals decimeter-scale cumulative subsidence over a seven-year period and widespread buried ice. I validated these results using very-high-resolution satellite stereo digital elevation models. The observed displacement patterns are consistent with year-round ice flow and warm-season ice melt. These results provide new constraints on the processes contributing to moraine dam degradation and have direct implications for glacial lake outburst flood (GLOF) hazard assessment.
In Chapter 3, I extended this approach to the 23 moraine-dammed glacial lakes in Nepal which are the highest priority for monitoring. I used seasonal change in InSAR coherence as a proxy for buried ice presence. I found that most moraine dams contain buried ice that produces surface displacement of centimeters to tens of centimeters per year. Analysis of displacement components indicates that the observed deformation reflects a combination of ice melt and ice flow, with the relative contribution of each process varying between sites. I found evidence for extensive buried ice in several moraine dams previously classified as ice-free, which substantially changes the conclusions of prior hazard assessments. These results can be used to improve GLOF hazard assessments and modelling studies.
In Chapter 4, I developed a deep-learning approach for regional snow depth prediction across the Western United States. I trained a U-Net CNN using a large archive of airborne lidar snow depth measurements and multi-modal inputs including SAR backscatter, optical imagery, topography, and coarse-resolution physical model outputs. The final CNN substantially outperforms existing approaches for near real-time prediction of Western U.S. snow depth in accuracy, precision, and resolution. It can be applied to create spatially continuous maps of snow depth over the entire Western U.S. and dense snow depth time series over the past decade. This work establishes a new benchmark for regional snow depth prediction performance, with implications for future operational forecasting
The Intersection of Culture, Eating Habits and Eating Competence Among U.S.-born vs Immigrant Southeast Asian College Students
No full textThesis (Master's)--University of Washington, 2025Southeast Asian (SEA) countries have a shared culture that is distinct from the vaguely defined "Asian culture," a nuance that is lost in U.S.-based research that reports racial demographic groups as opposed to ethnic ones. SEA cultures share characteristics in their relationship to food and mealtimes, and understanding these cultural influences on eating habits can better inform nutrition interventions and barriers to eating competence (EC) among SEA Americans. EC emphasizes positive attitudes, internal cues, food enjoyment, and meal planning without restrictive rules. This mixed methods analysis examines the association between culture, eating habits, and EC among U.S.-born (n=77) versus immigrant (n=36) SEA undergraduate college students in the U.S. We hypothesized that EC would be higher in the immigrant SEA population due to aspects of traditional food culture that may align with the Satter Eating Competence Model (ecSatter). EC was measured via the Satter Eating Competence Inventory (ecSI 2.0TM) and perceived influence of culture was analyzed through written responses to the question, "How does your culture and/or upbringing inform what and how you eat?". Results showed no statistically significant difference in ecSI 2.0TM scores between the U.S.-born and immigrant SEA groups (p=0.8302). However, qualitative analysis revealed insights into SEA culture and wider "Asian culture" on food. These insights include an emphasis on balanced meals, rice as a staple grain, traditional food as inherently "healthy," and aspects of SEA culture that align with EC. While there may be features of SEA food culture that promote (or hinder) EC, future research is needed to further explore how EC appears in SEA cultures
Data-driven approaches to disaster preparedness: Integrating natural language processing and machine learning for enhanced infrastructure system resilience
No full textThesis (Ph.D.)--University of Washington, 2025In an era marked by intensifying disasters caused by natural hazards, improving resilience and preparedness has become increasingly important for safeguarding communities and the critical infrastructures on which they depend. Earthquakes, in particular, pose severe threats to human well-being and the integrity of critical infrastructure systems, such as bridges, which serve as key conduits for transportation, emergency response, and economic continuity during and after crises. The rapid acceleration of data generation presents opportunities and challenges; if effectively leveraged, diverse and complex data sources can support more robust, data-driven decision-making for disaster preparedness. This dissertation presents three analytically rigorous and adaptable frameworks to address key challenges using high-dimensional, real-world data to improve disaster preparedness and infrastructure assessment. This dissertation first presents a text mining framework and an accompanying open-source code designed to extract insights from a novel corpus of practical disaster reports. This chapter highlights the ability to synthesize lessons from past disasters, providing observations that inform future preparedness initiatives. Second, this work introduces a machine learning-based framework for predicting key bridge characteristics related to seismic vulnerability. This framework reduces the need for manual data collection while increasing the availability of network-level characteristics, thus allowing for a more comprehensive understanding of infrastructure resilience. Third, this work proposes an integrated framework that combines natural language processing, feature engineering, and uncertainty quantification to improve the reliability and interpretability of seismic vulnerability assessments based on small, information-rich datasets. Together, these contributions lay the groundwork for future research and practice in disaster resilience, particularly in contexts characterized by limited data availability and high data complexity. By demonstrating how natural language processing and machine learning can be used to harness textual, structured, and high-dimensional data, we aim to advance intelligent, data-efficient methods that support more informed and effective decision-making in disaster preparedness and infrastructure system management