VictorJuiz/thesis huggingface dataset:
Radiation-balanced silica fiber lasers and amplifiers

In optically pumped lasers, the quantum defect generates heat that causes detrimental fluctuations in the output mode, frequency, and power. Common heat-mitigation techniques use bulky mechanical coolers that introduce vibrations, leading to laser frequency and amplitude noise. Anti-Stokes fluorescence (ASF) cooling is a promising vibration-free alternative that involves pumping the laser's active ions above their mean fluorescence wavelength. Ultimately, this enables the creation of "radiation-balanced lasers", in which the heat generated from lasing is negated by cooling due to ASF. This form of cooling was first demonstrated in 1995 and, since then, there has been extensive research on cooling in crystals and other bulk samples. ASF cooling in fibers, however, remained largely unexplored. This work presents a thorough investigation of ASF cooling in fibers and demonstrates that fibers are viable candidates for applications to practical radiation-balanced fiber lasers. We start by demonstrating cooling in various Yb-doped fiber hosts including silica, which was previously regarded as a highly improbably host for cooling. We then use the best performing silica fiber to make a 22-dBm amplifier with no net heating along the length of the fiber. Then, finally, we present a radiation-balanced silica fiber laser with 115 mW of output power. By eliminating the need for conventional coolers, this result will enable fiber lasers with lower relative intensity noise and greater temporal coherence than possible until now

Keywords: Electrical Engineering.
Advisor: Digonnet Michel J. F.
Miller D. A. B.
Solgaard Olav
Random constraint satisfaction problems and high-dimensional estimation

In this thesis, we consider random constraint satisfaction problems(rCSP's) that exhibit sharp phase transitions. In the first part, we study a rCSP with discrete variables, namely the random regular NAE-SAT model. In a broad class of discrete rCSP's, one-step replica symmetry breaking cavity heuristics from statistical physics predict that there is the condensation threshold before the satisfiability threshold where most of the solutions lie inside a bounded number of solution clusters and the overlap between two independently drawn solutions concentrates precisely at two values. We give the first rigorous confirmation of this prediction for the random regular NAE-SAT problem. The second part of the thesis studies the binary linear classification problem in high dimensions, which can be viewed as a rCSP with continuous variables. We determine the satisfiability threshold for the data to achieve a positive margin assuming the covariates are Gaussian and the labels are drawn from a generalized linear model. Moreover, we characterize the limiting test error of the max-margin estimator

Keywords: Statistics.
Advisor: Dembo Amir
Chatterjee Sourav
Montanari Andrea
Randomized methods in optimization : fast algorithms and fundamental limits

High-dimensional machine learning models trained on massive datasets have shown exceptional predictive ability over the last decade. Their adoption has created novel challenges and paradigms for practitioners and researchers. Typically, stochastic first-order methods have become the method of choice for training these models, due to their small per-iteration computational cost. Yet, these are known to be sensitive to hyperparameters' choices with weak or even no convergence guarantees. Alternative (e.g., second-order) optimization methods have not received the same amount of attention nor proved to be as effective in practice. A key bottleneck usually lies in their computational requirements, way more intensive than first-order methods. In this thesis, we are interested in random projections -- also known as (a.k.a.) sketching or subsampling methods -- in the context of large-scale optimization problems, which either involve massive datasets or high-dimensional optimization variables. The generic purpose of using random projections here is to reduce the dimensionality of the data matrix and/or the optimization variable, to obtain both faster convergence and smaller memory requirements. Naturally, random projections incur a loss of information, whence a loss in statistical performance. We consider the fundamental questions that arise when compressing information for faster computational procedures. What is the minimal embedding dimension one can use to project the data that guarantees convergence of an optimization solver? In fact, how to optimally couple random projections of the data with an optimization solver? And how to achieve optimal trade-offs between statistical and computational performance in terms of the choice of the random projection and the embedding dimension (a.k.a. sketch size)? To answer these questions, we will first focus on unconstrained convex quadratic objectives, and consider a broad class of first-order optimization solvers that involve a sketching-based preconditioner. We will explore and compare different random projections and classical first-order methods. We gradually extend our analysis and methodology to regularized convex quadratic optimization, and then to generic convex optimization problems

Keywords: Electrical Engineering.
Advisor: Pilanci Mert
Boyd Stephen P. 
Wootters Mary
Rare-variant approaches to complex traits across population biobanks

Complex diseases are a significant global burden, accounting for 70% of deaths in the U.S. annually. For example, 70,000 new cases of inflammatory bowel disease are diagnosed every year. Many such diseases have underlying genetic etiologies responsible for their pathology. Understanding their genetic basis could lead to more timely diagnosis and improved prognosis. Furthermore, human genetics presents an opportunity to identify new therapeutic targets. However, while much of the disease-causative common variation is well-documented, our understanding of rare, disease-contributory variation is sparse, largely due to the lack of power (limited sample size) and ascertainment to detect such variation with accuracy. While the above goal of understanding rare variation is not achievable with small cohort (n < 100,000) studies, population-level biobanks (n ~ 500,000) offer the ability to study this type of genetic variation. These datasets present a unique opportunity for rapid discovery of robustly-validated disease-causative variants because they possess large sample sizes, better population-level annotations, and next-generation sequencing technologies like whole-exome and -genome sequencing. This dissertation contains six Chapters. In Chapter 1, I introduce key terms and methodologies and then outline my contributions to the human genetics space that are a) key and b) ancillary to the dissertation. Chapter 2 explores the human leukocyte antigen (HLA) region in the UK Biobank, cataloging rare variants that explain additional heritability of complex diseases. In Chapter 3, I introduce Multiple Rare-variants and Phenotypes (MRP), a novel, flexible, Bayesian framework for rare-variant signal aggregation across variants, studies, and phenotypes. Chapter 4 details a corollary method to MRP, the Multiple Rare-variants and Phenotypes Mixture Model (MRPMM), which clusters rare variants into groups based on their effects on a multivariate phenotype. In Chapter 5, I, along with collaborators from the International Inflammatory Bowel Disease Genetics Consortium, identify rare coding variants newly associated with Crohn's disease using a mixed-model approach with a software called SAIGE. I conclude in Chapter 6 with a summary of the value in studying rare variation in the genome, the takeaways from my research, and the areas in which future research should go

Advisor: Montgomery Stephen
Rivas Manuel (Manuel A.)
Bustamante Carlos (Carlos D.)
Hastie Trevor
Kundaje Anshul
Rational preparation of broadband-light-emitting lead-halide materials

Among the various properties that two-dimensional lead-halide perovskites exhibit, their ability to emit strong, tunable photoluminescence at room temperature is particularly attractive for solid-state lighting applications. These materials' emission originates from the recombination of excitons (bound electron-hole pairs). For the majority of lead-bromide and iodide perovskites, excitonic recombination results in a narrow emission feature with a small Stokes' shift. However, in a small subset of perovskites, an additional broadband emission feature is observed which covers most of the visible light spectrum. This behavior has been attributed to exciton self-trapping, a process by which an exciton produces a lattice distortion around itself and, subsequently, becomes trapped in that distortion. The combination of the narrow free excitonic emission and broad self-trapped emission results in a photoluminescence spectrum which appears white to the human eye. While the ability for perovskites to emit broadband white light is attractive, it is currently not possible to reliably prepare perovskites which exhibit white-light emission at room temperature. In an effort to develop rational routes to obtaining white-light emission in lead-halide materials at room temperature, I studied the emission properties of lead-chloride perovskites and non-perovskite lead-bromide materials. Through observation of the lead-chloride perovskites, I find that all the materials examined exhibit broadband emission at room temperature. Further, I demonstrate that the (001) perovskites, the most common structure type, can reliably emit white light at room temperature when prepared as mixed chloride-bromide materials. I then sought to expand the search for broadband-light emission to octahedral lead-bromide materials in general. After surveying a wide variety of lead-bromide hybrid materials with varying dimensionality and interoctahedral connectivity, I find that broadband emission is common across this large and diverse family despite the differences in material dimensionality or interoctahedral connectivity. Additionally, I also find that there is no correlation between any ground state structural parameters and the broadband emission's position or breadth. These data contradict ideas which have previously been proposed for perovskites and non-perovskite materials and suggest that more detailed study of these materials is required

Keywords: Chemistry.
Advisor: Karunadasa Hemamala
Solomon Edward I.
Waymouth Robert M.
Reclaiming Indigenous Women’s Reproductive Health: The Impacts of Colonization on Indigenous Motherhood and the Revitalization of Traditional Birthing Practices

Prior to the Allotment and Assimilation Era in 1887 and the introduction of Western medicine, Indigenous women’s reproductive health was immersed with cultural practices, medicinal plant remedies, and ultimately handled by women of the community. However, with time and assimilation, these practices began to fade and be forgotten. This thesis explores the following questions: How has assimilation and western expansion impacted the reproductive health and rights of Indigenous women and how do these continue to impact them in today’s current society? Looking into the oral histories of traditional birthing practices and the herbal medicines used for women’s health, how can these teachings be integrated or reintegrated back into the culture and potentially into Western medicine? What steps can be taken to reclaim this traditional knowledge and reverse the current state of Native women’s reproductive health and rights? I examine the impact of colonization and assimilation on the current state of Indigenous Women’s Reproductive Health to provide insight as to why there is a loss of Indigenous traditions. Looking at Navajo, Lakota, and Dakota Sioux birthing knowledge specifically, I explain their traditional birthing practices based on examinations of ethnographies from 1920-1940, interviews I conducted with elder women affiliated with these tribes, along with other research interviews on the topic. With this knowledge of the birthing practices, I explore the medicinal plants that aided in the births, as well as in serving as a contraceptive, abortifacient, gynecological aid, and reproductive aid to women in these tribes. By understanding the history of Indigenous women’s reproductive health, this paper aims to help in revitalizing Indigenous knowledge around birth and women’s medicine and possibly integrating these practices into Western medicine.

Keywords:  Department of Science
Indigenous women
reproductive health
tradition birthing practices
traditional medicinal plants
Science
Technology
and Society
STS
Thesis
Advisor: Schiebinger Londa
Sato Kyoko
Recommendations for algorithmic fairness assessments of predictive models in healthcare : evidence from large-scale empirical analyses

The use of machine learning to develop predictive models that inform clinical decision making has the potential to introduce and exacerbate health inequity. A growing body of work has framed these issues as ones of algorithmic fairness, seeking to develop techniques to anticipate and proactively mitigate harms. The central aim of my work is to provide and justify practical recommendations for the development and evaluation of clinical predictive models in alignment with these principles. Using evidence derived from large-scale empirical studies, I demonstrate that, when it is assumed that the predicted outcome is not subject to differential measurement error across groups and threshold selection is unconstrained, approaches that aim to incorporate fairness considerations into the learning objective used for model development typically do not improve model performance or confer greater net benefit for any of the studied patient populations compared to standard learning paradigms. For evaluation in this setting, I advocate for the use of criteria that assess the calibration properties of predictive models across groups at clinically-relevant decision thresholds. To contextualize the interplay between measures of model performance, fairness, and benefit, I present a case study for models that estimate the ten-year risk of atherosclerotic cardiovascular disease to inform statin initiation. Finally, I caution that standard observational analyses of algorithmic fairness in healthcare lack the contextual grounding and causal awareness necessary to reason about the mechanisms that lead to health disparities, as well as about the potential for technical approaches to counteract those mechanisms, and argue for refocusing algorithmic fairness efforts in healthcare on participatory design, transparent model reporting, auditing, and reasoning about the impact of model-enabled interventions in context

Keywords: Biomedical Informatics.
Advisor: Shah Nigam
Goel Sharad
Hernandez-Boussard Tina 
Zou James
Recovering infallers from black holes

In this thesis, we investigate quantum and stringy phenomena that allow infalling information to escape a black hole. The mechanisms we present are limited in their scope, either requiring a special black hole or a special infaller, but they are also highly transparent and tractable. Rather than address the monumental question of black hole unitarity, we instead focus on special cases where the mechanisms of information recovery can be precisely understood

Keywords: Physics.
Advisor: Silverstein Eva
Hayden Patrick (Patrick M.)
Stanford Douglas
Reduced order model predictive control of high-dimensional systems

Many physical systems are modeled by ﬁnite-dimensional sets of ordinary diﬀerential equations (ODEs). Others have dynamics that evolve over a continuum (i.e. are inﬁnite-dimensional) and are best modeled by partial diﬀerential equations (PDEs), including systems with ﬂuid ﬂows, deformable/ﬂexible structures, or ﬂuid-structure interaction. In practice, PDE models are generally semi-discretized to produce high-ﬁdelity ﬁnite-dimensional ODE models. Since the dimension of these models can range from thousands to millions, computational challenges severely limit the use of standard approaches to model-based controller design. In this thesis, we propose an approach for efficiently designing high-performing controllers based on high-dimensional models. Specifically, we develop a model predictive control (MPC) algorithm for solving constrained optimal control problems that leverages high-fidelity, but low-dimensional, reduced order approximations of the original model to satisfy practical computational requirements. In the linear setting, we combine existing ideas from tube MPC with novel approaches for controller synthesis and analysis to develop a reduced order MPC (ROMPC) scheme for solving robust, output feedback control problems, and we provide theoretical closed-loop performance guarantees that explicitly account for model reduction error. We also extend the ROMPC scheme to the nonlinear setting by exploiting piecewise-affine reduced order models. We motivate and validate the proposed approach through two case studies. First, we use a linear, coupled rigid-body/fluid dynamics model for aircraft control, where the high-dimensional computational fluid dynamics (CFD) model has over one million dimensions. Second, we use a nonlinear finite element model (FEM) with over ten thousand dimensions to control a soft robot. Simulation and hardware experiments are used in both studies to demonstrate the practicality and performance of ROMPC

Keywords: Aeronautics and Astronautics.
Advisor: Pavone Marco
Farhat Charbel
Gerdes J. Christian
Rock Stephen M.
Re-engineering Digital Privacy for Domestic Violence Survivors

In recent decades, technology has increasingly been used as a tool for domestic violence perpetrators to harass, stalk, and surveil victims. Subsequently, survivors of domestic violence face critical digital privacy needs that are inadequately addressed by modern digital privacy practices and tools. Drawing from interviews with survivors and domain experts, this paper first presents a graphical framework to model digital privacy attacks in domestic violence. It then outlines five key digital privacy needs of domestic violence survivors— awareness, coverage, convenience, maintenance, and alerts. Next, it introduces the Berrier system, a single control tower for cross-platform privacy settings designed specifically for survivors’ privacy needs. Finally, the paper presents a 10-participant user study for Berrier, which finds that using Berrier sharply increases the average total accuracy (+10.0%), speed (+39.5%), and perceived ease (+51.0%) of changing one’s online privacy settings. These findings present a crucial step forward in re-engineering digital privacy for domestic violence survivors.

Keywords: digital privacy
domestic violence
cybersecurity
privacy
human-computer interaction
design thinking
computer science
Text
Thesis
Refraction of Dirac Fermions across Molecular Graphene Junctions

Graphene has been a subject of great interest in condensed matter physics due to its exotic topological properties. We have used an STM capable of individual atomic manipulation to engineer molecular graphene nanostructures which host relativistic massless Dirac fermions and constitute a fully-tunable electronic system. Motivated by this light-like behavior, our ultimate goal is to create a Veselago lens out of flakes of doped molecular graphene. This work explores the refraction of Dirac fermions across various types of molecular graphene junctions, including neutral-p, neutral-n, n-n, and neutral graphene - 2 dimensional electron system junctions. Principle to our analysis is a new technique combining the traditional quasi-particle interference method used in scanning tunneling microscopy with individual atomic manipulation, which allows us to investigate the scattering of electron wave functions in 2D systems with great precision. Finally, we also explore the possibility of ’tachyonic’ behavior of Dirac fermions as they refract across junctions from areas with higher to lower group velocities. We find significant evidence of refraction of Dirac fermions across various junctions, as well as propose a theoretical explanation for the angles of refraction based off of band structure. We also find evidence of ’tachyonic behavior’ in Dirac fermion refraction.

Keywords:  Program in Engineering Physics
graphene
molecular graphene
condensed matter physics
scanning tunneling microscopy
individual atomic manipulation
Veselago lens
Dirac fermions
refraction
tachyonic behavior
Text
Thesis
Advisor: Manoharan Hari
Dionne Jennifer
The regulation of GLI1 in basal cell carcinoma

Investigations into the hedgehog (Hh) signaling cascade in increasingly complex metazoan model systems has revealed complex regulation of the GLI transcription factors to control proliferation, stemness, and ultimately oncogenic transformation. Through the study of perturbations to the hedgehog pathway in the setting of drug-resistance in Basal Cell Carcinoma (BCC), we elucidate a previously unappreciated nuclear GLI1 maturation cycle. Upstream blockade of cytoplasmic Hh activators in BCC results in the hyperactivation of the kinase aPKC and the deacetylase HDAC1 to sustain high Hh output. We find that these resistance genes act sequentially to promote the nuclear deacetylation of GLI1. Acetylated GLI1 (AcGLI1) is locked in the nucleus in a transcriptionally inactive complex on the inner nuclear membrane comprised of LAP2beta. The sequential activities of aPKC and HDAC1 rescue GLI1 off of the inner nuclear membrane, associating alternately with the nucleoplasmic co-activator LAP2alpha. The LAP2 proteins antagonize GLI1 nuclear export, ultimately facilitating transcriptional output by promoting nuclear accumulation and chromatin association. Mechanisms to disrupt all components of this GLI1 chaperoning system hold considerable promise for the treatment of advanced BCC

Advisor: Oro Anthony
Jackson Peter K. (Peter Kent)
Khavari Paul A.
Weis William I.
Regulatory and cellular dynamics in cancer

Improving and maintaining durable clinical responses to cancer therapies requires a detailed understanding of regulatory dynamics that underlie altered gene expression in cancer cells and as well as the cellular dynamics of cancer and immune cells during cancer progression and in response to therapy. Towards this goal, my dissertation research focuses on the following areas: First, I describe the development of a computational and experimental method to enable high-throughput, low-cost analysis of chromatin accessibility. Second, I characterize the immune cell response to immune checkpoint blockade and find that the clonal T cell response to PD-1 blockade consists of novel clonotypes that had not previously been observed in the same tumor, a process we term clonal replacement. Third, I identify Polycomb-associated DNA loops in stem cells that span tens to hundreds of megabases and enable long-range spreading of the repressive histone modification H3K27me3. Finally, I show that extrachromosomal DNA clusters in the nucleus of interphase cells to promote intermolecular enhancer-promoter interactions and high levels of oncogene expression

Advisor: Chang Howard Y. (Howard Yuan-Hao)
Curtis Christina
Greenleaf William James
Khavari Paul A.
The regulatory landscape of CAR T cell dysfunction

Chimeric Antigen Receptor (CAR) T cells have emerged as a promising immunotherapy strategy to target cancer in a subset of patients. T cell exhaustion presents a significant barrier to the progress of CAR T cell therapy development and efficacy. We use a human CAR T cell model system, utilizing the non-exhausting CD19-targeting CD19-28z CAR T cells and the in vitro exhausting GD2-targeting HA-28z CAR T cells, to investigate the genetic interactions and regulation underlying CAR T cell exhaustion and dysfunction. We nominate regulatory transcription factors and enhancer loci that control the expression of exhaustion-associated and -promoting genes and perturb these regulatory networks to modulate the exhaustion phenotype and prevent T cells from exhibiting the hallmarks of exhaustion

Keywords: Genetics.
Advisor: Chang Howard Y. (Howard Yuan-Hao)
Bassik Michael
Greenleaf William James
Mackall Crystal