VictorJuiz/thesis huggingface dataset:
Micromechanical modeling of rate-dependent multiphysics fracture propagation

Rate-dependent behavior associated with deformation and fracturing of materials poses significant challenges for modeling. In addition to the complications of the viscoelastic response, the speed of fracture propagation reflects micromechanical mechanisms in the fracture process zone (FPZ). In order to represent these complicated behaviors, a thermodynamically consistent, rate-dependent fracture model is required. Based on rigorous thermodynamic principles, we derive a rate-dependent phase-field mechanical model coupled with single-phase fluid flow in both the matrix and the fracture. The model is guaranteed to satisfy energy conservation during fracture propagation. The model serves as a strong basis for investigating rate-dependent fracturing experiments and for making predictions of material behaviors under new conditions. In addition, we extended the model to different domains such as core-scale breakdown experiments, internal pressure-driven fractures, and multi-scale constitutive modeling. We demonstrated the accuracy of our model in different contexts by performing benchmark experiments against analytical solutions or by comparing the numerical results with experimental measurements. The multi-scale constitutive modeling framework allows us to simulate materials with microscopic fracture networks. We also presented a semi-analytical solution for fracture-matrix imbibition that does not require any tuning/matching parameters. We demonstrated that our model is capable of accurately capturing the nonlinearity of the displacement curves with very little computational cost

Keywords: Energy Resources Engineering.
Advisor: Kovscek Anthony R. (Anthony Robert)
Tchelepi Hamdi
Tartakovsky Daniel
Mineralogy and Scaling Relationships in Shale During Acidification: Models and Experiments

Acidic reactive flow and the dissolution of carbonate minerals in porous media is important in many subsurface activities, such as hydraulic fracturing, carbon geological sequestration and wastewater disposal. The mineralogy of the rock and their evolution in pore structure during acidization are critical factors for predicting subsurface flows, such as leakage, injectability and fluid production. In this work, we studied the interplay between flow, reaction and boundary motion of real rock samples incorporated in microfluidic chips. In particular, we examined how the mineralogy of shale, a fine-grained sedimentary rock, controlled the evolution of its interface, surface area and porosity during acidification, including the scaling between such quantities. The two shale samples used in this study were Marcellus (MCL) and Wolfcamp 2H5 (WFC). The MCL sample is a homogeneously carbonate-rich shale, while the WFC sample is characterized by its grain-like distributions of carbonate. Each shale sample was polished, fractured and positioned inside a fabricated microfluidic chip. Once the system reached steady-state flow conditions, 1% hydrochloric acid was injected into the inlet of the shale embedded microfluidic chip at a flow rate of 1000μL/hour for 3 minutes. Preferential dissolution of carbonate over inert minerals led to the creation of channels and cavities and an increased fracture roughness in the WFC sample. Meanwhile, uniform dissolution into the matrix had smoothened the surface of the MCL sample. Video segmentation and image analyses were performed to quantify the changes in porosity and interfacial length. The results show that the evolution of surface geometry depended strongly on the carbonate component of the shale and their spatial distributions, with a positive correlation between the porosity and interfacial length in the WFC sample and a negative correlation in the MCL sample. In addition, a novel approach was devised to model the observed correlation between mineralogy and changes in surface geometry from acidization mathematically. The devised model captured the scale of the changes in porosity and interfacial length effectively. However, a fitting parameter was necessary to shift the intercept of the model results to align with the experimental results. The evolution of shale interface and porosity depends in a complex way on the carbonate minerals and their spatial distribution. The scale of these changes due to the mineral composition and patterning can be captured through a simplified model.

Keywords:  Department of Energy Resources Engineering
School of Earth Energy &amp; Environmental Sciences
Department of Energy Resources Engineering
Stanford University
acidic reactive flow
shale-embedded microfluidic
Thesis
Advisor: Battiato Ilenia
Minimax regret bounds for stochastic linear bandit algorithms

The multi-armed bandit problem is a popular framework to model online experiments and the ideas in this domain are intended to help making better decisions as more data becomes available to a learner. Stochastic linear bandits are an important subclass of this general problem that have many applications in areas such as dynamic asset pricing and healthcare. In this dissertation, we study this problem and provide an analysis that produces many existing results as well as some new bounds. Our analysis also naturally leads to a new algorithm, called Sieved-Greedy (SG), that reduces the exploration while maintaining strong theoretical guarantees. Furthermore, we show that all of these bounds can be improved significantly under additional gap assumptions. In particular, we show that the regret of Optimism in Face of Uncertainty Linear bandit (OFUL) and Thompson Sampling (TS), two popular algorithms in this domain, scale poly-logarithmically under these assumptions (rather than as $\sqrt T$ ). Next, we focus on TS and prove that, although it achieves near optimal Bayesian regret bounds, it may perform poorly under mild distributional mismatches. Finally, we introduce a data-driven version of TS and prove that, under mild conditions, it achieves minimax optimal regret even in the worst case. An important property of both of our proposed algorithms (SG and improved TS) is that they use the observed data to adjust the rate of exploration

Keywords: Statistics.
Advisor: Bayati Mohsen 
Candès Emmanuel J. (Emmanuel Jean)
Lai T. L.
Minimum-distortion embedding

We consider the vector embedding problem. We are given a finite set of items, with the goal of assigning a representative vector to each one, possibly under some constraints (such as the collection of vectors being standardized, i.e., having zero mean and unit covariance). We are given data indicating that some pairs of items are similar, and optionally, some other pairs are dissimilar. For pairs of similar items, we want the corresponding vectors to be near each other, and for dissimilar pairs, we want the corresponding vectors to not be near each other, measured in Euclidean distance. We formalize this by introducing distortion functions, defined for some pairs of the items. Our goal is to choose an embedding that minimizes the total distortion, subject to the constraints. We call this the minimum-distortion embedding (MDE) problem. This monograph is accompanied by an open-source Python package, PyMDE, for approximately solving MDE problems. Users can select from a library of distortion functions and constraints or specify custom ones, making it easy to rapidly experiment with different embeddings. Because our algorithm is scalable, and because PyMDE can exploit GPUs, our software scales to data sets with millions of items and tens of millions of distortion functions. Additionally, PyMDE is competitive in runtime with specialized implementations of specific embedding methods. To demonstrate our method, we compute embeddings for several real-world data sets, including images, an academic co-author network, US county demographic data, and single-cell mRNA transcriptomes

Keywords: Electrical Engineering.
Advisor: Boyd Stephen P. 
Pilanci Mert
Srivastava Ashok N. (Ashok Narain)
Mining electronic medical records for cancer treatment decisions

With the growing amount of medical data available and the increasing reliance of the medical community on artificial intelligence (AI) tools, there is an emerging demand for techniques that support medical decisions based on patient outcomes. To be adopted, machine learning (ML) tools must be accurate, trustworthy, and interpretable. Clinicians and patients should be able to understand the reasoning due to the high-stakes and sensitive nature of most medical decisions. Ultimately, medical ML tools will inform the decision-making process, empower clinicians and patients, and help improve clinical outcomes. We focus on using electronic medical records (EMRs) to improve retrospective comparative effectiveness research for reliable and interpretable decision-making in oncology. Decision making is fundamentally causal, intervening to improve outcomes. EMRs are a rich source of information that can be used to inform those decisions. Natural language processing (NLP) can analyze the unstructured clinical notes. Our research adapts causal inference, ML, and NLP techniques to discover insights from high-dimensional and high-noise EMR data. We study how we can 1) use NLP to identify cancer treatments, 2) reduce selection bias in observational studies, and 3) build ML tools to supplement radiologist decision making. In the first study, we use EMR notes to identify cancer treatments. We apply the method to prostate, esophagus, and oropharynx cancer datasets. It achieves over 90% accuracy for treatment identification. The method can be used to supplement cancer treatment records and help with future research on treatment planning and comparison. In the second study, we show how clinical notes can be used to uncover potential confounders and adjust for bias in retrospective comparative effectiveness studies. We apply our approach to prostate and lung cancer cohorts and found that we reduce the amount of bias when compared against established randomized control trials. In the third study, we develop a tool that could aid radiologist decision-making for mammogram diagnosis. It quantifies the decision threshold of each radiologist and can improve radiologist consistency and practice

Keywords: Management Science and Engineering.
Advisor: Athey Susan
Shachter Ross D.
Rubin Daniel (Daniel L.)
Misallocation of Capital across Firm Size in Developing Countries

In the development literature, there is a common conception that small firms disproportionately lack access to credit compared to large firms, leading them to under-invest in high return opportunities. Using firm-level data from the World Bank Enterprise Surveys in 106 developing countries, I investigate whether the distribution of returns to capital across firm size is consistent with this conception. Instead of finding higher returns to capital for smaller firms as predicted by the hypothesis, I find that the marginal return to capital increases with firm size in 43 countries. I then explore possible explanations behind this relationship involving heterogeneity in production elasticities, adjustment costs, markups, and risk. I find that a trade-off exists between the higher returns for large firms and higher levels of risk. After determining the relationship between returns to capital and firm size, I estimate that reallocating capital to eliminate misallocation related to firm size would lead to a 5% productivity gain on average.

Keywords:  Department of Economics
Department of Economics
misallocation
developing countries
marginal returns to capital
small and medium sized enterprises
Thesis
Advisor: Klenow Peter
Misconstruction of Older Adults: The Use of Sociotechnical Imaginaries in the Design of Older Adult Monitoring Technologies and the Conflicting Ethical Perspectives of Stakeholders

The older adult population in the U.S. is expected to more than double over the next 40 years and as this segment of the population expands, society will encounter an eldercare crisis. One promising solution is the use of monitoring technologies to ease the burden of care, however as described by STS scholar, Shelia Jasanoff, these technologies are constructed with certain assumptions in the design and implementation process that can be referred to as ‘sociotechnical imaginaries’. Through this thesis I highlight the social impetus, promise, and importance of the development of these technological solutions for senior care. I then explore technologists’ construction of the older adult lived experience – the ‘sociotechnical imaginaries’ - as made known through two real life case studies showcasing misuses of an older adult monitoring tool. My assessment concluded that these sociotechnical imaginaries are biased in their construction of older adult life and do not account for older adults' preferences. These limitations can make eldercare monitoring technologies unable to sufficiently care for older adults, can heighten older adult’s vulnerability, and can negatively impact older adult’s abilities to thrive. Finally, this misalignment of values in the design and implementation of older adult monitoring technologies highlights the conflicting ethical perspectives of eldercare stakeholders.

Keywords: monitoring technologies
eldercare
ethics of care
surveillance
sociotechnical imaginaries STS
Thesis
Misuse and manipulation : the strategic politics of military capacity building

How do states exercise control over military partners, particularly when building their military capacity? Put another way, how do states prevent their partners from misusing transferred military power? Typical solutions to these delegation or principal-agent problems focus on the selection of partners with shared interests or the use conditional inducements that reward compliance and punish misuse. While these strategies are employed by states, they are often insufficient. This dissertation develops the argument that states also turn to a divierse set of strategies with a common theoretical logic that I call "strategies of manipulation." These strategies take a variety of forms including tailored capacity building, resource restrictions, counterbalancing and conflict mitigation. While inducements alter the partner's payoffs to alternative outcomes, manipulation alters the partner's efficiency in producing these alternative outcomes. Specifically, strategies of manipulation create incentives for the partner to use transferred resources to advance the delegating state's objective by mitigating the partner's opportunities to bring about other outcomes through misuse. Using a difference-in-differences analysis of panel data on post-World War II interstate arms transfers and U.S. military training, policymaker interviews and illustrative historical cases, I show that concerns about misuse and strategies of manipulation motivate decision makers at important moments in history and can help explain broad patterns of military capacity building

Keywords: Political Science.
Advisor: Schultz Kenneth A.
Fearon James D.
Krasner Stephen D.
Weinstein Jeremy M.
Mixed-method approaches to employment relationships in team-based online gig work

Fueled by internet-based technologies that enable seamless transactions between globally dispersed individuals, new online platforms are emerging that compose "flash" teams of remote freelancers to collaborate on complex projects in diverse industries. In these provisional teams, relationships are transient, virtual, and often algorithmically mediated. This presents organizational scholars with new intriguing puzzles. First, how do flash teams achieve effective coordination despite their lack of shared context and minimal opportunities for rich interactions? Second, how do workers navigate the uncertainties associated with this way of working, including those arising from being evaluated by algorithmic systems? To answer these questions, I draw on extensive online communication data (2 million Slack messages), the performance records of 117 teams, and over 18 months of field observation from a single "gig" labor platform company that composes virtual project teams of freelance software development professionals. Study 1 develops a natural language processing-based measure of teams' cognitive diversity as expressed in their online interactions and demonstrates its task-contingent relationship with performance. In a complementary inductive approach, study 2 theorizes how leaders' situational behaviors aimed at creating role clarity explain variation in team performance outcomes. Study 3 inductively theorizes the conditions under which freelancers developed heightened levels of trust and engagement in response to an algorithmic ranking system. My findings contribute to research on team effectiveness, team temporal dynamics, and leadership, as well as to the nascent literature on algorithms at work. They also point to several empirical and methodological opportunities for organizational scholars seeking to study novel technology-enabled organizational forms

Keywords: Management Science and Engineering.
Advisor: Goldberg Amir 
Valentine Melissa (Melissa A.)
Christin Angèle
Clement Julien (Julien Alexandre Romain)
Model interpretation and data valuation for machine learning

Machine learning is being applied in various critical applications like healthcare. In order to be able to trust a machine learning model and to repair it once it malfunctions, it is important to be able to interpret its decision-making. For example, if a model's performance is poor on a specific subgroup (gender, race, etc), it is important to find out why and fix it. In this thesis, we examine the drawbacks of existing interpretability methods and introduce new ML interpretability algorithms that are designed to tackle some of the shortcomings. Data is the labor that trains machine learning models. It is not possible to interpret an ML model's behavior without going back to the data that trained it in the first place. A fundamental challenge is how to quantify the contribution of each source of data to the model's performance. For example, in healthcare and consumer markets, it has been suggested that individuals should be compensated for the data that they generate, but it is not clear what is an equitable valuation for individual datum. In this thesis, we discuss principled frameworks for equitable \emph{valuation} of data; that is, given a learning algorithm and a performance metric that quantifies the performance of the resulting model, we try to find the contribution of individual datum. This thesis is divided in 3 sections, machine learning interpretability and fairness, data valuation, and machine learning for healthcare - all linked by the common goal of making the use of machine learning more responsible for the benefit of human beings

Keywords: Electrical Engineering.
Advisor: Zou James
Pauly John (John M.)
Weissman Tsachy
Model-free methods for multiple testing and predictive inference

Recent advances in technology have allowed us to collect, store and process an enormous amount of data, bringing unprecedented challenges to interpretable data analysis: first, the structure of data is often complicated, while model assumptions are hard to justify in practice; second, the algorithms used to analyze the data can be extremely complex---think of the convolutional neural nets---making it difficult to develop validity guarantees for the results. Indeed, it has been noticed by researchers that many of the classical statistical methods fail when applied to the modern type of problems---we need a new set of tools to conduct statistical data analysis in the modern era. This dissertation contributes to the toolbox of statistical data analysis in the modern world by presenting several model-free methods for multiple testing and predictive inference. The methods proposed in this dissertation, building upon knockoffs and conformal inference, bypass the modelling of the data structure and the analysis of complex algorithms, and work as wrappers of other (potentially black-box) existing algorithms. Despite the flexibility of these methods, they are guaranteed to achieve statistical validity under the minimal set of assumptions. The validity and efficacy of these methods are evaluated in extensive numerical experiments. Applying these methods to real genetic and clinical data has led to new scientific insights

Keywords: Statistics.
Advisor: Candès Emmanuel J. (Emmanuel Jean)
Owen Art B.
Tibshirani Robert
Modeling and statistical analyses in global health policy

There is a need for more robust research that can inform decisions which will improve the health of low- and middle-income populations, especially those living in poverty. Enteric water-borne infections, such as cholera and typhoid, and undernutrition persist as leading causes of global child deaths and disproportionally affect the world's poor. This dissertation consists of three chapters aimed at informing policy that can reduce the burden of enteric infections and undernutrition. In the first chapter, I estimated the impact and cost-effectiveness of treatment and vaccination strategies to address cholera in Bangladesh and explored methods to more robustly and efficiently calibrate disease models to available data. I identified techniques to adapt a Bayesian calibration technique, Incremental Mixture Importance Sampling, to address large prior uncertainties in model parameters and biases in cholera reporting data caused by passive hospital-based surveillance systems. I found that routine cholera vaccination of young children is likely to be cost-effective in Bangladesh, but that uncertainties in cholera natural history identified in calibration translate to substantial uncertainties in the optimal vaccination policies that would remain unidentified without proper model calibration. My second chapter focused on using econometric techniques to evaluate the impact that a large US Government global nutrition and agriculture program, Feed the Future, has had on child nutrition outcomes in Sub-Saharan Africa. Feed the Future's focus countries experienced substantially larger declines in child stunting prevalence after the program's introduction, compared to non-focus countries. For my third chapter, I constructed a geographic- and age-stratified dynamic model of typhoid fever in India using primary data from an ongoing surveillance study and projected the incidence reductions and cost-effectiveness of several typhoid vaccination strategies. By incorporating new data on vaccine efficacy, costs of typhoid illness, and typhoid burdens within India, I found that typhoid vaccination is likely to result in net cost savings across most states in India, due to high costs of illness under the status quo and the concentration of burden among children, who are the most likely target population for vaccination efforts. My dissertation research highlights several methodological themes, including the importance of considering population heterogeneities in both statistical analysis and simulation modeling, and yields policy-relevant findings that can aid in more evidence-based decision-making

Keywords: Health Policy.
Advisor: Goldhaber-Fiebert Jeremy D.
Bendavid Eran
Salomon Joshua A.