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Weathering Inequality: The Racial Stratification of Relocation as Climate Adaptation Following Major Environmental Hazards
No full textClimate change is intensifying environmental hazards, exacerbating inequalities, and making
relocation a critical, yet potentially unequal, adaptation strategy. Existing research, often limited
by aggregate data, overlooks how racial stratification shapes community- and household-level
mobility post-disaster. This research addresses this gap by examining nuanced relocation patterns
following major hurricanes, using unique longitudinal consumer data combined with high resolution flood and wind hazard metrics. This research involves three distinct analyses. First,
examining community relocation rates following Hurricane Harvey's flooding in Harris County,
TX reveals persistently higher relocation occurring only in majority Black, Hispanic, or Asian
block groups facing extreme flooding. Second, investigating whether post-Harvey movers
relocated to areas with lower future flood risk finds that the capacity to reduce risk is
significantly stratified by the racial composition of households’ origin communities, even
following severe impacts. Third, broadening the focus beyond a single event, an analysis of
household relocation and instability following wind impacts from four major hurricanes
demonstrates that storm intensity effects are moderated by the racial composition of households’
origin communities, creating divergent and potentially unequal pathways. Collectively, these
chapters demonstrate that vulnerability and adaptive capacity – encompassing community-level
relocation rates, household ability to reach safer destinations, and patterns of relocating versus
instability within individual households – are deeply shaped by racialized spatial inequalities.
Findings challenge narratives of relocation as a universally accessible or effective adaptation
solution, revealing how racial inequality structures mobility patterns and outcomes across
different hazard types and geographic scales, thereby underscoring the urgent need for equitable
climate adaptation
Exploratory Analysis of Anxiety in College Students
Full text linkMany established predictors of anxiety (e.g., socioeconomic status, sleep, loneliness) have been studied, yet research investigating how these factors interact with anxiety, specifically in the college student population, is sorely lacking. This exploratory study surveyed Rice University undergraduates on predictors typically shown to predict anxiety in non-college student populations, including social media use, gratitude, and self-efficacy, along with anxiety levels. Together, the predicting factors of neuroticism, problematic social media use, gender, general self-efficacy, and loneliness explained 43.3% of the variance in anxiety scores. Unexpectedly, alcohol/drug use, phone use before bed, and socioeconomic measures (parental income and education) did not correlate with anxiety. These findings can help universities understand how to ease the burden of anxiety that is particular to the college student experience. Future research endeavors will incorporate other validated measures and explore how demographic differences affect the relationship between anxiety and its predictors
Motif, Rhythm, and Stylistic Influences in Béla Bartók’s Two Elegies, Op.8b: A Stylistic Analysis
No full textThis document presents a stylistic analysis of Béla Bartók’s Two Elegies, Op. 8b, composed in 1908 and 1909. The study focuses on how motivic development and rhythmic design contribute to the elegiac character of each piece, with consideration given to stylistic influences drawn from late Romanticism, the harmonic color and pianistic textures associated with Debussy, and Bartók’s early engagement with folk elements. The findings reveal two distinct compositional approaches. The first Elegy is marked by heightened emotional intensity, supported by stylistic features such as late Romantic pianism, chromatic harmonic language, and Debussy-influenced coloristic effects including quartal harmony. Structurally, it employs two central motifs—a three-note figure and a leitmotif—that undergo extensive variation in contour and intervallic content. These motivic transformations are paired with frequent meter changes, displaced accents, and syncopation, contributing to the piece’s emotionally charged atmosphere. In contrast, the second Elegy presents a more restrained expressive character, with Romantic influences more subdued. Compared to the first, it reflects a more modern compositional approach, as the piece is constructed almost entirely from a five-note motto motif. This motivic economy supports cohesion within a rhythmically flexible environment shaped by irregular meters, tempo fluctuations, and ad libitum passages. The result is an improvisatory quality that heightens the work’s insistent character. By analyzing the Two Elegies, this study contributes to a more comprehensive understanding of these underexamined works and provides insight into the structural means by which expressive character is articulated
Learning with Distribution Shift: Theory and Practice
No full textThis thesis investigates algorithm design and theoretical analysis in continual learning (CL), a machine learning paradigm where learning agents sequentially encounter tasks with distinct distributions. While state-of-the-art machine learning algorithms (e.g., deep neural networks) designed for single-task problems achieve remarkable success in static environments, they suffer from \emph{catastrophic forgetting} in CL scenarios - a phenomenon where performance on earlier tasks deteriorates after learning new ones. Although significant efforts have been devoted to developing practical CL algorithms, the theoretical understanding of catastrophic forgetting remains underdeveloped, leaving existing empirical approaches without rigorous theoretical foundations. This research aims to address these problems in two aspects: 1) on the practical side, we develop novel CL method with theoretical guarantees, and 2) on the theoretical side, we provide new theoretical insights into catastrophic forgetting and the ability of CL algorithms to mitigate them by analyzing the CL risk within modern settings that mirror real-world CL applications.
The first part of the thesis focuses on the practical design of structural regularization (SR) algorithms, a prominent category of CL algorithms that uses a quadratic regularizer to mitigate catastrophic forgetting. Due to computational constraints, existing regularization-based continual learning methods apply diagonal approximation that crudely recovers the previously learned information. We present a sketching-based SR method for continual learning that maintains computational efficiency while providing theoretically bounded approximation error. Our experimental results demonstrate consistent performance improvements across various SR algorithms on benchmark CL datasets.
The second part of the thesis establishes theoretical foundations on the regularization effect on catastrophic forgetting in CL problems. For two-task linear regression under fixed design, we examine an -regularized CL algorithm and provide tight bounds on the risk across both tasks. Our risk bounds reveal that a well-tuned -regularization can partially mitigate catastrophic forgetting by introducing intransigence. Furthermore, we extend the results to general structural regularization methods in random design, showing that SR algorithms can fully avoid forgetting in the setting with sufficient memory.
By investigating practical algorithms and theoretical properties of continual learning, this thesis sheds light on the catastrophic forgetting phenomenon, offering new paths and perspectives for future CL research
Hurricane Beryl: Recovery One Year Later
No full textOn July 8, 2024, Hurricane Beryl made landfall as a Category 1 hurricane with sustained winds of 80 mph. Across the Houston area, millions of households were left without power for days as the heat index climbed above 100, wind damages were estimated to cost between 4.5 billion, and 42 deaths were attributed to the storm. Prior research has described the recovery that took place within the first several weeks following the storm, as well as nearly 6 months after Beryl made landfall, finding that in the more immediate aftermath of the storm, more than 2 in 10 residents in Houston and Harris County said their lives were still either somewhat or very disrupted, and about 4 to 6 months after the storm, just over 1 in 10 residents reported the same. This survey snapshot uses recently collected data from the Kinder Institute for Urban Research’s Greater Houston Community Panel to follow up on earlier work and explore what recovery from Hurricane Beryl looks like 1 year later
Greater Houston Community Panel 2023 and 2024 User Guide
No full textThe Greater Houston Community Panel (GHCP) is a longitudinal, cohort study of adults living in the Houston area who respond to multiple surveys each year. The study is housed in the Houston Population Research Center (HPRC) at the Kinder Institute for Urban Research at Rice University and is conducted in partnership with the Institute for Health Policy at the UTHealth Houston School of Public Health. This user guide is meant to provide an overview of the project and its data, as well as instructions for working with the public-use files that are periodically released. The sections throughout the rest of the document are organized to give an overview of the survey contents, data collection practices, responses and response rates, sampling procedures, and best practices for data analysis. GHCP survey data can be used to analyze trends on the values, tastes, preferences, experiences, and circumstances of Houston-area residents, providing unparalleled insights into one of the largest and most diverse areas in the nation
Development of Novel Fluorescent Structures with Biological Applications
No full textBased on the rapid advancement of optical imaging technologies, in recent decades, an increasing number of fluorescent molecules, with various fluorescent signals, have been developed. These colorful fluorescent scaffolds, including organic or inorganic dyes, fluorescent proteins, or encapsulated nanoparticles, have become essential and indispensable tools in various fields of study, enabling disease diagnosis, active visualization of lesions, dynamic tracking of biological processes, and precise sensing of environmental changes. The advancements in modern fluorescent microscopy and imaging devices in recent years have further fueled enthusiasm for the development of next-generation imaging probes.
This dissertation presents the rational design and synthesis of a series of novel fluorescent probes, exploring their frontier applications in biological and physiological research, including transformation from regular fluorophores to photosensitizers through a single atom shift to facilitate drug release from a novel prodrug platform, designing fluorescent noncanonical amino acids to introduce new functionalities to proteins through genetic code expansion, and construct pH-sensitive bone-targeting fluorescent nano probe with pretty long emission in the second near-infrared region to realize precise bone metastases tracking. By fine-tuning their photophysical properties, such as brightness, photostability, and emission wavelength, and engineering them to possess new characteristics like viscosity sensitivity, pH sensitivity, and photoactivatable capacities, these fluorescent probes were optimized for targeting and investigating specific biological processes. The developed small-molecule fluorophores offer enhanced compatibility with live cells and animal models, as well as improved performance in terms of imaging depth and resolution. Together, these innovations expand the toolbox of fluorescence-based technologies, opening new avenues for visualizing and understanding complex biological systems at the molecular level
Understanding Deficits in Novel Storytelling to Inform Assessment and Treatment of Discourse in People with Aphasia
No full textMany people with aphasia (PWA) who have been discharged from speech-language pathology services continue to experience and report persistent difficulties in communication that continue to negatively impact their ability to socialize and return to work. This gap in research was addressed by focusing on discourse abilities to better understand this population using two familiar stories, Cinderella and Goldilocks and the Three Bears, and two novel stories, Pigeon: Impossible and Snack Attack. Novel storytelling versus familiar was demonstrated to be a better and more sensitive measure of narrative discourse level deficits in PWA, likely because novel stories are rich in content and impose much higher cognitive demands compared to familiar stories, requiring integration of multiple events as the stories unfold. Use of novel stories allowed for elimination of familiarity effects and schema-based processing present in familiar stories, and consequently, for testing the discourse production abilities beyond what is masked by familiarity. Another important finding through this work was that specifically for novel storytelling it was the interaction of event segmentation, working memory abilities, and planning aspects of executive functions that together predicted performance above and beyond demographic parameters and story comprehension ability, while none of these cognitive predictors explaining performance independently. Finally, novel storytelling treatment designed to train patients to identify and attend to verbs/events in stories depicting people engaged in routine activities of daily living was shown to be effective, demonstrating response to treatment and generalization in two patients considered “recovered” from aphasia. This work makes a much needed contribution to understanding functional communication deficits in people with aphasia given that discourse is the basis of all daily communication, as well as to understanding the relationship between language and cognition
Data for Lattice-induced spin dynamics in Dirac magnet CoTiO3
No full textRaw data for the figure plots in the journal article doi: 10.1063/5.028288
Mutational Profiling of the Adeno-Associated Virus Rep Protein for Gene Therapy Production
Full text linkAdeno-associated virus (AAV), a federally approved gene therapy vector that is currently in clinical trials for hundreds of diseases, often presents suboptimal characteristics for therapeutic applications, such as suboptimal tissue specificity, limited cargo size, undesired immune responses, and costly manufacturing. To improve this gene therapy vector, AAV proteins are frequently studied via rational design and combinatorial engineering. While the latter approach increases the sequence space that can be explored, it also presents unique challenges such as genotype-phenotype mismatches, noise arising from mutational errors in cloning, and bias arising in amplicon preparation and sequencing.
In AAV, Rep proteins mediate DNA packaging and virus assembly, suggesting that changes in Rep activity, expression, or DNA binding might affect genome packaging. However, these proteins are not as well-understood as the proteins that make up the virus shell. I sought to understand how mutations in the Rep protein affect activity by selecting a library of Rep mutants for their ability to produce virions. To do this, I designed large protein libraries to examine a broad sequence space, I designed a selection strategy that couples genotype-phenotype characteristics of the mutants, and I designed a single-stranded DNA isolation workflow that enabled me to sequence winning variants in deep sequencing. By sequencing the rep gene following the purification of viruses that package AAV genomes, I identified Rep mutants having non-synonymous mutations with a range of cellular activities. Surprisingly, synonymous mutations within the p19 promoter were enriched to the greatest extent, increasing in abundance by 102 to 104-fold. When the most highly enriched mutant was used to package a synthetic DNA cargo into the AAV capsid, the packaging efficiency could not be differentiated from native Rep. These findings suggest that these synonymous mutations enhance AAV genome packaging into capsids by affecting Rep-genome interactions. They also suggest that silent sequence changes in the DNA cargo packaged by Rep can be used to tune packaging DNA packaging efficiency.
Additionally, I designed a sequential cloning method for developing barcoded chimeric protein libraries, which enables easier analysis of deep-sequenced these. This cloning strategy has been partially validated. Future work should be done to optimize this cloning method, as it would be applicable for chimeric library design for any protein engineering experiment.
Lastly, this work outlines considerations in high-throughput protein engineering experimental design. I hope this section of my thesis enlightens those who wish to begin high-throughput protein experiments and learn from some of the critiques I have of my own work