VictorJuiz/thesis huggingface dataset:
Living up to the dream? African American school administrator counternarratives about race, leadership and retention

A growing body of research shows that principal turnover is higher among people of racialized backgrounds (Meyer et al, 2020), associated with lower rates of student proficiency (Grissom & Bartanen, 2021) and most often is due to environmental conditions (Levin, Bradley & Scott, 2019). In partnership with the San Francisco Unified School District, I sought to explore the tenure experiences of their Black school leaders. I conducted 20 semi-structured interviews with African American principals and assistant principals. I combine social cognitive framework about administrator career decisions (Fairley-Ripple et al, 2012) with critical organizational theory (Ray, 2019) to demonstrate how SFUSD operates as a racialized organization despite espoused district commitments to African American students, Black educators and antiracist pedagogy. By looking at the district's new leader induction program, I show that there are unintended consequences to their efforts. For example, through helping new principals and assistant principals to name, interrogate and challenge unjust systems, Black leaders are more aware of the ways in which white supremacy culture is negatively impacting their work.

Keywords: Education.
Advisor: Brown Bryan
Kozleski Elizabeth
Labaree David
Local thermodynamic probe of correlated electrons in moiré systems

The emergence of "moiré materials", heterostructures formed by twisting and stacking atomically thin layers, has inaugurated a new era for investigating correlated electronic systems. They offer unprecedented tunability over band structure, electronic interactions and topology. In this dissertation, I will present my works on the use of a specialized scanning probe microscope, a scanning single-electron transistor (SET), to locally investigate correlated states in a diverse array of moiré systems. The SET probes the local compressibility and chemical potential of an electronic system with unparalleled resolution, enabling access to the delicate interaction-driven incompressible (insulating) phases and their phase transitions. In this dissertation, I will first summarize instrumentation efforts to set up two different scanning SET systems at Stanford University, which were used to carry out scientific projects described in this dissertation. I will then describe measurements on a selection of representative examples from two distinct classes of moiré materials. In magic-angle twisted bilayer graphene, I explore both the interacting Chern and Hofstadter physics in the high magnetic field regime, as well as the enigmatic correlated insulators found at low magnetic fields. In a semiconductor moiré heterobilayer WSe2/MoSe2, I describe measurements that reveal the coexistence of flat and dispersive moiré bands that leads to intricate competitions between Hofstadter and unconventional charge-ordered states. In a twisted double bilayer WSe2 system, I discuss how the introduction of Γ−valley moiré bands leads to new correlated ground states and charge excitations. Through these measurements, I will demonstrate how the local thermodynamic sensing capabilities of scanning SET synergize with the high tunability of moiré materials in uncovering novel correlated electronic phases

Keywords: Humanities and Sciences.
Applied Physics.
Advisor: Feldman Ben (Benjamin Ezekiel)
Goldhaber-Gordon David
Heinz Tony F.
Locating legacies of environmental racism in park-going : a study of urban nature, race, and spatial interactions

Environmental justice research has highlighted how contemporary patterns of provision and accessibility of urban parks reflect a long legacy of environmental racism and injustice. However, the limited availability of large-scale data on trips to parks has precluded examining whether the legacy of environmental racism in park allocation manifests in park use behavior. This three-paper dissertation leverages SafeGraph cell-phone mobility data of trips to parks in America's ten most populous cities as of 2019 to understand whether patterns of park-going replicate the racial inequality in the provision of nature in urban areas. Paper 1 relies upon mixed effects regression models of park trips to assess explanations provided by leisure scholars for racial differences in park use. Paper 2 uses spatial regression analysis to test Jane Jacobs' theory that social diversity in the residential neighborhood of a park is critical for attracting high rates of foot-traffic to the park. Paper 3 leverages an Infomap network clustering algorithm to quantify the level of socio-spatial homophily in park-going and the tendency for whiter and affluent neighborhoods to use different types of parks than residents in neighborhoods of color. Collectively, the papers suggest that park use is less geographically constrained and less racially segregated for park-goers in neighborhoods of color than prior research has presumed.

Keywords: Humanities and Sciences.
Sociology.
Advisor: Grusky David B.
Asad Asad L.
Correll Shelley Joyce
Long horizon planning in the real world

Enabling robots to perform everyday tasks such as cooking a meal or doing laundry requires giving them the ability to plan future actions over long horizons. Task and Motion Planning (TAMP) seeks to achieve this by combining high-level symbolic reasoning with low-level geometric reasoning to produce long horizon plans that are grounded with actionable motor commands. However, prior TAMP works have largely been limited to simulation due to their computational complexity and brittleness to perception and control noise. This thesis presents the first full-stack TAMP system designed to handle the unpredictability of the real world in real time. We demonstrate the ability to perform TAMP on a real robot with real-time reactive behavior and planning times no longer than several seconds. This system includes a symbolic perception pipeline that enables robust closed-loop task planning, efficient TAMP algorithms that are faster than previous state-of-the-art by an order of magnitude, and a method for integrating TAMP planning with reactive controllers that can adapt to unexpected environmental changes in real time. This thesis also addresses the challenge of scaling TAMP to large domains by proposing an alternative framework that can perform TAMP with a library of learned skills

Keywords: Engineering.
Advisor: Bohg Jeannette
Khatib Oussama
Sadigh Dorsa
Long-horizon prediction for human-robot collaboration

Robots are mainly seen as tools that offer passive assistance to humans or operate independently in close proximity to them. However, we are still far from witnessing robots that can collaborate with humans on tasks wherein both agents' actions and dynamics are interdependent and achieve synergistic results. As an example, and one that serves as the central motivation of my work, robots carrying a table towards a goal location and avoiding obstacles must be able to predict their partners' actions and mutually adapt to changing strategies during collaboration. Key challenges have been accounting for diverse, multimodal human behaviors over both short and long horizons, and producing joint actions that are temporally consistent. To address these issues, I leverage generative modeling for accurate sequence prediction of human-like motion and behaviors. I focus on two approaches to model interaction: first, by predicting team subgoals and executing cooperative actions; and second, by predicting partner actions and executing collaborative actions that would account for the partner's efforts. In the first part of this thesis, I develop a Variational Recurrent Neural Network-based planner trained with human-human demonstrations that learns to sample sequences of future waypoints autoregressively to achieve successful synergy with a human-in-the-loop on the table-carrying task. Next, I show that mixture density models can capture multimodality in human-human demonstrations, but fail to predict action sequences accurately for the cooperative carrying task. Finally, I show that a co-policy developed with a Transformer-based diffusion model conditioning on past human actions can not only plan action sequences with real humans-in-the-loop to achieve high success rates, but also display compelling collaborative behaviors in novel, out-of-training-distribution settings

Keywords: Engineering.
Mechanical Engineering.
Advisor: Kennedy Monroe
Sadigh Dorsa
Schwager Mac
Longitudinal intestinal microbiome collection and cultivation reveals site-specific compositions and functions

The spatiotemporal structure of the human microbiome, proteome, and metabolome reflects and determines regional intestinal physiology and may have implications for disease. Yet, we know little about the distribution of microbes, their environment, and their biochemical activity in the gut because of reliance on stool samples and limited access to only some regions of the gut using endoscopy in fasting or sedated individuals. To address these deficiencies, my thesis work aimed to profile the composition and function of the microbiome along the spatial gradient of the intestinal tract using a variety of different techniques and model systems. In CHAPTER 2, I work with the Tabula Sapiens consortium to collect immune cells and microbial contents from matched regions of the human intestinal tract of organ donors. We found spatially distinct microbial communities and immune transcriptomic profiles. In CHAPTER 3, I work with the Tabula Microcebus consortium to elucidate the composition of the microbiota along the mouse lemur intestinal tract in two lemurs: one with recent antibiotic history and one without recent antibiotics. Similar to human organ donors, we found incredibly diverse and spatially distinct microbial communities in a mouse lemur that did not recently take antibiotics. However, we found profoundly lower diversity all throughout the intestinal tract of the mouse lemur that had recently taken antibiotics, highlighting how perturbances to the microbiota can influence both small intestine and large intestine communities. In CHAPTER 4, I attempt to address many of the limitations of using organ donors by studying the human intestinal microbiota using an ingestible sampling device that allows for noninvasive collection of human intestinal contents during physiological conditions. We were able to profile spatially distinct microbiotas, bile acids, and secreted human proteomes along the intestinal tract of 15 healthy human subjects. Further, we were able to integrate this multi-omics dataset to provide important biological insight into microbial interactions with bile acids and the human proteome that have important implications for human health. In CHAPTER 5, I use cultivation techniques to acquire small intestine and stool-derived in vitro communities, along with a comprehensive library of isolates (> 400), from the human small intestine and stool of a single subject. Mouse colonization of these communities and isolates reveals how taxa maintain a preferential spatial niche, as well as highlight the importance isolate ancestry in undergoing spatially distinct dynamics in response to a diet switch. Ultimately, I was able to annotate compositional and functional differences in the microbiome along the intestinal tract using a variety of different methods, highlighting the need for investigation beyond the stool microbiota to be able to further harness and tune the effects of the microbiota on human health

Keywords: Medicine.
Genetics.
Advisor: Huang Kerwyn Casey
Bhatt Ami (Ami Siddharth)
Fordyce Polly
Sherlock Gavin
Losing touch : essays

"Losing Touch: Essays" is a book about connection, disconnection, and what happens in between. "Losing Touch" makes reference to multiple kinds of contact and distance, from the embodied, literal experience of losing another's physical touch through separation or social distancing, to "losing one's touch" and being more prone to failure, to "losing touch with someone" across physical or virtual time and space, and even becoming "out of touch" with a cultural zeitgeist or "losing touch with reality." The chapters that follow are correspondingly organized into two distinct but related groups, "Missed Connections," centering on the desires wrought by and despite gender and sexuality in the years since the #MeToo movement, and "Connection Failed," devoted to virtual and digital intimacies in the wake of the COVID-19 pandemic.

Keywords: Humanities and Sciences.
Comparative Literature.
Advisor: Saldivar Jose
Kantor Roanne
Thiranagama Sharika
Lost in translation : investigating new roles for methyl lysine signaling in the regulation of protein synthesis and cancer pathogenesis

Protein lysine methylation is a common type of post-translational modification that plays a crucial role in various cellular processes. The enzymes responsible for catalyzing these methylation reactions are known as lysine methyltransferases (KMTs). While KMTs have been extensively studied in the context of chromatin regulation and histone methylation, it is increasingly recognized that non-histone protein methylation also plays a significant role in fundamental biological and pathological processes. However, numerous human KMTs remain uncharacterized, and the biological significance of many lysine methylation events is still unknown. In this work, I characterize SMYD5 (SET and MYND domain containing 5), an orphan human KMT. I demonstrate that SMYD5 methylation regulates protein synthesis, particularly active ribosomal function, and specifically enhances mRNA translation for oncogenic programming, facilitating the evolution of malignant gastric cancer. I also propose that inhibiting SMYD5 could serve as a potential cornerstone for combination therapy to treat GAC. The following chapter presents rigorously validated resources that enhance our understanding of the physiological functions of eEF1A methylation, a key modulator of the protein synthesis machinery. These tools will facilitate subsequent research aimed at exploring fundamental biological process. In summary, this study broadens our understanding of methyl-lysine signaling by examining an unidentified enzyme's physiological roles in cancer pathology. Moreover, this work provides robust methodological resources that expedite advancements in the study of protein methylation-based cellular signaling, particularly in the context of human health and disease.

Keywords: Humanities and Sciences.
Biology.
Advisor: Gozani Or
Dixon Scott
Morrison Ashby
van Rechem Capucine
Low dimensional motor neural structure of freely moving ambulation and reaching

Motor systems neuroscience is an interdisciplinary field that studies how the brain controls voluntary movement. Mechanistic and correlational understandings gained from decades of animal studies have been translated into brain machine interfaces, devices that can potentially help individuals with motor injury. However, most of these studies have been done in classically constrained settings. There is growing theoretical evidence, but limited experimental proof, that suggests that these models of neural control may not generalize to naturalistic settings. Thus, there is a knowledge gap in both mechanistic understanding and clinically translatable findings of how the brain controls unconstrained behavior. My PhD work focuses on bridging this knowledge gap by exploring the neural control of free locomotion and unconstrained reaching, broken up into 3 main projects: 1. Building a platform that collects wireless neural data and full-body markerless kinematics to study naturalistic behavior. 2. Performing computational analyses on a reach-walk task to quantify the neural differences of similar behaviors performed under different levels of cognitive difficulty. 3. Decoding locomotion to quantify the neural relationships between the gait cycle and total body position. My experimental work and computational analysis advance the field of motor systems neuroscience by representing a novel experimental platform to uncover the structure of ambulatory control. Many results from my freely moving experiments can only be captured with this platform, and show that previous constrained experiments are limited in their ability to capture the full activity of the motor cortex. My research is foundational for subsequent studies that may inform human clinical studies leveraging new insight about free movement.

Keywords: Engineering.
Electrical Engineering.
Advisor: Nuyujukian Paul
Delp Scott
Osgood Brad
The LZ dark matter WIMP search and treatment of fundamental signals

While we can observe dark matter gravitationally, we have yet to determine its exact physical properties through non-gravitational means. One of the most historically favored models for dark matter is the weakly-interacting massive particle (WIMP). The LUX-ZEPLIN (LZ) dark matter experiment seeks to detect WIMPs through the use of a dual-phase time projection chamber (TPC) filled with a 7 tonne active xenon target. LZ published world-leading limits on WIMP-nucleon cross-sections for WIMP masses above 9 GeV/c^2 following its first WIMP search. In addition to summarizing the full WIMP search, this work discusses the development of key efforts including the treatment of fixed and time-varying conditions within both reconstruction and analysis frameworks, studies of high-activity periods likely resulting from fluorescence of the TPC walls, and the development of novel methods to model backgrounds and test analysis cut efficiencies. LZ's future WIMP searches will incorporate a form of bias mitigation known as "salting" in which unknown quantities of fake signal events are injected into WIMP-search data. This work discusses the methods used to reliably model, create, and inject fake yet unidentifiable signal events into LZ's data stream. This work concludes with results from the application of autoencoders on PMT waveforms to remove unknown backgrounds.

Keywords: Humanities and Sciences.
Physics.
Advisor: Akerib Daniel
MonzaniMaria Elena
Allen Steven
Tompkins Lauren
Machine learning and statistical methods for physical systems

Physical systems often exhibit highly complex, non-linear behaviors, making their characterization without a profound understanding of underlying dynamics a challenging endeavor. In this presentation, I will explore several methodologies for integrating valuable prior knowledge into machine learning-based and statistical methods, aimed at predicting physical system behaviors and bolstering the control of engineering systems. The talk will be divided into three main parts. In the first part, I will present several deep-learning-based interpolation methods for estimating nearshore bathymetry with sparse measurements. By applying deep learning-based models from a Bayesian perspective, we provide uncertainty quantification for the underlying bathymetry profile and compare our predictions with those from Gaussian Process Regression (GPR). Results show that our approach provides more accurate posterior estimates, particularly when encountering patterns with sharp gradients, such as those found around sandbars and submerged objects. In the second part, I will introduce a novel framework, namely physics-based Parameterized Neural Ordinary Differential Equations, for predicting laser ignition inside a rocket combustor. A 0D reacting flow model is combined with neural ordinary differential equations to deliver accurate solutions, even with limited training samples, that adhere to physical constraints. We validate our physics-based PNODE using solution snapshots of high-fidelity Computational Fluid Dynamics (CFD) simulations, demonstrating its capability to accurately learn complex combustion dynamics with high-dimensional parameters of laser ignition. In the third part, we consider the use of a periodic Martingale Model for Forecast Evolution (MMFE) to integrate weather forecasts into decision-making processes for energy system controls. We demonstrate that, when combined with a linear state space model, our periodic MMFE yields computationally tractable Markov Decision Processes (MDPs) with low-dimensional state variables. We provide algorithms for the maximum likelihood estimation of model parameters for our periodic MMFE and showcase the corresponding asymptotic convergence. Furthermore, we validate the generative power of our framework for weather forecast errors by calibrating our periodic MMFE using real data collected from hourly commercial weather forecasts.

Keywords: Engineering.
Advisor: Darve Eric
Glynn Peter W.
Kitanidis P. K. (Peter K.)
Magnetic field amplification and radiation emission in relativistic beam-plasma systems

Magnetic field amplification by relativistic plasma instabilities is crucial to many beam-plasma systems. In high-energy astrophysical environments, these instabilities can mediate collisionless shock formation, particle acceleration, and radiation emission, while in the laboratory they can affect laser-matter interaction related to compact radiation sources and inertial fusion. In this Thesis, we report the discovery of a new nonlinear plasma streaming instability which generates much stronger, larger-scale magnetic fields than previously thought possible in dilute, relativistic beam-plasma systems. Our kinetic theory for the growth and saturation of this instability is validated in plasmas of varying composition by multidimensional particle-in-cell simulations and indicates that it could lead to significantly enhanced particle acceleration in gamma-ray bursts. We further show that modern high-power laser and accelerator facilities can drive lepton beams large enough to probe multiple relativistic instabilities. We derive requirements on the lepton beam characteristics needed to study these instabilities in astrophysically-relevant laboratory conditions and show that they can enable unprecedented x-ray flux needed for high energy density science experiments

Keywords: Humanities and Sciences.
Physics.
Advisor: Fiuza Frederico
Glenzer S. H.
Blandford Roger D.
Bucksbaum Philip H.
Making small embedded systems secure and dependable

Low-power, low-cost embedded microcontrollers (MCUs) are frequently used in security-critical settings. Recent systems advances, such as type-safe languages and standardized protocols, improve security and dependability, but add additional software to a system. MCU-based embedded systems, however, have limited flash memory, so minimizing code size is extremely important. As a result, code size limits lead many embedded systems to use C code and custom protocols, with all of the security implications that brings. This dissertation shows that new techniques and principles allow embedded systems to have the security and dependability benefits of modern "big system" programming practices and protocols while keeping code size small. First, this dissertation focuses on the use of Rust --- a modern, memory-safe programming language --- in embedded systems. I present evidence that idiomatic Rust code generates binaries 50-100% larger than similar C code. I characterize and quantify several major sources of this increase. I describe a set of Rust programming principles that reduce the size of Rust binaries, and evaluate these on an industrial Rust firmware application. I also discuss a collection of compiler optimizations that could further reduce Rust binary sizes. Second, this dissertation examines 6LoWPAN, the standard IP protocol design for low-power devices. Despite 6LowPAN being an IETF standard, different 6LoWPAN implementations often fail to communicate with each other. Their failures stem from 6LoWPAN's design, particularly the implications code size limits have on implementations. I propose three design principles for Internet protocols on low-power networks that prevent such failures, and evaluate these by using them to design a modified 6LoWPAN specification. This modified 6LoWPAN enables interoperable implementations across a wide variety of code size budgets

Keywords: Engineering.
Electrical Engineering.
Advisor: Levis Philip
Engler Dawson R.
Levy Amit (Amit Aryeh)