VictorJuiz/thesis huggingface dataset:
Large Scale CO2 Injection into Basalt Formations: Is it Viable?
Keywords:  Earth System Science
Carbon storage
Basalt
permeability
mineral carbonation
Earth System Science
Earth
Thesis

Geologic storage of CO2 in the subsurface is vital if we are to reach the UNFCC’s goal to limit warming to below 1.5oC or even the less stringent goal of 2oC. Recently, there has been a rise in interest in geologic storage of CO2 storage in Basalt formations, as the mineral make-up presents the opportunity to lock CO2 as thermodynamically-stable carbonate minerals. However, basalt formations are less permeable with depth due to secondary alteration mineral filling and compaction and therefore have limited injectivity. Further, the majority of lateral storage and transmissivity in basalt formations at depth is through interbedded formations, flow contact zones and hyaloclastite formations. We take a hydrologic perspective on CO2 storage in basalt by reviewing the hydrogeology and lithology of major formations from around the world to assess their true storage potential. Using reported permeability and lithological data, we demonstrate that large scale storage of CO 2 (on the scale of a 1Mt/yr CO 2 source), is highly unlikely due to the low permeability, limited thickness and limited availability of highly permeable and thick basalt reservoirs that promote safe storage of CO2 . We find highly permeable interbeds within basalt formations and hyaloclastite formations (marine basalt flows) tend to preserve enough permeability for safe injection. However, they are limited in their distribution and availability when it comes to large CO2 sources.
Large-scale and high-dimensional statistical learning methods and algorithms
Keywords: Statistics.

In the past two decades, many areas such as genomics, neuroscience, economics and Internet services have been producing increasingly big datasets that have high dimension, large sample size, or both. This provides unprecedented opportunities for us to retrieve and infer valuable information from the data. Meanwhile, it also poses new challenges for statistical methodologies and computational algorithms. On the one hand, we want to formulate a reasonable model to capture the desired structures and improve the quality of statistical estimation and inference. On the other hand, in the face of increasingly large datasets, computation can be a big hurdle for one to arrive at meaningful conclusions. This thesis stands at the intersection of the two topics, proposing statistical methods to capture desired structures in the data, and seeking scalable approaches to optimizing the computation for very large datasets. We propose a scalable and flexible framework for solving large-scale sparse regression problems with the lasso/elastic-net and a scalable framework for solving sparse reduced rank regression in the presence of multiple correlated responses and other nuances such as missing values. Optimized implementations are developed for genomics data in the PLINK 2.0 format in R packages snpnet and multiSnpnet respectively. The two methods are demonstrated on the very large and ultrahigh-dimensional UK Biobank studies and see significant improvement over traditional predictive modeling methods. In addition, we consider a different class of high-dimensional problems, heterogeneous causal effect estimation. Unlike the setting of supervised learning, the main challenge of such problems is that in the historical data, we never observe the other side of the coin, so we have no access to the ground truth of the true difference among treatments. We propose adaptation of nonparametric statistical learning methods, in particular gradient boosting and multivariate adaptive regression splines, to the estimation of treatment effect based on the predictors available. The implementation is packaged in an R package causalLearning
Large-scale electronically steerable millimeter-wave antennas for wireless power delivery
Keywords: Electrical Engineering.

The recent expansion of wirelessly connected devices (often termed the Internet of Everything) has intensified the need for safe, long-range wireless power delivery systems. Approaches to long-range wireless power delivery that have been demonstrated to date are lacking in the amount of power than can be safely delivered to small receiving devices. Radio frequency power delivery systems operating at millimeter-wave frequencies open up the far-field distance of the transmitter as a largely unexplored degree of freedom to achieve this goal. However, such systems inherently require a highly directional beam to be steered toward the intended receiver, and electronic means of achieving this goal are typically cost-prohibitive for consumer and industrial applications. The reconfigurable reflectarray antenna architecture enables the design of large, low-complexity electronically steerable antennas, but implementations in the literature use either boutique processes that are difficult to scale or tunable components that consume excessive static power. This work presents the design of a large, 2-d steerable millimeter-wave reflectarray, including a CMOS reflective phase shifter IC. The CMOS phase shifter IC simplifies the design of large arrays by integrating control logic and enables milliwatt-level static power consumption even for large arrays. Flip-chip on printed circuit board construction ensures small silicon area even for large antenna apertures. Measurements of an 874-element demonstration array operating at 61.5 GHz are presented showing beam scanning in two dimensions. The measured aperture efficiency is 8.7 percent with a silicon area of only 0.21 square millimeters per element and a power consumption of 1.1 milliwatt for the entire array
Launch: Learning, Design and Technology Master’s Project Report
Keywords: Graduate School of Education
LDT
social-psychological interventions
mobile technology
stereotype threat
belonging
Thesis

My LDT Final Project aims to utilize the recursive benefits gained from social-psychological interventions - often called WISE Interventions. These interventions have the capacity to improve academic performance for students who are historically and systematically disenfranchised in schools. WISE Interventions can also mitigate negative academic trends that befall students who are habitually disenfranchised by the dominant culture. These interventions show success in laboratory and school settings. The project will build on these successes but will deliver the interventions to secondary students via an app. Initially, the app will reach to students in traditional middle and high school settings be it a charter, an independent, or a district school. In subsequent phases of the project, the app’s use will extend to students re-entering into those schools from an alternative education setting or a juvenile justice center.
Learning from unknown information sources
Keywords: Business.

When an agent receives information generated by a source whose accuracy might either be high or low, standard economic theory dictates that she update as if the source has medium accuracy. In a lab experiment, I find that subjects' updating behaviors deviate from this benchmark. First, subjects under-react to information when the source is uncertain. Second, the under-reaction is more pronounced for good news than for bad news. These two patterns, under-reaction and pessimism, are consistent with a theory of belief updating where agents are insensitive and averse to compound uncertainty and ambiguity. I also find that subjects' reactions to information with uncertain accuracy are uncorrelated with their evaluations of bets with uncertain odds. This suggests that people have distinct attitudes toward uncertainty in information accuracy and uncertainty in economic fundamentals. The experimental results are validated using observational data on stock price reactions to analyst earnings forecasts, where analysts with no forecast records are classified as uncertain information sources
Learning genomic and molecular mediators of genotype-phenotype associations
Keywords: Biomedical Informatics.

The vast majority of genomic variants are non-coding, and many disrupt regulatory elements, causing dysregulation of gene expression. However, the functional mechanisms by which non-coding variants operate at the molecular level, as well as their tissue-specific downstream effects on cellular, organismal and disease phenotypes remain challenging to decipher. Firstly, complex phenotypes such as physical activity patterns are difficult to characterize and measure. Secondly, even after inferring statistical associations between genetic loci and complex phenotypes, identifying the causal variants is challenging due to the issues posed by linkage disequilibrium. Finally, the elucidation of functional molecular mechanisms that mediate the manifestation of genotypic variation to phenotypic effects remains an open challenge in the field. This thesis attempts to address these three challenges via the development and application of statistical and deep learning approaches to mine large genomic, molecular and phenotypic datasets. The MyHeart Counts study serves as an example of how wearable and mobile technologies enable unobtrusive real-time measurements of complex phenotypes such as exercise and physical activity patterns. These technologies also enable rapid recruitment of large study cohorts and facilitate fully digital randomized controlled trials with low barriers to entry. Such technologies also facilitate the compilation of population-level biobanks, such as the UK Biobank by enabling acquisition of lifestyle and activity data at scale. Having acquired complex phenotypes on large data cohorts, we can begin to investigate the effects of genomic variation on these phenotypes by performing genomewide association studies (GWAS). Functional GWAS SNPs can be identified via in silico interrogation of predictive deep learning models of regulatory DNA. Here, I present convolutional neural network models trained on genome-wide chromatin profiling experiments to interpret and finemap GWAS SNPs by leveraging their ability to learn predictive DNA sequence syntax. Case studies in colorectal cancer and Alzheimer's disease are presented to illustrate the application of these methods. To improve the model stability and interpretability, I developed deep learning models that can predict regulatory chromatin profiles at single base resolution, accounting and correcting for confounding experimental biases. I also contributed to several collaborative investigations of the molecular basis of complex cellular phenotypes. We identified the Sp1 regulatory protein as a key regulator of matrix stiffness and induction of tumorigenic phenotypes in mammary epithelium; the PI3K pathway as a key modulator of efficiency of stem cell differentiation and transcription factor networks that regulate murine muscle stem cell aging through differentiation. In summary, this thesis presents new computational approaches for linking genotype to phenotype through mechanistic molecular mechanisms
Learning is its Own Reward: Exploring Worlds with Curiosity-driven Spiking Neural Networks
Keywords:  Department of Symbolic Systems
reinforcement learning
symbolic systems
curiosity
spiking neural networks
neural networks
sample-efficiency
generalization
machine learning
Thesis

While curiosity is an important part of human learning, it is rarely explicitly a part of machine learning: given some objective, machine learning algorithms optimize directly to get the best possible score. This has led to numerous impressive results but needs many examples and fails in situations where performance feedback is limited. Approaches attempting to incorporate curiosity generally still use a top-down optimization paradigm.
However, spiking neural networks (SNNs), which aim to better mimic biological neurons, present an alternative. Research has shown that local, spike-timing-dependent learning rules allow for the objective-free and bottom-up learning of complex patterns in data. But, limited work has been done applying spiking neural networks in contexts where they must interact with a simulated world making a series of actions.
We show that by combining bottom-up and top-down learning mechanisms in an SNN, namely spike-timing dependent plasticity (STDP) and backpropagation, and rewarding the SNN agent for the amount that it learns, as measured by its STDP update before modulation, our agent quickly learns to explore its environment in increasingly complex ways and seek increasingly novel situations. We demonstrate that, in terms of extrinsic rewards, this approach often results in better performance much more quickly (Up to 1,510 times faster with better performance for some environments and state-of-the-art models) than other curiosity-driven and general reinforcement-learning methods.
Left to Our Own Devices: Smartphone use, Mental Health, and Tech Responsibility
Keywords:  Symbolic Systems Program
Smartphones
Mental Health
Phone Addiction
Ethics of Technology Companies
Thesis

Smartphones are often necessary for daily life. Yet there is growing scholarly and public awareness of negative associations between smartphone use and mental health. Over the past decade, three narratives have emerged. First, the Attention Narrative argues that smartphones undermine focused attention and that this, in effect, is by design. Second, the Poison Narrative argues that prolonged, regular smartphone use causes mental illness, particularly depression. Third, the Problematic Use Narrative argues that excessive smartphone use is a pathological behavioral addiction. Ultimately, strong evidence supports only the Attention Narrative. Research studies demonstrate that smartphones undermine focused attention, and case studies show that popular apps are specially designed to maximize engagement, leading to overuse. Given that most users cannot abstain from their smartphones, technology companies ought to make it easier for users to manage their screen-time. Technology companies are responsible for enabling users to choose more freely how they spend their attention.
Leveling down, all things considered
Keywords: Political Science.

Because all cases of leveling down involve harms without offsetting gains along some dimension of well-being, resources, or opportunities, there is a broad agreement across the political spectrum regarding cases like these: essentially, that one must never level down to equality, all things considered. And yet, this never-level-down conclusion would seem to rule out some practices that, at least in democratic societies, are widely held—and for good reason. Consider, for instance, the egalitarian practice of giving one vote—and one vote only—to all members of the political community. According to the leveling down objection, this practice can only be defended on instrumental or non-egalitarian grounds. "One person, one vote" (OPOV) should be otherwise abandoned if it does nothing to serve the cause of utility, freedom, autonomy, or the like. This sort of thinking seems to me deeply misguided. Of course, having the right to vote in the upcoming election might make my life go better, but that stipulated fact does not seem to capture all the ways in which it might matter for me to have, like you, one vote. And more importantly, this way of thinking about the value of "one person, one vote" (OPOV) gives the wrong sort of response to a hypothetical case in which we would all be better off under epistocratic or other forms of non-democratic authority, both of which seem objectionable under straightforwardly egalitarian grounds. I argue in this dissertation that a reasonable theory of distributive justice will level down to equality in some cases, as evidenced by the principle of "one person, one vote." I argue that this practice is valuable given a certain understanding of what sorts of relationships are required by justice: egalitarian relationships not marked by different forms of domination and subordination. And I argue, too, that this grounding of the value of OPOV might have implications for the distribution of other things that matter: resources among them
Leveraging large-scale genetic data for drug discovery and mechanistic understanding
Keywords: Bioengineering.

Genotype-phenotype associations, which have been discovered in abundance via genome-wide association studies (GWAS) during the past 15 years, offer a valuable roadmap to elucidate the mechanistic underpinnings of disease. One of the primary objectives of this "post-GWAS era" is to identify causal genes that mediate these associations with the broader aim of developing effective therapies. Large-scale biobank datasets offer the possibility of conducting data experiments with human data in addition to in vitro and in vivo data to identify causal genes and evaluate the long term impact of therapies targeting them. Individuals who harbor genetic variants that alter the function of disease-causing genes form a valuable cohort that can be used to evaluate these long term effects. In this thesis, I demonstrate the utility of genetic evidence to predict therapeutic effects for metabolic diseases and liver diseases with drugs currently in clinical trials. I present methodological advances to evaluate genetic evidence, and present results that were subsequently confirmed in clinical trials. In addition, I identify the causal gene at a GWAS locus for metabolic disease and delineate its mechanism of action using multi-omic data from humans and in vitro/in vivo knockout models. Identifying the causal gene is quite challenging in such cases, where associations occur in non-coding regions and phenotypes are complex combinations of biological effects in different tissues. I demonstrate a tissue specific mechanism of action for variants in the locus that confer carriers with a predisposition to a complex normal-weight "metabolically obese" phenotype. I present lessons learned from integrating multi-omic data to discover causal genes and develop a method for conducting these data integration studies
Leveraging simulations to improve focused ultrasound brain treatments
Keywords: Bioengineering.

Transcranial focused ultrasound (tcFUS) is a non-invasive therapeutic modality that can be used to induce thermal or biomechanical effects in the brain. The most common applications include thermal ablation as an alternative to open brain surgery, neuromodulation to probe the circuitry of the brain, and blood brain barrier opening to improve targeted drug delivery into the brain. For these tcFUS applications, the intact skull poses a major technical challenge. It defocuses the ultrasound focal spot and shifts the focal spot away from the target location. To overcome this, a common approach is to model the skull and estimate corrections that can account for its aberrating effects. Although skull modeling and correction estimation is currently utilized for thermal ablation treatments, there is ample room for improvement in terms of treatment safety and efficiency. In this dissertation, I present three contributions towards improving the safety and efficiency of tcFUS procedures: (1) a rapid beam simulation framework to predict focal spot position and temperature rise prior to the delivery of acoustic energy, (2) use of the same simulation framework to improve focusing through the intact skull, and (3) application of magnetic resonance images as an alternative to computed tomography images for generating skull models used for correction estimation
Leveraging structure in the analysis of high-dimensional biological data
Keywords: Electrical Engineering.

With the advancement of technologies to collect data automatically, researchers now have access to large datasets unimaginable until recently. In biology and medicine, multiple types of data have been generated in massive quantities with the promise to explain complex biological phenomena. While previously data acquisition was the most costly process, the main bottleneck today has become data analysis because many datasets also contain hyper- informative details which result in high dimensionality. Detecting signals in such data is like searching for a needle in a haystack. Fortunately, certain structures underlying the data can provide opportunities to make the analysis statistically powerful and computationally efficient. Borrowing insights from different structures in the data, I have developed efficient, principled, and interpretable methods to analyze high-dimensional data, with a focus on the modern biological applications. In this thesis, I first introduce a method to analyze high-throughput single-cell data, combining gene expression and immuno-sequencing data. Next, I propose GLISS, a novel framework that integrates Spatial Gene Expression data with single-cell RNA-sequencing data to simultaneously select spatial gene features and identify hidden spatial cellular structures. GLISS utilizes a graph-based feature selection method that is sensitive to non-monotonic associations to determine spatial genes. Finally, I present AEGIS, an exploratory data analysis method for Gene Ontology applications. AEGIS entails new visualization strategies that leverage the Directed Acyclic Graph structure of the Gene Ontology to facilitate information retrieval and power calculations for research study design
Levraging multi-omics integration to characterize human cancer
Keywords: Genetics.

Omic technologies have been applied to cancer since they were invented and have yielded extremely powerful results. Common tumor types have been profiled extensively for their cancer drivers and genetic landscapes, identifying the extent of to which mutations alter the gene expression in cancer. However, other molecular features have not been comprehensively studied. As omic technologies become more widely available and more accurate, the unprofiled molecular landscape of cancer is ripe for investigation. This dissertation focuses on scientific investigations that utilize state-of-the-art omic tools to characterize the molecular phenotypes in human cancers. In addition, this dissertation addresses the topic of diversity, equity, and inclusion (DEI) and shares results from a program that I founded for Stanford Biosciences and the Medical Scientist Training Program (MSTP). The first chapter provides an overview of gene regulation studies based on multi-omics methodology, current molecular knowledge of thyroid cancer, and the state of DEI in biomedical graduate training programs. Chapter 2 clarifies mechanisms in which chromatin regulation affects protein-transcript correlation in progression and development of thyroid and breast cancer. It points to an interesting mechanism by which transcription factors mediate coordinated transcription and translation. Chapter 3 outlines the preliminary findings from a multi-omic analysis of genotype, chromatin regulatory landscape, transcripts, and proteins in metastatic thyroid cancer. This analysis classifies tumors and metastases according to regulons that have direct and indirect effects on the comprehensive profile of biomolecules. Chapter 4 describes an approach to a diversity recruitment program that has potential to scale across biomedical graduate programs. Results from the program show there are many underrepresented minorities that have the potential to thrive in biomedical training if supported. Lastly, Chapter 5 discusses the potential impact of multi-omics integration on the cancer field and other disease types. Taken together, these chapters represent key insights, improvements, and applications of multi-omics integration on human disease and opportunities for our graduate programs to be more engaged with DEI