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Algorithms for Fair Redistricting
No full textRedistricting is the act of dividing land into geographic regions, called districts, for political or administrative purposes. The most notable---and contentious---example comes from the United States House of Representatives, where seats are apportioned among states according to their respective populations following a decennial census, then assigned to political districts, with each district holding a separate election for a single representative. Political redistricting occurs periodically at other state and local levels, alongside other forms of redistricting for non-political purposes, such as the determination of school attendance zones. In all of these settings, there is a vast space of possible outcomes to consider and a compelling need for fairness and transparency.
In this thesis, we design algorithms for various redistricting tasks and provide theoretical underpinnings for existing algorithms. We begin by considering the initial task of seat apportionment, exploring the space of randomized allocation methods satisfying strong fairness guarantees while eliminating unwanted correlations across states. We then turn to the task of drawing redistricting maps that are provably fair, using ideas and results from the Cake-Cutting model in fair division. Finally, we contribute new algorithms and theory for the task of sampling random redistricting maps, with the aim of building robust statistical tests for assessing partisan fairness.Engineering and Applied Sciences - Computer Scienc
Quantifying the Health Impacts of Air Pollution: Methods for Causal Exposure–Response Estimation and Policy-Relevant Evidence
No full textAmbient fine particulate matter (PM2.5) remains one of the most consequential environmental risks to human health worldwide, contributing to millions of premature deaths each year. Yet determining how mortality risk changes across the full PM2.5 exposure range—particularly at low concentrations where regulatory decisions are most sensitive—remains a fundamental scientific and methodological challenge. Observational air pollution data are high-dimensional and subject to complex confounding structures, spatial heterogeneity, and model misspecification. Recovering credible causal exposure--response functions (ERFs) in this setting requires methods capable of flexibly addressing heterogeneous effects, nonlinear relationships, and uncertainty in both exposure and outcome models. This dissertation develops, evaluates, and applies such methods to strengthen the evidence base for air-quality regulation and policy-relevant health-impact assessment in the United States.
The overarching theme of this dissertation is connecting rigorous statistical methods with meaningful public health and policy insight. First, I evaluate the performance of widely used ERF estimators and synthesize guidance for when different approaches are most appropriate. Second, I introduce a new causal inference method designed to address a pervasive but under-recognized source of bias—local confounding—that arises when the strength and type of confounding vary across the exposure distribution. Third, I demonstrate how these methodological advances can be applied to a real policy context through a transparent, reproducible health-impact assessment of proposed energy infrastructure. Across all three aims, I emphasize design-based workflows, principled diagnostics, and reproducibility to support robust inference in settings where regulatory stakes are high.
Chapter 1 addresses a longstanding gap in understanding which statistical methods reliably estimate ERFs under realistic confounding and exposure–outcome structures. I compare seven commonly used ERF estimators across a comprehensive set of simulation scenarios that vary the true ERF shape, the confounding mechanism, the degree of effect heterogeneity, and sample size. These include traditional regression models (linear, spline-based, and threshold) and design-based causal estimators that use entropy balancing or generalized propensity score matching. Two key insights emerge. First, regression-based ERFs can exhibit substantial bias when confounding is nonlinear or heterogeneous, even when the resulting curves appear smooth and precise. Second, design-based causal estimators that explicitly balance covariates across the exposure distribution tend to be more robust, particularly with large sample sizes. Applying all methods to a national cohort of more than 68 million Medicare beneficiaries reveals a distinctly nonlinear ERF: mortality risks rise steeply at lower PM2.5 concentrations and attenuate at higher levels. This chapter concludes with concrete methodological recommendations and fully reproducible code to support adoption.
Chapter 2 fills a critical methodological gap by providing a causal inference framework specifically tailored to settings where confounding varies across the exposure distribution. I introduce REBEL (Rolling Entropy Balancing for Exposure--response functions under Local confounding), a new design-and-analysis pipeline for continuous exposures. REBEL (i) constructs overlapping exposure windows, (ii) achieves covariate balance within each window through entropy balancing, (iii) calibrates each window to the full target population to recover population-level effects, and (iv) aggregates local estimates via an overlap-aware meta-estimator. I also develop diagnostics for detecting local confounding and propose a counterfactual cross-validation approach for tuning algorithmic parameters. In simulations, REBEL consistently outperforms existing ERF estimators when local confounding is present and remains competitive when it is not. Applied to 68.5 million Medicare beneficiaries, REBEL uncovers a steep, supralinear increase in all-cause mortality at low exposures to coal-derived PM2.5 —a pattern masked by global models—suggesting that traditional approaches may materially understate coal’s health burden. This chapter also derives a coal-specific exposure–response function that explicitly adjusts for potential local confounding, providing one of the first flexible, population-based ERFs for coal-derived PM2.5 in the literature and demonstrating how source-specific toxicity can be estimated with improved causal validity.
Chapter 3 demonstrates how these methodological tools can be translated into actionable evidence through a rigorous, policy-relevant assessment of PM2.5 impacts from a proposed 2,200-MW natural-gas combined-cycle plant in Colleton County, South Carolina. The analysis integrates source-specific emissions estimation, reduced-complexity atmospheric dispersion modeling (InMAP), population-weighted exposure assessment, environmental justice profiling, and health and economic valuation using U.S. EPA tools. Under conservative assumptions, the plant would expose more than 2.09 million people across South Carolina and Georgia to measurable increases in annual PM2.5, with the highest burdens concentrated in nearby census tracts characterized by lower incomes, lower property values, and higher proportions of Black residents. Estimated health damages reach up to $27.9 million annually, rising further under higher-capacity operating scenarios. Sensitivity analyses reveal that operational decisions (e.g., capacity factor) have substantially greater influence on community exposure than modest variations in design parameters such as stack height. This chapter provides a transparent, reproducible template for early-stage health-impact assessment of energy facilities.
Taken together, these chapters (i) provide principled guidance for choosing among ERF estimators and diagnosing when causal designs are needed, (ii) introduce a new method that addresses locally varying confounding in continuous-exposure settings, and (iii) demonstrate how causal evidence can be scaled to evaluate community-level risks from proposed energy infrastructure. By bridging methodological rigor with policy relevance, this dissertation advances the tools needed to credibly quantify the health impacts of air pollution and to inform air-quality and energy decisions.Biostatistic
Architecture Within Reason: Construction, Labor, and Rationalization in Weimar Germany
No full textWorld War I shattered millions of lives and left much of Europe in ruins. In postwar Germany, artists, architects, politicians, and industrialists cultivated utopian visions of the future that were motivated by hopeful optimism for renewal and redemption despite a degraded reality. Weimar never became what its greatest visionaries hoped, but the impulses for these imaginary pursuits were nonetheless real. Importantly, postwar architectural currents converged with the rise of rationalization in Weimar Germany. This dissertation contends with the “constructed meanings” of rationalization after World War I and examines how this concept came to encompass much more than a technical doctrine. Rather, rationalization is investigated as a complex cultural and technical terrain that saw the embrace of science, technology, and industry by architects, in parallel with reinvigorated beliefs in social transformation as the ultimate objective of art.
Rationalization was inflected by, and also hybridized with, other cultural and artistic concepts. The legacy of older artistic ideals, such as the Romantic Gesamtkunstwerk, remained pivotal even as rationalization ascended to epistemic dominance. In turn, the concept was directed toward numerous purposes of politics and social reform. While the Gesamtkunstwerk ultimately comprised a failed framework for artistic and social renewal—what Andreas Huyssen describes as “a false totality and … an equally false monumentality”—it nevertheless exerted a powerful influence in cultural discourse after the cataclysm of the Great War and profoundly shaped the subsequent rise of rationalization in architecture.
This study defines a culture of reason that was specific to architecture in Weimar Germany, plural in its manifestations, and unresolved in its ideological ambitions—a complex of thought and practice that embraced industrial techniques, state interventions, and scientific management just as fervently as it did idealist aspirations of utopian renewal and Romantic conceptions of spiritual and communal redemption. Considering historical developments as varied as colonial building, wartime resource management, state-led housing initiatives, and industrial psychotechnics demonstrates how rationalization was often taken as the means to ends not reducible to reason alone. Rather, Weimar rationalization enjoined science, technology, and construction to numerous purposes that were variously pragmatic, artistic, political, or militaristic. Historicizing rationalization as an elastic concept opens the door to a richer understanding of its multiple configurations as well as its participation in a multitude of historical domains. The dissertation pursues this project across five chapters that examine the negotiations of German Kultur and industrial Zivilisation in Weimar Germany.Architecture, Landscape Architecture and Urban Plannin
Characterization and Optimization of a Murine Engineered CAR T cell Surrogate for the Treatment of Autoimmune and Autoinflammatory Conditions
No full textThe adaptive immune system can occasionally generate cells that are autoreactive,
leading them to trigger autoimmune responses led by diminishing function or numbers of
Tregs. Regulatory T cells (Tregs) are a rare subpopulation of T cells that play a critical
role in the maintenance of homeostasis and immune tolerance. While Chimeric Antigen
Receptor (CAR) T cell therapy is already an important therapeutic modality for tackling
complex autoimmune conditions, the generation of a CAR Treg may prove more
effective and specific, treating the autoimmune condition more directly without the off
target toxicity effects. This thesis evaluates preclinical surrogates for CD19 CAR T cells
which, following optimization, were compared to CD19 CAR Tregs in a murine
inflammatory model in terms of safety, engraftment, off-target effects, and efficacy.
Following optimization, the newly engineered CAR T cell was an effective control tested
in co-culture assays with B cells and T cells, showcasing the off-target immune response
that was not observed with the specific CAR Treg cultures. In an in vivo model of
systemic lupus erythematosus, CAR T cells generated a significant immune response
compared to the CAR Tregs. This suggests that a specific CAR Treg would be a safer
alternative to a CAR T cell-based therapy.Extension Studie
Multicellular Media: Visual Practice in Developmental Biology, 1860-present
No full textTo animate–derived from the Latin animāre–is to give life. Since the emergence of cinema in the last decades of the nineteenth century, animation has also referred to a media practice in which the motion of objects or bodies is produced through the synthesis of a sequence of images. This dissertation draws on the history of biology, science and technology studies, contemporary experimental practice, and media and feminist theory to trace how developmental biologists have crafted narratives about multicellular life through animation since the end of the nineteenth century. In Chapters 1 and 2, I argue that animation, a practice historically rooted in drawing, has been shared across art and biology and that the embodied and media-based synthesis of images have given rise to explanations of morphogenesis, the emergence of form during development. Chapter 3, a demonstration of contemporary animation, describes how cell-lineage domains and tensile cytoskeletal cables organize a grid of square cells as well as the segmentation gene engrailed in the embryo of the amphipod Parhyale hawaiensis. Chapter 4 synthesizes historical and recent literature that provide evidence for cell and tissue mechanics being instructive for morphogenetic processes, including genetic regulation. It proposes that more systematic studies of morphogenesis based in the making and synthesis of images are needed to understand how mechanics can be a substrate for evolutionary change.Biology, Organismic and Evolutionar
Effects of chronic social isolation on somatosensory circuit development and behaviors
No full textLight touch is critical for social communication across the lifespan for humans and other mammals. The COVID-19 pandemic demonstrates how social and sensory inputs, including touch, are deprived due to chronic social isolation, which can be detrimental to our mental and physical health. Previous studies on animal models of chronic social isolation have demonstrated various behavioral changes, while the relationship between changes in tactile circuits and other complex
behaviors remain unclear. Light touch processing from the body skin is initiated through the activation of low-threshold mechanoreceptor neurons (LTMRs), which synapse onto heterogeneous populations of spinal cord interneurons and projection neurons that process and transmit tactile information to the brain. In addition, corticospinal neurons with cell bodies in the primary somatosensory cortex innervate the spinal cord dorsal horn; through connections with a range of spinal cord interneurons, corticospinal neurons play a significant role in the gating of touch information and act as a ‘filter’ to selectively control which tactile information ascends to higher brain centers. Whether and how chronic social isolation affects the development of tactile circuits and behaviors is of particular interest of my thesis study.
Using conditional mouse genetics, histology, electrophysiology, and behavioral assays, we found that chronic social isolation during the post-weaning period leads to profound behavioral changes, as well as anatomical, and functional changes in somatosensory circuits. We found that post-weaning social isolation leads to behavioral hyperreactivity to innocuous light touch stimuli. We also found that post-weaning social isolation leads to an increased number and increased firing rates of spinal cord neurons in response to light touch stimuli. Further, we found that post-weaning social isolation leads to decreased corticospinal neuron innervations in the spinal cord dorsal horn, as well as decreased firing rates of spinal cord neurons in response to direct somatosensory cortex stimulation. More specifically, we found that post-weaning social isolation leads to decreased firing rates of spinal cord inhibitory parvalbumin (PV) interneurons in response to somatosensory cortex stimulations, suggesting that post-weaning social isolation weakens the connections between corticospinal neurons and inhibitory PV interneurons that ultimately causes tactile hypersensitivity in mice. In line with this, modulating distinct subsets of spinal cord interneurons, such as inhibitory Ror interneurons and excitatory PV interneurons, might be sufficient to affect
tactile sensitivities. Together, this work reveals the importance of social touch during post-weaning development in governing the normal development of tactile behaviors and circuits in mice.Neuroscienc
Facing the Monster: The Ethical Challenge of Weird Fiction
No full textThis dissertation argues that the prioritization of empathy as the foundation of ethical reading, especially through identification, obscures the alterity of the other and forecloses truly ethical engagement. Through close readings of texts that center the literary monster as a site of estrangement, I propose that Weird fiction, defined not as a genre but as a mode, disrupts empathic strategies and creates affordances for ethical reading rooted in attentiveness, humility, and refusal of mastery. Chapter One establishes a theoretical framework grounded in Emmanuel Levinas’s ethics of the face and James J. Gibson’s theory of affordances to critique aesthetic mechanisms of empathy. Chapter Two examines the fiction of Mariana Enriquez, whose grotesque figures resist empathic access but demand ethical attention despite discomfort. Chapter Three turns to Antoine Volodine’s post-exoticism, in which formal and meta-fictional hostility disorients the reader and renders them complicit as a spectator of violence as well as intruder. Chapter Four explores Old French texts by Marie de France and Chrétien de Troyes where shapeshifters, werewolves, and other monsters prefigure Weird encounters that unsettle chivalric epistemologies and challenge the legibility of the human. The conclusion reflects on the pedagogical and political implications of ethical estrangement, proposing a critical posture for reading and teaching literature that centers the irreducible other. Across these chapters, I argue that the Weird monster functions as an interruption; it is an aesthetic, ethical, and affective provocation that challenges reading for identification. I advocate for an ethics that, rather than beginning with empathy and falling victim to its limitations, allows one to turn their attention towards the face of the monster.Romance Languages and Literature
Ecological Activities in Childcare Microbiome
No full textThe field of human and environmental microbiome research has undergone remarkable growth over the past two decades, driven by technological innovations that allow us to characterize complex microbial communities without traditional culture-based approaches. Despite these advances, several fundamental challenges persist: terminology inconsistencies that hinder communication among microbiome researchers, incomplete characterization of microbial transmission dynamics in key developmental settings like childcare centers, and difficulty distinguishing viable from non-viable microorganisms in community samples. This dissertation addresses these interconnected challenges through three distinct yet complementary investigations that collectively advance both the technical foundations and applied aspects of microbiome science, with particular attention to early-life exposures in built environments where children spend significant portions of their developmental years.
Chapter 1 examines the evolving lexicon of microbiome research, highlighting how inconsistent terminology has created confusion in the field. Through critical analysis of common terms like "microflora" and "metagenomics," we demonstrate how shifting definitions and misapplications have complicated research interpretation. The chapter provides a structured review of computational strategies for microbiome data analysis, distinguishing between taxonomic and functional approaches derived from various meta'omic methods. By organizing and clarifying vocabulary for major classes of microbiome analysis and their resulting feature tables, this work establishes a framework for more precise communication, enhancing the field's ability to address specific questions about microbial community composition and function with appropriate methodological alignment.
Chapter 2 applies advanced multi-omic techniques to investigate microbial transmission in childcare facilities—critical environments where young children experience formative microbial exposures during immune development. This represents the first comprehensive study integrating microbiome samples from childcare environments and children using full-length amplicon sequencing and both short-read and long-read metagenomic approaches. The findings reveal distinct environmental microbial signatures, with human-associated microorganisms predominating on high-touch surfaces while greater taxonomic diversity characterizes low-touch areas. The identification of transmission pathways between children and their environment, including food-associated microbes like Lactococcus lactis and Streptococcus thermophilus, provides insights into microbial exposure routes during early development. Additionally, the improved genomic resolution afforded by paired sequencing technologies enabled detection of lateral gene transfer events and characterization of bacteriophage ecology, highlighting how enhanced methodological approaches can reveal previously understudied aspects of environmental microbiomes.
Chapter 3 addresses the fundamental challenge of distinguishing viable from non-viable microorganisms in complex communities. This work develops a novel, high-throughput approach based on sequencing multiple optimized marker gene transcripts to identify actively transcribing microbes. Through rational marker gene selection, in silico primer design, and optimized amplification protocols validated against paired metagenomic and metatranscriptomic datasets, the method provides an accessible alternative to more costly and technically demanding techniques. The resulting protocol demonstrated effectiveness in distinguishing viable microbes across synthetic, human, and built environment samples, offering a scalable solution for identifying and quantifying functional community members in diverse microbiome samples.
Together, these chapters represent a comprehensive contribution to microbiome research methodology and application. By addressing terminological precision, environmental context, and viability assessment, this work provides a foundation for more rigorous and biologically meaningful microbiome studies with applications spanning environmental monitoring, clinical diagnostics, and public health interventions.Population Health Science
Who Fears Job Losses from U.S. Tariff Hikes? Evidence from Cambodia
Get PDFapplication/pdfIDP000985_001In April 2025, the U.S. introduced sharply higher tariff rates under a new “reciprocal tariff” system, raising serious concerns about potential employment impacts on export-oriented industries in developing countries. This paper examines Cambodian public perceptions of the tariff hikes using a phone-based survey of 600 households conducted between September and October 2025. We find that 58.5% of respondents disagreed that the tariff increases negatively affect their jobs, indicating limited perceived employment risk, while 25.9% expressed agreement. Regression analysis reveals substantial heterogeneity across income groups: low-income respondents employed in export industries are significantly more likely to perceive negative job impacts, whereas middle- and higher-income respondents show no meaningful direct-exposure effects. Indirect exposure reduces perceived risks among higher-income groups, reflecting their greater job stability and financial resilience.technical repor