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Veiled Visibility: Spatial Memory and Queer Identity in Shinjuku Ni-Chome - Understanding Ni-Chōme as Urban Queer Sanctuary And Its Role in Fostering a Spatial Identity and Cultural Enclave
Get PDFShinjuku Ni-Chōme, nestled in Tokyo’s dense urban heart, is more than
nightlife—it’s memory, movement, and meaning for Japan’s LGBTQ+
communities. This thesis traces Ni-Chōme as a “Queersphere” shaped by both
historical refuge and contemporary tension. Amid Japan’s cultural norms of
discretion, queer identity fragmentation, and a shifting global discourse on
“gayborhoods,” Ni-Chōme emerges as a paradox: visible yet veiled, open yet
exclusive. The study asks how queer identity, spatial memory, and urban
transformation converge in this district—and for whom. Through ethnographic
fieldwork, interviews, spatial analysis, and archival research, it uncovers how
Ni-Chōme navigates pressures of commercialization, political shifts, and
internal exclusions, particularly toward non-cisgender male identities. Findings
show a space constantly reinventing itself—vulnerable, resilient, and deeply
community-driven. By bridging queer theory and urban planning, this thesis
offers not only a portrait of this sanctuary but a vision for inclusive,
intergenerational, and culturally rooted queer spaces in Japan’s urban future.Department of Urban Planning and Desig
Building a Foundation: Developing a Middle School Number Theory Course to Enhance Mathematical Understanding
Get PDFThis thesis addresses the lack of emphasis on number theory in middle school mathematics by developing an elementary number theory curriculum. Core concepts, including divisibility, prime numbers, even and odd numbers, and rational numbers, are taught alongside arithmetic skills, culminating in a cryptography unit. The curriculum is designed to strengthen students’ number sense and conceptual understanding while building on existing research in K-12 number theory education. By integrating targeted number theory instruction with arithmetic and early algebra, this curriculum aims to enhance mathematical reasoning and provide middle school learners with a more robust foundation for future mathematical study.Extension Studie
A Trioxacarcin Prodrug with Accelerated Reversion and an Enterobacteriaceae-Selective Antibiotic with Low Induction of Resistance
Get PDFThe trioxacarcins are highly oxygenated, complex bacterial fermentation products first isolated from Streptomyces bottropensis DO-45 in 1981 with remarkable broad-spectrum antiproliferative activity against both Gram-positive bacteria and eukaryotic cells. This antiproliferative activity motivated a phase I clinical trial for LL-D49194α1, a closely related natural product, which ended with one woman dying from myocarditis and the abandonment of further development. The more recent advent of antibody-drug conjugates (ADCs), a class of targeted chemotherapeutics consisting of a monoclonal antibody conjugated to a toxic payload through a linker system, has promise for attenuating the off-target toxicity of the trioxacarcins. However, to realize a stable trioxacarcin ADC, it has been found that a prodrug of the electrophilic spiro-epoxide functionality responsible for the activity of the class is necessary. In chapters one and two of this dissertation, I detail efforts to optimize a trioxacarcin prodrug for application in an ADC by developing a simplified diversifiable pharmacophore and probing structure-activity relationships (SAR) for both antiproliferative activity and reversion kinetics. In doing so, I identify an optimized pharmacophore possessing identical potency, a three-fold increase in prodrug reversion rate, and increased hydrophilicity, as compared to previous trioxacarcin prodrug ADC warheads.
Antibiotic-resistant pathogens of the Gram-negative family Enterobacteriaceae are among the deadliest of the multidrug-resistant (MDR) bacteria emerging today, taking the top two spots in the 2024 edition of the Bacterial Priority Pathogens List published by the World Health Organization. In chapters three and four, I detail the discovery and gram-scale synthesis of ciprimelpin, a potent Enterobacteriaceae-selective antibiotic that acts by inhibiting the LolCDE component of the localization of lipoproteins pathway. Unlike previously reported LolCDE inhibitors, ciprimelpin has a low propensity for resistance development: it induces a very low frequency of spontaneous resistance (FoR), and isolated mutant strains contain at least two and as many as five mutations in the target protein complex yet remain highly susceptible. Ciprimelpin is well tolerated, orally bioavailable in mice, and highly efficacious in murine infection models of MDR Enterobacteriaceae infection, either by intravenous or oral administration. Ciprimelpin’s in vivo efficacy and tolerability, low propensity for resistance development, and novel mechanism of action make it a promising preclinical candidate for the treatment of infections caused by MDR-Enterobacteriaceae, including those for which there are currently no treatment options.Chemistry and Chemical Biolog
Street Politics in Istanbul: Thresholds of Dissent, Endurance, and Agency
No full textThis dissertation examines revolutionary politics in Candere, a working-class district in
Istanbul once known for its militant leftist past. Drawing on 36 months of ethnographic fieldwork,
it develops foreclosure as an analytic to trace how political, ethical, and spatial possibilities are
systematically closed off under contemporary governance. As revolutionary telos fades, endurance
emerges not as heroic survival but as a political form shaped by structural constraint. Through an
analysis of revolutionism in post-revolutionary times, the research shows how dissent is made to
signify more than it can do, offering a diagnostic of governance and a rethinking of agency under
conditions of constraint.Anthropolog
“Still Nothing About Silesia”: Codifying Regional Family Memory in Polish Literature After 1989
No full textThis dissertation examines how Upper Silesia is represented in post-1989 Polish literature. A historical region currently divided between Poland and the Czech Republic, Upper Silesia was once a contested borderland and site of contact between Polish and German cultures, as well as a hub of heavy industry, especially coal mining. After Poland’s current borders were decided in 1945, numerous factors—including mass migration and deportation, the repression of regional differences by the communist regime, Polish-language education, and ongoing processes like deindustrialization—had begun to radically alter the region’s cultural identity, drawing it closer to the Polish mainstream. Starting in the period of Poland’s transition from communism to democracy and ending in the present-day, my work asks how Upper Silesia is constructed within the Polish-language literary imagination, as exemplified by several books about the region that have gained prominence among national audiences and critics. The works studied are stylistically divergent, ranging from from an autobiographical essay, to historical novels, to book-length nonfiction, to a realist novel. Close readings of these texts identify genre conventions, as well as the cues that serve to code works as Upper Silesian or not. I argue that across formal conceits and time, a certain set of conventions, i.e., occasional Silesian language use and dramatic references to specific events of twentieth-century history, have become a shorthand for Upper Silesian identity on the contemporary Polish book market. Crucially, when these conventions are absent from literary works—-even ones that are set in Upper Silesia and centered on local life—the texts in question are not necessarily read as “Upper Silesian” or “regional” by audiences and critics. Nonetheless, images of family life, inheritance, and memory come into sharp focus in all of the texts I study, implying that works about life in Upper Silesia that have attained some degree of national prominence share themes of family and memory, regardless of how “regional” they are considered to be. Chapter One begins with the seeming absence of Upper Silesia from the regional writing boom of the 1990s. First, I argue that certain regions were better represented than others in this period due to genre conventions and the critical discourse surrounding nostalgic writing. Then, I turn to Adam Zagajewski’s 1991 autobiographical essay, “Dwa miasta” (“Two Cities”), suggesting that Zagajewski’s choice not to include references to regionally-specific language and history led many critics not to label the work as Upper Silesian, despite its setting. Chapter Two identifies the 2010s as a moment of emergence for Upper Silesian themes on the Polish book market, due in no small part to the historical novels of Szczepan Twardoch. I analyze his 2014 novel, Drach (Dragon or Paper Dragon in Silesian), one of the first texts centered exclusively on Upper Silesian history to achieve national success. I contend that Twardoch engages with and ultimately defies both the left- and right-coded tropes of the Polish media discourse as he constructs a compelling narrative about Upper Silesian anti-heroes and other flawed regional characters. Twardoch’s work is contrasted with Anna Dziewit-Meller’s 2016 novel, Góra Tajget (Mount Taygetus), which educates national audiences about the wartime and postwar suffering of Upper Silesians, at times referencing the Holocaust. Despite their differences, both novels seem to situate Upper Silesian cultural distinction in the dramatic events of the twentieth-century, just as they deploy the Silesian language. Chapter Three studies Zbigniew Rokita’s 2020 reportage, Kajś (Somewhere in Silesian), book-length nonfiction centered on the author’s decision to learn more about his Upper Silesian heritage in the hope of claiming a regional identity for himself. While this work offered national audiences an accessible guide to Upper Silesian history and an introduction to debates on the Silesian language, its very premise implies that regional belonging is in some way tied to familiarity with twentieth-century history and a basic knowledge of Silesian, rather than simply living in the region. Finally, Chapter Four reads Anna Cieplak’s 2021 novel, Rozpływaj się (Melt Away), which lays bare the inner voices of four struggling residents of Upper Silesia spanning from the 1990s to contemporary period. The novel’s protagonists do not display an interest in or knowledge of regional history and only speak Silesian when forced to by their elders. I conclude that Cieplak’s novel points to the contemporary ambivalence of Upper Silesian identity when it is not fixed on the events of the early to mid twentieth-century that are writ large in regional history. The dissertation ends with a brief epilogue about other art forms that express regional culture, emphasizing that Upper Silesian cultural expression should not be seen as exclusive to the particular conventions and audiences of Polish-language literature as they have developed in the past thirty years.
Keywords: Poland, Upper Silesia, Literature, Prose, RegionalismSlavic Languages and Literature
Lysosomal Membrane Permeabilization Triggers GSDMD-mediated Pyroptosis through Cytosolic Release of Cathepsin B and L
No full textLysosomal membrane permeabilization (LMP) in macrophages by detergents, crystals and infectious agents triggers an inflammatory cell death, called pyroptosis, which is caused by cleavage and activation of a pore-forming protein, called gasdermin D (GSDMD), that disrupts mitochondrial and cell membranes and serves as a conduit that releases inflammatory cytokines. LMP-mediated cell death has been shown to play an important role in atherosclerosis caused by cholesterol crystals, gout caused by uric acid crystals, silicosis caused by silica crystals and asbestosis caused by asbestos fibers. Pyroptosis in macrophages is often caused by activation of cytosolic sensors of invasive infection or signs of intracellular damage, called inflammasomes, that recruit a family of inflammatory death-inducing proteases called caspases to cleave gasdermin D. However, the mechanism responsible for LMP-induced inflammatory cell death is not well studied. Here, we show that LMP in mouse and human macrophages triggers gasdermin D-dependent pyroptosis that is unexpectedly independent of the caspases and the inflammasome NLRP3 previously thought to cause pyroptosis in response to LMP. Instead, we show that LMP caused by crystals and lysosomal detergents releases the lysosomal proteases, cathepsins B and L, into the cytosol, where they activate gasdermin D to induce pyroptosis. Pharmacological inhibition of cathepsin B and L suppresses LMP-induced pyroptosis. Knockout of either cathepsin B or L on its own does not inhibit LMP-induced pyroptosis, but knockout of both cathepsins strongly blunts pyroptosis, indicating that either cathepsin can activate gasdermin D. Genetic deficiency of both cathepsins also reduces the release of the inflammatory cytokine interleukin-1beta (IL-1beta) ) from LMP-induced pyroptotic macrophages. However, unlike cell death, IL-1beta release also requires NLRP3 activation of caspase-1 to cleave pro-IL-1beta to its mature form, since it is blocked by the NLRP3 inhibitor MCC950, indicating that pro-IL-1beta is not a substrate of the cathepsins. Thus, lysosomal rupture triggered by macrophage phagocytosis of disease-causing crystals leads to release of cathepsins B and L into the cytosol, where they activate gasdermin D to form membrane pores, triggering caspase-independent pyroptosis.Graduate Educatio
Modeling Health Equity and Financial Risk Protection Impacts of Cardiovascular Disease Prevention in Low- and Middle-Income Countries
No full textMany low- and middle-income countries (LMICs) are embarking on journeys towards universal health coverage to achieve health system goals of improving health and financial risk protection. The large and growing burden of noncommunicable diseases, including cardiovascular diseases (CVDs), poses a real threat in delivering on those goals. Cost-effective interventions exist to prevent CVD through treatment and control of risk factors such as hypertension. However, there is poor and unequal health systems performance for managing hypertension in LMICs, leading to severe underuse of high-value healthcare. In addition to the health threat, the growing burden of CVDs and expensive healthcare costs may exacerbate already poor financial protection outcomes in many countries. These impacts can be especially pronounced among poor households who often bear a disproportionate burden of CVDs and face greater financial risk from seeking care.
There is a huge opportunity to improve the level and distribution of health and financial protection in LMICs through enhanced management of hypertension. In this dissertation, I quantified the health equity and financial protection impacts of improved CVD prevention in both a global and local (Uganda) context to inform the design of efficient and equitable health systems that can provide high-value primary care to the large and growing population of people living with CVDs or at-risk of developing CVDs in LMICs.
In Chapter 2, I developed a microsimulation model to evaluate the potential health equity impacts of improving hypertension management across socioeconomic groups in 44 LMICs. I find that eliminating socioeconomic-based disparities in hypertension diagnosis and treatment can reduce CVD risk disparities within countries. Importantly, implementing equity-sensitive hypertension management programs requires reducing disparities in linkage to treatment.
In Chapter 3, I extended the simulation modeling framework to estimate the financial protection impacts of improved hypertension management. I show that targeted interventions to eliminate socioeconomic-based disparities in hypertension treatment coverage in LMICs can also provide financial protection through avoided hospitalizations, particularly for the poorest. However, directly covering inpatient costs would provide greater financial protection benefits at a lower cost, especially when considering socioeconomic-based disparities in healthcare use after a CVD event. This demonstrates the need to combine financing mechanisms and health interventions to address both out-of-pocket costs and disease prevention, especially for the poor, to jointly improve population health and financial protection.
In Chapter 4, I further adapted the microsimulation model to the local context of Uganda where national health insurance is high on the policy agenda. Using a participatory modeling approach with stakeholders, I simulated the potential distributional health and financial protection impacts of a national health insurance scheme that generates demand for preventive healthcare among Ugandan adults living with hypertension. I find that the potential health and financial protection impacts of national health insurance are dampened by staggered enrollment into insurance (compared to universal enrollment) and constrained by supply-side capacity to diagnose and treat hypertension. Foregone health and financial protection benefits would be greatest among the informally employed sector and unemployed. Uganda should aim to pursue policies to reach universal insurance enrollment with focused efforts to enroll the informal sector and unemployed population and invest in improving quality-of-care and service delivery infrastructure to maximize the potential benefits of a national health insurance program.
As countries across the world face the growing challenge of caring for aging cohorts, enhanced understanding of the role of the health system in improving health, reducing health disparities, and contributing to financial protection can inform targeted population-based risk reduction strategies and health system performance improvements that eliminate, rather than exacerbate, health and economic disparities. In this dissertation, I have shown that high performing primary healthcare systems in LMICs can improve the level and distribution of health and financial protection in the population.Population Health Science
Learning gene regulation with cross-modal integration of observations and perturbations
Get PDFBiological systems are studied by observing and perturbing. Recent abundance in omics data in various modalities with higher resolution and scale, along with an explosion in perturbation techniques, poses critical opportunities in learning gene regulation. This PhD dissertation presents innovative cross-modal integration approaches for understanding gene regulatory mechanisms. It demonstrates how the integration provides multi-dimensional insights on gene regulation, namely how different components in multiple modalities interact across cellular contexts, and the perturbational and sequence variant impacts on those axes of gene regulation.
Specifically, I have developed four computational methods, namely CORAL, SIMBA+, BEAN, and Labeled Gromov-Wasserstein Optimal Transport, each of which has tackled distinct tasks for a better understanding of gene regulation mechanisms using different statistical and machine learning models. CORAL allows upstream and downstream transfer learning of single-cell multiomics data. SIMBA+ provides cell-state-specific mechanistic insights on sequence variants and phenotypes by integrating single-cell multiomics data and genome-wide association studies. BEAN integrates genotype and phenotype from high-throughput CRISPR base editing screens to improve the power of the screen. Labeled Gromov-Wasserstein Optimal Transport integrates multimodal single-cell perturbation screens for cross-modality response prediction and feature interpretation.
Overall, this PhD dissertation took a major step forward in machine learning and statistical approaches for learning gene regulation through cross-modal integration of observational and perturbational datasets. Through the integration, I provide major insights into gene regulation, variant interpretation, disease mechanism, and clinical discovery.Biomedical Informatic
Scheduling Algorithms for Low-Precision Accumulation on Energy-Efficient Deep Neural Network Hardware
Get PDFIn just a few years, deep neural networks (DNNs) have advanced from image classification and machine translation to large language models (LLMs) capable of processing and generating hundreds of thousands of tokens. Like their predecessors based on convolutional and transformer architectures, modern LLMs rely on extensive matrix multiplications interleaved with non-linear operations. Unlike earlier models, however, LLM dot products routinely exceed 30,000 elements, imposing severe costs in energy and latency—particularly in edge deployments where resources are limited. Enabling efficient inference in such settings demands both novel hardware architectures and new algorithmic strategies for computation scheduling.
At the core of these workloads lies accumulation: the repeated addition of products within large dot products. Narrowing the bitwidth of accumulators can dramatically reduce hardware complexity, energy consumption, and latency. Yet overly narrow accumulators increase the risk of numerical overflow, degrading model accuracy. Existing solutions mitigate this by retraining models with modified weights, a process that is costly for large models and often infeasible due to data access constraints.
This thesis introduces algorithmic and architectural techniques that enable low-bitwidth accumulation without retraining or altering the model weights. We first present Alternating Greedy Schedules (AGS), which partitions the summation into positive and negative subarrays and orders their accumulation to avoid overflow. We then propose Markov Greedy Sums (MGS), which leverage the statistical structure of DNN partial products to maximize use of a narrow accumulator and resort to a wide accumulator only when necessary. Finally, we develop Alternating Balancing Sums (ABS), a buffer-based reordering scheme that provably maintains narrow accumulation for most operations, deferring wide accumulation to a short final phase. Together, these techniques establish a family of scheduling strategies that systematically balance precision, energy, and latency.
We evaluate our designs across diverse DNN inference tasks, showing that they substantially reduce accumulator bitwidth requirements and energy consumption while preserving accuracy—all without retraining. These results demonstrate a path toward scalable, energy-efficient hardware for next-generation DNNs and LLMs, enabling their deployment across a broader range of platforms from cloud servers to edge devices.Engineering and Applied Sciences - Computer Scienc
Latent Structure in Cancer Genomics: Methods for Reproducibility, Computational Efficiency, and Causality
No full textCancer arises from genomic changes and complex interdependencies among latent, or unmeasurable, biological mechanisms. Latent mechanisms include transcriptional programs (gene expression patterns governing cellular activity) and mutational processes (patterns of DNA sequence changes). These mechanisms are central to cancer development and can influence clinical prognosis and response to treatment. Understanding these latent processes is critical for modern cancer research and requires statistical models that can recover them from high-dimensional data.
This dissertation focuses on biologically interpretable latent-variable models. Latent variables allow high-dimensional genomic data to be represented in a low-dimensional space, capturing underlying biological mechanisms. The three chapters of this dissertation introduce new methods utilizing latent variable models for reproducibility, computational efficiency, and causality.
Reproducibility is key for strong, credible findings in cancer genomics. Chapter 1 introduces fdrSAFE, a selective ensembling algorithm for estimating local false discovery rates. We show this approach achieves robust near-optimality, performing well when baseline approaches perform poorly in at least one setting. In addition to improved accuracy, this method eliminates the need for arbitrary model choice, improving reproducibility of multiple test corrections using local false discovery rates.
With rapidly advancing technologies and expanding data sources, computational efficiency is essential for scalable genomic analysis. Chapter 2 develops bayesNMF, a computationally efficient Gibbs sampler for Poisson Bayesian NMF, where learned latent factors model underlying mutational processes. It utilizes Metropolis-Hastings steps to avoid computationally intensive Poisson augmentation and defines high-overlap, geometry-informed proposal distributions. We show that it performs as well as current MCMC methods while being up to 30 times faster.
Translating methods into clinical use requires causal understanding to distinguish correlation from causation. Chapter 3 introduces a framework for causal inference on latent biological outcomes, enabling causal interpretation of treatment effects. It formalizes and quantifies the concept of learning-induced (li-)interference, which arises when learned latent outcomes depend on other samples' treatments, biasing causal estimates. This chapter also proposes an algorithm to mitigate li-interference and demonstrates promising results in both simulated and real cancer data.
Together, these methods contribute to a unified view of latent structure in cancer genomics. Each chapter is accompanied by an open-source R software package (fdrSAFE, bayesNMF, and causalLFO) to promote reproducibility and usability of these methods.Biostatistic