VictorJuiz/thesis huggingface dataset:
From Leaks to Ripples? An Analysis of Brazilian Political Discourse on Social Media

Using text-as-data methods, we describe and analyze the political agenda of Brazilian politicians, members of the public, and media outlets during the first year of Jair Bolsonaro’s presidency. In this period, the first Car Wash Leaks were released: a series of journalistic articles suggesting improper behavior of judges and prosecutors involved with Brazil’s main anti-corruption operation. Our study of over 5.5 million tweets is based on Barberá et al. (2019)’s framework: We use an LDA model to uncover agenda topics and a vector-autoregressive approach to study the association between shifts in attention to a topic by one group and subsequent shifts of attention by other groups. We find that (i) the agenda of right and left-wing members of the public have a greater correspondence with each other than with the agenda of politicians with similar ideological leaning; (ii) attention shifts of the public have a greater association with subsequent attention shifts of politicians than the inverse relationship. In mapping the political agendas to the Car Wash Leaks, we find evidence that (iii) right-wing politicians ignore the Car Wash Leaks during their release whereas other actors increase their attention to the topic and (iv) the framing of corruption-related topics when new leaks were released was polarized.

Keywords: Brazil
agenda-setting
political discourse
Stanford Economics Department
Thesis
Advisor: Gentzkow Matthew
From molecules to medicine : expanding the therapeutic streetlight for inflammatory bowel diseases

Inflammatory bowel diseases (IBD) comprise a set of largely heterogeneous chronic intestinal disorders and includes ulcerative colitis (UC) and Crohn's disease (CD). Compounding prevalence of IBD, exacerbated by relatively early disease onset, low mortality, and lack of curative therapies, has led to an increasing economic burden on the healthcare system. There may be subsets of patients with IBD whose molecular underpinnings are not yet addressed in the current paradigm of IBD translational research and drug development. This includes epigenetics, which is understudied and could provide mechanistic links between environmental influences and IBD pathology. It is imperative to continue the investigation of novel modalities that may explain more nuanced manifestations of IBD and develop new therapies with the ultimate goal of improving care for patients with IBD. Here, we aimed to address the critical need for novel drug treatments for patients with IBD in two ways. First, we implemented computational drug repositioning via signature matching using publicly available gene expression datasets to identify FDA-approved drugs that can be repurposed to treat patients with UC. We identified atorvastatin as a lead candidate for reversing inflammatory genes from the disease signature, and we validated our findings using a retrospective cohort study design modeled after a target trial and showed that atorvastatin had a protective effect on colectomy risk in patients with UC. Our findings provide a systematic framework for integrating at a large scale to repurpose existing FDA-approved drugs for a wide range of human diseases beyond UC. Secondly, we report single-cell histone acetylation and methylation shifts in multiple hematopoietic cell types from patients with IBD. Using epigenetic landscape profiling by time-of-flight mass cytometry (EpiTOF), we investigated histone and chromatin modification changes in peripheral blood mononuclear cells (PBMCs) from patients with IBD that may peripherally inform local tissue immune responses under disease conditions. Our findings support a role for histone modifications in IBD pathology that can offer new insights into the epigenomic landscape of IBD and ultimately opens the door to a novel class of therapeutics that may be efficacious for patients with IBD

Advisor: Habtezion Aida
Khatri Purvesh
Sage Julien
Utz Paul
Full-field analysis : a machine learning approach

The numerical reservoir simulation model is a stable among the tools used to address the problem of describing the fluid flow behavior of producing reservoir from wells. The downside of using these models is the inherent cost of development and deployment, especially for small fields, and resources restrained projects. The recent advances in machine learning methods in well testing domain, ignited an interest to bring these capabilities to reservoir simulation and management. In this work, supervised and unsupervised machine learning techniques were applied to reservoir management tasks, that are usually dealt with using a numerical reservoir simulation model. Starting with well level analysis, simulation of downhole pressure response in producing wells, as a function of production and /or injection rate, using only field data is investigated. The methods used include algorithms composed of feed forward, recurrent, and convolution layers. The same is used to simulate water cut response as a function of operational parameters. The results suggest that it is possible to generate accurate prediction using these techniques. Extending the analysis to field level, we showed how using unsupervised learning techniques helped in guiding samples selection for training, then we applied generative modeling techniques using variational autoencoder to the problem of spatial control in the reservoir form data. We compared the performance of it to autoencoder, and other machine learning algorithms to predict multiphase production profiles form wells. Our investigation indicates that it can be done successfully in undrilled locations. We further applied conditional variational autoencoder along with deep feature interpolation methods to generate novel simulations that are not available in the training data set, extending the range of available simulated profiles without additional training or the need to conduct numerical simulation runs. Finally, we showed, using real field examples, how the methods developed can be used in predicting wells' production performance in the future, using past production data, and wellhead surface measurements, and control as an input. We applied this to single, two-, and three-phase flow examples successfully. We compared the liquid rate production performance to Gilbert correlation, a commonly used solution for this problem. We showed that using machine learning algorithms carries less uncertainty in cumulative production estimation, compared to Gilbert. The solutions carries implications for cost, and field data collections strategies. The methodologies we employed assumes no reservoir model and are purely data driven

Keywords: Energy Resources Engineering.
Advisor: Horne Roland N.
Mukerji Tapan
Tartakovsky Daniel
Functional analysis of polarity protein dynamics in the Arabidopsis stomatal lineage

Cell polarity features the asymmetric distribution of molecules and structures. The establishment of cell polarity allows cells to spatially organize diverse cellular processes, including orienting cell division planes, assuring directional transport of nutrients and hormones, and determining cell morphology and fate. In both animals and plants, cell polarity is established and maintained by so-called "polarity proteins", whose subcellular localization is markedly asymmetric. Precision in the polar localization pattern of polarity proteins, in both space and time, is crucial for the development and functions of the hosting cells. In the past two decades, many polarity proteins have been identified; the cellular functions of most of these polarity proteins, however, remain to be determined. In the stomatal lineage of Arabidopsis thaliana, polarity proteins BASL (BREAKING OF ASYMMETRY IN THE STOMATAL LINEAGE) and BRXf (the protein family of BREVIS RADIX) interact and polarly localize to a fraction of the membrane before the onset of an asymmetric cell division (ACD). How BASL and BRXf achieve and maintain their striking polarity and how the dynamics of their polarity in different stages of ACD are connected to their functions are largely unknown. In this dissertation, I combine long-term live imaging, quantitative image analysis, genetics, and developmental biology approaches to explore different avenues of BASL/BRXf polarity and the connections of their polarity to their functions in regulating ACD, thereby providing a mechanistic view of cell polarity and ACD regulations in plants

Keywords: Biology.
Advisor: Bergmann Dominique
Ehrhardt David (David Walter)
Feldman Jessica L.
Stearns Tim
Wang Zhi-Yong
Function-oriented synthesis : design, synthesis, and evaluation of novel protein kinase C modulators

Protein kinase C (PKC), a family of central signaling kinases, is of immense research interest due to its prominent roles in various devastating diseases for which there are no cures. The first part of my dissertation research is concerned with bryostatin 1, a scarce marine natural product and a potent PKC modulator. Even though bryostatin 1 is in the forefront of multiple clinical investigations (HIV/AIDS eradication, Alzheimer's disease, cancer immunotherapy etc.), it is not evolved nor optimized for human use, and is unlikely to succeed in this one-size-fits-all approach. Therefore, design, synthesis, and evaluation of synthetically accessible bryostatin analogs through function-oriented synthesis continues to be important. From the scalable synthesis of bryostatin 1, I participated in a collaborative effort to prepare and evaluate close-in derivatives of bryostatin 1, adjuvant leads for enhanced cancer immunotherapy. Using computer-guided, function-oriented synthesis, I also developed an accessible synthesis of the most simplified bryostatin analogs to date, in which one analog possesses strikingly similar potency to that of bryostatin 1, the parent compound. I also developed an asymmetric bifunctional allylation platform that is amenable to broad substrate scopes, further improving the scalable synthesis of bryostatin 1. This methodology successfully led to the synthesis of a novel des-A-ring bryostatin analog that possesses single digit nanomolar binding affinity to a conventional and a novel PKC isoform. Lastly, I am also working on tigilanol tiglate that shares very close structural similarity with phorbol esters. However, unlike phorbol esters, a class of potent tumor promoters, tigilanol tiglate can rapidly ablate tumor and at the same time induce remarkable wound healing activity via intratumoral injection. Tigilanol tiglate is currently being clinically evaluated (Phase II) with a broad range of tumors, especially accessible cutaneous, subcutaneous, and nodal tumors that are refractory to conventional therapy. Its activity is proposed to be the result of selective activation of conventional PKC isoforms. Our work is the first, and currently the only, demonstration of laboratory preparation of tigilanol tiglate and analogs, which would have a huge impact on their future supply

Keywords: Chemistry.
Advisor: Wender Paul A.
Burns Noah
Du Bois Justin
Fundamental and applied physics with optically levitated microspheres

In this collection of work, I will first guide the reader toward an understanding of optical tweezers, a technique used to trap small refractive particles, such as glass microspheres or living cells. In 2019, Arthur Ashkin was awarded part of the Nobel Prize in Physics for his development of this technique, and he made many of the initial strides in the field. With a general understanding of optical tweezers, I will discuss the specific setup and implementation constructed in our group at Stanford, especially those aspects that are particularly unique or novel to the apparatus. All of the work presented has been performed with optically trapped silica microspheres in vacuum, using a single-beam trapping technique with interferometric readout. I will then move to a discussion of a number of technical developments with metrological applications including: force field microscopy, direct measurements of a levitated microsphere's mass and radius, control of the microsphere's rotational degrees of freedom, and residual gas sensing. To conclude, I will explore some of the fundamental science goals of the project, including results from searches for fifth forces related to both dark energy and more general modifications to Newtonian gravity that might arise from new physics that manifest in the submillimeter regime

Keywords: Physics.
Advisor: Gratta Giorgio
Fox John D. 
Hollberg Leo (Leo William)
Fundamental limits and optimal uses of data in modern learning problems

What is the best we can do with the amount of data at our disposal with a given learning task? Modern learning problemsâwith a modest amount of data or subject to data processing constraintsâfrequently raise the needs to understand the fundamental limits and make judicious use of the available small or imperfect data. Given a learning problem, the aim of this thesis is to exploit its key structure, as well as optimally trade between real-world resources, to achieve statistical efficiency. This thesis covers three examples spanning statistics, information theory, theoretical computer science, operations management, and economics. The first example is the estimation of entropy of continuous distributions, an important and classic problem in information theory and statistics. In this problem, we identify and overcome the bottleneck caused by small densities, and provide the first characterization of the minimax risk without the prevalent assumption that the densities are bounded away from zero. The second example is the unified estimation of properties of discrete distributions. On one hand, we show that two approaches, based on either local moment matching or profile maximum likelihood, could both be universally optimal. On the other hand, we determine the fundamental limits of all unified estimators, and identify a tight separation between ad-hoc and unified approaches emerged only in the high-accuracy regime. The third example concerns the optimal bidding in first-price auctions, which have very recently swept the online ad industry. We characterize the optimal performances and propose computationally efficient algorithms, under various assumptions on the characteristics of the other bidders' bids, of the bidder's private valuation, of the feedback structure of the auction, and of the reference policies with which our bidder competes. Experimentation on first-price auction datasets from Verizon Media demonstrates the promise of our schemes relative to existing bidding algorithms

Keywords: Electrical Engineering.
Advisor: Weissman Tsachy
Montanari Andrea
Özgür Ayfer
Fusion for robot perception and control

Humans have long dreamed of robots that can perform a wide variety of tasks, such as cooking, cleaning, and exploring potentially dangerous environments. However, robotics adoption still struggles even in highly-structured environments. In factories, robots currently account for less than one third of the manufacturing workforce. Because many robots need to be hardcoded for every task, they often cannot deal with any errors in their models nor any changes to the environment. In academic research, recent works in machine learning are enabling robots to learn directly from data. Particularly in the areas of learning-based perception and control, we see advancements in deep learning for visual perception from raw images as well as deep reinforcement learning (RL) for learning complex skills from trial and error. However, these black-box techniques often require large amounts of data, have difficult-to-interpret results and processes, and fail catastrophically when dealing with out-of-distribution data. In order to create robotic systems that can flexibly operate in dynamic environments, we want robot perception and control algorithms that have three characteristics: sample efficiency, robustness, and generalizability. In this dissertation, I introduce the concept of ''fusion'' in robot perception and control algorithms to achieve these three characteristics. On the perception side, we fuse multiple sensor modalities and demonstrate generalization to new task instances and robustness to sensor failures. On the control side, we leverage fusion by combining known models with learned policies, making our policy learning substantially more sample efficient

Keywords: Mechanical Engineering.
Advisor: Bohg Jeannette
Okamura Allison
Li Fei Fei
Gauge-theoretic invariants in algebraic geometry

In this thesis, we examine certain questions relevant to the study of sheaf-theoretic invariants of complex projective surfaces and beyond. In the first part, the virtual localization technique is applied to the problem of showing blow-up formulae for virtual invariants on a smooth projective surface by using the moduli spaces of m-stable sheaves in the sense of Nakajima and Yoshioka, who already proved analogous results for equivariant moduli spaces of framed sheaves on P2. This yields a very general blow-up formalism that is applied to prove a blowup formula for virtual Donaldson invariants. In the second part, the theory of the Atiyah class on algebraic stacks is developed. Here, several technical constructions are given. Some of these may have been known to experts but no suitable reference was found. The results provide the fundamental building blocks for the construction of perfect obstruction theories. They are used throughout the first part and may prove useful beyond this setting

Keywords: Mathematics.
Advisor: Mazzeo Rafe
Conrad Brian
Vakil Ravi
Genotype-specific chromatin accessibility states in cancer

Cancer is most often classified as a genetic disease, with cells accumulating mutations over time that allow for uncontrollable proliferation. However, mutations alone cannot account for all of the phenotypes that cancers exhibit. For example, metastatic cells have distinct morphologies, characteristics, and migratory abilities compared to their primary tumor counterparts, despite having nearly identical genotypes. To understand why this might happen, we must look beyond the genotype of cancer cells and instead focus on their epigenetic landscape, which involves how the genome is folded within the cell and which parts are readily accessible for transcription. This work first investigates how a mutation in the gene liver kinase B1 (LKB1) in lung cancer has the potential to enable metastasis, even though most cells with an identical mutation will never metastasize. Instead, it appears that a specific epigenetic switch must occur in order to initiate the metastatic cascade. In addition, this work describes a high-throughput method to profile the chromatin landscape of cancer cells after introducing engineered mutations or perturbations. This method minimizes the time and effort required to investigate the epigenetic consequences of additional genetic mutations in cancer, beyond just LKB1. Following decades of research into the origins of metastasis and the interplay between genetics and epigenetics, these studies highlight the importance of chromatin context in understanding the genetic underpinnings of cancer. These results have implications for predicting which patients are at highest risk for metastatic progression, understanding molecular subtypes of cancer as they relate to clinical stages, and identifying stage-specific vulnerabilities in cancer

Advisor: Greenleaf William James
Winslow Monte
Bassik Michael
Khavari Paul A.
Geometric aspects of deep learning

Machine learning using deep neural networks -- deep learning -- has been extremely successful at learning solutions to a very broad suite of difficult problems across a wide range of domains spanning computer vision, game play, natural language processing and understanding, and even fundamental science. Despite this success, we still do not have a detailed, predictive understanding of how deep neural networks work, and what makes them so effective at learning and generalization. In this thesis we study the loss landscapes of deep neural networks using the lens of high-dimensional geometry. We approach the problem of understanding deep neural networks experimentally, similarly to the methods used in the natural sciences. We first discuss a phenomenological approach to modeling the large scale structure of deep neural network loss landscapes using high-dimensional geometry. Using this model, we then continue to investigate the diversity of functions neural networks learn and how it relates to the underlying geometric structure of the solution manifold. We focus on deep ensembles, robustness, and on approximate Bayesian techniques. Finally, we switch gears and investigate the role of nonlinearity in deep learning. We study deep neural networks within the Neural Tangent Kernel framework and empirically establish the role of nonlinearity for the training dynamics of finite-size networks. Using the concept of the nonlinear advantage, we empirically demonstrate the importance of nonlinearity in the very early phases of training, and its waning role farther into optimization

Keywords: Physics.
Advisor: Ganguli Surya
Shenker Stephen Hart
Yamins Daniel
Geometric learning of biomolecular structure

The shape of a macromolecule such as a protein, RNA, or DNA, is intrinsically linked to its biological function. Better reasoning about these shapes may unlock new scientific discoveries in human health and open a path towards the rational design of novel medicines and materials. I demonstrate the potential of machine learning in this area by discussing the design of a new class of neural networks that are 'geometric' in nature: they exploit the three-dimensional arrangement of atoms—thereby modeling the underlying physical processes of molecular structure—to generalize to new and unseen molecules. These results point to machine learning as an area of great promise for structural biology

Advisor: Dror Ron
Altman Russ 
Das Rhiju
Kundaje Anshul
Geometric techniques in multiterminal communication and estimation

Since its inception in 1948, one of the main goals in information theory has been to extend its original scope of point-to-point communication to include networks of nodes exchanging information. Towards this goal, we develop tools from high-dimensional geometry to analyze the fundamental limits of communication and statistical estimation tasks in a networked setting. In the first half of the thesis, we describe a problem in network communications -- the relay channel -- and prove a new upper bound on the capacity of this channel which resolves an open problem posed by Thomas Cover in "Open Problems in Communication and Computation", Springer-Verlag, 1987. The proof is highly geometric, with its main ingredient being a new isoperimetric result on high-dimensional spheres that builds on a Riesz-type rearrangement inequality. In the second half of the thesis, we consider a collection of networked statistical estimation problems modeling bandwidth and privacy constraints in distributed and federated learning systems. In these problems, data is distributed across many nodes in a network and must be communicated to a centralized estimator under communication, privacy, or mutual information constraints. We show how a geometric interpretation of Fisher information from the processed statistical samples can derive tight minimax lower bounds for many distributed estimation problems of interest

Keywords: Electrical Engineering.
Advisor: Özgür Ayfer 
Osgood Brad
Weissman Tsachy