VictorJuiz/thesis huggingface dataset:
Decision-making with matrix-shaped data

Research on matrix-shaped data has focused on conducting statistical inferences from a matrix based on a sparse number of its observations in a high-dimensional setting, with important applications for recommendation systems, revenue management, marketing, and healthcare operations. In response to the increasing popularity of online platforms, experimentation and causal inference, this thesis addresses the following new and pressing problems: how to leverage non-uniform data patterns in a matrix to recommend favorable products and estimate treatment effects more accurately, how to adapt low-rank matrix structure in online experimentation to save costs, and how to better optimize directly from the matrix-shaped data without the estimation interlude. The first part of this thesis starts with an algorithm called NU-Recommend for learning user preferences to optimize recommendation systems using non-uniform matrix-shaped data. It then ventures into the causal inference domain and applies the NU-recommend algorithm to recover missing values in the panel data, leading to more accurate "synthetic control" methods to measure the efficacy of treatment as the missing pattern in the panel data is highly non-uniform. Online learning is state-of-the-art practice in many companies, as they have widely adopted online experimentation such as multi-armed bandit (MAB) to seek the best product for their customers among periodically refreshed product catalogs. This thesis then introduces the Low-Rank Bandit algorithm that can reduce the opportunity cost of online experimentation by leveraging low-rank structure in matrix-shaped data. This allows for a transition from conducting a one-time prediction with offline data as performed by NU-Recommend to conducting prediction online as the data is collected in real time. Finally, this thesis goes beyond the traditional "estimate then optimize" regime which all previous three parts have followed, and instead designs a direct method that goes from data to decision in one step. This method paves the road for future work of going directly from matrix-shaped data to decision making by leveraging the properties of the matrix structure

Keywords: Business.
Advisor: Bayati Mohsen
Spiess Jann
Xu Kuang
Deep generative modeling for structure-guided protein design

In recent years, deep generative modeling has garnered attention as a powerful way to approximate the probability distributions of high-dimensional data, and synthesize an infinite amount of novel data from finite datasets. In the field of protein design, where structural data is low in abundance and expensive to obtain, generative modeling has the potential to "fill" many gaps in known conformational space, increasing the scope of designable proteins, and allowing us to automate design in a data-guided way. This thesis describes my work in developing and training generative models for computational protein design, and describes the reformulation of a general protein design task as a conditional generation problem, solved by latent variable optimization within a deep generative model. I begin with a description of early explorations of protein representation and computer vision methods, leading to the development of a new 3D generative model for proteins, and culminating with the invention of an algorithm that uses generative design to create binding proteins against arbitrary user-specified epitopes. We name this algorithm "Sculptor"

Keywords: Biochemistry.
Advisor: Huang Possu
Das Rhiju
Harbury Pehr
Kim Peter
Deep learning for inverse design of photonic devices

Inverse design of photonic devices is to use optimization algorithms to discover optical structures for desired functional characteristics. However, most of these inverse design problems are non-convex in a very high-dimensional space. This thesis will discuss the use of deep learning as an efficient tool for the inverse design of photonic devices. First, I apply Generative Adversarial Networks (GANs) to 3D metagrating design to augment high-performing device patterns. Next, I introduce Global Optimization Networks (GLOnets) which replace the discriminator in GANs with an electromagnetic solver and then train the generator directly from the solver by backpropagating gradients calculated by the adjoint variable method. Then we apply GLOnets to optical multi-layer thin-film stack design. Next, we analyze the mathematical principles behind GLOnets and explain their advantages. Finally, I discuss the application of neural networks as surrogate simulators to speed up simulations in the inverse design. Overall, we envision that combing the modeling capability of deep neural networks and existing physics knowledge could transform the way photonic systems are simulated and designed

Keywords: Electrical Engineering.
Advisor: Fan Jonathan Albert
Wetzstein Gordon
Zou James
Deep representations with learned constraints

Learning to extract the task-relevant features from high-dimensional data is an important challenge in machine learning. The recent success of machine learning is largely attributable to the advancement of deep neural networks, which transform the data into a new representation that is amenable to downstream machine learning algorithms. Deep neural networks thus treat the extraction of task-relevant features as a representation learning problem to be solved jointly with the task of interest via end-to-end training. This dissertation dives deeper into the process of representation learning and argues that it is often possible to supplement the existing training signal by imposing additional constraints on the learned representations. These constraints allow us to imbue the representation space with desirable traits that are known a priori to benefit the downstream task of interest. In this dissertation, we shall consider examples of various tasks and then show how to leverage insights about the task to constrain the representation in a beneficial manner. These insights are task-specific, taking advantage of specific characteristics of the task to determine a suitable constraint on the latent space (e.g., imposing smoothness, imposing a information-prioritization scheme, or capturing a specific explanatory factor of interest, et cetera). Our demonstrations broadly cover task categories ranging across domain alignment, control, and generative modeling---thus demonstrating the ubiquitous efficacy of designing and imposing task-specific constraints during representation learning

Advisor: Ermon Stefano
Bui Hung 
Kochenderfer Mykel J.
Ma Tengyu
Deforming modernity : the experience of horror in Italian literature

This dissertation studies horror as a form of experience and its aesthetics in works of modern Italian literature not belonging to the horror genre. My phenomenology of horror detaches it from the monstrous, the supernatural, and gore, to reconnect it to lived experience and show how it can erupt into a life despite modernity's claims of safety and control. I argue that horror is underpinned by contextual and contingent elements: a corpse is not horrifying for an autopsist at work. Similarly, depending on factors such as gender, traversing a dark alleyway can be horrifying. Featured are works from Giacomo Leopardi, Carlo Michelstaedter, Luigi Pirandello, Curzio Malaparte, Primo Levi, Anna Maria Ortese, and Giuseppe Berto

Keywords: French & Italian.
Advisor: Wittman Laura
Denson Shane
Gumbrecht Hans Ulrich
Harrison Robert Pogue
Harrison Thomas J.
Deliberation and communication under Chinese authoritarian influence : the cases of Macau and Hong Kong

This dissertation examines public discourse in two semi-authoritarian contexts, the Chinese territories of Macau and Hong Kong, through two empirical studies. It concludes that while it is possible to satisfy the high standards of a successful deliberative mini-public in these environments, under current political conditions, significant barriers remain to the development of deliberative authoritarianism

Keywords: Communication.
Advisor: Fishkin James S.
Carroll Glenn
Hancock Jeff 
Pan Jennifer
Democratic phantasies : political imagination and the Athenian democracy

This dissertation explores the role of imagination in the ancient Athenian democracy and classical political thought. I argue that the imagination has several political functions crucial for our capacity to balance innovation and tradition, stability and change, and individual freedom and collective identity. The imagination is responsible for human creativity and innovation, which tend to thrive and flourish in a free society. Yet, such imaginative creativity can become destabilizing and self-undermining. To flourish, society must ensure some degree of continuity, tradition, and stability. The imagination allows for this as well, supporting a wide range of institutions, norms, ideas, and values that foster society's predictable operation. Understanding this balance of imagination sheds new light on the success of the Athenian democracy and carries significant insights into some fundamental tensions at the heart of liberal democracy today.

Keywords: Political Science.
Advisor: Ober Josiah
McQueen Alison (Alison E. J.)
Nightingale Andrea Wilson
Demographic Differences in COVID-19 Vaccine Hesitancy Among Stanford Healthcare Workers

Despite the widespread availability of the COVID-19 vaccine in the United States, 30% of
healthcare personnel (HCP) were still not vaccinated by the end of December, 2021. The purpose
of my thesis was to identify characteristics of hospital-based healthcare personnel that are
associated with COVID-19 vaccine hesitancy and with changing one’s mind about getting
vaccinated; knowledge of the types of healthcare personnel who are vaccine hesitant will help
our public health system better address gaps in HCP vaccine coverage. I analyzed surveys from
over 15,000 hospital-based HCP from Stanford Healthcare in December 2020 to understand
employee intentions with respect to COVID-19 vaccination. I found that female healthcare
workers, Black / African American healthcare workers, and younger healthcare workers were
more likely to delay or refuse the vaccine. HCP from counties of high social vulnerability and
HCP that interface less with patients also showed higher rates of vaccine hesitancy and higher
rates of changing their minds about getting vaccinated. Furthermore, vaccine newness and fear of
side effects were the most cited reasons for vaccine hesitancy. Healthcare workers in sub-groups
with high rates of vaccine hesitancy should be specially considered in the design of future
vaccination campaigns for hospital workers.

Keywords: Vaccine hesitancy
COVID-19 (Disease)
Medical personnel
Text
Thesis
Design affordances for online interactions

Online interactions are part of our social fabric. We are becoming increasingly reliant on online platforms, such as social media and collaborative tools, to enable and facilitate our social interactions and collaborations. The ways in which these platforms are designed greatly influence the way we interact with one another. Yet many commercially available tools for computer-mediated user-user interactions rely on existing generic approaches without adequate consideration of contextual specificity or user needs. This often leads to ill-designed collaborative tools, frequently impairing communication and collaboration, rather than empowering users to interact seamlessly online. To build a better understanding of the impact of current platform designs, I have investigated user perceptions around online actions. Online actions are actions that we take on social platforms that have communicative value and are one of the most active ways in which we interact with others online. I have studied how ambiguities inherent in the current design of these actions make users think, feel, and interact. I have found that ambiguous online actions lead to inaccurate conjectures, attribution biases, and interpretation discrepancies by users both in social media and on collaborative platforms. These, in turn, affect users' perception of others and comfort in taking online actions, especially in contexts where perceptions of ownership abound. Consequently, my investigations elucidate where and how misunderstandings may occur, and underscore the importance of providing rationales for online actions. From synthesizing these findings, I have formulated principles of design affordances that can enable platform developers and designers to help build better collaborative experiences for users

Keywords: Mechanical Engineering.
Advisor: Leifer Larry J.
Berger Jonathan
Follmer Sean
Hancock Jeff
Design and manufacture of a 3-D smart skin for non-developable geometries

A smart skin is a sensing component comprising a network of sensors distributed at specific locations inside a soft polymer skin; this component can be integrated with a structure or part, allowing it to make control decisions. Such skins have compelling applications in the robotics field, potentially enabling dexterous manipulation and facilitating human-robot interactions as in the field of nursing assistant robotics. In this thesis, an investigation was performed to design and fabricate a multifunctional, thin, 3-D polymer skin that is doubly-curved with an embedded distributed sensor network. Piezoelectric sensors and RTD sensors were integrated with the network to functionalize the skin to be able to sense contact and temperature at different locations on the skin. This thesis addresses the above problem by synthesizing two key technologies. Specifically, a stretchable "net" of sensors is used to cover a target part conformally, and this sensor network is covered using a conformal dip coating process to form the complete skin. To apply the key technologies above, several tasks are required, all of which are detailed in this thesis. The network must first be designed using a simulation-based particle swarm optimization algorithm to ensure that it effectively covers the target part. Once manufactured, the network is expanded from its manufacturing footprint and deployed over the surface of the object using a specially developed "stretch tool." After deployment, the network must be encapsulated within a physical skin. Multiple materials are surveyed for suitability in this effort, and a process for encapsulating the network is engineered. After the completion of the skin article, tests are conducted to validate the functionality of the sensors using a data acquisition unit The resulting skin article covers a finger-like demonstrator form, and is capable of sensing temperature differences across its surface, as well as sensing the presence or absence of contact

Keywords: Aeronautics and Astronautics.
Advisor: Chang Fu-Kuo
Senesky Debbie
Springer George S.
Design and synthesis of polymechanophore systems with dramatic mechanochemical response

Polymer mechanochemistry is an emerging area of research that investigates the use of mechanical force to promote chemical transformations. Embedding properly designed mechanochemically active motifs, termed mechanophores into polymers can induce various force triggered responses including coloration, chemiluminescence, small molecule release, and catalysis. However, most examples in this field only incorporate a tiny amount of mechanophores into a mechanochemically inert polymer. The low content of mechanophore loadings limits the magnitude of mechanical response of the materials. In this thesis, we introduce a family of mechanophore monomers based on fused bicyclohexene structure, which can be efficiently polymerized through ring-opening metathesis polymerization (ROMP) into well-controlled polymechanophores with unprecedented mechanophore loading. The modular mechanophore design can be divided into three parts: a readily polymerizable cyclobutene head, a mechanochemically active cyclobutane core and a fused cycle with different functionalities. Upon force activation, cyclobutane can readily isomerize into a pair of alkenes, while revealing the "hidden" functionalities attached and incorporating them into polymer main chain. Through the choice of different functionalized cycles, we can achieve dramatic changes in polymer properties including conjugation state, degradability, contour length and binding affinity towards certain guest molecules. In Chapter 1, we will introduce the history, recent developments, and current challenges in this field. Chapter 2 and 3 summarize our efforts to develop mechanochemically generated conjugated polymers with better scalability and structural tunability. Chapter 4 introduces a polymer system with force-enhanced degradability by mechanochemically transforming hydrolytically stable cyclic ether into acid labile enol ether. Chapter 5 describes the development of a rotaxane based mechanophore that exhibits controlled force-triggered release behavior. Chapter 6 describes a side project I worked on about the controlled synthesis of metathesis degradable polycyclohexenes through the alternating copolymerization between butadiene and methyl methacrylate

Keywords: Chemistry.
Advisor: Xia Yan
Burns Noah
Martinez Todd J. (Todd Joseph)
Design considerations of a novel three-phase multi-level inverter

Countries around the world are committing to renewable generation and electrification targets in an effort to reduce greenhouse gas emissions. During this transition, power electronic inverters are becoming increasingly ubiquitous as the interface between different renewable sources and electrical loads. Today, utility-scale solar and battery energy storage systems mainly rely on single-stage central inverters to connect to the grid. Operating at a string voltage around 1kV, utility-scale solar often needs a medium voltage (MV) transformer to interface with the MV grid. Due to the MV transformer's cost, maintenance, and losses, multilevel inverters (MLIs) are investigated as a promising transformer-less alternative. Existing three-phase MLIs are designed with single-phase inverter modules or three-phase inverter modules as basic building blocks. Single-phase modules suffer from the need for large DC link capacitance to buffer the 120 Hz ripple for grid-tied applications. Three-phase modules do not require bulky input capacitance but rely on more complex topologies and result in poor efficiency. In this thesis, we propose a novel two-phase module-based MLI, that eliminates the need for bulky input capacitance while also enhancing the efficiency of a typical three-phase module. We achieve this by designing a power sharing scheme among inverter modules that allows the inverter module to operate at peak or zero power for most of the grid cycle. The proposed MLI design and power sharing scheme pose challenges for a distributed controller. This work leverages iterative and structurally constrained linear quadratic regulators (LQRs) to control the MLI system, and PLECS simulations are conducted to verify the efficacy of the controller. We build an experimental 110V DC input, 208 Vrms three-phase AC output, 1~kW 5-level prototype with > 95% efficiency to demonstrate the proposed MLI and controller design. With the same inverter module, we compare the efficiency of the proposed design to the conventional MLI design, where all modules share phase power evenly. We demonstrate that the proposed design consistently outperforms the conventional design in efficiency, with > 25% loss reduction at light load conditions. Lastly, we propose a formal way of optimizing the power sharing scheme for any multi-converter system based on the efficiency characteristics of the converter module

Keywords: Electrical Engineering.
Advisor: Dally William J.
Rivas-Davila Juan
Onori Simona
Design of robust closed-loop ultrasonic implant systems

Implantable medical devices for physiological monitoring and therapeutics are important tools for treating chronic diseases and restoring impaired body functions. Recent advances in wireless power and data transfer have driven the development of smaller, minimally invasive implants with real-time connectivity and new capabilities. In particular, ultrasound wireless power and communication has been successfully used for many proof-of-concept single implant sensors and stimulators. Next-generation therapies will need to precisely coordinate multiple implants combining sensing, stimulating, and processing to enable applications like bioelectronic medicine and brain-machine interfaces. In order for ultrasonic implant systems to support these applications, they will require more robust and scalable power and data transfer as well as closed-loop functionality to operate reliably in the body. In this work, we present approaches for building each aspect of a robust ultrasonic implant system. First, we discuss how to increase ultrasonic communication data rates using line-of-sight MIMO techniques. By taking advantage of the spatial degree of freedom, we show how to potentially scale the channel capacity with the number of implants. Using this concept, we demonstrate a 2x2 communication link with double the data rate of a conventional ultrasound link. Next, we propose an implant localization scheme using harmonic backscatter and demonstrate sub-millimeter accuracy. Using this location data with an ultrasound transducer array we can selectively and efficiently beamform power to implants. Finally, we describe the design of a closed-loop implantable drug delivery system, combining an electrochemical drug release mechanism with a custom CMOS chip. The implant includes power sensing and combining for more reliable power transfer as well as a potentiostat with a ±1.5 V stimulation range and ±100 uA readout range for programmable drug release. The techniques and systems presented in this work demonstrate how to build an ultrasonic implant platform for next-generation closed-loop medical applications

Keywords: Electrical Engineering.
Advisor: Arbabian Amin
Dahl Jeremy J.
Murmann Boris
Design techniques for wireless data and power transfer

Consumer electronics are ubiquitous -- in cars, in phones, in smart homes, in everything. This huge, multi-billion-dollar industry continually pushes the technological cutting edge to offer more performance at lower cost to improve our quality of life. Cutting edge techniques and transmitter architectures drive the innovation in the circuit design field today. Furthermore in the age of smart homes, ubiquitous connectivity, and internet of things, the push to make everything connected wireless includes the need for wireless powering. This wireless power delivery shares mathematical similarities to optimum data transmission through a channel. The desire to continuously increase the data rate and the bandwidth and throughput leave engineers with the unique challenge to develop the techniques and radio transmitter architectures of the future. This work summarizes work done to explore two techniques for integrated circuits spanning the entire transmitter chain from mixed signal baseband to RF. The first part discusses a self-calibration algorithm embedded in the digital control of a Digital-to-Analog Converter (DAC) that automatically calibrates for process mismatch which can significantly reduce the contribution of the DAC to the transmit error vector magnitude. The next technique discusses a phase-shifting amplifier (PSA) which is used to generate high phase resolution tunability while also providing gain. The PSA uses techniques inspired from distributed amplification and with proper alignment of phase delays along transmission lines, the signal can obtain the desired phase shift. This segues nicely into projects in the medical field where 60GHz has been a hot research topic. This work also presents the source design optimization problem for a planar tissue model for wireless powering of medical implants. This regime is different from the traditional near-field and far-field transmission regimes and is known as mid-field

Keywords: Electrical Engineering.
Advisor: Wong S. Simon
Pauly John (John M.)
Rivas-Davila Juan