VictorJuiz/thesis huggingface dataset:
Cruel Streets: Criminalizing Homelessness in San Francisco

Over the past thirty years, cities across the US have adopted variants of “quality-of- life” policing. Central to these efforts have been local ordinances aimed at curbing visible poverty, suppressing “anti-social behavior,” and removing the homeless from public space. My dissertation examines the causes, practices, and consequences of criminalizing homelessness in the contemporary metropolis. By relating ethnographic observations in the political and bureaucratic fields with those between interactions of state officials and homeless individuals, the dissertation reveals novel forms of the criminalization of poverty, tracing how homelessness is turned into a criminal activity by state classifications, institutional transformations, and populist politicization thanks to, rather than in-spite of, provisions of welfare and rhetoric of assistance. It also uncovers novel forms of the penalization of poverty, disclosing how policing can be directed by urban change, economic organizations, community groups, and agencies of poverty governance tangential to the criminal justice system. Expanding the conception of the criminalization of poverty, which is often centered on incarceration or arrest, the study reveals previously unforeseen consequences of move- along orders, citations, and threats that dispossess the poor of property, create barriers to services and jobs, and increase vulnerability to violence and crime.

Advisor: Wacquant, Loic
The Analysis of Cluster-Randomized Test-Negative Designs: Eliminating Dengue

According to the World Health Organization, dengue is the most critical and most rapidly spreading mosquito-borne viral disease in the world and is responsible for the infection of an estimated 380 million people across the globe annually. There is no cure for dengue, making/nprevention key to disrupting the rapid progression of this disease into the world's population./nRecent scientific advances target the mosquito's ability to carry and transmit viral diseases. The method motivating this research injects a safe, naturally occurring bacterium called Wolbachia into the mosquito population responsible for the spread of dengue and other arboviruses including Zika, chikungunya, and yellow fever. When successfully introduced into the mosquito population, Wolbachia prevents these viruses from replicating, which reduces the potential of transmission to humans./nThis dissertation addresses the statistical evaluation of the impact of studies of such mosquito-based interventions. Collecting reliable evidence for mosquito-borne interventions is often expensive and logistically prohibitive. The Cluster Randomized Test-Negative Design/ndiscussed in this thesis addresses many of the barriers to such vital research. In this trial setting and several variations, I propose and evaluate estimators of intervention impact. These results can be used to better inform policies and protect vulnerable populations.

Advisor: Jewell, Nicholas P
Designing for Health Accessibility: Case Studies of Human-Centered Design to Improve Access to Cervical Cancer Screening

Our world faces immense challenges in global health and equity. There continue to be huge disparities in access to health care across geographies, despite the massive strides that have been made to address health issues. In this dissertation, I explore the role of human-centered design to improve global health access and reduce disparities. Human-centered design, a cross-disciplinary creative problem-solving approach, has been applied and studied in both academic research and practice, but its role in improving global health access remains poorly understood./nIn this dissertation, I present research on designing for health accessibility in the context of one particular disease: cervical cancer. Every year, 300,000 women around the world die of cervical cancer and ninety percent of these deaths occur in low- and middle-income countries. Cervical cancer is an illustrative example of the global disparities in access to health care, given that cervical cancer is preventable and the majority of global cervical cancer mortality is in low- and middle-income countries./nMy research examines the work of two organizations that created unique solutions to improve access to cervical cancer screening in India and Nicaragua. I develop case studies of each organization grounded in ethnographic fieldwork, including over 250 hours of observation and 15 interviews over two years. Through these case studies, I show how early efforts to understand the barriers inhibiting cervical cancer screening access allow design practitioners to create novel and feasible ways to address these barriers. This demonstrates the importance of design practitioners considering multiple dimensions of accessibility, including availability, physical accessibility, accommodation, affordability, and acceptability, while conducting design research in order to improve the potential impact of their ideas and prototypes. Overall, this dissertation establishes the foundation of a new paradigm to “design for accessibility” that can inspire further application and research across sectors to address the many social equity and accessibility challenges facing our world.

Advisor: Agogino, Alice M.
The Interplay between Sampling and Optimization

We study the connections between optimization and sampling. In one direction, we study sampling algorithms from an optimization perspective. We will see how the Langevin MCMC algorithm can be viewed as a deterministic gradient descent in probability space, which enables us to do convergence analysis in KL divergence. We will also see how adding a momentum term improves the convergence rate of Langevin MCMC, much like acceleration in gradient descent. Finally, we will study the problem of sampling from non-logconcave distributions, which is roughly analogous to non-convex optimization./nConversely, we will also study optimization algorithms from a sampling perspective. We will approximate stochastic gradient descent by a Langevin-like stochastic differential equation, and use this to explain some of its remarkable generalization properties.

Advisor: Bartlett, Peter L
Jordan, Michael I
Impacts of Cloud Microphysics on Extreme Precipitation and Lightning

The microphysical processes involving water droplets and ice crystals in clouds are too small to be explicitly simulated by climate and weather models. Nevertheless, they play a critical role in the large-scale energy balance of the Earth and its atmosphere, as well as smaller-scale phenomena such as storms. This dissertation examines the impact of microphysics on the latter, specifically extreme precipitation and lightning. Climate change threatens to exacerbate such events, making the understanding of such extremes crucial./nWe focus primarily on the effects of microphysical processes as they are simulated in a superparameterized climate model, which is better suited to studying clouds and the associated extreme weather events than conventional models. We find statistically significant differences in extreme precipitation rates via two separate mechanisms when replacing one commonly used microphysics parameterization with another. We also find that the sign of changes in lightning flash rates with global warming depends on the microphysics representation used. Finally, we employ observations to address a longstanding question about the necessity of ice as a precursor of lightning. With the data available it is concluded that there is insufficient evidence to suggest that thunderstorm electrification can occur in the absence of ice.

Advisor: Collins, William D
Stochastic Capacity and Facility Location Planning with Ambiguous Probabilities

Motivated by the pharmaceutical industry, we consider two strategic models - a capacity planning model and a facility location planning model - where demand in each period of the problem horizon will be determined by the outcome of series of random events with two possible outcomes. These strategic planning problems are complicated by the fact that the probability of each of the alternative outcomes of these events is uncertain. Given this setting, we develop approaches for solving these models that are robust to ambiguities in outcome probabilities./nIn the first model, we consider a capacity planning model under this setting. We develop approaches to solving capacity expansion models in this setting that are robust to ambiguities in probability of success, and we consider a variety of different objective functions, including minimizing expected cost, minimizing value at risk and minimizing conditional value at risk. We formulate these models as multistage (stochastic) robust integer programs. For cases where these integer programs are challenging to solve to optimality, we propose two heuristic approaches, a straightforward rolling horizon approach and the more sophisticated event spike approach. The idea of event spike is adapted from Beltran-Royo et al. (2014), and in our version, we relax the stage-wise independence restriction which is the assumption in the original approach. These approaches are further developed to be applicable in the robust setting with different objectives. The effectiveness of these heuristics is shown through computational experiments. We also explore how alternative objectives and parameters affect solutions, and when explicitly modeling ambiguous probabilities adds value./nIn the second model, we extend the capacity planning model by considering the additional decisions of facility location and demand allocation. In addition to ambiguous probabilities of binary event outcomes, we assume that the uncertainty of demand has not completely resolved after the binary event occurs. We formulate models that are robust to ambigui- ties in both probabilities and demands by using (stochastic) robust integer programs. We propose variants of Nested Decomposition and Stochastic Dual Dynamic Programming to solve these models more efficiently. We extend these approaches by incorporating ambigu- ities in probabilities and demand. We also propose approaches that involve decomposing our large problem into a smaller number of sub-problems with longer horizons, and then applying Nested Decomposition and Stochastic Dual Dynamic Programming in this revised setting. In the computational study, we explore the effectiveness of the robust approach in this setting, and demonstrate the performance of the proposed heuristic approaches.

Advisor: Kaminsky, Philip M
Approaches to Safety in Inverse Reinforcement Learning

As the capabilities of robotic systems increase, we move closer to the vision of ubiquitous robotic assistance throughout our everyday lives. In transitioning robots and autonomous systems from traditional factory and industrial settings, it is critical that these systems are able to adapt to uncertain environments and the humans who populate them. In order to better understand and predict the behavior of these humans, Inverse Reinforcement Learning (IRL) uses demonstrations to infer the underlying motivations driving human actions. The information gained from IRL can be used to improve a robot’s understanding of the environment as well as to allow the robot to better interact with or assist humans./nIn this dissertation, we address the challenge of incorporating safety into the application of IRL. We first consider safety in the context of using IRL for assisting humans in shared control tasks. Through a user study, we show how incorporating haptic feedback into human assistance can increase humans’ sense of control while improving safety in the presence of imperfect learning. Further, we present our method for using IRL to automatically create such haptic feedback policies from task demonstrations./nWe further address safety in IRL by incorporating notions of safety directly into the learning process. Currently, most work on IRL focuses on learning explanatory rewards that humans are modeled as optimizing. However, pure reward optimization can fail to effectively capture hard requirements, such as safety constraints. We draw on the definition of safety from Hamilton-Jacobi reachability analysis to infer human perceptions of safety and to modify robot behavior to respect these learned safety constraints. We also extend this work on learning constraints by adapting the framework of Maximum Entropy IRL in order to learn hard constraints given nominal task rewards, and we show how this technique infers the most likely constraints to align expected behavior with observed demonstrations.

Advisor: Sastry, S. Shankar
Impacts of Rising American Partisanship: Stability, Accountability, and Hostility

Partisanship has always been one of, if not the most important, characteristics for understanding American political behavior, both at the elites and mass levels. Partisan identity, and the strength of that identity, functions as a key determinant of vote choice, voter turnout, political activism, social affect, and most other outcomes important to Americanists. Since the 1980s, the power of partisanship has only grown stronger – ideological polarization among elites and ideological, demographic and regional sorting among masses has led partisanship to be seen by some as a “mega-identity”, functioning as a highly salient proxy for any number of social group conflicts. In turn, national politics has disintegrated into gridlock, hostility, and mistrust./nThis dissertation explores three novel roles partisanship plays in the political behavior of American mass publics. In each case, partisanship operates as a challenge to democratic accountability in the American system. In Chapter 1, my coauthors and I present evidence that/nattitudinal stability in the mass public is dependent largely upon knowledge of where the parties stand on key issues. Not only do a significant portion of Americans lack this information, but our findings imply that, even among those who do have stable preferences, they often do not choose their parties because of their policies, but rather the reverse, highlighting the difficulty of accountability and representativeness in a hyper-partisan system. In Chapter 2, I argue that one of the few longstanding non-partisan determinants of the presidential vote – the state of the economy – is decreasing over time in its influence; as partisanship becomes increasingly important, voters are less likely to vote retrospectively, and more likely to vote in defense of their tribe. Finally, in Chapter 3, I focus on affective polarization, and the role of mass partisan attribution. From a series of novel survey experiments, I present evidence that partisans are increasingly likely to see the other side as primarily motivated by negative desires – bigotry, selfishness, hatred, etc. – and that the attributions we make about the preferences of those with whom we disagree play a key independent role in exacerbating and reinforcing partisan hostility./nTaken together, the studies in this dissertation suggest several ways in which hyper-partisanship will remain a significant challenge in American politics for the foreseeable future. Partisan attitudes, towards both policies and out-partisans, are stable and highly resistant to/ncorrection. Performance assessments of elites are increasingly colored by tribal considerations. Though I present some evidence for the possibility of attenuating partisan polarization, the overall findings in this dissertation suggest that doing so will be no easy feat.

Advisor: Lenz, Gabriel
Stoker, Laura
Number Theory in 3d Gravity and from 4d Gauge Theory

String theory is very successful in connecting deep results between physics and mathematics. However, besides the appearance of modular forms in many supersymmetric partition functions, the relations between string theory and number theory are relatively weak, compared with geometry, algebraic/geometric topology and representation theory. In this thesis, we aim to strengthen the tie between high-energy physics and various aspects of number theory. We start by investigating Euclidean AdS$_3$ quantum gravity, with asymptotic boundary as either genus-one, namely torus, or higher-genus Riemann surfaces. We will see the appearance of the asymptotic analysis on coefficients in the Klein’s j-function in Chapter 2, and two exotic finite-index subgroups of $SL(2,\mathbb{Z})$, with indices 24 and 384 respectively, in Chapter 3. In the end of that chapter, we also conjecture the existence of a series of finite-index subgroups for conformal boundary of any genus. Then we switch gear to the realm of $p$-adic numbers, and study the discrete semiclassical gravity on a Bruhat-Tits tree and its quotient, where we encounter a special representation of $PGL(2,\mathbb{Q}_p)$ and we explore its surprising relation to Chebyshev polynomials. At this point, we will have observed fascinating number-theoretic objects in studying 3d gravity and 2d conformal field theory. Finally, we present an example in which we are able to derive sophisticated number-theoretic identities, including the classic quadratic reciprocity by Gauss, from a careful use of first principles in string theory and 4d supersymmetric gauge theories.

Advisor: Ganor, Ori J.
Investigating Low Energy Wireless Networks for the Internet of Things

The Internet of Things (IoT) encompasses a broad array of technologies that connect the physical world with large-scale data processing and storage. Today, we can build ultra-low power devices that last for decades on ambient energy and we can deploy highly scalable internet services to process streams of IoT data. The dominant challenge of IoT lies in connecting these two domains. To meet this challenge, many new networks have been developed with low-energy, low-throughput, and predominantly uplink use cases in mind./nIn this work, we explore the capabilities and limitations of several recent IoT-focused networks. While these novel networks are not yet deployed at scale, modeling aspects of them enables us to predict the success or failure of particular applications and explore the ramifications of potential protocol improvements./nIn the local-area domain, Bluetooth Low Energy (BLE) has arisen as an ubiquitous method of connecting deployed devices directly to users' smartphones. One communication mechanism that BLE provides is the advertisement a simple, periodic, broadcast message intended for device discovery. With advertisements, we can create a single-hop, star-topology network in full compliance with the BLE specification that allows any number of devices to send data to any number of gateways. By modeling advertisement transmissions, we can accurately predict congestion in advertisement networks, which enables an a priori understanding of performance and an in-situ adaptation mechanism to meet reliability expectations./nIn the wide-area domain, multiple low-power, wide-area networks (LPWANs) have arisen to enable city-scale deployments. Their ability to transmit at ranges over a kilometer while drawing only a few hundred milliwatts could enable exciting new applications. To understand LPWAN capabilities, we propose a new metric, bit flux, which describes communication in terms of throughput over a coverage area. By using bit flux to model the performance of networks and the needs of applications, we demonstrate problems with the existing designs of several LPWANs that make them unsuitable for many real-world deployments./nIn both domains, we explore the strengths and weaknesses of communication technologies and their potential to serve real-world applications. The potential for these networks to promote a new generation of easy-to-deploy sensors is high. However, through the application of communication models, we demonstrate both potential use cases and also very real concerns that may limit them. By uncovering concerns that future deployments will face, we hope to guide improvements to these protocols that will improve their use and support the growth of the Internet of Things.

Advisor: Dutta, Prabal
Mitotiliztli ←→ Teochitontequiza: Danza as a Way of Knowing

This dissertation explores questions of embodied knowledge, power and consciousness through the study and practice of Anahuacan ceremonial dance. Danza – also known as Danza Azteca, Danza Mexica, Danza Tolteca-Chichimeca, or Mitotiliztli, Macehualiztli and Chitontequiza – is a rich tradition of Indigenous and syncretic dance rooted in transnational movements across Mexico, Central America and the United States. Reflections are offered on the nature of danza as a human movement system and as a social, cultural, political and spiritual movement. Centered in critical and comparative Ethnic Studies, theories of decoloniality, and “theory in the flesh” (Moraga and Anzaldúa 1983) this work thinks from and about danza as an embodied modality of Anahuacan science, art, spirituality and activism. Research has been conducted through transdisciplinary, scholar-practitioner methodologies: combining analysis of archival sources, secondary research and Indigenous and Performance Studies approaches to ethnography, creative writing and “research as ceremony” (Wilson 2008). Body chapters consider questions of decolonial aesthetics, poetics, ethics and philosophy of science through danza theory and praxis. Evidence demonstrates the necessity of integrating embodied, communal and ceremonial ways of knowing into existing critiques of epistemology, cosmology and cultural memory. Re-membering to relate, danza serves as a medium of intergenerational and community-based healing for Indigenous Latinx, Xicanx communities in Abya Yala.

Advisor: Pérez, Laura E