VictorJuiz/thesis huggingface dataset:
The high frequency optimization of the wide bandgap power devices

The development and commercialization of wide-bandgap power semiconductors enable new opportunities in the applications that extend the switching frequency of power conversion systems from hundreds of kilohertz to the multi-MHz range. The increasing switching frequency can reduce the energy storage requirements of the passive elements, leading to higher power densities (which reduces size) or even eliminating magnetic cores (which reduces weight) for the inductors in the power converters. Candidates like gallium nitride (GaN) and silicon carbide (SiC) devices lead this evolution with excellent electrical properties like high critical fields and thermal conductivity. Prior literature has characterized numerous commercial GaN and SiC devices and discovered significant large-signal off-state Coss charge-voltage hysteresis (namely, Coss loss). Unfortunately, the loss is not currently documented in the datasheet or modeled in manufacturers' SPICE models, which imposes challenges on circuit designers since it significantly degrades the efficiency and performance of power converters. This thesis addresses the issues of Coss loss in GaN and SiC power devices from the characterization, loss trends, root locations and causes, and optimization guidelines using circuit techniques and TCAD simulations

Keywords: Engineering.
Electrical Engineering.
Advisor: Rivas-Davila Juan
Chowdhury Srabanti
Plummer James D.
Higher order MDS codes

In the field of coding theory, maximum-distance separable (MDS) codes are a class of codes which have optimal guarantees for correcting worst-case Hamming errors. Geometrically, such codes correspond to configurations of vectors in $ dimensions such that no $ vectors are linearly dependent. By imposing more geometric conditions, one can define a novel family of codes known as higher order MDS codes. These codes unify a number of concepts in coding theory -- such as connecting the recoverability guarantees of a real-world distributed storage architecture with fundamental questions in list-decoding. One application of this theory is that Reed-Solomon codes, as well as many other polynomial codes, generically achieve list-decoding capacity.

Keywords: Engineering.
Advisor: Charikar Moses
Rubinstein Aviad
Guruswami Venkatesan
Wootters Mary
High-integrity urban localization : bringing safety in aviation to autonomous driving

Localization is a crucial function for autonomous vehicles (AVs), as it influences critical tasks such as perception, planning, and control. However, most existing localization systems focus on accuracy and may fail unexpectedly in challenging conditions, posing safety risks. To address this issue, this thesis proposes novel algorithms that prioritize safety along with accuracy for localization. These algorithms not only robustly estimate the location, but also continuously assess its reliability. Drawing inspiration from the principles of integrity monitoring -- a safety-centric framework used in aviation for reliable Global Navigation Satellite System (GNSS)-based localization -- this thesis aims to adapt these principles for assessing location reliability in autonomous driving. However, directly applying this framework to autonomous driving, particularly in urban environments, presents significant challenges. Unlike the well-characterized GNSS errors observed in open-sky conditions of aviation, localization errors in urban settings are more unpredictable and severe. Moreover, the variety of sensors employed in AV localization, such as GNSS, cameras, and inertial sensors, require effectively handling the limitations and combined effects of each sensor on the localization process. In this thesis, we present novel algorithms that utilize different sensors to improve localization and enable integrity monitoring in urban environments. These algorithms are tailored to handle various challenges that affect the reliability of the localization process, such as large errors in multiple GNSS measurements, high-dimensional errors in visual measurements, and combined effects of errors from different sensors. For these challenges, we develop strategies to mitigate the impact of large errors on localization, estimate the error bounds of the localization solution, or detect when the localization error exceeds a threshold. We also describe how these algorithms can be implemented efficiently for fast computation in AVs and show how they provide a better understanding of the localization reliability than existing methods. We test our algorithms on simulated and real-world data from urban environments. The results show that our algorithms can estimate the vehicle's position reliably and robustly, and also evaluate the integrity of the localization solution.

Keywords: Engineering.
Electrical Engineering.
Advisor: Gao Grace X. (Grace Xingxin)
Lall Sanjay
Weissman Tsachy
High-throughput and single-cell methods for measuring gene expression and chromatin modification changes produced by viral proteins in human cells

Viruses encode transcriptional regulatory proteins critical for controlling viral and host gene expression and chromatin state. Given their multifunctional nature and high sequence divergence, it is unclear which viral proteins can affect transcription, which specific sequences contribute to this function, and how these sequences alter the underlying chromatin. In this dissertation, I demonstrate how high-throughput synthetic biology and genomics can address these questions via functional and mechanistic investigation at scale. First, using a high-throughput assay, I measure the transcriptional regulatory potential of over 60,000 protein tiles across more than 1,500 proteins from 11 coronaviruses, all nine human herpesviruses, and other viruses that infect humans. These efforts massively expand viral protein annotations through the discovery of hundreds of new transcriptional effector domains, including a conserved repression domain in all coronavirus Spike homologs, effector domains in completely uncharacterized proteins from highly ubiquitous viruses, dual activation-repression domains in viral interferon regulatory factors, and an activation domain in six herpesvirus homologs of the single-stranded DNA-binding protein that we show is important for viral replication and late gene expression in a system with live virus. For the effector domains identified, I employ high-throughput mutagenesis to pinpoint sequence motifs essential for function and chemical inhibition screens to identify domains that are dependent on the activities of major chromatin-modifying enzymes. In addition to discovering viral effector domains and their potential interaction partners, we need to understand how they change the chromatin state of their target genes. As such, I next review myriad methods to map chromatin modifications, with a focus on single-cell approaches that can measure heterogeneity such as the kind we observe across many viral transcriptional effectors. I discuss recent advances that enable single-cell measurements, including optimization to reduce DNA loss, improved DNA sequencing, barcoding, and antibody engineering. Subsequently, I apply two existing methods to measure chromatin modification changes produced by well-characterized human chromatin regulators upon transcriptional silencing and demonstrate the ability of these methods to quantify the spread of classically associated histone and DNA modifications. Finally, I propose novel methods for the simultaneous measurement of multiple chromatin modifications at the single-cell level to investigate how these modifications change in relation to one another. I discuss the promise these methods hold for characterizing viral protein-induced changes to chromatin, which will be critical for studying their biological implications and for developing new compact gene regulation tools

Keywords: Engineering.
Bioengineering.
Advisor: Bintu Lacramioara
Covert Markus
Qi Lei (Professor of Bioengineering)
High-throughput discovery and optimization of high affinity and high specificity aptamers

Aptamers are synthetic affinity reagents useful for measuring and studying small molecules in biological systems. Unfortunately, the bottleneck in aptamer discovery platforms is the low throughput and high failure rate of canonical aptamer selection methods. To address these issues, I created a high-throughput screening platform to enable multiplexed, simultaneous discovery of specific aptamers against different targets. Canonically, aptamer selections must use techniques such as "counterselection" to ensure aptamer specificity. Unfortunately, using counterselection forces aptamer selections to be single-plex. I developed a platform to identify specific aptamers and forgo the need for techniques such as counterselection. Using a modified a benchtop DNA sequencer, I measured the affinity and specificity for ~10^6 aptamer clusters. I identified rare, specific aptamers that are able to distinguish between small-molecule metabolites differing by a single hydroxyl group. Further, the platform allows us to optimize and utilize existing aptamers previously limited to fixed environmental conditions. Many published aptamers have been discovered under non-physiological environments. However, changing from selection conditions can reduce aptamer affinity and prevents the use of many aptamers. I demonstrated that small mutations in aptamer sequence can stabilize aptamer affinity in physiological conditions. Understanding structure-mechanism relationships that enable aptamer specificity has often been limited to specialized groups. Thus, improving aptamers through rational design guided by structural information has been largely inaccessible. I developed a novel method to investigate aptamer specificity; the pipeline uses experimental data to guide a computational pipeline for reliable structure determination. My work seeks to address the need for high affinity and high specificity aptamers by enabling multiplexed aptamer discovery and broadening aptamer usability for previously discovered aptamers.

Keywords: Engineering.
Bioengineering.
Advisor: Soh H. Tom
Duchi John
Huang Possu
High-throughput quantum chemical validation of machine learning on solid-state batteries and sustainable plastics

My work applied a vast array of materials discovery techniques to identify polymers which would be suitable for biodegradation and recycling and materials for the improved performance of solid-state batteries. New chemistries in both of these fields will allow for optimization to specific tasks without compromise in performance. Chemically recyclable plastics are a necessary step in order to minimize our feedstock usage which drains earth resources and solid-state batteries are an avenue to bring lithium metal batteries to market. In my attempts to quantify the kinetic barriers for polymers, I found that most techniques are not fast enough to screen a wide field of polymers accurately. Through a tight-binding-density-functional-theory protocol I found patented polycarbonates which can undergo the entropically controlled ring-closing depolymerization for infinite chemical recycling. To mitigate mechanical failure for batteries, I found a zero-strain cathode NbFe3(PO4)6 that has a theoretical capacity orders of magnitude larger than current cathode materials. I also developed a parameter to quickly calculate adhesion between two solid materials and identified electrolyte coating materials which could increase surface contact for lower interfacial resistance. In addition I quantified errors between experimental and computational values of diffusion in solid electrolyte materials. The range of data science, quantum, and classical methods have limitations and trade offs with speed, accuracy, and breadth of application, which I discuss extensively to qualify all presented results. This work contributes to accelerating the commercialization of necessary environmentally-friendly products by presenting a range of viable options to be further optimized by experimentalists.

Keywords: Engineering.
Materials Science and Engineering.
Advisor: Qin Jian
Reed Evan
Devereaux Thomas
SendekAustin
The holistic voice : examining vocal expression through context, perception, and production

In this dissertation, I explore the complex, crucial role of acoustic paralinguistic attributes in vocal expression, emphasizing the need for speech technologies to accurately capture these nuances. Central to my research are two key propositions: (1) the integration of context awareness and adaptive modifications in vocal expression production and perception significantly enhances the alignment of speech technologies with human communication, and (2) the introduction of the "vocal persona," a concept defined as a chosen set of vocal expressions that orient and respond to a communication context, enriching our understanding of both natural and synthesized voices. Employing a blend of qualitative research, audio signal processing, and machine learning, these studies examine the production, encoding, modification, and perception of paralinguistic attributes in both speech and singing. Together, the studies clarify and formalize the influences of multimodal context on vocal expression alongside the role of acoustic paralinguistic cues in communication. They result in the introduction of the vocal persona as a novel framework for holistic vocal expressiveness. The dissertation encompasses a comprehensive literature review on vocal expression and affect, expressive speech technologies, and the intersection of voice with context and personality, along with the role of machine learning in speech and audio processing. Perception studies on how affect is perceived in speech and song are included, as well as machine learning experiments, such as accent classification and prosodic context embeddings. The research extends to analysis for re-synthesis techniques and specific applications like tracking acoustic speech features post-pediatric traumatic brain injury. A significant portion of the dissertation is dedicated to the thematic analysis of vocal persona, proposing a model and framework for natural persona-guided expression. This research culminates in synthesizing these insights to propose a framework for persona-guided speech synthesis, including context-adaptive voice conversion and text-to-speech applications.

Keywords: Humanities and Sciences.
Music.
Advisor: Berger Jonathan
Chafe Chris
Smith Julius O. (Julius Orion)
Holy smokes : measuring wildfire smoke and its impacts using machine learning and causal inference

Responding to global environmental change and adapting to future climate uncertainty requires accurate measurement, monitoring, and understanding of environmental impacts and estimates of the effects on society. In this dissertation, I focus on wildfire smoke-related air pollution and apply a variety of methods to better quantify the harms from this environmental stressor. In my first chapter, I develop a computer vision method to characterize the spatial extent of wildfire smoke from geostationary satellite imagery with high temporal resolution. When applied to external validation data, the trained model is able to segment smoke plumes even with noisy input labels and is able to capture more within-EPA-monitoring-station variation in fine particulate matter (PM2.5) than existing annotations of wildfire smoke extent. In my second chapter, I use causal inference approaches to examine the effect of wildfire smoke exposure on student learning outcomes as measured by test scores across nearly all public school districts in the US from 2009-2016. I find that smoke exposure negatively affects student learning and that the effects are more negative for younger students, present across test subjects, and affect communities with varying levels of economic disadvantage and racial-ethnic composition. In my third chapter, I develop a new wildfire severity metric that links source fires to their downwind smoke PM2.5 impacts and allows us to quantify the societal harms of individual fires. I find that the proportion of smoke transported across county or state boundaries has increased in recent years and that 7 out of the top 9 most severe smoke-generating fires originated in California and that 6 are from the 2020 fire season. This approach enables assessment of whether specific fire characteristics affect smoke toxicity and helps clarify the growing transboundary impacts to local air quality, which has historically relied on local emissions regulation in order to reduce local exposure to pollutants. These chapters contribute to our understanding of wildfire-related harms and inform better decision making to adapt to future environmental impacts.

Keywords: Sustainability.
Earth System Science.
Advisor: Burke Marshall
Ermon Stefano
Lobell David (David Brian)
Homologous recombination during C. elegans meiosis : regulation of chromosome organization and DNA repair machinery

Engagement of the homologous chromosome is an essential aspect of meiosis, the conserved, developmental program which allows diploid organisms to create haploid gametes. For their faithful segregation in the first meiotic division, homologs must identify each other and become stably connected. In my thesis work, I have sought to understand the cellular mechanisms that promote homolog pairing and connection in C. elegans meiotic prophase cells. First, in chapter 2, I attempt to shed light on the long-enigmatic question of how homolog partner recognition is achieved in C. elegans. Homologs are paired in C. elegans through cis-acting chromosomal regions called pairing centers (PCs), but how PCs function in homolog partner recognition is unclear. To better understand the mechanism of action of PCs, I sought to characterize their 3D physical structure by adapting the Optical Reconstruction of Chromatin Architecture (ORCA) method to C. elegans germ cells. Because of technical challenges encountered during analysis of ORCA data, I did not obtain new biological insights about PC function. However, my work on ORCA adaptation contributed to a different project focused on meiotic chromosome organization by the chromosome axis proteins. The findings of this project are detailed in chapter 3. The primary contribution of my thesis work is described in chapter 4, which is broadly focused on the cellular mechanisms that promote homolog connection during meiotic prophase. Homologs become physically connected during meiosis by a crossover, which is formed through repair of programmed DNA double-strand breaks (DSBs). Once programmed DSBs have been induced, recombinase proteins conduct critical early steps of the DSB repair (DSBR) pathway: the repair template search and strand exchange reactions. In this chapter, I investigate how C. elegans recombinase RAD-51 is regulated during meiotic DSBR by partner proteins in the RAD-54 family, showing that two RAD-54 family paralogs, RAD-54.L and RAD-54.B, make distinct contributions to the dynamics of RAD-51 association with DNA and DSBR progression. We propose that this division of labor between the two RAD-54 family paralogs enables the meiotic DSBR machinery to ramp up their capacity for efficient DSB processing, while also counteracting the potentially deleterious effects of hyperactivation of the DSBR machinery.

Keywords: Medicine.
Developmental Biology.
Advisor: Villeneuve Anne
Boettiger Alistair
Fire Andrew
Goins Lauren
Hope for US Healthcare

US Healthcare is built on free market principles meant to embrace individual liberty in their healthcare choices, and to leverage competition to incentivize cost effective, high quality care. The reality is a highly fragmented ecosystem whose total expenditures eclipse comparable OECD countries amounting to nearly 20% of GDP, with overall worse outcomes. This paper leverages a literature review approach to analyze each sector of healthcare to quantify its cost drivers, market position, and how they each impact overall cost and quality of US healthcare. Rather than aligning profits with health and
creating competition to reduce prices and improve care, the fragmented market is rife with anti-competitive behavior, pitting each sector against the other, and continuously driving up prices. It is proposed that policy be adjusted to align profits with health, legal kickbacks distorting the economy be eliminated, and markets be truly transparent and free to negotiate their best interests.

Keywords: Public Policy
Healthcare Cost and Utilization Project (U.S.)
Health insurance > Government policy
Health care reform
Pharmaceutical policy
Medicare
Medicaid
Pharmaceutical Research and Manufacturers of America
Physicians
Antitrust investigations
Hospital care
Value Based Care
Stanford University
Humanities and Sciences
Public Policy Program
Text
Thesis
Advisor: Polyakova Maria
Rosston Greg
Host determinants of enterovirus neuropathogenesis

Enteroviruses (EVs) comprise a large genus of positive-sense single-stranded RNA viruses and are among the most common human infectious agents. EVs cause a wide range of diseases and recent outbreaks of several emerging EVs have been associated with severe neuropathogenesis highlighting their ongoing threat to public health. Apart from a vaccine against PV and two vaccines against EV-A71 in China, there are currently no approved antivirals or therapeutics to treat enterovirus infections. Thus, there is a pressing need to develop novel strategies for combating these medically important pathogens. Host-directed therapy (HDT) is an emerging strategy in the field of antivirals that aims to control viral infections by interfering with host cellular pathways. Evaluation of host targets for use in HDT is dependent on a comprehensive understanding of how a pathogen causes disease. In recent years, our understanding of how EVs co-opt cellular functions and cause disease has greatly accelerated with the advent of unbiased genetic and proteomic technologies and the use of human organoids to more accurately represent human viral infections. This thesis presents examples of applying genome-scale genetic screens to identify host proteins hijacked by enteroviruses to promote their replication and the use of human spinal cord organoids to better understand the mechanisms by which enteroviruses cause disease.

Keywords: Medicine.
Microbiology and Immunology.
Advisor: Carette Jan
Gozani Or
Kirkegaard Karla
Monack Denise