VictorJuiz/thesis huggingface dataset:
Machine learning for computational engineering

This dissertation presents my work on solving inverse problems in computational science and engineering using a combination of partial differential equations (PDEs) and deep neural networks (DNNs). By substituting unknown parts of a physical model with DNNs and expressing known physical laws with PDEs, we preserve physical structures (e.g., conservation of momentum) to the largest extent while leveraging DNNs for data-driven function approximation. To train the DNNs within a physical system, we express both numerical simulations (e.g., finite element method) and DNNs as computational graphs and calculate the gradients using reverse-mode automatic differentiation. We have built a system of reusable and flexible numerical simulation operators in our software, ADCME, and AdFem, to help users implement sophisticated numerical schemes using a high-level language, Julia, without constructing computational graphs explicitly. Our software has benefited a variety of engineering applications, such as seismic inversion, neural-network-based constitutive modeling, inverse modeling of Navier-Stokes equations, etc. We have also extended ADCME to large-scale inverse modeling using MPI and successfully deployed distributed inverse modeling optimization algorithms across thousands of cores. My major contribution includes: - Algorithms: first order physics constrained learning for calculating gradients of implicit operators, and second order physics constrained learning for calculating Hessians. These techniques find novel applications in coupled systems of PDEs and DNNs. For example, we proposed trust region methods for training deep neural networks embedded in a PDE solver, which enjoy remarkably faster convergence and better accuracy than first order optimizers in our cases. - Software: ADCME, a general automatic differentiation framework for inverse modeling, and AdFem, a computational graph based finite element simulator. We also extended their capability to large-scale problems via MPI. - Applications: many fruitful applications, such as DNN-based constitutive modeling for solid mechanics and geomechanics, calibrating Lévy processes from sample paths, and learning unknown distributions from indirect data in a physical system

Advisor: Darve Eric
Ermon Stefano
Iaccarino Gianluca
Machine learning for FEL optimization

With improvements in linear accelerator (Linac) technology, high-energy electron beams accelerated and compressed by Linac systems can radiate in a high-gain X-ray Free Electron Laser (XFEL). After more than a decade of successful operation at the LINAC Coherent Light Source (LCLS) and other facilities around the world, the scientific community from various fields has a strong demand for further increase in the peak and average power of the next generation of XFELs. Reaching higher peak power is critical for coherent diffraction imaging of complex molecules, a key scientific motivation for the LCLS, which could lead to medical discoveries and drug development, for example, by imaging the structure of a complex virus in situ. In addition, higher power X-rays can facilitate fundamental research in strong field physics and other fields. The first part of the work to be discussed in this dissertation is the theoretical study of achieving higher XFEL power by implementing undulator tapering and related optimization algorithms. Second, simulation and experimental results of taper optimization are presented, showing that with the new devices in the LCLS-II including phase shifters and the variable gap undulator, and with the advent of different kinds of Machine Learning (ML) algorithms, FEL power can be successfully optimized. The next part of this work investigates methods of retrieving transverse and longitudinal information of the X-ray pulses including both power and phase and illustrates the impact of the optimized FEL power. Finally, the longitudinal field reconstruction research is carried out for explanation of the longitudinal pulses, including both power and phase. This research establishes novel techniques for FEL optimization and demonstrates that the improved devices and ML algorithms are promising experimental techniques to maximize FEL power

Keywords: Applied Physics.
Advisor: Huang Zhirong
Raubenheimer Tor O.
Aiken Alexander
Wu Juhao
Machine learning for satellite imagery when labels are scarce

As the world aims to achieve the UN Sustainable Development Goals by 2030, data gaps in the developing world make it difficult to measure progress and target interventions. Recent rapid advances in computer vision and satellite imagery acquisition offer opportunities for automatically extracting knowledge about our planet from space. Compared to field surveys, which are the traditional source of knowledge about human activities and natural ecosystems, satellites offer global coverage at low marginal cost. However, many regions of interest around the world lack ground truth labels on which to train machine learning models. This dissertation will cover two strategies for mapping our planet when labels are scarce: (1) learning better features on satellite imagery to maximize label use efficiency and (2) using non-traditional data sets as ground truth. The application area of focus is agriculture, with a particular focus on cropland and crop type mapping, but the methods and data modes considered can be applied to any domain that uses remotely sensed data

Advisor: Lobell David
Ermon Stefano
Gerritsen Margot (Margot G.)
Machine learning methods for prediction of cardiovascular diseases

Artificial intelligence and machine learning methods in cardiology are increasingly used in a wide array of remote monitoring applications and within clinical workflows. The acceptance of such methods is due to the demonstrated benefits of deep learning algorithms to achieve state-of-the-art results and surpass human accuracy in challenging clinical tasks. Concurrently, the advancements in wearable technology have allowed for an unprecedented look into human health and activity. This snapshot into the objective measurements of human health can enable clinicians, researchers, and patients to measure and track a new dimension of human health at a granularity not previously possible. Herein, I present work that focuses on methods to advance remote monitoring and prediction models for cardiovascular diseases. First, I introduce a multi-task deep learning method to jointly assess signal quality and arrhythmia event detection in wearable photoplethysmography devices for real-time detection of atrial fibrillation. Second, I describe a multimodal neural network model to predict the etiology of left ventricular hypertrophy from electrocardiograms and echocardiograms. Together, these results demonstrate new applications of machine learning tools that may assist in the study of cardiovascular disease progression inside and outside a clinical setting

Keywords: Medicine. Department of Biomedical Data Science
Advisor: Bustamante Carlos (Carlos D.)
Wall Dennis Paul
Zou James
Making connections and investments : an ethnography of China-to-US venture capital business

From the second half of 2014 to the first half of 2018, with Chinese government's financial and policy support and also a relatively relaxed US-China trade relation, unprecedented amounts of money from Mainland China flooded Silicon Valley and became an important new source of capital for tech startups in the US. This wave of China-to-US venture investment, which is a reversal of more than two decades ago when the US money poured into China, was channeled by a class of Chinese transnational venture capitalists who shuttled between the two countries, navigated and mediated flows of information, capital and people. Given the changing US-China relation, Chinese venture capitalists have to develop different strategies to craft their Chinese identities in order to secure, legitimize and even obtain an advantage when competing with their US counterparts. With their Chinese characteristics, they are not "just a member" of the local investor community in Silicon Valley. Rather, their status and business activities have always been embedded in and understood through the transnational context. My dissertation studies this wave of China-to-US venture capital business from 2016 to 2018. Starting with a historical review of its emergence and decline, my dissertation is mainly concerned about exploring the central ideas and practices through which Chinese venture capitalists manage uncertainty and maximize capital gains from transnational investment. As they describe venture capital being a "people-centered" business, networking, or in their words, "jianli guanxi" (building guanxi/建立关系), has been the key. Through actively making and managing their web of guanxi, Chinese venture capitalists recruit talents, sort startup deals, evaluate investment cases and exit at the right time and at the right price. Contrary to the stereotype of venture capital being a male-dominated industry, my fieldwork reveals that Chinese women have taken a leading position in cultivating and maintaining these human relationships which support the transnational venture capital business. My dissertation thus has a special focus on women venture capitalists and explores how they, as active and full participants who are emotional and rational beings, initiate and engage in cultivating guanxi. In addition to accomplishing business goals, I argue that the process of making connections also produces values and sentiments which structure the business and also shape their life as venture capitalists

Keywords: Anthropology.
Advisor: Yanagisako Sylvia Junko
Ferguson James
Kohrman Matthew
Zhou Xueguang
Mano a mono : monocytes across disease and vaccination

The innate immune system is the first line of defense against pathogens. Known best for its quick but short-lived effects, the role of the innate immune cells in chronic disease and vaccination has remained relatively unexplored. This thesis first describes the powerful prognostic use of monocytes, a component of the innate immune system, to better stratify patients with idiopathic pulmonary fibrosis for organ transplant. Next, we demonstrate the long-term response of monocytes in vaccines adjuvanted with 3M-052, a novel innate-immune specific adjuvant. Finally, we show that monocytes are critical responders to the Pfizer-BioNTech COVID mRNA vaccine in the largest single-cell study of an mRNA vaccine to date

Advisor: Davis Mark M.
Khatri Purvesh
Pulendran B. (Bali)
Utz Paul
Mapping the structure-activity landscape of ARHGAP36

ARHGAP36 is an atypical Rho GTPase-activating protein (GAP) family member that drives both spinal cord development and tumorigenesis, acting in part through an N-terminal motif that suppresses protein kinase A and activates Gli transcription factors. ARHGAP36 also contains isoform-specific N-terminal sequences, a central GAP-like module, and a unique C-terminal domain, and the functions of these regions remain unknown. In this dissertation, I describe our three-part approach to map the ARHGAP36 structure-activity landscape. Using splice variants and truncation mutant analyses, we identified an isoform-specific N-terminal sequence that inhibits ARHGAP36-mediated Gli activation and suppresses ARHGAP36 recruitment to the plasma membrane. The GAP-like and C-terminal domains can counter these inhibitory effects, in part by promoting trafficking to the plasma membrane and primary cilium, respectively. Through a high-throughput mutagenesis screen, we further discovered several residues in the GAP homology domain that are essential for Gli activation. Finally, we applied these variants in a comparative proteomic analysis that identified several GAP-like domain-dependent binding partners and demonstrated one interactor, prolyl oligopeptidase-like protein, to be a direct ARHGAP36 antagonist. In combination, these systems-level analyses have revealed an ensemble of mechanisms that could enable context-specific regulation of ARHGAP36 activity, establishing an experimental framework that can be applied towards other signaling proteins

Keywords: Chemical and Systems Biology.
Advisor: Chen James Kenneth
Beachy Philip Arden
Jackson Peter K. (Peter Kent)
Meyer Tobias
Market design for platforms, large games, and comparative statics

In recent years the amount of data collected by online platforms has increased massively. These data, together with the unique ability of online platforms to design their marketplaces, provide platforms with an unprecedented opportunity to make better market design choices to enhance the welfare of platforms' participants and increase platforms' revenues. The first chapter of my dissertation (co-authored with my advisors Ramesh Johari and Gabriel Weintraub) studies one such market design problem that relates to quality selection. Online markets typically consist of many small buyers and sellers, and thus, in order to analyze market design decisions in online platforms it is crucial to model and to have a better understanding of large games, i.e., settings with many interacting agents. The second chapter of my dissertation (co-authored with Gabriel Weintraub) studies some properties of mean field models which are used to model settings with a large number of interacting agents. The third and fourth chapters of my dissertation provide tools that enable deriving comparative statics results in complex uncertain environments

Keywords: Business.
Advisor: Johari Ramesh
Weintraub Gabriel 
Bimpikis Kostas
Ostrovsky Michael
A mass-conserving, cut-cell method to simulate scour formation with vertical Cartesian coordinates

Scour is the primary cause of bridge failures in the United States and accurate field-scale simulations of scour are required for bridge stability analysis. In current bridge design practice, empirical equations based on laboratory flume tests are utilized to determine scour depth. These equations have been proven to be conservative due to scale effects when applied to field-scale scour conditions. Therefore, computational fluid dynamics (CFD) solvers have been utilized to simulate flow behavior around bridge piers. However, the majority of current CFD solvers do not accurately estimate the large-scale scour depth in field conditions, and mass conservation during scour is typically not satisfied. Therefore, a new CFD solver is proposed in this dissertation with new features including a moving-bed, cut-cell model, and mass-conservative sediment transport. The new moving-bed cut-cell model is based on a Cartesian grid system in the vertical coordinate (z-coordinate), and the bed depth is defined at vertices of the unstructured horizontal mesh. This discourages discontinuities in the bed and allows a more accurate representation of the bathymetry. The new model ensures that the total sediment mass within the bed and flow is conserved during bed movement and sediment transport. The proposed three-dimensional (3D), moving-bed and mass-conserved CFD solver has been applied to various scour studies and validated with flume tests in terms of flow velocity and scour patterns. The moving-bed cut-cell model provides accurate, continuous, and smooth simulation of the flow field and scour patterns and has been validated with the results of several experiments

Advisor: Billington Sarah
Fringer Oliver B. (Oliver Bartlett)
Freyberg David L.
Materials design and electrochemical engineering for pollution control

The ultimate goal of sustainable development is to preserve the Earth for our future generations. Clean water, fertile soil and fresh air are basic necessities for human wellbeing; however, they are among the natural resources most vulnerable to pollution. Using my expertise in materials science, catalysis, and electrochemistry I developed during my doctoral studies, I synthesized unique materials and designed novel reactors to find solutions for various environmental problems. In Chapter 1, I will motivate the need for developing electrochemical technologies for solving environmental problems and give a brief introduction to the thermodynamics and kinetics in electrochemical systems. Chapter 2 will describe my efforts in reducing the energy and chemical demands for organic wastewater treatment. Specifically, I discovered that copper single atoms incorporated in graphitic carbon nitride can catalytically activate hydrogen peroxide to generate hydroxyl radicals, which then non-selectively oxidizes organic pollutants into harmless small molecules. To further eliminate the dependence of the treatment system on H2O2, I developed a H2O2 electrolyser for on-site generation of H2O2 from air, water and renewable energy. These two innovations work in tandem to deliver a wastewater treatment system with substantially reduced energy and chemical inputs compared with conventional advanced oxidation processes. Chapter 3 will demonstrate a method for removal of heavy metals from contaminated soil. This method is composed of a recirculating soil washing system using EDTA solution and an electrochemical filter that removes heavy metals from the EDTA solution by electrodeposition. I discovered that applying an alternating-current bias with an optimal waveform mitigates side reactions and enhances the electrodeposition efficiency of the electrochemical filter compared with applying a direct-current bias. In addition, the regeneration ability of the electrochemical filter makes this platform suitable for recovery of heavy metals from a diverse range of waste streams. Chapter 4 will present a way to promote the catalytic activity of platinum for the degradation of formaldehyde in air, namely by covering the platinum surface with a nanoscale-thin layer of aqueous electrolyte. This discovery was made possible by a platinum-coated nanoporous polyethylene membrane, which is able to construct and retain an evaporation-stable aqueous electrolyte layer over platinum with an effective thickness of just a few tens of nanometers. In a broader context, my work demonstrates that the knowledge obtained through the development of gas diffusion electrodes in fuel cells can be applied to gas-phase heterogeneous catalysis beyond electrochemical systems

Keywords: Materials Science and Engineering.
Advisor: Cui Yi
Mitch William A. 
Prinz F. B.
Mean field methods for stochastic control and optimization problems

In probability theory and physics, mean field methods study the dynamics of high dimensional stochastic models by applying the law of large numbers in a sophisticated form, in order to obtain simpler models that approximate the original system. In the setting of a multi agent system, the basic idea is to replace the individual interaction effects induced by other agents upon a single agent with an averaged effect. We can reduce a complex, multi agent system to a single agent problem. In this thesis, we study the use of mean field methods in some stochastic control and stochastic optimization problems. In particular, we study mean field analysis of distributed optimization algorithms, and mean field analysis of a class of stochastic control problems with many entities

Keywords: Mathematics.
Advisor: Blanchet Jose H.
Glynn Peter W.
Papanicolaou George
Measurement of hall conductivity and gated magnetization by torque magnetometry

This thesis details the development of a new method to measure Hall conductivity for highly resistive samples utilizing a torque magnetometry method. Traditional methods to measure the Hall effect are inaccurate for highly resistive samples due to pollution from longitudinal resistance and sample heating. We have developed a new technique to measure the Hall effect using dissipationless currents and without point contacts to address such problems. A Corbino disk exhibits a magnetic dipole moment proportional to Hall conductivity when voltage is applied across a test material. This magnetic dipole moment can be measured through torque magnetometry. The symmetry of this contactless technique allows for the measurement of Hall conductivity in previously inaccessible materials. I first describe the method for fabricating cantilevers with integrated Corbino disks and the procedure used to measure Hall conductivity with such devices. The primary measurements of Hall conductivity through the new Corbino disk torque technique were performed on indium tin oxide (ITO). After performing tests for systematic errors with applied voltage and current, we measured the Hall conductivity of conductive ITO as a reference material to establish the accuracy of the Corbino disk torque technique. We next fabricated Corbino disks with resistive ITO and annealed across the metal-insulator transition to measure Hall conductivity of highly resistive samples. After annealing, we found magnetism in the ITO through both bulk torque magnetometry and the anomalous Hall effect. Such findings demonstrated both the accuracy of the new technique and the presence of debated inherent magnetism in annealed ITO. Having integrated a Corbino disk on a cantilever to measure the Hall effect, we are now expanding our ability to fabricate and measure cantilevers with integrated circuit elements. We have begun to measure torque magnetometry of miniaturized cantilevers with integrated gates. The initial miniaturized cantilevers were constructed to hold exfoliated graphene-hBN stacks. Smaller designs improved the torque sensitivity by a factor of 1000. Finally, such miniaturized cantilevers with metal layers could be extended in the future to measure Hall conductivity of smaller exfoliated flake samples

Keywords: Physics.
Advisor: Kapitulnik Aharon
Hwang Harold Yoonsung
Kivelson Steven
Measuring and enhancing the security of machine learning

The surprising failure modes of machine learning systems threaten their viability in security-critical settings. For example, machine learning models are easily fooled by adversarially chosen inputs, and have the propensity to leak the sensitive data of their users. In this dissertation, we introduce new techniques to proactively measure and enhance the security of machine learning systems. We begin by formally analyzing the threat posed by adversarial examples to the integrity of machine learning models. We argue that the security implications of these attacks has been overstated for many applications, yet demonstrate one application where these attacks are indeed realistic---for evading online content moderation systems. We then show that existing defense techniques operate in fundamentally limited threat models, and therefore cannot hope to prevent realistic attacks. We further introduce new techniques for protecting the privacy of users of machine learning systems---both at training and deployment time. For training, we show how feature engineering techniques can substantially improve differentially private learning algorithms. For deployment, we design a system that combines hardware protections and cryptography to privately outsource machine learning workloads to the cloud. In both cases, we protect a user's sensitive data from other parties while achieving significantly better utility than in prior work. We hope that our results will pave the way towards a more rigorous assessment of machine learning models' vulnerability against evasion attacks, and motivate the deployment of efficient privacy-preserving learning systems

Advisor: Boneh Dan
Liang Percy
Valiant Gregory