Situating Racialized Genders: An Analysis of Identity Development, Cultural Influences, and State-level Policy Impact on the Health of Transgender People of Color

This research provides an in-depth exploration and definition of the multifaceted experiences of transgender (trans and nonbinary) individuals of color in the United States (US), balancing both a broad legislative analysis and a focused qualitative perspective. This dissertation explores how to more rigorously define the epidemiological concept of “exposure” to impact upon health inequities for individuals who experience oppression on multiple axes (e.g., race and gender). Initially, this research employs a polytomous latent class analysis to classify US states into three categories—Mostly Protective, Mixed, and Mostly Harmful—based on trans rights and structural racism policies. By drawing upon data from the Movement Advancement Project’s Gender Identity Tally and a structural racism legal database, the analysis reveals that even within seemingly inclusive states, protective measures are not uniformly applied, pointing to the necessity of comprehensive, intersectional policy evaluations. These insights underscore the variable impacts of state policies, advocating for more inclusive legislative frameworks that adequately consider the intersecting identities of race and gender. The second study shifts to a qualitative focus, examining the experiences of 8 Black transmasculine individuals in the Black culturally rich and historically significant city of Detroit, Michigan—a pivotal site during the Great Migration – and surrounding counties in Southeastern Michigan. Using Interpretative Phenomenological Analysis and object elicitation, the study investigates how participants, aged 25-35 years, navigate the tapestry of Black gender norms, family belonging, and community interactions in shaping their masculine and Black identities. A key finding is the role of alcohol serving not only as a medium for gender affirmation and personal identity exploration but also as a crucial element in familial and community bonding and coping with structural anti-Black oppression. Amid structural discrimination, alcohol emerges as a tool for coping and solidarity, facilitating connections and collective support within the family and community. Together, these quantitative and qualitative insights offer a holistic view of how historical, cultural, and political determinants intersect with localized, cultural dynamics to influence the well-being and identity development of marginalized communities. The research stresses the importance of crafting policy solutions that are both broad in scope and finely attuned to the specific needs and challenges faced by these communities. By detailing the interplay between legislation and lived experience, the study advocates for interventions and policies that are both inclusive and sensitive to the diverse realities of trans individuals of color, particularly those identifying as Black and transmasculine.Population Health Science

Cash Country: A Revolutionary Biography of the Tunisian Dinar

Cash Country is an ethnography of money in times of political upheavals. Following the 2011 popular uprisings in Tunisia and across the Arab region, I investigate how Tunisia’s national currency, the dinar, has become a public object. From widely commented-upon devaluations, media scandals on disappearing banknotes, currency trafficking at borders, new practices of hoarding money, and changing monetary policies, I argue that the Tunisian dinar structures revolutionary aspirations and their disenchantments, tying political horizons to unchanging economic conditions. Cash Country devises a methodology to “follow the money”, centering its materiality and circulations. I track the struggles that invest the dinar’s material forms, from cash whose visual aspect strives to mirror the transition between political regimes, to attempts by financial actors to digitalize currency. I follow the dinar from inside the Central Bank, showing how the institution transforms into the mediator of financial capitalism at home, to the nation’s borders where the dinar’s illicit circulations run in friction with transnational surveillance regimes. Cash Country’s premise is the relation between money and its material forms, the exercise of materializing a universal media into a localized currency. I expand from the social theory of money which understands money as an object that evades definition because it exists mutually as a universal medium and a locally embedded form. I pay attention to social actors’ attempts to define money by bridging the gap between the idea of money – universal and commensurable – and its materialization into a national currency – depreciated and barely convertible. I argue that if the dinar has become a site of effervescence and interventions, it is because it articulates the scales people are caught in, between the frame of the national, where revolutionary transformations are imagined to take place, and the workings of global capitalism from where the imaginable gets produced. By following the social life of money, Cash Country writes a different story of uprisings and their afterlives in North Africa and the Middle East. Instead of assessing the successes or failures of revolutions, this dissertation highlights how struggles for freedom are struggles that invest the terms of capitalism. As political transitions give way to economic encroachment, Cash Country reveals how money operates as an object that structures horizons of possibility today.Middle Eastern Studies Committe

Mate Choice in Phlox Wildflowers

Mate choice determines when, where, and how reproduction occurs. The summed effect of these decisions across generations drives evolutionary trajectories and patterns diversity across biological scales. Inspired by these far-reaching impacts, I focus on two primary arenas where plant mate choice decisions occur: 1) organismal interactions between plants and their pollinators and 2) cellular interactions between reproductive structures within the flower (pollen and pistil). In Chapter 1, I quantify the pollination environment in the Texas wildflower, Phlox drummondii, and identify high pollinator specialization towards a single butterfly species. Building on this empirical work in Chapter 2, I generate a novel theoretical framework for the role of pollinators as agents of dispersal. In Chapter 3, I test the hypothesis of increased self-fertilization in P. cuspidata as an adaptation to avoid costly hybridization with its related congener, P. drummondii. In Chapters 4 and 5, I use quantitative and functional genetics to investigate the genetic basis of the self-incompatibility mechanism active in P. drummondii. I map the genetic basis of intraspecific variation in the self-pollen rejection response and identify a novel gene causing self-pollen recognition in Phlox wildflowers. Taken together, my work integrates broad experimental approaches to explore how mate choice mechanisms function across biological scales.Biology, Organismic and Evolutionar

Investigating Differential Expression on Birds from Mongolia Based on Aridity

The Mongolian climate has a division between northern and southern locations based on arid environmental variables: mean annual temperature and precipitation. This study used RNA-seq data from three different species and tissue types to test for differential gene expression. There were nine combinations of species and tissue types and all but one had at least one differentially expressed gene. One combination had significant differences in expression based on northern and southern locations, the Daurian Redstart (Phoenicurus auroreus) and muscle tissue with 800 differentially expressed genes at p.05 significance. The findings demonstrate that there is differential expression based on aridity for at least one species and tissue from Mongolia.Extension Studie

Designing for Decentralized Finance through Differentiable Optimization, and a Study of Bayesian Optimization

Decentralized finance (DeFi) is the concept of building financial infrastructures without relying on centralized intermediaries. A notable development in DeFi is the creation of decentralized exchanges (DEXs), which operate as smart contracts on a blockchain. Due to the high cost of on-chain operations, automated market makers (AMMs) such as Uniswap v3 have emerged as the prevailing model of liquidity provision on DEXs. Two closely related research questions arise in the DeFi space: (1) What are the optimal strategies of liquidity providers given an AMM design such as Uniswap v3? (2) How should the design of AMMs be optimized to achieve objectives such as profit maximization? This thesis addresses these two central research questions using computational methods, in particular, through differentiable optimization. Chapters 2 and 3 study the optimal strategies of liquidity providers (LPs) in Uniswap v3. In both chapters' formulations, the expected utility of an LP is differentiable with respect to its liquidity allocation under any exogenous price sequence, enabling differentiable optimization of LP strategies. With the formulation of a convex stochastic optimization problem that can be solved in a differentiable manner, Chapter 2 explores optimal static LP strategies in economic settings with varying factors such as an LP's belief about price dynamics, risk aversion, and for different specifications of the Uniswap v3 liquidity pool. Understanding LP strategies also leads to insights into the design of Uniswap v3 liquidity pools. Under a similar optimization framework, Chapter 3 extends from static LP strategies to dynamic LP strategies, specifically LP strategies that reallocate liquidity whenever the price movement reaches a certain threshold. These proposed dynamic strategies—particularly context-dependent variants modeled by a neural network, which adapt the shape of liquidity allocation to contextual information such as price and moving average of non-arbitrage trade volume at the time of reallocation—are shown to lead to significant gains compared to static LP strategies. Taking a broader perspective on AMM design, Chapter 4 optimizes market-making mechanisms for a single trade in settings with multiple traded goods, seeking market maker profit maximization under adverse selection. Conjectures of optimal mechanisms are generated using tools of differentiable economics, which uses differentiable optimization for economic design. To prove the optimality of proposed mechanisms, a duality theorem is established between the market-making mechanism design problem and an optimal transport problem. This approach of combining differentiable economics with theoretical analysis is used to develop a parameterized class of optimal market-making mechanisms. These results also establish that, in some cases, the optimal market maker across multiple goods must use complex bundling. Additional conjectures about the structure of optimal mechanisms are presented, and an empirical optimality bound is established for some conjectures by approximately solving the dual with linear programming. The second part of this thesis studies transfer learning of the Gaussian process (GP) prior in Bayesian optimization (BO), a widely used black-box function optimization method. Previous GP-based transfer learning methods for BO are limited to utilizing historical data collected from black-box functions with the same domain as the new black-box function to be optimized. The proposed method, model pre-training on heterogeneous domains (MPHD), employs a neural network that maps from domain-specific contextual information to specifications of hierarchical GPs for a given domain. As a result, MPHD is able to transfer knowledge across heterogeneous domains such as hyperparameter-tuning for different machine learning models. It is shown through theoretical analysis and empirical results that MPHD is a practical transfer learning method for BO, with demonstrations of competitive performance on challenging real-world hyperparameter-tuning tasks.Engineering and Applied Sciences - Computer Scienc

Worker-Centric AI for Decision Support

Just as the Industrial Revolution reshaped manual labor, AI technologies are now transforming cognitive work—offering intelligent support and fundamentally changing the nature of workers' tasks and workflows. The design of AI-powered decision-support systems affects not only output-centric values of work --- such as quality, efficiency, and creativity ---but also human-centric values, including workers' skills in AI-supported tasks, their agency, collaboration, and the meaning they derive from their work. However, current AI decision-support paradigms typically focus only on output-centric metrics (e.g., decision accuracy) and overlook how people process information, their motivation to engage with AI recommendations, and their ability to critically assess AI outputs. As a result, these systems often lead to overreliance, fail to enable human-AI complementarity, and may even deskill workers. For both moral and economic reasons, AI tools must empower workers, help them develop new skills, and truly complement human expertise. Technologies that support worker agency and skill development are more likely to lead to long-term organizational performance, job satisfaction, and economic resilience. To achieve this, I argue that AI systems must be designed from a worker-centric perspective --- one that optimizes both output- and human-centric outcomes and is grounded in human cognition: how people think, decide, learn, and apply expertise. In the first part of this dissertation, I demonstrate that existing AI decision-support systems do not sufficiently account for human cognition. They are built on the implicit --- yet incorrect --- assumption that users consistently engage cognitively with AI support. Challenging this assumption within the field of human-AI decision-making, I show that cognitive engagement is an essential mechanism for critically evaluating and effectively incorporating AI advice into decision-making. Drawing on the dual-process theory of cognition, I demonstrate that current paradigms of AI support exacerbate heuristic (System 1) thinking by offering readily available decisions and explanations that users can adopt with minimal effort. To counteract this, I introduce cognitive forcing functions ---interaction interventions that elicit cognitive engagement by disrupting heuristic processing at decision time --- and show that these significantly reduce overreliance on AI. Building on these findings, in the second part of this dissertation, I introduce a suite of novel systems that operationalize worker-centric AI --- systems that are grounded in human cognition when optimizing output- and human-centric outcomes of work. I present AI decision-support systems that: (1) complement human judgment through adaptive AI support policies, learned via reinforcement learning, that personalize assistance based on contextual and cognitive factors; (2) augment human skills through human-centered contrastive explanations that address knowledge gaps by contrasting AI decisions with likely human reasoning; and (3) extend human perspective-taking capabilities in decisions requiring cognitive empathy by simulating diverse viewpoints, as demonstrated with the AHA! system for AI deployment decisions. Together, these contributions advance the field of human-AI decision-making and chart a path toward worker-centric AI systems --- designed not only to enhance productivity but also to sustain workforce development. Such systems ensure that AI complement human expertise, preserves pathways for skill acquisition, and ultimately strengthens the workforce.Engineering and Applied Sciences - Computer Scienc

Manipulation of NK-cell immunity via metalloproteinases and chemokine receptor redirection in viral infection and cancer

Natural killer cells perform their role of immunosurveillance and target killing by active engagement with ligands in the environment with their activating and inhibitory receptors, as well as their cytokine and chemokine receptors. It is well known that metalloproteinases can cleave ligands and chemokines to modify ligand-receptor interactions, chemotactic activity, and overall function. However, studies addressing how metalloproteinases manipulate these processes and how we can harness these to enhance natural killer cell activity in viral infection and cancer are lacking. Here, we discover the underlying mechanisms by which SARS-CoV-2 and related sarbecoviruses induce metalloproteinase-mediated cleavage of critical NK cells ligands, MIC-A/B, to evade natural killer cell immunity. We also probe the role of the metalloproteinase-cleaved chemokine CXCL16, which has demonstrated an increasingly important role in triple negative breast cancer. Lastly, as an extension of our work in probing chemokine-chemokine receptor axes, we set the groundwork to rewire NK-cell chemotaxis by introducing novel chemokine receptors as a therapeutic strategy to redirect them into tumor tissues and fully harness their cytotoxic potential.Graduate Educatio

Computational Methods of Advancing Therapeutic Genome Editing

The cells of all living organisms across every domain of life contain a heritable DNA genome that encodes all of the requisite information needed to recapitulate their structure, function, and behavior. The development of programmable tools capable of editing DNA in living cells has enabled a revolution in the biological and biomedical sciences. Despite their enormous potential, the application of these tools to personalized medicine has been challenging due to wide variability in editing efficiency across different sequences. In this thesis, I describe the development of computational tools for predicting and analyzing the outcomes of therapeutic genome editing experiments. I show that these tools enable the rapid development of editing strategies for correcting both common and rare pathogenic mutations. In Chapter 2, I describe the development and exploratory data analysis of pooled lentiviral screens for rapidly assessing the outcomes of prime editing experiments. First, I detail the design and construction of paired gRNA–target site libraries for high-throughput evaluation of editing efficiencies for both pegRNAs and nsgRNAs. Then, I use the results from paired pegRNA–target site screens to characterize the sequence determinants of mammalian mismatch repair. I show that mismatch repair efficiency depends on both the specific mismatched bases as well as the length of uninterrupted mismatches. Using data from paired nsgRNA–target site screens, I show that prime editing efficiency with PE3 systems is not correlated with predicted Cas9-nuclease efficiency scores, motivating the development of predictive machine learning models specific for complementary-strand nicking. In Chapter 3, I formulate mechanistic machine learning, a paradigm for performing machine learning on chemical systems wherein domain knowledge about reaction mechanisms can be directly incorporated into the underlying structure of data-driven models. Using mechanistic machine learning, I describe the development of OptiPrime, a model of prime editing efficiency and show that its exquisite predictive performance is dependent on its mechanistic formulation. Additionally, I show that the intermediate values computed by OptiPrime are physically interpretable and can be used for accurate predictions of outcomes of prime editing experiments with complementary strand nicking guides (i.e., PE3) and with paired prime editing guide RNAs (i.e., twinPE). Next, in Chapter 4, I demonstrate several prospective use-cases of OptiPrime towards the development of therapeutic approaches for correcting pathogenic mutations in human and mouse models of disease. Using cystic fibrosis as a test case, I show that OptiPrime can be used to generate pegRNA sequences that achieve high editing efficiencies at three common pathogenic mutations in CFTR, including one that resulted in double the editing efficiency of a pegRNA that required 3 years to hand-optimize. I then show that OptiPrime-generated sequences can be used directly in primary cells for correction of pathogenic mutations in mouse models of Alport syndrome and KIF1A-associated neurological disorder. Moreover, I show several "nonconventional" use cases for OptiPrime, including for T cell engineering in primary human cells, generating a pair of pegRNAs capable of installing a recombinase landing site that enabled over 10\% integration efficiency into CFTR intron 1, and combining OptiPrime with SpliceAI to correct a cause of HLA class II immunodeficiency. In Chapter 5, I describe the development and application of powTNRka, a dynamic programming algorithm for assessing the outcomes of base and prime editing experiments at highly repetitive genomic loci. PowTNRka enabled the development of base editing and prime editing strategies in the trinucleotide repeat tracts of HTT and FXN, the genes associated with Huntington’s disease and Friedreich’s ataxia, respectively. Base editing was able to abate somatic repeat expansion in HTT and FXN in both in vitro and in vivo models, providing a potential strategy for preventing repeats from reaching pathogenic length. Moreover, prime editing was able to precisely excise repeats at HTT and FXN in models that contained pathogenic numbers of trinucleotide repeats. In an in vivo model of Friedreich’s ataxia, prime editing-mediated repeat excision resulted in successful restoration of FXN transcript levels. Lastly, in Chapter 6, I provide a brief outlook on the state of current research at the intersection of computation and genome editing technologies, along with future research directions that will further pave the path for the field’s continued development.Chemistry and Chemical Biolog

Novel mechanisms of gene regulation driving aggressive leukemias

Acute myeloid leukemias (AMLs) have an overall poor prognosis with many high-risk cases coopting stem cell gene regulatory programs, yet the mechanisms through which this occurs remain poorly understood. Increased expression of the stem cell transcription factor, MECOM, underlies one key driver mechanism in largely incurable AMLs. How MECOM results in such aggressive AML phenotypes remains unknown. To address existing experimental limitations, I engineered and applied targeted protein degradation with functional genomic readouts to demonstrate that MECOM promotes malignant stem cell-like states by directly repressing prodifferentiation gene regulatory programs. Remarkably and unexpectedly, a single node in this network, a MECOM-bound cis-regulatory element located 42 kb downstream of the myeloid differentiation regulator CEBPA, is both necessary and sufficient for maintaining MECOM-driven leukemias. Importantly, targeted activation of this regulatory element promotes differentiation of these aggressive AMLs and reduces leukemia burden in vivo. In an effort to translate these biological insights into a therapeutic strategy, this work also explored the use of heterobifunctional small molecules and chemically induced proximity to rewire MECOM transcriptional activity. While efficacy was limited, this portion of the study revealed key principles to consider when aiming to therapeutically engineer and reprogram transcription in this manner. In sum, these findings suggest a broadly applicable approach for functionally dissecting oncogenic gene regulatory networks to inform improved therapeutic strategies.Biological and Biomedical Science

A Causal Inference Framework for Identifying Critical Windows of Time-Varying Exposures

There has been great clinical interest in the concept of ‘critical windows’ or ‘sensitive periods’ of environmental exposures. The concept of a critical window, defined formally in this dissertation, refers to a specific time period during which an individual is more susceptible to developing an outcome in response to a particular exposure than at other times. The statistical methods used to identify these critical windows have several limitations, and applied researchers are often limited to using methods developed for different research questions which can lead to bias, inflated Type I error rates, and low power. Studies on environmental exposures are almost always observational by necessity, and it remains an ongoing challenge to interpret results as the causal effect of intervening on the exposure rather than merely as an association between the exposure and the outcome. Because the methods currently in use have not been previously examined through a causal inference lens, results across studies are difficult to compare, even if the same covariates are used. This dissertation seeks to combine these two areas of interest by proposing a framework for the identification of critical windows from a causal inference perspective. Throughout this work, we demonstrate how different methods should be employed to answer subtly different research questions and compare our methods to existing approaches through simulations where appropriate. In Chapter 1, we introduce our novel flexible CAusaL Identification of Critical windOws - Modified Treatment Policy (CALICO-MTP) framework, extending previous work on using a dose modification scheme to estimate the causal effect of continuous exposures. We propose dividing the concept of critical window identification into three distinct research questions, each addressed with different approaches. These questions are: 1) Curve estimation: what does the exposure-outcome relationship look like over time? 2) Hypothesis testing: is there any time window during which there is an effect of intervening on the exposure? and 3) Window selection: after determining that there is a causal relationship, what is the critical window for that exposure? For the first question, we propose a curve estimation strategy to yield results similar to those of the commonly used distributed lag model (DLM). For the second, we propose estimating the effect of intervening on all biologically plausible windows and combining the p-values using the Aggregated Cauchy Association Test (ACAT), a p-value combination method that accounts for strong correlations between test statistics. For the third, we discuss strategies for selecting the window once the global null has been rejected. We apply these methods to a dataset from Beth Israel Deaconess Medical Center (BIDMC) and compare them to previous results regarding the effect of Nitrogen Dioxide (NO2) exposure on the 32-40 week fetal head circumference as measured by ultrasound45, and we present a novel visualization for the causal effect of intervening on time intervals. In Chapter 2, we present a variant of this framework, CALICO-ADRF, that explicitly models nonlinear dose-response relationships by estimating the Average Dose Response Function (ADRF) for each time window. This nonlinear relationship is particularly relevant for environmental exposures such as metals, where some are necessary minerals at low exposures but act as toxins at high exposure levels, and temperature, which may exhibit a thresholding effect for certain outcomes. We use a scalar test statistic that is the integrated squared derivative of the estimated ADRF to perform global hypothesis testing with Type I error control and improved power compared to the methods of Chapter 1 for biologically-plausible nonlinear dose-response curves. We demonstrate these results looking at the effect of maternal prenatal temperature exposure and birthweight for full-term deliveries in the same BIDMC cohort. In Chapter 3, we present a discussion of causal inference concepts specifically tailored to the methods most commonly used for time-varying environmental exposures, offering a novel perspec- tive for researchers. We present a framework through which the target estimand of different mod- eling approaches can be compared, improving the ability to draw meaningful and comparable conclusions across studies. We explore the different estimands that these methods target and illustrate when these estimands align or diverge depending on the underlying causal structure of the exposure. Finally, we provide guidance for researchers on how to appropriately align their methodological choices with their research questions.Biostatistic