VictorJuiz/thesis huggingface dataset:
Towards expressive and scalable deep representation learning for graphs

The ubiquity of graph structures in sciences and industry necessitates effective and scalable machine learning models capable of capturing the underlying inductive biases of the relational data. However, traditional representation learning algorithms on graph structure faces many limitations. Firstly, traditional methods including matrix factorization and distributed embeddings cannot scale to large real-world graphs with billions of nodes and edges due to their sizes of parameter space. Secondly, they lack expressiveness compared to recent advances of deep learning architectures. Lastly, they fail in inductive scenarios where they required to make prediction on nodes unseen during training. Finally, interpretation of what model learns from data is elusive to domain experts. In this thesis I present a series of work that pioneers the use of graph neural networks (GNNs) to tackle the challenges of representation learning on graphs in the aspects of explainability, scalability, and expressiveness. In the first part, I demonstrate my framework of GraphSAGE as a general but powerful overarching graph neural network framework. To tackle the challenge of model interpretability with the new GraphSAGE framework, I further introduce an extension model to obtain meaningful explanations from the trained graph neural network model. Under the framework of GraphSAGE, the second part presents a series of works that improves the expressive power of GNNs through the use of hierarchical structure, geometric embedding space, as well as multi-hop attention. These GNN-based architectures achieved unprecedented performance improvement over traditional methods on tasks in a variety of contexts, such as graph classification for molecules, hierarchical knowledge graphs and large-scale citation networks. In the third part, I further demonstrate a variety of applications of GNNs. Based on GraphSAGE, I developed PinSAGE, the first deployed GNN model that scales to billion-sized graphs. PinSAGE is deployed at Pinterest, to make recommendations for billions of users at Pinterest. In the area of grahics and simulations, we apply expressive architectures to accurately predict the physics of different materials and allow generalization to unseen dynamic systems. Finally, I discuss BiDyn, a dynamic GNN model for abuse detection before concluding the thesis

Advisor: Leskovec Jurij
Ré Christopher
Yamins Daniel
Towards low temperature solid source synthesis of monolayer molybdenum disulfide and low resistance contacts to molybdenum disulfide-based devices

Silicon (Si) transistors have recently reached nanometer-scale dimensions. Further gate length scaling requires thinner body channels and low resistance contacts. Yet, Si carrier mobility severely degrades at thicknesses below ~3 nm due to surface roughness, leading to lower on-state current for short gate-length transistors. MoS2 is a 2-dimensional (2D) atomically smooth semiconductor that retains its band gap and electrical properties down to single monolayers (3 atoms thick), making it appealing for transistor gate length scaling. In addition, stacking of transistors in 3 dimensions (3D) will be needed to further increase their density when 2D scaling reaches diminishing return. However, synthesizing high mobility MoS2 with thin layers and low resistance contacts has been an ongoing challenge in developing MoS2 for such applications. In addition, for many 3D heterogeneous integration applications, relatively low temperature (400-600°C) material deposition techniques are necessary, on time scales of 2 hours or less. In the first part of this thesis, we describe the chemical vapor deposition (CVD) growth of MoS2 using solid source precursors. By carefully controlling precursor amount and placement as well as optimizing the temperature heating ramp, sulfur (S) injection is optimally timed for efficient reaction with molybdenum trioxide (MoO3). In the second part, we describe efforts to make low-resistance metal contacts to MoS2. By inserting a Ta2O5 insulator layer between metal contacts and multi-layer MoS2, contact resistance can be reduced by up to 3 orders of magnitude along with significant Schottky barrier height reduction. In the final part, we report electrical characteristics of 1L MoS2 grown at 560°C with carrier mobility comparable to 1L MoS2 grown at higher temperatures, within the 600°C thermal budget for back-end-of-line (BEOL) processing and 3D integration. The record high drive currents and effective mobilities achieved on these 560°C 1L films are the best reported for solid source CVD-grown 1L MoS2 below 600°C providing an encouraging path towards utilizing MoS2 to continue transistor density scaling into the future

Keywords: Electrical Engineering.
Advisor: Wong Hon-Sum Philip
Pop Eric 
Saraswat Krishna
Towards semantic representations of tissue organization from high-parameter imaging data

Viewed at a high spatial and molecular resolution, every individual instance of a biological tissue is unique. However, it is a tenet of evolution that prior evolutionary successes are adapted and repurposed for variations on function and for the creation of new functions. Thus, it would be expected that the organization characteristic of a tissue's type can be expressed in terms of a collection of repeated biological units that respect some rules governing their assembly and collective functionality. Advances in high-parameter imaging technologies present the opportunity to observe tissues at high spatial and molecular resolution. What, then, are the repeated biological units and the rules (governing the units' assembly and collective functionality), characterizing the organization of tissue types viewed through the lens of such technologies? We present conceptual, mathematical and algorithmic tools towards possible answers of this question, and their applications to high-parameter tissue imaging data of lymphoid tissues and the tumor microenvironment. The results yield clinically relevant insights into the specific biology of these tissues, as well as hint at general principles of tissue organization that become apparent only when tissues are assayed in such detail

Keywords: Bioengineering.
Advisor: Nolan Garry P.
Altman Russ 
Covert Markus
Towards understanding nanoscale and mesoscale order in organic semiconductors

Organic semiconductors are emerging as promising semiconductor materials for a wide range of electronic and electrochemical device applications including displays, electronics, solar cells, chemical sensors, and energy storage. Recent advances in molecular structure-property relationships and backbone and side chain engineering have enabled significant progress in improving their charge transport properties and carrier mobilities, which are now competitive with amorphous silicon. Moreover, over the past few years, a series of conjugated polymers with polar side chains have been developed to operate as mixed ionic/electronic conductors in aqueous media. Despite these advances, an understanding of the multiscale mechanism for ionic and electronic charge transport remains elusive, limiting further progress in materials development. Charge transport requires continuous coupling across all length scales (i.e., atomic to mesoscale). Understanding the local environments where ionic and electronic charges reside in a semiconducting polymer and the energetic landscape for charge transport are key questions to address in the search for designing better materials. The focus of this dissertation has been on attempting to improve our understanding of the relationship between structure and its impact on macroscopic properties. In this dissertation, I present several methods to characterize the structure of semiconducting polymers and discuss the role of structure on optoelectronic and electrochemical properties. In the first part of this dissertation, I present a series of methods to extract and measure the nanoscale and mesoscale order of semiconducting polymers using low-dose, high-resolution transmission electron microscopy images and data analytics. These results showcase the applicability of developing novel methods to characterize the rich, complex mesoscale structure of conjugated polymers with unprecedented detail. In the second part of this dissertation, I combine a series of electrochemical and structural characterization techniques to study polymer volumetric charging in a series of aqueous electrolytes. These novel semiconducting polymers have been specifically designed for electrochemical device applications that leverage ionic/electronic charge transport. This work elucidates complex polymer- and electrolyte- dependent structure-property relationships, which will help further developments on the rational design and understanding of this new family of materials. Overall, these studies suggest design principles to continue to advance the field of organic electronics

Keywords: Materials Science and Engineering.
Advisor: Salleo Alberto
Appel Eric (Eric Andrew)
Spakowitz Andrew James
Township-level Positions in the Chinese Bureaucracy: A Study on Tenure and Personnel Mobility Patterns

As the lowest level of government in the Chinese bureaucracy, Township-level government serves as the link both between the state and the society and between urban cities and rural villages. Using the personnel records of the bureaucrats at and beyond the township level among 13 Chinese counties between 1990 and 2012, this thesis studies the structural relationships of the township-level government in the Chinese bureaucracy to understand the interaction pattern among the governments in different townships and between the township- and the upper-level governments. Specifically, this thesis examines 1) the structural characteristic of the township-level government reflected in the tenure of township-level positions; 2) the structural relationships of the township-level government reflected in the personnel mobility; and 3) how the tenure and personnel mobility shape the social relationships among township- and upper-level bureaucrats. This thesis quantitatively reveals the formal organizational rules and patterns at the lowest levels of the Chinese bureaucracy.

Keywords:  Stanford Global Studies
East Asian Studies
China
Bureaucracy
Social sciences > Network analysis
Text
Thesis
Advisor: Zhou Xueguang
Training and deploying visual agents at scale

Autonomous agents that perceive and interact with the world, such as home robots and self-driving vehicles, hold great promises to a future that automates mundane tasks and improves the living standards for billions of people. However, two major obstacles stand in our way towards this grand goal. First, modern AI systems require huge amount of data to learn meaningful behaviors, yet training them directly on physics robots is unscalable due to high cost and low efficiency. Second, mobile robot platforms typically have limited onboard computing resources but demand low reaction latency, which hinders the mass deployment of large-capacity visual models. In this dissertation, we will explore an effective recipe towards developing algorithms and systems that are able to train and deploy visual agents at scale. The key idea is to train the agents in rich simulation, then overcome the sim-to-real gap, and finally deploy efficiently on edge devices with lightweight video processing architectures. This dissertation is organized around 4 primary components in the pipeline. First, we propose an open-source distributed framework that provides a full-stack solution to accelerate reinforcement learning (RL) significantly for complex robotics tasks. Second, we construct an ecologically valid and visually realistic simulator for home robotic tasks. Third, we introduce a novel policy learning method that achieves zero-shot generalization to unseen visual environments with large distributional shifts, which facilitates sim-to-real transfer. Finally, we design a new family of video learning architectures that enables deep video understanding for visual agents on resource-constrained devices. We hope that the techniques and ideas presented in this dissertation will bring us one step closer to the future where intelligent robots will become as ubiquitous as smartphones in our lives

Advisor: Li Fei Fei
Niebles Duque Juan Carlos
Wu Jiajun (Computer scientist)
Trajectory forecasting in the modern robotic autonomy stack

Autonomous systems are increasingly nearing widespread adoption, with new robotic platforms constantly being tested and deployed alongside humans in domains such as autonomous driving, service robotics, and surveillance. Accordingly, human-robot interaction will soon be present in many everyday scenarios. However, there are still many challenges preventing autonomous systems from safely and smoothly navigating interactions with humans. For example, while merging into traffic is one of the most common day-to-day maneuvers we perform as drivers, it poses a major problem for state-of-the-art self-driving vehicles. The reason humans can naturally navigate through many social interaction scenarios, such as merging in traffic, is that humans have an intrinsic capacity to reason about other people's intents, beliefs, and desires, applying this reasoning to predict what might happen in the future and make corresponding decisions. As a result, imbuing autonomous systems with the ability to reason about other agents' potential future actions is critical to enabling informed decision making and proactive actions to be taken in human-robot interaction scenarios. Indeed, the ability to predict other agents' behaviors (also known as "trajectory forecasting") has already become a core component of modern robotic systems, especially so in safety-critical applications such as autonomous vehicles. Towards this end, this dissertation tackles the development of trajectory forecasting methods, their effective integration within the robotic autonomy stack, and the injection of task-awareness in their performance evaluation

Keywords: Aeronautics and Astronautics.
Advisor: Pavone Marco
Sadigh Dorsa
Schwager Mac
Transcriptional and functional convergence of coronary endothelial lineages in mouse and human

Coronary blood vessels comprise the arteries, veins and capillaries that supply the heart. In mice, the endothelial cells (ECs) that form the inner lining of these vessels are derived primarily from two lineages during embryonic development. The first lineage comes from the endocardium (Endo), the layer of cells lining the inside of the heart chambers. The second lineage comes from the endothelium of the sinus venosus (SV), the major inflow tract of the developing heart. Although these two lineages are known to contribute to complementary regions of the heart during both development and adulthood, it is unknown whether there is any difference in their function or whether they retain any transcriptional markers of their origins. In addition, it is unknown whether ECs in human hearts also originate from the same sources. In order to address these questions, I combined lineage tracing of Endo-derived ECs in murine hearts at various stages with single-cell RNA sequencing. I discovered that while Endo- and SV-derived ECs can be transcriptionally distinguished at e12, they had equivalent transcriptional patterns at e17.5. Additionally, at e17.5 capillary ECs segregated into two populations based on the influences of blood flow and hypoxia. On the other hand, adult ECs from both lineages were transcriptionally homogenous with respect to both lineage and location, and responded similarly to ischemia-reperfusion injury. Finally, developing human hearts contained capillary ECs in similar cell states as e17.5 mouse hearts, indicating that mouse and human coronary development follow similar trajectories

Keywords: Genetics.
Advisor: Red-Horse Kristy
Baker Julie
Bhatt Ami (Ami Siddharth)
Kundaje Anshul
Transforming empire : the Genoese from the Mediterranean to the Atlantic, 1282-1492

Between the crusades and Columbus, the Genoese maritime empire centered in the eastern Mediterranean and the Black Sea transformed into a financial empire based on the Iberian peninsula. From the thirteenth century, Mongol, Mamluk, Turcoman, and Latin formations took advantage of Byzantine weakness; and against this backdrop the Genoese established a network of fortified ports and islands. With their maritime dominance, the Genoese rendered themselves essential to rulers from Plantagenet England to Mongol Baghdad, as mercenaries, envoys, navigators, bankers, and merchants. Yet the fifteenth-century rise of the Ottoman and Spanish empires eroded Genoese naval superiority. Traditionally, Venice is seen to have triumphed over Genoa in the geopolitical competition -- after all, the Venetian empire endured far longer than did that of the Genoese. Yet it was a pyrrhic victory, and the Genoese defeat a salutary trauma. Losing their eastern possessions to a rising Ottoman power and enduring foreign domination at home, the Genoese adapted and innovated, becoming dominant on the Iberian peninsula and in the Atlantic world in banking, the sugar industry, and the slave trade. Based on research conducted in two dozen archives and manuscript libraries around the globe, this dissertation weaves together Ottoman Turkish and Latin sources and brings together fragmented historiographies in an integrated narrative and analytical arc from the Mediterranean to the Atlantic. It was no accident that Columbus was Genoese. As pirates and slave traders, navigators and bankers, they brought extensive resources and long experience to their infiltration of the Iberian empires. Whether we demonize Columbus or lionize him, the Genoese trajectory brings us into an era we recognize readily as our own. Yet this study also forces a radical reassessment of a constellation of broad themes which we usually examine only in isolation: the legacy of the crusades and the interaction between Islam and Latin Christianity; the roots and consequences of Atlantic slavery; and the relationship of violence and colonial exploitation to the more subtle and hidden sovereignty of financial power

Keywords: History.
Advisor: Findlen Paula
Como David R.
Dorin Rowan
Yaycioglu Ali
Transition metal dichalcogenides for next-generation photovoltaics

Conventional silicon solar cells dominate the photovoltaics market with a market share of about 95% due to their low-cost manufacturing and reasonable power conversion efficiency. However, the low optical absorption coefficient and brittle nature of silicon lead to degraded performance in ultrathin, flexible silicon solar cells and therefore prevent their broader usage in next-generation photovoltaic applications demanding high specific power (power per weight) and flexibility, for example in aerospace, transportation, architecture and self-powered wearable and implantable electronics. In addition, for widespread adoption of solar energy, new materials need to be integrated on silicon to form tandem solar cells with higher efficiency, lower cost per Watt and therefore higher affordability. Semiconducting transition metal dichalcogenides (TMDs) are promising for flexible high-specific-power photovoltaics and for silicon-based tandem solar cells, due to their ultrahigh optical absorption coefficients, desirable band gaps, self-passivated surfaces, high stability and lifetime, biocompatibility, eco-friendliness, and compatibility with existing nanoelectronic fabrication processes. However, challenges such as Fermi-level pinning at the metal contact-TMD interface and the inapplicability of traditional doping schemes have prevented most TMD solar cells from exceeding 2% power conversion efficiency. In addition, fabrication on flexible substrates tends to contaminate or damage TMD interfaces, further reducing performance. In this work, we investigate the promise and challenges of TMDs as next-generation photovoltaic materials and provide novel and scalable solutions for their challenges, leading to record high performance on par with prevailing thin-film solar technologies

Keywords: Electrical Engineering.
Advisor: Saraswat Krishna
Chowdhury Srabanti
Pop Eric
Transmission of the human microbiome : from infants to infections

The human body is colonized by trillions of microorganisms, the vast majority of which reside in the large intestine. This collection of bacteria, fungi, archaea and viruses is collectively called the gut microbiome. Large enough to be considered its own organ, the gut microbiome has vast impacts on every day human function, including digestion, the immune system, even the brain and a host's mood. New research also suggests the gut microbiome can modulate an individual's response to anti-cancer immunotherapy. The anaerobic, nutrient rich environment of the mammalian intestine provides bacterial populations with everything needed to grow, proliferate and differentiate. While certain bacterial species that inhabit the gut microbiome may live within the food we eat, many species are uniquely adapted to the human intestine and do not live in other environments. The following conclusions logically follow: 1) Bacteria uniquely adapted to the human gut must be transmitted between individuals, 2) The human body has the ability to acquire new microbes from the environment and other individuals. Proper functioning of the gut microbiome is required for health, therefore this process is beneficial to the human body. 3) The gut microbiome is stable over time in most individuals, therefore selection takes place for which microbes engraft in the gut long-term. However, these conclusions raise many more questions about the acquisition and transmission of gut microbes, including: At what age do humans begin to acquire and develop a microbiome, and at what age does acquisition of new microbes slow down or stop? Where do the new microbes colonizing an infant come from? How are healthy, commensal microbes selected from pathogens? Does transmission between individuals also spread fungi, archaea and phages? Does transmission occur at the community level, or is it a single species at a time? Throughout my graduate work in Dr. Ami Bhatt's laboratory at Stanford University, I have attempted to shed light on a small portion of the microbiome transmission problem. Using advanced metagenomic sequencing techniques, I have tracked bacteria and phages down to the strain level to understand if and how they pass between individuals. This research has been limited to transmission between humans in non-experimental settings - no specific interventions or animal models were used. In these observational studies, I desired to capture acquisition and transmission events as they happened naturally by using the precise archaeological record stored in an individual's microbiome. I hope that my modest contributions will advance the field, improve understanding of how humans acquire and transmit members of their gut microbiota and provide stepping stones for future research to expand upon my findings

Keywords: Genetics.
Advisor: Bhatt Ami (Ami Bharat)
Good Benjamin H. 
Sidow Arend
Tang Hua (Geneticist)
Transport in gapped topological surface states

Three-dimensional topological insulators feature two-dimensional metallic surface states. These surface states are protected by the topological nature of the insulating bulk, meaning they must remain gapless present even when the surface is perturbed by, for example, disorder. Topological protection, however, has limits. Perturbations that affect the topological description of a system can open a gap in its surface states. Using electronic transport measurements of devices based on the topological insulator Sb2Te3, this thesis explores three such perturbations

Keywords: Applied Physics.
Advisor: Goldhaber-Gordon David
Kapitulnik Aharon
Moler Kathryn A.