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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
Just Economics: Inequality and Political Culture in Cross-National Perspective
No full textEconomics is widely viewed as a key participant in the construction of the pathologies of market societies. Critical scholars in fields such as Science and Technology Studies (STS) see it as the black sheep of the social sciences. Economics is characterized as promoting a view of society entirely through the prism of markets, individual self-interest and the imperative of growth at the expense of other values such as equality or human rights. In this dissertation, I look at the work of inequality economists at the Poverty Action Lab (J-PAL), World Inequality Lab (WIL), and the Delhi School of Economics to portray a different picture of the field. These subsets of economists are explicitly committed to values of tackling poverty, reducing inequality and promoting human rights and social development, but they define the problem of inequality differently and propose very different solutions for it.
It remains a puzzle how—despite these cross-national differences—economics, a field long seen as promoting efficiency over equality, comes to be accepted as a source of solutions to the problem of inequality. I argue that economists gain legitimacy to address inequality by constructing a field of expertise that I call “just economics.” This economics is just in three ways: it gets the right answers (as in the French expression, c’est juste!); it aims toward normatively desirable (i.e. just) ends; and it is “just” economics, providing neutral policy advice that leaves citizens free to make their own normative decisions. Just economics taps into cross-national differences in what makes for good evidence and morally desirable outcomes in the eyes of experts and lay citizens in a given political culture. I draw on co-productionist scholarship in STS to show how just economics appeals to culturally-embedded understandings about whose responsibility it is to taking on problems of inequality, the extent to which the problem is one of social structure or individual agency, and the appropriate institutional channels through which responses should be coordinated.
I develop this argument in thematically connected chapters. Chapters 1-2 take up economists’ own disciplinary self-awareness, showing how the discipline has adopted different thought styles to get at right answers, specifically, causal, historical and experiential approaches. I describe how economists draw on experiments, statistics and experiences in order to satisfy their reflexive questions about their own expertise. Chapters 4-6 are country case studies of approaches to inequality in the United States, France and India, as a question of opportunity, distribution, and rights, respectively. In the United States, a privatized welfare model focuses on unlocking “poverty traps” and making the poor into good entrepreneurial capitalists. In France, the system of social welfare is oriented toward reducing the potential for class conflict, and focuses on the use of expertise to inform reasoned public debates about inequality. In India, the rights-based approach is rooted in the country’s constitutional “Directive Principles,” realized through contemporary efforts in activism and legislation to secure socioeconomic rights. Chapter 7 explores how these different thoughts styles synergize with distinct constituents of the social compact—markets, states and (civil) society—while Chapter 8 looks at education as a specific policy domain where these styles are mobilized to implement visions of a just society. Inequality, I conclude in Chapter 9, is more than a statistical property to be studied by expert social scientists: By comparing approaches to inequality cross-nationally, I show that how we address inequality through policy rests on disparate understandings of science, politics and faith in society.Public Polic
Magnetic Flux and Nonlinear Dynamics of Classical and Quantum Superconducting Hardware
Full text linkSuperconducting quantum computing is a promising path towards achieving fault-tolerant quantum computation. Control and readout of signals at mK temperatures and subsequent amplification to room temperature electronics has allowed for impressive feats such as the first demonstration of quantum supremacy / advantage. The scalability of current systems that enable quantum computation are hindered by signal latency, excess heat loads, and overcrowding of cables in the dilution refrigerator. In this thesis, we propose cryogenic solutions using magnetic flux that improve scalability of superconducting quantum processors. In an effort to bridge the energy gap between the mK and 4 K stages of the dilution refrigerator, we simulate a flux soliton amplifier that can provide up to 10x gain to flux soliton pulses with low-loss in a resistance free traveling-wave bias scheme. To address latency and spatial cable considerations, we simulate a flux soliton cryogenic pulse generator that uses breather oscillations to create microwave pulses in the range of 15 - 24 gigahertz with over 97\% energy efficiency. In addition, we present an experimental investigation of transmission properties for resonantly phase-matched Josephson traveling-wave amplifiers in magnetic fields to develop useful intuition towards the challenge of operating cryogenic parametric amplifiers in high magnetic fields. Our work presents paths for utilizing magnetic flux as a resource to advance large-scale high-fidelity quantum computing.Engineering and Applied Sciences - Applied Physic
Nasal immunity: from understanding CD8+ T cell accumulation following vaccination to exploring the role of nociceptors in nasal infection
No full textMany respiratory pathogens first take their foothold in the nasal mucosa (NM), from which they can replicate, disseminate, and cause life-threatening complications. Having a strong line of defense at this primary site of infection can substantially help curb disease severity. The barrier function of the nasal mucosa rests on several actors from innate and adaptive immune cells to structural cells and secreted factors. In an effort to better understand nasal immunity and develop strategies to enhance immune defense at this site, we first explore how long term CD8+ T cell mediated immunity is established in the nose and in a second chapter ask the question of whether sensory neurons, particularly nociceptors, participate in nasal immune defense.
Tissue resident adaptive immune cells, particularly CD8+ Tissue resident memory (TRM) T cells, have been shown to provide rapid pathogen control upon infection. However, current clinical peripheral vaccination strategies do not elicit strong CD8+ TRM responses in barrier tissues. Here, using model antigen ovalbumin with adjuvant poly(I:C), we describe a peripheral vaccination strategy coupled to a noninvasive intranasal engagement step that allows the formation of CD8+ TRMs capable of significantly reducing viral burden following Influenza A virus (IAV) infection. We dissect the steps leading to robust effector CD8+ T cell accumulation starting with the recruitment of a small population of antigen-specific effector CD8+ T cells to the non-inflamed nasal mucosa, which we call pioneer cells. We show that following antigen delivery pioneer cells produce IFNγ leading to CXCL9 and CXCL10 production by neighboring cells, which allows further recruitment of effector CD8+ T cells. We also show that blocking α4 integrin prevented recruitment of pioneer cells to the nasal mucosa. We further investigate the molecular identity of peripheral cells generated following vaccination using single cell RNA sequencing and identify a putative pioneer cell population characterized by high Itgb4, Itgb1, as well as Runx1 expression and transcriptional activity.
In the next chapter, we examine the role of nociceptors, which have increasingly been shown to modulate host defense in other barrier tissues, in nasal host defense. We first characterize sensory neuron populations in the nasal mucosa, showing dense nociceptive innervation in the respiratory tissue, as well as around the Nasal-Associated Lymphoid Tissue (NALT) and sparser nociceptive innervation in the olfactory tissue. We address the role of nociceptors in shaping nasal immunity by using genetic, systemic or local depletion of nociceptors and follow up with nasal infection models using IAV or Streptococcus pneumoniae. Following nasal influenza infection, there was no difference in viral titers between wild-type and nociceptor-ablated mice but there were trends towards more neutrophils and macrophages in nociceptor-ablated mice. However, following S. pneumoniae infection, we obtained mixed results with several independent experiments showing significantly lower bacterial burden in nociceptor depleted animals (locally and systemically), reminiscent of studies in the lungs and meninges, while more recent experiments show no difference in titers despite successful nociceptor ablation.
Overall, this investigation into two very different aspects of nasal immunity revealed in one part that boosting existing known effectors of host defense namely CD8+ TRMs can be crucial against viral pathogens while in a second part showed that although the nasal tissue is densely innervated by somatosensory neurons, the role of nociceptive fibers in nasal host defense remains to be ascertained and perhaps needs to be evaluated while accounting for variables such as their interactions with olfactory neurons, or the microbial communities of the nasal cavity.Immunolog
Embryonic plasticity through evolution and development in the acoel Hofstenia miamia
Full text linkThe single-celled zygote ultimately develops into an adult animal with many diverse terminal cell types and tissues. This totipotency is lost during subsequent cleavages as specification begins in the embryo. In Chapter 1, I reviewed and synthesized literature on the divergent modes of early cleavage across animals. Through this study, it was revealed that many phyla, including Xenacoelomorpha, are understudied in regard to early embryonic plasticity. To investigate the potentials of embryonic cells in xenacoelomorphs, I studied the acoel worm Hofstenia miamia, which enables investigations of embryos in the lab. Though H. miamia adults are known for their tremendous regenerative capacity, no prior study has examined the embryo’s capacity to compensate for missing cells. In Chapter 2, I conducted an exhaustive examination of the post-zygotic totipotency and embryonic plasticity of the early embryo, from the 2-cell stage to the 8-cell stage. I isolated blastomeres at the 2- and 4-cell stage and found that both 2-cell blastomeres and the 4-cell stage macromeres were competent to develop into complete adult worms, exhibiting post-zygotic totipotency. Examination of the progeny of the micromeres and macromeres at the 4- and 8-cell stage showed that micromeres are specified upon birth and cannot develop cell types beyond what they endogenously produce in the wild type embryo, whereas isolated macromeres can develop cell types beyond their endogenous fates. A dissociation and reconstitution assay found that all blastomeres at the 8-cell stage can expand their developmental potential and contribute to exogenous cell types in response to the perturbation, despite earlier specification. The H. miamia embryo exhibits extraordinary plasticity despite having an invariant cleavage program with early, fate-specifying cleavages. In Chapter 3, I attempted to uncover differentially expressed transcripts between the micromeres and macromeres of the early-stage H. miamia embryos. I assembled a new version of the H. miamia transcriptome, enriched for embryonic stages, adding an additional 6,839 transcripts to the previous transcriptome assembly. After remapping newly and previously collected data to the updated transcriptome, I was able to identify cell type marker genes that were expressed earlier during embryogenesis. Altogether, my thesis research provides a summation of previous experimental embryology experiments across animals, a characterization of the plasticity in the early embryo of the acoel H. miamia and identified putative mRNA maternal determinants in the early embryo.Biology, Organismic and Evolutionar
Robust Uncertainty Quantification for Non-Negative Matrix Factorization with Applications to Mutational Signatures Analysis
Full text linkMutational signatures are distinctive patterns of mutations resulting from cancer-causing molecular processes, such as defective DNA repair mechanisms or UV radiation. The study of mutational signatures in cancer has provided insights into the molecular epidemiology of cancer and helped guide clinical decisions and therapies. Non-negative matrix factorization (NMF) methods have been foundational to the discovery of many mutational signatures and their respective loading or activity level in individual tumor genomes. In this dissertation, we explore topics related to robust uncertainty quantification for NMF methods with applications to mutational signatures analysis in cancer. Chapter 1 provides an overview of current literature and key concepts. Chapters 2 and 4 introduce and characterize specific models for mutational signatures analysis, while Chapter 3 addresses a general statistical challenge in developing and testing robust NMF models.
In Chapter 2, we introduce BayesPowerNMF, a Bayesian NMF method with uncertainty quantification for mutational signatures analysis that is robust to model misspecification. While existing NMF models have been successful in discovering many mutational signatures with verified etiologies, the NMF model is ultimately only a rough approximation to reality. Model misspecification, or using a model that deviates from reality, can lead to poor inference, like failing to detect important mutational processes or inferring spurious ones that do not actually exist. In BayesPowerNMF, we leverage power posteriors for nonparametric robustness to misspecification. By performing full Bayesian inference, we are able to report uncertainty in both the signatures and loadings inferences. In simulations of both well-specified and plausibly misspecified genomic data, we illustrate the limitations of two leading NMF methods for mutational signatures discovery and demonstrate that BayesPowerNMF discovers more true processes and fewer spurious processes than these leading NMF models. Finally, we demonstrate BayesPowerNMF's performance on whole-genome sequencing data from the ICGC/TCGA Pan-Cancer Analysis of Whole Genomes project.
In Chapter 3, we formulate a maximum density method for specifying the parameters of Dirichlet distributions that provides better control over the location and scale of the density near the boundary of the simplex than conventional approaches. Dirichlet distributions are widely used in Bayesian models and are a natural choice when modeling mutational signatures, such as for formulating informative priors based on known signatures for Bayesian NMF models and generating realistic mutational signatures for simulation studies. In the maximum density method, we tune the parameters to maximize the Dirichlet density at a specified target location point, subject to a scale constraint. The scale constraint is very flexible: for instance, for modeling mutational signatures we constrain an approximation to the cosine error, which is the preferred similarity metric in this setting. We demonstrate several desirable features of our maximum density method in a series of examples, including defining Metropolis--Hastings proposals for MCMC, constructing prior distributions for rare events, and generating simulated probability vectors for mutational signatures analysis.
In Chapter 4, we propose methods for uncertainty quantification for non-negative least squares (NNLS) regression. NNLS is a popular method for loadings-only inference in mutational signatures analysis, where we wish to estimate the activity of a fixed set of signatures in a single tumor genome. These tools can be used to estimate the activity of known mutational processes in tumor genomes to guide clinical treatment and validate biomarker mutational signatures discovered via NMF studies. While there is extensive work in loadings-only NMF inference methods to improve point estimates, these methods lack uncertainty quantification. We consider two approaches to building uncertainty quantification for NNLS regression: first, by leveraging the equivalency between NNLS and ordinary least squares linear regression for non-zero loadings to build confidence intervals, and, second, by resampling the observed mutation counts vector and repeating NNLS on each replicate to build bootstrap confidence intervals. In simulation studies, our resampled NNLS method performs well in both estimating the loadings and classifying active vs inactive signatures, with interpretable uncertainty quantification for both tasks and well-calibrated confidence intervals for loadings estimates.Biostatistic
Contexts of Care: Supporting Immigrant-Origin Students and Families in a New Destination
No full textThis dissertation examines how educators, families, and youth collectively build caring contexts of reception in a growing, diversifying school district in the United States. Drawing on over 800 hours of embodied ethnographic research conducted during the 2023–2024 school year, the study explores the daily experiences of immigrant educators, students, and families. Merging critical qualitative ethnographic research with applied experience, the author worked as an Amharic- and Spanish-speaking family liaison across the district and as a college access mentor for immigrant-origin students in a college preparation program for immigrant-origin youth.
First, the study demonstrates that immigrant educators develop creative strategies to meet both individual and collective needs within institutional constraints. These educators actively work to interrupt systemic harm while navigating educational structures that often perpetuate inequities for immigrant-origin students. Second, the research illuminates how multiple actors—including students, families, and educators—interact within layered institutional contexts. An intersectional analysis reveals that these interactions are significantly shaped by factors beyond immigration status alone, including income, housing status, and national origin. Third, the findings show that caring educational practices emerge in complex and sometimes contradictory ways. While symbolic gestures of inclusion exist, the most effective practices address material needs and structural barriers facing immigrant communities. Fourth, the data indicates that student outcomes are closely tied to the availability of multilingual staff and resources that reflect the diverse backgrounds of the student population. Schools with more comprehensive linguistic support systems demonstrated more positive integration experiences. Finally, the research documents how immigrant youth themselves develop resilience strategies and peer support networks that function as protective mechanisms against exclusion and discrimination. These student-led initiatives often operate alongside formal institutional supports but fill critical gaps in the educational context of reception.
The dissertation concludes with policy recommendations that address structural barriers facing immigrant communities, including expanding higher education access for students with liminal immigration statuses, implementing staffing practices that reflect student diversity, providing attendance stipends for low-income students, and developing welcoming school cultures that address material needs beyond symbolic gestures. These recommendations are grounded in transformative care ethics and target multiple dimensions of contexts of reception, recognizing that meaningful immigrant integration requires coordinated interventions across educational and social domains.Educatio
Essays in Behavioral Dynamics
Full text linkThis thesis consists of three chapters that study behavior when people make correct inferences from observed events but make mistakes when reasoning about hypothetical events. This type of behavior is documented in a growing experimental literature on failures of contingent thinking. The first chapter proposes a theoretical framework for analyzing this behavior in competitive markets, introducing Dynamic Cursed Expectations (DCE) and the corresponding equilibrium concept, Dynamic Cursed Expectations Equilibrium (DCEE). The second chapter studies an asset pricing model and shows that DCEE leads to overvaluation of risky assets and overtrading relative to the Rational Expectations Equilibrium benchmark. The third chapter introduces Sequential Cursed Equilibrium (SCE) for extensive games and shows that multiple experimental results on failures of contingent thinking can be explained by SCE behavior.Economic
Task-Relevant Generative Models and Safe Reinforcement Learning with Applications to Clinical Decision-Making
Full text linkClinical decision-making is inherently complex and high-stakes, frequently requiring sequential decisions under uncertainty.
Automated discovery of clinical concepts and reinforcement learning (RL) have great potential to assist clinicians, yet current approaches face several practical challenges.
First, generative models often fail to differentiate clinically relevant structures from irrelevant noise, compromising interpretability and predictive utility.
Second, traditional RL approaches optimize for fixed objectives and thus cannot adequately accommodate varying clinical goals or patient-specific preferences.
Finally, prevalent offline RL methods tend to be unsafe, overly conservative, or reliant on impractical assumptions, such as access to the behavior policy, which restricts their real-world usability.
This thesis introduces methodologies designed specifically to address these challenges.
First, Chapter 3 introduces prediction-focused Gaussian Mixture Models (pf-GMM) and Hidden Markov Models (pf-HMM) for identifying and clustering clinically relevant features from noisy, high-dimensional data.
Second, Chapter 4 proposes a Robust Decision-Focused (RDF) model-based RL framework. This framework learns transition dynamics that perform consistently well across changing clinical reward preferences, ensuring high-quality decisions in diverse clinical scenarios.
Third, Chapter 5 presents Decision-Point RL (DPRL), an offline RL methodology that identifies high-confidence ``decision points" in clinical data for targeted, minimal policy adjustments, backed by theoretical safety guarantees.
Validation of these contributions includes rigorous theoretical analyses and extensive empirical evaluations using synthetic benchmarks, medical simulators (e.g., cancer treatment), and real-world clinical datasets (e.g., a hypotension cohort from MIMIC-IV dataset, an HIV dataset, and electronic health records from a hospital system).
The methods presented demonstrate improvements in predictive accuracy, clinical interpretability, robustness against reward shifts, and safety compared to conventional approaches.
Collectively, this thesis advances the development of interpretable, robust, and safe machine learning methods, effectively bridging the gap between machine learning methods and real-world clinical practice, enhancing decision-making in healthcare.Engineering and Applied Sciences - Computer Scienc
Regulation of homotypic and heterotypic interactions in transcription factors
No full textFine-tuned regulation of transcription in cells is essential for proper differentiation, development, and signaling. Transcription factors (TFs) represent an important class of transcriptional regulators that recognize specific DNA sequences and contribute to changes in gene expression. TFs engage in various homotypic and heterotypic interactions with protein cofactors that provide opportunities for their regulation. Aberrant TF function is implicated in a wide range of diseases, but small molecule-mediated modulation of TFs remains a challenging task. Recent advances in chemically induced proximity (CIP) and targeted protein degradation (TPD), however, have highlighted novel ways to perturb TF function, stability, and localization. In this dissertation, I use structural, biochemical, and computational approaches to investigate homotypic interactions among the ZBTB family of TFs and drug-induced heterotypic interactions between the E3 ligase CRBN and ZF domain-containing TFs. Our results from the former study indicate that polymerization in ZBTB TFs enhances their transcriptional effector functions and presents opportunities for therapeutic modulation. Moreover, our latter study defines the landscape of ZF domain-containing TFs amenable to CRBN-focused TPD approaches. Taken together, this dissertation augments our understanding of how TF-mediated interactions and their regulation can be utilized for pharmacological benefit.Biological and Biomedical Science