VictorJuiz/thesis huggingface dataset:
"Dark inscrutable workmanship" : rereading mystery through riddle in the visionary poetry of Wordsworth, Shelley, and Keats

It is a paradox of the Romantic imagination that the source of poetic power is a mystery beyond the poet's reach. While the paradox has been explicated as a metaphysical problem reflected in the corpus of major Romantic poets, this dissertation models a different interpretive methodology that renders the existential predicament analytically accountable within the poem itself, in order to provide a new understanding of how Romantic visionary poetry works. To this end, it departs from the cosmological drama most commonly studied as visionary poetry, and analyzes instead three poems on the quest for vision: William Wordsworth's The Prelude, Percy Bysshe Shelley's "Alastor, " and John Keats's The Fall of Hyperion. While the poems concern an impossible quest for a transcendent power by definition beyond the poet's grasp, reading them as riddlic quest poems reveals that each poem's vision is double: at the farthest reach of mystery, another, literal meaning stealthily arises. The quester's fervent attempt to peer through the dark mirror produces even deeper enigmas, but the reflections in the dark mirror itself yield unexpected insight into the nature of the quest. I argue that scenes of mystery in Romantic visionary poetry contain the structure of the riddle, in which one reads the unknown in terms of the known, so that the appearance of mystery can be read as a special kind of appearance— and thereby, the very mystery that seems to lock us out can lead us in. My broader argument about the genre is that the most emphatic layer of Romantic visionary poetry —the one whose ardent assertion of visionary power (or its inverse, visionary despair) aims straight for our spiritual throats— is also the one that the poem's own structure impels us to see through. Rather than a demystifying reading that reveals the poem as less than what it seemed, however, my reading shows how the poems are more than what they seem, by unfurling the full depth of meaning invested in the adverb through. Therefore, if the first power of the riddle is to lead us into the mystery, its second power is how it leads us out of it. The project takes the riddle as an enabling possibility for approaching the mystery in each individual poem, in order to discern structures that render the most mysterious passages inescapably accountable to the poem, to bridge the two normally opposed senses of vision —a glimpse at a realm beyond, or outlook on the one we occupy— and finally to develop a renewed understanding of the Promethean quest at the heart of Romantic visionary poetry

Advisor: Gigante Denise
Greene Roland
Vermeule Blakey
Data science for social equality

Recent work in algorithmic fairness has highlighted the ways in which machine learning and data science can exacerbate already profound social inequalities. While invaluable, this work should not cause us to lose sight of the more optimistic counterpoint: that machine learning and data science have the potential to also reduce social inequality if properly applied. This dissertation explores this potential. In the first half of the dissertation, we provide two examples illustrating how data science and machine learning can improve healthcare for underserved populations. We first develop a deep learning algorithm which identifies pain-relevant features in knee osteoarthritis x-rays which conventional severity measures overlook, but which help explain higher pain levels in black, lower-income, and lower-education patients. Secondly, we use data from a women's health app to decompose women's mood, behavior, and vital signs into four simultaneous cycles --- daily, weekly, seasonal, and menstrual --- and reveal that the menstrual cycle, though often invisible in past analyses, is the largest of the four cycles. In the second half of the dissertation, we provide two examples illustrating how data science and machine learning can detect bias in human decision-making, focusing on policing as an application domain. We first describe a new family of probability distributions and use them to accelerate a Bayesian test for discrimination by two orders of magnitude, allowing it to scale to much larger datasets. We then apply this test to a national dataset of traffic stops which we collect via public records requests and publicly release. The methods we develop are more broadly applicable to assessing bias in many other human decisions

Advisor: Leskovec Jurij
Jurafsky Dan
Zou James
Data summaries for scalable, high-cardinality analytics

Sensor data, network requests, and other machine generated data continue to grow in both volume and dimensionality. Users now commonly aggregate over billions of records segmented by fine-grained time windows or a long tail of device types. To support this growth, an emerging class of data systems including Apache Druid and Apache Kylin precompute approximate summaries for different segments of a dataset, partitioned by contextual dimensions. These systems can avoid expensive full data scans by operating directly over segment summaries. However, existing streaming and mergeable summarization techniques do not scale to settings where summaries must be aggregated over hundreds or millions of segments. In this thesis, we develop data summaries tailored for high-cardinality aggregation. We find that an end-to-end approach to algorithm design, where data summaries and aggregation are designed for specific high-cardinality workloads, can yield dramatic improvements in accuracy, space usage, and runtime. We demonstrate this approach with three summarization techniques. First, we address runtime bottlenecks for quantile queries by introducing the Moments sketch, which uses compact moment statistics and efficient distribution solvers to reduce aggregation and query overhead compared to existing mergeable summaries. Second, we address accuracy and space bottlenecks for item frequency and quantile queries by developing Cooperative summaries optimized for aggregate (rather than individual) summary error, improving upon the error guarantees provided by mergeable summaries. Third, we address runtime bottlenecks for thresholded kernel density estimation queries by extending k-d tree summaries with a new aggregation procedure, TKDC, and show that it achieves asymptotic improvements in runtime compared with direct computation

Advisor: Bailis Peter 
Charikar Moses 
Zaharia Matei
Data-driven adaptive traffic signal control via deep reinforcement learning

Adaptive traffic signal control (ATSC) system serves a significant role for relieving urban traffic congestion. The system is capable of adjusting signal phases and timings of all traffic lights simultaneously according to real-time traffic sensor data, resulting in a better overall traffic management and an improved traffic condition on road. In recent years, deep reinforcement learning (DRL), one powerful paradigm in artificial intelligence (AI) for sequential decision-making, has drawn great attention from transportation researchers. The following three properties of DRL make it very attractive and ideal for the next generation ATSC system: (1) model-free: DRL reasons about the optimal control strategies directly from data without making additional assumptions on the underlying traffic distributions and traffic flows. Compared with traditional traffic optimization methods, DRL avoids the cumbersome formulation of traffic dynamics and modeling; (2) self-learning: DRL self-learns the signal control knowledge from traffic data with minimal human expertise; (3) simple data requirement: by using large nonlinear neural networks as function approximators, DRL has enough representation power to map directly from simple traffic measurements, e.g. queue length and waiting time, to signal control policies. This thesis focuses on building data-driven and adaptive controllers via deep reinforcement learning for large-scale traffic signal control systems. In particular, the thesis first proposes a hierarchical decentralized-to-centralized DRL framework for large-scale ATSC to better coordinate multiple signalized intersections in the traffic system. Second, the thesis introduces efficient DRL with efficient exploration for ATSC to greatly improve sample complexity of DRL algorithms, making them more suitable for real-world control systems. Furthermore, the thesis combines multi-agent system with efficient DRL to solve large-scale ATSC problems that have multiple intersections. Finally, the thesis presents several algorithmic extensions to handle complex topology and heterogeneous intersections in real-world traffic networks. To gauge the performance of the presented DRL algorithms, various experiments have been conducted and included in the thesis both on small-scale and on large-scale simulated traffic networks. The empirical results have demonstrated that the proposed DRL algorithms outperform both rule-based control policy and commonly-used off-the-shelf DRL algorithms by a significant margin. Moreover, the proposed efficient MARL algorithms have achieved the state-of-the-art performance with improved sample-complexity for large-scale ATSC

Advisor: Leckie Jim
Lepech Michael 
Wang Jie (Researcher in adaptive computational learning)
A data-driven ontology of brain function : engineered, interrogated, and clinically applied

In modern healthcare, there is no accepted system for diagnosing disorders of mental function based on the altered brain systems that cause them. In neuroscience, there is no consensus on what a brain system is. This thesis takes a data-driven approach to mapping domains of brain function across the human neuroimaging literature. Neural circuits and associated mental functions were mapped from the brain coordinate data and full texts of nearly 20,000 studies. The resulting domains characterize several novel brain circuits that are absent from the conceptually dominant expert-determined frameworks in the field. Having engineered a framework for brain function, the next aim was to interrogate whether its domains adhere to basic organizational principles. Are the domains reproducible, meaning that their circuits and functions predict one another in held-out articles? Are they modular, partitioning the literature into homogeneous and separable subsets? Are they generalizable, serving as prototypes of circuits and functions observed in single studies? It is demonstrated here how competing knowledge frameworks can be evaluated against a common set of standards. The data-driven framework is consistently seen to offer leading performance. The second aim of the thesis was to apply the data-driven framework to predict clinical outcomes. The notes from over 20 million healthcare visits were rated by semantic similarity to domains, and these dimensional phenotypes were found to outperform binary diagnoses in predicting future psychotropic prescriptions, hospital visits, and all-cause mortality. Though the data-driven framework was not designed to have clinical relevance, its prognostic performance is on par with frameworks crafted specifically for psychiatric applications. The third and final aim of the thesis was to systematically quantify discrepancies between article texts and brain coordinate data. Computational approaches to ontology engineering are expected to mitigate certain forms of bias, while others may be inherent to the neuroimaging literature. The findings point to the influence of the publication incentive structure on how neuroimaging data are interpreted by neuroscientists. The collective results illustrate the scientific, clinical, meta-scientific interest in engineering a data-driven framework for brain function. They suggest a potential for biological knowledge frameworks to go beyond guiding diagnosis and to generate the prognoses that have long eluded the standard of care for disorders of brain function

Advisor: Etkin Amit
McFarland Daniel A. 
Poldrack Russell A.
Potts Christopher
Data-driven strategies towards grid-interactive and energy-efficient buildings

Buildings use more energy than any other sector and are widely considered as lowest-hanging fruit for reducing energy demand. But if the energy savings potential is so great, then why do buildings continue to be so inefficient? One major cause is due to informational gaps that prevent critical stakeholders—from policymakers to facility managers—from properly identifying poor performing buildings and diagnosing their sources of inefficiencies. New sensors and data sources are, however, enabling newfound opportunities to monitor and track our buildings like never before. In this work, I aim to address informational gaps that hinder energy savings by proposing data-driven strategies that leverage smart meter and open data sources. Empowered with such tools, critical stakeholders can make more informed decisions to reduce energy waste in buildings

Advisor: Jain Rishee 
Fischer Martin A.
Rajagopal Ram
"Death, Where Is Thy Sting?" : British spiritualists and the First World War

This dissertation examines the widespread yet underexamined phenomenon of spiritualism in Britain during and immediately following the First World War. While contemporary and historiographical interpretations of the war tend to emphasize its futility and senselessness, this dissertation highlights a population of Britons who, in their belief that death was not extinction, managed to find meaning and consolation in the midst of a seemingly irredeemable conflict. Primarily cultural in its approach, the dissertation draws on a range of sources, including séance transcripts, spirit photographs, letters, diaries, and spiritualist publications. The dissertation examines the complex ways in which spiritualism incorporated and refigured elements of British culture. It highlights the women at the forefront of wartime spiritualism who, by developing their own psychic faculties, sought direct communion with their dead. While the patriarchal British state was sending their loved ones to die and dictating their burials, spiritualist women re-domesticated death, re-feminized mourning, and found novel forms of agency. Meanwhile, séance rituals also provided a way to re-imagine elements of the traditional Victorian "good death." In the religious realm, spiritualism was, for some, an alternative to orthodox Christianity; for others, Christianity and spiritualism were complementary and compatible, with spiritualism providing objective assurance of life after death at a time when the Church's response to the war seemed insufficient. Wartime spiritualists also had complex and contradictory understandings of colonial subjects and racial others, particularly in their conceptions of the far and Middle East: many spiritualists revered the East as a paragon of spiritual evolution—yet with this evolution came a certain naïveté and childishness. Mediums channeled not only dead soldiers, but also Eastern spirits who delivered psychic wisdom in broken English. Though sometimes unruly, these subaltern spirits were always eventually subdued, reaffirming British supremacy at a time of unstable imperial politics and colonial agitation. Finally, wartime spiritualism not only reconnected the living and the dead, but also provided a philosophical and theological justification for the conflict. Spirits relayed redemptive prophecies, often socialist in tone, declaring that the war would result in the spiritual evolution of humanity, the end of sectarianism and national strife, and the dawn of universal brotherhood

Keywords: History.
Advisor: Satia Priya
Como David R.
Stokes Laura
Decentralized data analysis : genome-wide association studies and other biomedical applications

The recent growth of data with potential medical ramifications has led to a better understanding of complex disease pathways and risk factors. Currently, much of the medically and clinically-relevant data is generated and maintained in decentralized silos. As the size and sensitivity of this data increases, it is becoming exorbitantly expensive, unsafe, and impractical to host entire datasets in a centralized location. Meta-analysis techniques offer a feasible solution; however, they can introduce bias or may not be applicable in certain cases (e.g. small sample sizes). Federated learning can be used to combine some of the advantages of both data centralization and meta-analysis. I will describe how three optimization algorithms (Newton's method, alternating directions method of multiplier and Anderson accelerated Douglas-Rachford splitting) can be used with secure sum or partially-homomorphic encryption techniques to perform decentralized regression-based association analysis. I will show that these techniques are practical in many low-dimensional settings and that some can be scaled to computationally intensive tasks such as genome-wide association studies (GWAS) at consortium scale. Finally, I will introduce our federated GWAS platform, HyDRA

Keywords: Physics.
Advisor: Bustamante Carlos (Carlos D.) 
Chu Steven
Leskovec Jurij
Deep learning for predicting antigen presentation

The presentation of antigens by Human Leukocyte Antigen (HLA) class II molecules is an essential component of adaptive immune responses. Accurate prediction of which antigen fragments are likely to be presented by class II molecules will be of great value for vaccine development and cancer immunotherapies. However, current computational methods trained on in vitro binding data have limitations for accurately predicting class II antigen presentation. Here, we describe MARIA (MHC Analysis with Recurrent Integrated Architecture), a multimodal recurrent neural network for predicting the likelihood of presentation for a given candidate peptide. In addition to in vitro binding measurements, MARIA is trained on peptide HLA ligand sequences identified by mass spectrometry, antigen gene expression levels, and protease cleavage signatures. By integrating these diverse features and leveraging both improved training data and a novel machine learning framework, MARIA (AUC=0.89-0.92) outperformed existing methods in validation data sets and allowed identification of immunogenic epitopes in lymphomas and celiac disease. Across several independent early phase cancer vaccine studies, peptides with high MARIA scores were more likely to elicit strong CD4 T-cell responses. In summary, MARIA improves identification of human peptide immunogens for diverse applications and can be accessed at https://maria.stanford.edu

Keywords: Genetics.
Advisor: Altman Russ 
Alizadeh Ash
Fire Andrew Zachary
Fordyce Polly
Deep learning in vision-based robotic manipulation : towards generalization and fast inference

In the past decade, researchers are looking at bringing robots into our daily lives, and automating services such as taxis, delivery, house-works, and even medical procedures. One of the major roadblocks in making this leap is the diversity and uncertainty in the environments that the robots need to work in. Machine perception, i.e. understanding of the environment through visual, audio, and contact signals, is indispensable in such diverse and uncertain environments, and is a hard problem in itself. Further, the environment is changing, due to human activities and other factors, and robots need to react to the changes quickly. Recent developments in deep learning, especially computer vision, has brought us closer to achieving the goal of bringing robots into our daily environments. However, deep learning methods require a large amount of data with annotated labels, and new datasets and annotations need to be collected for each new task. Deep reinforcement learning algorithms have also achieved good performance on a range of locomotion or manipulation tasks, but the amount of interactions required to train most algorithms is so large that it could take days even with parallel simulation engines. Highly data-efficient models and learning algorithms are needed to help robots learn faster and with less human effort. Additionally, when designing a learning-based solution to a robotics task, inference speed needs to be taken into consideration so that the robot can respond to changes quickly. This thesis introduces methods to improve training data efficiency and inference speed for vision-based robotic manipulation. To improve data efficiency of models, we analyze properties and structures of the specific problems, and build structural biases into the models based on the insights obtained. In addition, we demonstrate self-supervised learning of the perception model on real images, enabling robots to collect their own training data without requiring human annotations. To improve robots' response speed, when learning motion policies we design learning algorithms to always explicitly learn the distribution of promising actions, instead of learning an action evaluation function which requires online optimization during runtime. The proposed methods are integrated into end-to-end systems and tested on real robots on two tasks: vision-based robotic grasping, and rope manipulation and knotting

Keywords: Electrical Engineering.
Advisor: Bohg Jeannette
Finn Chelsea 
Sadigh Dorsa
De-essentializing 'Hispanic': A Critical Approach to Ancestry Estimation with FORDISC 3.1

Latin American populations exhibit a high degree of admixture due to the region’s complicated history of colonialism, migration, and settlement. Commonly used methods currently employed in forensic anthropology, such as FORDISC 3.1, often fail to account for the diversity in morphological signatures of the peoples who comprise this region and, in turn, effectively estimate ancestry or affinity. This thesis explores this program’s limits and challenges, using both ideal and realistic preservation case measurements for Latin American populations. Mosaic plots, multiple correspondence analyses, principal components analyses, and mean posterior probability bar graphs were employed to examine the program’s outputs. It was found that FORDISC 3.1 needs improved reference samples and socially-relevant classification labels in order to more accurately classify Latinx individuals by ancestry. These results are especially useful and important now, given the context of the humanitarian crisis along the U.S.-México border. This thesis concludes by promoting the use of admixture approaches and a fuller disentanglement of the biological science of forensic anthropology from the socially-driven frameworks of law enforcement investigation.

Keywords:  Center for Latin American Studies
forensic anthropology
FORDISC
biological anthropology
physical anthropology
admixture
Stanford University
Center for Latin American Studies
Thesis
Advisor: Algee-Hewitt Bridget