VictorJuiz/thesis huggingface dataset:
Three essays on intergenerational mobility in the U.S.

The papers in this dissertation investigate the patterns and consequence of intergenerational mobility in the United States. First, I examine changes in the share of Black and white children earning more than their parents. I find that declines in absolute income mobility for Black children, from 92% to 41% between 1940 and 1987 birth cohorts, are steeper than for whites. In the preferred specification, the racial gap increases from 2 to 8 pp. For Black men, a principal driver of low mobility is their high rate of institutionalization. For white women, family formation plays a key role in achieving upward mobility. Black women have much higher mobility in individual income, but not in family income. Mobility declines are largest in the South, where Black parental income was particularly low in the early cohorts. Second, I investigate the consequences of class mobility for people's beliefs. Do children growing up in a particular class retain its beliefs? And is the process of moving between classes itself associated with shifts in beliefs? I find evidence that people's values show relatively strong, and their material interests comparatively weak associations with parental class. Moreover, people who move from one class to another are more likely to hold the beliefs of the higher-status class across a number of domains, such that the upwardly mobile are more tolerant, the downwardly mobile more hostile to redistribution. I also find evidence for resentment regarding political ideology, where mobility is associated with lower chances of holding the beliefs of the higher-status class. Third, I analyze whether changes in educational stratification have resulted in greater parental influence on people's level of social distrust. Compared to own education, has parental education grown in significance? I find evidence that men, for whom educational expansion has stalled, saw increases in the relative weight of parental education on social distrust. At the same time, women saw continued increases in educational attainment and decreases in the weight of parental background, relative to their own educational attainment.

Keywords: Sociology.
Advisor: Grusky David B. 
Jackson Michelle Victoria 
Reardon Sean F.
Three essays on money in politics in the United States

I study democratic representation and accountability in the United States with a focus on campaign finance. My dissertation uses large panel data sets of campaign donors to examine what motivates campaign donors to give money, and the implications of their campaign contributions for three different aspects of American politics: 1) interest groups (how do the partisan preferences of individual group members constrain interest groups' ability to buy political favors with PAC contributions?), 2) political extremism (how did localized exposure to the subprime mortgage crisis affect Republican donors' support for the Tea Party movement?), and 3) electoral accountability (how much can voters learn about candidates' issue-specific positions based on who donate to these candidates?).

Keywords: Business.
Advisor: Bonica Adam 
Shotts Kenneth W. 
Broockman David E.
Thwarting Electoral Revolution: The Communal State and Authoritarian Consolidation in Venezuela

In 2017, Venezuela appeared to be on the brink of democratic transition. After former president Hugo Chavez’s death and amid a crippling humanitarian crisis, Venezuela’s quasi-authoritarian regime lost in the country’s 2015 parliamentary elections. According to studies on rapid transitions out of autocracy, the electoral defeat, combined with deteriorating socioeconomic conditions and prolonged social unrest in 2016 and 2017, should have resulted in democratic transition. Today, however, Venezuela remains under the regime’s control and continues to sink deeper into authoritarianism. This thesis argues that the Venezuelan regime’s political survival was the result of two strategies for authoritarian consolidation that began developing once Chavez came to power in 1999. The first was a military strategy focused on maintaining the Armed Forces’ loyalty and protecting against popular mobilization. The second was a bottom-up strategy centered on a series of communal governance structures that later evolved into mechanisms for social control. The gradual development of these consolidation strategies over the years is what ultimately allowed the Venezuelan regime to thwart democratic transition in 2017.

Keywords:  Fisher Family Honors Program in Democracy
Competitive Authoritarianism
Authoritarian Consolidation
Venezuela
Hugo Chavez
Nicolas Maduro
Electoral Revolution
Communal State
Estado Comunal
Communal Councils
Communal Governance
Center on Democracy Development and the Rule of Law
CDDRL
Thesis
Advisor: Magaloni Beatriz
Trinkunas Harold
Tissue mechanics control T cell activation and metabolism through YAP

T cells encounter forces at the tissue scale as they traverse through a variety of microenvironmental mechanical contexts, including sites of infection, inflammation, and cancer. The transcriptional coactivator Yes-associated protein (YAP) is a well-known sensor of tissue mechanics that is dominantly regulated by the stiffness of extracellular matrix. Understanding microenvironmental mechanosensing has offered revolutionary leaps in the fields of cellular development, organ size regulation, stem cell differentiation, cancer metastasis, and programmed cell death. The role of YAP has been well established in cancer metastasis and recent works have implicated YAP in immunity, but there have been no links yet between YAP and mechanics in immune cells, and environmental mechanosensing has never been demonstrated for T cells. To shed light on T cell pathophysiology in disease-associated tissues, this dissertation sought to determine how mechanical signals translate to regulation of the T cell effector and metabolic program through YAP. We describe a conditional knockout mouse model to analyze the effects of YAP on T cell activation and metabolism. We discover YAP to be an unexpected suppressor of T cell activation and metabolic reprogramming and demonstrate that this inhibition extends to in vivo T cell responses to infection and autoimmunity. We report a mechanism of YAP-mediated suppression of T cell proliferation and metabolism. Finally, we address a long-standing question of how T cells respond to tissue mechanics using biomimetic matrices and identify YAP as a key player in T cell mechanotransduction. In summary, this dissertation reveals a new paradigm whereby tissue mechanics fine-tunes adaptive immune responses in health and disease. Our work sets a foundation for the exciting therapeutic potential of either inhibiting YAP to promote T cell response while simultaneously impairing tumor growth, or conversely inducing YAP activity to relieve T cell-dependent autoimmune disease and blood cancers. Altogether, we highlight the importance of future ventures toward understanding how T cells respond to their mechanical microenvironment.

Keywords: Microbiology and Immunology.
Advisor: Bollyky Paul
Butte M. (Manish)
Davis Mark M. 
Idoyaga Juliana
Sage Julien
Tissue-mimicking electronics : development of miniaturized, soft, stretchable and developmentally morphing bioelectronics

Implantable devices, such as the vagus nerve stimulator (VNS) and the implantable cardioverter defibrillator (ICD), have been widely used to treat cardiac and neurological diseases. However, they are made of rigid electronic materials. The significant mechanical mismatch between stiff electronics and soft biological tissues causes negative immunoresponses, tissue damage, and device dislocation. To address those problems, we developed soft and electronically active materials with tissue-like Young's modulus and microfabrication methods for stretchable microelectronics. We demonstrated intimate electrical coupling with neural tissues and localized ultralow-voltage electrical stimulation on the peripheral nerves in vivo. To accommodate organ dynamics, such as the beating of the heart, we fabricated a fully elastic microelectrode array for stable electrophysiological recording without electrode displacement during cardiac contraction-relaxation cycles. Epicardial electrophysiological mapping on a chronic atrial fibrillation porcine model showed excellent signal-to-noise ratio and significantly higher spatial resolution compared with the state-of-the-art cardiac mapping system. Finally, we developed an implantable device that can accommodate rapid tissue growth without negatively influencing normal developmental functions. Current implantable bioelectronic devices mechanically constrain the growing tissue due to their fixed shapes and sizes. For infants, children, and adolescents, once implanted devices are 'outgrown', additional surgeries are usually needed for device removal, followed by replacement. These tedious processes inevitably lead to repeated intervention and elevated complication rates. We addressed this limitation by developing Morphing Electronics (termed MorphE), which are designed and fabricated to suitably adapt to in vivo tissue growth with minimal mechanical constraint. Animal studies showed that the soft and self-adapting MorphE caused no damage to a rat nerve after it had grown to 2.4 times its initial diameter. The stable neural interface allowed chronic electrical stimulation and monitoring without disruption of functional behavior in developing animals. As such, MorphE creates a new avenue for adaptive pediatric electronics medicine.

Keywords: Bioengineering.
Advisor: Bao Zhenan 
Cui Bianxiao 
George Paul M. (Paul Matthew) 
Yock Paul G.
A toolbox of peptide-drug conjugates for targeted chemotherapy

Traditional cancer chemotherapeutics often have narrow ranges between their effective and toxic doses. To improve efficacy and minimize side effects, protein-drug conjugates (PDCs) can be used to selectively deliver chemotherapeutics to cancerous tissue. Existing targeting agents rely nearly exclusively on high molecular weight (MW) proteins, namely antibodies. Low MW targeting agents offer a dramatically altered pharmacokinetic profile and have potential to overcome historical limitations of antibody-drug conjugates including limited efficacy against solid tumors and difficulty targeting brain tumors due to an inability to cross the blood-brain-barrier. Despite the theoretical advantages of low MW targeting proteins, there exists some skepticism that this class of PDCs would be highly effective due to rapid systemic clearance or toxicity. The work presented here includes the development and testing of a toolbox of PDCs comprising an identical tumor targeting moiety and toxic warhead but having diverse molecular properties including varying MW and valency. This set of PDCs enables investigation of the impact of varying features on circulatory half-life, tumor penetration, and therapeutic efficacy. Chapter 1 provides background on tumor-targeting and motivation for this work. Chapter 2 discusses the engineering of a broadly specific tumor targeting agent, a detailed investigation of the structural basis of its targeting mechanism, and an exploration of its use as a tumor imaging agent. Chapter 3 describes the development of an ultra-low MW PDC from this tumor targeting agent and an investigation of its mechanism of drug delivery. Chapter 4 focuses on the development and in vivo testing of an intermediate MW PDC with pharmacokinetic properties between an antibody-drug conjugate and an ultra-low MW PDC. Chapter 5 presents the entire toolbox of PDCs and demonstrates the in vivo potency of monovalent, low MW targeting proteins as alternative scaffolds for drug delivery. Finally, Chapter 6 summarizes the existing clinical landscape of PDCs and highlights the role of this work in promoting further research into targeted chemotherapy.

Keywords: Bioengineering.
Advisor: Cochran Jennifer R. 
Bertozzi Carolyn R.
Huang Possu
Tools to understand how students learn

As classroom sizes grow, instructor workload also increases. Despite innovations in technology to scale education, little has been done to improve upon the most critical component of student learning: unsupervised work on assignments. In computer science education, learning process---the way in which students design, debug, and explore programming assignments---is instrumental to performance and mastery. Yet few studies have defined assignment-centric metrics to measure learning process, much less design systems that transform the way we think about unsupervised work today. My work explores how to improve student assignment work so that both the teacher and learner benefit. While many tools analyze only a student's final submission, I focus on a paradigm to collect in-depth snapshots of in-progress student work. I first discuss the complexity of characterizing progress on a programming assignment with an abstraction called milestones, and I show how we can use machine learning methods to visualize how students work through an open-ended graphics-based assignment. Next, I present a tool, Pensieve, which organizes snapshots of student work so that teachers see a student's problem-solving approach. This tool facilitates sit-down student-teacher conversations, where teachers can give more in-depth feedback to each individual student. Thirdly, I present TMOSS, a tool to detect excessive collaboration---that is, when a student heavily relies on peer or online resources---at any point during unsupervised work on an assignment. For both Pensieve and TMOSS, I discuss pedagogical and cultural impacts on students as well as the classroom at large. This work points to a new paradigm for supporting learners and a path forward for designing new types of assignments that enhance the student experience. I close by discussing a graduating networking classroom project to reproduce existing research, which prepares students for research and industry careers in networking.

Keywords: Piech, Chris (Christopher) degree supervisor.
Electrical Engineering.
Advisor: McKeown Nick 
Prabhakar Balaji
Sahami Mehran
Topics in robust mean estimation and applications to importance sampling

The sample mean is often used to aggregate different unbiased estimates of a real parameter, producing a final estimate that is unbiased but possibly with high variance. This thesis proposes two new robust estimators that can adaptively trade off some bias for variance, resulting in more competitive non-parametric mean estimators. The first estimator winsorizes the sample mean in a data-dependent way. The threshold level at which to winsorize is determined by a concrete version of the Balancing Principle, also known as Lepski's Method. The procedure chooses a threshold level among a pre-defined set by roughly balancing the bias and variance of the estimator when winsorized at different levels. While the assumptions of the Balancing Principle are probabilistic, in the importance sampling setting it is possible to bound such probabilities to obtain a finite-sample theorem yielding a principled way to perform winsorization with optimality guarantees. The second estimator introduces an aggregation rule that roughly interpolates between the sample mean and median, resulting in estimates with much smaller variance at the expense of bias. While the procedure is non-parametric, its squared bias is asymptotically negligible relative to the variance, similar to maximum likelihood estimators. The estimator is consistent, and concentration bounds for the its bias and L1 error are derived, as well as a fast, non-randomized approximating algorithm that enjoys similar theoretical properties. The empirical performances of the estimators are examined in real and simulated data, with a focus on importance sampling. They generally match the performance of the sample mean in low-variance settings, while exhibiting far better results in high-variance scenarios.

Keywords: Statistics.
Advisor: Diaconis Persi 
Chatterjee Sourav 
Wong Wing Hung
Topics in selective inference and its application to instrumental variables

This thesis addresses problems in statistical inference after model selection procedures. The framework we adopt throughout the discussion is selective inference, which provides with valid inference conditioning on the model selection event. Chapter 1 gives a background introduction to the problem of interest and the guiding principle of selective inference, especially inference with randomization. Chapter 2 introduces the framework of inferactive data analysis, so-named to emphasize on inference after interactive data analysis. Chapter 3 discusses the problem of valid inference for the treatment effect after selecting invalid instrumental variables via a data-driven Lasso type selection procedure called SisVive. Instrumental variables models are widely used in Economics as well as Mendelian randomization in Genetics, and our method would be helpful for the practical use of instrument variables when it is not certain whether they are all valid or not. Our approach is conditional inference via selective inference with randomization, and fits into the general data analysis framework discussed in Chapter 2. We demonstrate the inference method through a development economics dataset and also a Mendelian randomization dataset with only summary statistics. Chapter 4 discusses the problem of valid inference for the treatment effect after pre-testing the strengths of instrumental variables via an F test. This is a widely used screening step in practical instrument variables data analysis, and people would only proceed to conduct inference and report results if the dataset passed the pre-test. We will show the common practice of ignoring the selection effect could result in significant bias in certain scenarios, while our inference method will correct for it. Again we adopt the conditional inference approach and demonstrate the method through two educational economics datasets.

Keywords: Statistics.
Advisor: Taylor Jonathan E. 
Lai T. L. 
Tibshirani Robert
Topics in supersymmetry and supergravity : applications to cosmology and scattering amplitudes

This thesis consists of two parts, which explore related but distinct directions. The first part concerns theories of supersymmetry and supergravity with a spontaneously broken supersymmetry -- specifically, a nonlinearly realized supersymmetry and nilpotent multiplets. From a phenomenological perspective, theories of spontaneously broken supersymmetry are relevant due to the lack of experimental evidence for unbroken supersymmetry at low energies. In recent years, theories with nilpotent multiplets have proven to be useful for constructing cosmological models in a supersymmetric context. To this end, we develop a method of obtaining actions (and multiplets) with a nonlinearly realized supersymmetry from the corresponding linear models by taking the masses of certain particles to infinity -- physically, these infinite-mass particles can be thought of as being integrated out. This generalizes previous work done by Komargodski and Seiberg to (a) all energy scales and (b) to theories with local supersymmetry (supergravity) as well. The supergravities with non-linearly realized supersymmetry have de Sitter vacua with a positive cosmological constant. In the second part, we study scattering amplitudes in two different physical systems -- the N=1 Volkov-Akulov theory, and U(1) anomalous amplitudes in extended supergravities. Anomalies have always been important in theoretical physics, as they give insight into the extent to which classical symmetries control quantum physics. Motivated by work done by Marcus in the 1980s, we study anomalies in extended supergravities from the perspective of anomalous scattering amplitudes. We develop the formalism of linearized superconformal chiral superfields for these theories, and construct candidate one-loop anomalous superinvariants. We find that in N = 5, 6, and 8 supergravities one-loop anomalous amplitudes are absent, which implies a possibility of an improved UV behavior.

Keywords: Physics.
Advisor: Kallosh Renata
Kachru Shamit
Linde A. D.
Silverstein Eva
Topographically-mediated variability in fog and belowground moisture and its role in redwood distributions and responsiveness to drought

Forests cover 30% of the land surface and absorb almost a quarter of annual anthropogenic carbon dioxide emissions, but are vulnerable to increasing drought stress as a result of climate change. Regions of high water availability embedded within topo-climatically diverse forest landscapes could protect forests during drought, but have been understudied. In this dissertation, I sought to develop a nuanced understanding of the role of fine-scale spatial variation in water availability on the habitat distribution and drought responsiveness of coastal redwoods (Sequoia sempervirens), the tallest tree species on earth. In the first chapter, I show that redwood distributions can be mapped at 10 m resolution with demonstrable accuracy over 34,800 hectares and three redwood forests from airborne imaging spectroscopy, field-collected training data, and an optimized machine learning image classification framework. In the second chapter, I found that mapped fog frequency from the MODIS satellite and airborne LiDAR-derived estimates of belowground moisture shaped microscale redwood distributions, and that interpolated height above a stream (IHAS) was consistently negatively related to redwood abundance. These results suggested that fog and topographically-defined gradients of water availability within redwood forests contribute to significant variation in redwood habitat suitability within regions where redwood range contraction has been predicted with coarse resolution data. The third chapter tests the hypothesis that access to moisture from fog and streams have a buffering effect on redwood response to drought. Cores were collected from redwoods across a gradient in fog frequency and at two IHAS positions at Mt. Tamalpais. Annual basal area growth rates calculated from ring width measurements revealed an amplified negative response of redwood annual growth to drought indices at ridge sites with low fog frequency, suggesting that redwoods with access to water from fog and streams were less responsive to drought, as we expected. However, streamside trees under the highest fog frequency regime were the most responsive to precipitation variability, challenging the simple interpretation that redwoods with access to water from fog and streams were always less responsive to precipitation deficits. Analyses of airborne LiDAR-derived tree height data revealed that these trees were significantly taller and therefore exposed to higher leaf water stress. We concluded that biological compensation for higher water availability during mean climate, through increased height growth, might reduce the buffering effect of fog and streams on redwood growth responsiveness to drought.

Keywords: Earth System Science.
Advisor: Field Christopher B. 
Asner Gregory P. 
Fendorf Scott 
Konings Alexandra 
Mach Katharine J.