VictorJuiz/thesis huggingface dataset:
Advances in data-driven financial econometrics and item response theory : theory and applications

Although option market is becoming increasingly important, its complex structure and abundant underlying assets make it very difficult to obtain relatively accurate pricing formulas or algorithms. The first part of this thesis focuses on a theoretical stochastic volatility model for option pricing. We propose a continuous-time stochastic volatility model based on an arithmetic Brownian motion: a one-parameter extension of the normal stochastic alpha-beta-rho (SABR) model. Using two generalized Bougerol's identities in the literature, the study shows that proposed model has a closed-form Monte-Carlo simulation scheme and that the transition probability for one special case follows Johnson's SU distribution---a popular heavy-tailed distribution originally proposed without stochastic process. It is argued that the SU distribution serves as an analytically superior alternative to the normal SABR model because the two distributions are empirically similar. The second part of this thesis gives a bias corrected algorithm of the least square Monte Carlo (LSM) algorithm for pricing American options. The traditional LSM estimator contains undesirable look-ahead bias, and the conventional technique of removing it necessitates doubling simulations. We present the leave-one-out LSM (LOOLSM) algorithm for efficiently eliminating look-ahead bias. We also show that look-ahead bias is asymptotically proportional to the regressors-to-simulation paths ratio. Our findings are demonstrated with several option examples, including the multi-asset cases that the LSM algorithm significantly overvalues. The LOOLSM method can be extended to other regression-based algorithms that improve the LSM method. In addition to option pricing, the third part of this thesis studies item response theory (IRT) and its application to foraging data analysis. A fundamental problem in biology is to understand how the differences among components, or variation in function, contribute to collective behavior. To tackle this problem, we first give a new approach to latent trait modeling in IRT, then design corresponding experiments and finally apply IRT to data analysis. We also give detailed explanation and further discussion for experiment results

Keywords: Management Science and Engineering.
Advisor: Lai T. L.
Blanchet Jose H.
Lu Ying
Advancing access to non-visual graphics : haptic and audio representations of 3D information and data

Graphical representations are pervasive in Science, Technology, Engineering and Math (STEM) content, often serving as cognitive aids for spatial reasoning tasks. Examples include data charts, 3D models and simulations, physics diagrams, and maps. The vast majority of this content is highly visual and often inaccessible to people who are Blind and Visually Impaired (BVI). Alternative representations are mostly text-based (e.g. image descriptions) or static and difficult to update, produced by skilled specialists (e.g. embossed graphics). The inaccessibility gap is growing as more content moves to a digital format and offers sighted users capabilities for interactive manipulation and exploration. This dissertation aims to increase access to non-textual information for BVI people by investigating representations of spatial information that exploit haptic and/or auditory perception towards supporting enhanced understanding, interaction, and manipulation. I explore these accessibility challenges motivated by two activity domains: 3D design (Part 1) and data visualization on the web (Part 2). In Part 1, I investigate supporting 3D information consumption as well as authoring through the development of a novel 2.5D haptic shape display. This display consists of a grid of 12x24 actuated pins that can render shapes at interactive rates. With this system, I conduct studies to assess haptic object recognition and spatial navigation of large-format layouts. Working with BVI co-designers, I develop interaction techniques to support 3D design tasks, allowing users to create and manipulate models. An evaluation demonstrates how the proposed 3D design workflow enables BVI makers to quickly understand 3D design concepts and apply them in creating their own designs. In Part 2, I investigate increasing the accessibility of data visualizations on the web. With the goal of understanding BVI users' current experiences when accessing data-driven media on the web, I present findings from two studies, an online survey and a remote contextual inquiry. I outline challenges with existing accessible data representations, and discuss compromises users make between the modalities and technology they would prefer for access vs. the practical feasibility of their availability. To address the need for data representations on the web that are updatable and multimodal, I propose an algorithmic approach to generating data narratives, combining textual descriptions and sonification (the mapping of data to non-speech sounds). This approach is driven by co-design workshops with BVI users and principles in auditory perception. An evaluation of this approach demonstrates that data narratives support users in better understanding and gaining insights from auditory graphics compared to the standard representation

Keywords: Mechanical Engineering.
Advisor: Follmer Sean
Landay James A.
Okamura Allison
Advancing optimization for modern machine learning

Machine learning is a transformative computational tool on its way to revolutionizing a number of technologies and scientific applications. However, recent successes in artificial intelligence and machine learning, and the resulting imminent widespread deployment of models have transformed the classical machine learning pipeline. First of, the sheer scale of available data---in both quantity and dimensionality---has exploded. Furthermore, modern machine learning architectures come with an exponential number of design choices and hyperparameters, yet they are all optimized using generic stochastic gradient methods. This highlights the need for adaptive gradient methods that perform adequately without prior knowledge of the instance they will be given. Institutions then deploy these models in the wild and expect them to provide good predictions even on out-of-distribution inputs---this emphasizes the need for robust models. Finally, as we collect evermore user data, we wish that models trained on this data do not compromise the privacy of individuals present in the training set as we release these models to the public. In this thesis, we show that solving these emerging problems require fundamental advances in optimization. More specifically, we first present new theoretical results on understanding the optimality of adaptive gradient algorithms and show a practical use case of adaptive methods in the context of gradient-based samplers. We then present scalable methods for min-max optimization with the goal of efficiently solving robust objectives. We conclude by developing private optimization methods that optimally learn under more stringent privacy requirements, as well as adaptive methods that add the "right amount of noise" and significantly decrease the price of privacy on easy instances

Advisor: Duchi John
Liang Percy
Ré Christopher
Sidford Aaron
Advancing photonic communication and sensing through novel 3D silicon photonic devices

From consumer products to specialized technology, it seems like everybody wants devices that are more powerful and yet smaller at the same time. Reliance on metallic devices and components serves to bottleneck any progress towards achieving both goals. One solution is to incorporate photonic technology and especially photonic crystals (PhCs), taking advantage of the speed, efficiency, and lossless nature of photons. PhCs, specifically three-dimensional (3D) PhCs, offer a promise of complete control over light inside a device through photonic band gap (PBG) confinement. But a big obstacle to integrating 3D PhCs into current processing and architecture is fabrication. Fabricated 3D PhCs (prior to this author's work in 2019) are not developed in a way that is considered silicon-compatible (Si-compatible) - using methodology that would fit into already-established silicon processing methods. Before widespread integration can be realized, a highly-customizable Si-compatible method for developing 3D photonic devices needs to be introduced. This dissertation explores the world of PhCs, offering novel fabrication methods and analyses of 3D silicon PhC devices. Both pure bulk 3D PhC structures and 3D PhC defect devices are examined, using theory and literature to provide a solid foundation and then building upon that foundation with new and innovative designs. The first truly Si-compatible fabrication of a 3D PhC is explored, yielding a wide omnidirectional and complete band gap. Then by introducing intentional defects into the device and still maintaining Si-compatible methodology, this thesis provides an avenue for a breadth of research applications to use 3D PhC defect devices that control light through PBG confinement

Keywords: Electrical Engineering.
Advisor: Saraswat Krishna
Miller D. A. B.
Vuckovic Jelena
Advancing retinal prostheses from two to three dimensions

Advanced age-related macular degeneration (AMD) results in loss of central vision due to the gradual demise of photoreceptors. Currently, no therapy exists for such condition, and the loss of sight is permanent. Subretinal prostheses aim to restore sight via electrical stimulation of the surviving secondary neurons in the retina. This is a rapidly expanding area of research, especially with the boom of neural interfaces in recent years, with many groups focusing on various aspects of such devices. Our group is pursuing a full-system approach, from fabrication of the devices to in-vivo testing and to clinical trials. In our system, silicon photovoltaic pixels convert light into electrical current to stimulate the nearby neurons. Clinical trial of the first version of such implants having 100 µm pixels confirmed safety and feasibility of this approach and demonstrated prosthetic acuity closely matching the sampling limit of such arrays (20/420). Further improvement of visual acuity requires miniaturization of the pixels and faces many challenges. Simply scaling the pixel size down with flat bipolar arrays decreases the penetration depth of the electric field into the tissue, increasing the stimulation threshold beyond the capacity of even the best charge-injection material. To enable smaller pixels, moving from flat electrodes into a three-dimensional configuration helps mitigate these issues. Additionally, with planar junction diodes isolated by deep reactive ion etched (DRIE) trenches, carrier recombination at the pixel side walls may limit the light-to-current efficiency, and growing side-wall oxide results in oxidation-related stress in the Si, especially as pixels scale down. We addressed these limitations by transitioning from planar to vertical junction diodes. In this thesis, I will discuss the limitations on reducing the pixel size and overcoming these limitations by using (1) pillar electrodes, (2) honeycomb electrodes, and (3) vertical junction diodes validated ex-vivo and in-vivo. I present the design and fabrication processes of such devices and also demonstrate the resulting photodiode functionality, electrode performance, retinal integration with 3-D devices in-vivo and electrophysiological responses. This work will enable prostheses with pixels down to 20 µm, which corresponds to visual acuity exceeding 20/100. I conclude by discussing the remaining work toward full utilization of such devices and moving toward single-cell resolution

Keywords: Electrical Engineering.
Advisor: Palanker Daniel
Harris J. S. (James Stewart)
Kamins Theodore I.
Adversarially robust machine learning with guarantees

Machine learning (ML) systems are remarkably successful on a variety of benchmarks across several domains. In these benchmarks, the test data points, though not identical, are very similar to the training data. On the other hand, success in the real world requires good performance across a broad range of inputs that are potentially very different from the training data. Self-driving cars encounter unexpected construction zones, predictive health-care systems run into unforeseen changes in demographics, and real world systems are exposed to attackers who strategically generate inputs. Unfortunately, current ML systems are brittle and fail even under extremely small changes to inputs, as demonstrated by the existence of adversarial examples. As ML systems are becoming widely deployed, we need to build robust ML models that are guaranteed to work well across a wide range of inputs. While adversarial examples attracted widespread attention, progress has been limited by critical computational and statistical roadblocks which we address in this thesis

Advisor: Liang Percy
Hashimoto Tatsunori 
Ma Tengyu
The age of the perplexed : translating nature and bodies between the Ottoman Empire and Europe, 1650-1730

"The Age of the Perplexed: Translating Nature and Bodies between the Ottoman Empire and Europe, 1650-1730" examines scholarly engagements across cultures as a lens onto debates about the relationship between knowledge and uncertainty in the early modern world. It reveals the intertwined stories of religious conflict, natural history, medical authority, and translation which illustrate an important episode of intellectual genealogy fostering the idea of human difference in physical, spiritual, and moral domains. Over the course of their increasing interactions stimulated by religious, diplomatic, commercial, and intellectual agendas, Ottoman and European scholars faced a set of questions that still continues to resonate. Do all human beings have an essential nature? Were human bodies interchangeable in the medical context, or were "Turkish" bodies essentially different both emotionally and physically? Perplexed by the dilemma of understanding one other, both Ottomans and Europeans saw cultural encounters as an opportunity to raise questions about the universality of knowledge and human nature. This project demonstrates the boundaries of what constituted knowledge exchange in an age of early modern globalization and religious turmoil. I approach the question of knowledge exchange from the differing perspectives of a diverse community of Ottoman and European scholars, historians, physicians, natural philosophers, apothecaries, drug traders, preachers, and jurists between Istanbul and Europe. Each of these episodes reveals how efforts to bridge and differentiate distinct human societies were conducted through the medium of translation. Rather than seeing translation as a unidirectional process, this dissertation delineates what made translations successful or contingent failures by explaining how they created new perceptions of nature, human bodies, faith, and uncertainty, both in the Ottoman Empire and Europe. "The Age of the Perplexed" advances scholarship on the history of knowledge and history of early modern science and medicine. While a growing number of Ottoman and European scholars were certain about why having foreign knowledge would make a difference for them, they also realized that knowledge in circulation was too unstable and uncertain to be definitive. Consequently, the rhetoric of uncertainty became a new mode of inquiry and a highly productive strategy; factual and trustworthy knowledge was not necessarily the first intention in cross-cultural scholarly engagements. The knowledge that emerged at the nexus of the Islamic world and Europe, with all of its contradictions and ambiguities, raised critical questions about the limits of human knowledge. Fundamentally, these seventeenth-century conversations offered new possibilities to accommodate anxieties about what it meant to essentialize other societies—the people, their bodies, and their faith.

Keywords: History.
Advisor: Findlen Paula
Crews Robert D.
Mikhail Alan
Riskin Jessica
Aire, Tierra y Fuego: Environmental Trauma and Grassroots Mobilization in Puerto Rico

“It’s not easy. I cry at night. I cry in the bathroom [...] I have to be strong.” Those
are words uttered by Diana Fernandez. According to the National Oceanic and Atmospheric Association, Hurricane María’s economic loss is estimated to be anywhere from US $43 to $159 billion. The cyclone leveled power grids, homes and other essential infrastructure in Puerto Rico. Scholarly work surrounding the impacts of hurricanes on the island have been widely documented. However, dedication to the storm’s effects on shaping social issues, identity, and political parties are seldom. This study applies text data of hurricane survivors and their testimonials, engages in empirical approaches to identity and leverages qualitative case studies of ecofeminism and the Movimiento Victoria Ciudadana to center gender disparities and grassroots mobilization in Puerto Rico. At its simplest, this study argues that environmental phenomena inform salient discourses of political status and social life on the island—because for many, like Diana Fernandez, life was, and still is, not easy.

Keywords: Puerto Rico
Identity
Grassroots Mobilization
Environment
Intersectionality
Ecofeminism
Politics and government
Social surveys
Text
Thesis
Advisor: Diaz-Cayeros Alberto
Algorithmic techniques for neural network training on memory-constrained hardware

Deep learning has enabled accurate inference for many real world problems, making it an attractive tool for embedded devices at the edge. Unfortunately, these devices are often limited by tight energy and memory budgets, prompting past research to optimize for neural network inference efficiency and size. In our work, we take some of the first steps towards enabling gradient descent-based neural network training on these inference-optimized devices. For example, power-of-two quantization is commonly used to reduce weight size, but training on quantized weights is known to be difficult. We discuss some potential causes of this difficulty and propose improved quantization techniques to enable training. More recently, emerging memory promises to obviate off-chip data movement, but is expensive to update. We present an algorithmic compression technique to enable training without incurring overwhelming write costs of emerging memory technology. When applying these techniques to perform transfer learning on CHIMERA, a chip with large scale resistive RAM (RRAM) macros, we observe a 101x reduction in the number of weight update steps and a 340x reduction in energy-delay product compared to a stochastic gradient descent baseline that achieves the same accuracy

Keywords: Electrical Engineering.
Advisor: Murmann Boris
Mitra Subhasish
Pilanci Mert
Algorithmically guided human decision making in criminal justice and beyond

With the proliferation of modern data infrastructure and computational resources, increasingly more decisions in almost every sector of public policy, including criminal justice, are informed by data-driven algorithms today. However, due to the high-stakes nature of decisions in criminal justice, the efficacy and equity of algorithms in judicial procedures are sometimes challenged and how algorithms can be appropriately incorporated into judicial processes remains a complex question to answer. In this dissertation, we present both applications demonstrating how algorithms may help human decisions in criminal justice and algorithmic tools for human-centered decision making. We start by showing risk assessment tools, one of the most common algorithm applications in criminal justice, can outperform humans on predicting recidivism under ecologically valid settings in a randomized controlled experiment. We then demonstrate how both predictive and non-predictive algorithms can help with better human decision making in criminal justice by reducing incarceration time and mitigating racial bias in two real-world applications. Finally, inspired by decision making problems encountered in aforementioned applications, we propose two algorithmic methods that aid human decision making through modeling the structure of evolving probabilistic predictions and estimating the preference of the human decision maker while searching for optimal policy configurations during sequential experimentation. Together, this dissertation contributes to the understanding of interplay between humans and algorithms in criminal justice and other domains by presenting an interconnected collection of randomized controlled experiments, real-world applications, and innovative computational methods

Advisor: Bernstein Michael S.
Goel Sharad
Brunskill Emma
Algorithms for fair public and private resource allocation

This thesis explores algorithms for resource allocation, with an emphasis on fairness. "Fairness" is an extremely complex concept and our goal in this thesis is not to design an algorithm that is "truly fair" (if such a thing even exists). Rather, we consider a variety of specific objectives inspired by fairness, and analyze their properties in different resource allocation models. The thesis is split into "private" resource allocation and "public" resource allocation. Private resource allocation handles items such as food and cars, where each individual receives a separate bundle of resources, with the key assumption being that their happiness (or "utility") depends only on what they receive, and not on what others receive. This process typically occurs through decentralized markets. In contrast, in public resource allocation, a centralized government makes a single decision that affects all citizens: for example, how to allocate a city budget, or whether to build a communal pool. Markets and governments are two of the most fundamental institutions in our society, and so any improvement to these systems can have far-reaching benefits. For both of these settings, we take on the role of the system designer (or "social planner"). Our goal is to design an allocation method (or "mechanism") to distribute these resources in a "good" way, where "good" can have many interpretations, including but not limited to fairness concerns. We consider two primary approaches to defining "good": axiomatic and welfarist. An axiom states a desirable property the outcome. For example, a common fairness axiom for private resource allocation is "envy-freeness": no individual should prefer another individual's bundle to her own. In contrast, the welfarist approach is predicated on a designated "welfare function", which assigns a single number to each possible outcome, with larger numbers being "better". Our goal then becomes to choose an outcome which maximizes the welfare function. Different welfare functions represent different priorities: for example, maximizing the sum of utilities focuses on overall societal happiness, and maximizing the minimum utility focuses on equality of happiness across individuals. For all of these settings -- private or public, axiomatic or welfarist -- we provide allocation mechanisms that are provably "good" in some way. That said, each of our mechanisms has its own drawbacks, and whether or not these mechanisms are truly "fair" depends on a myriad of logistical, cultural, and philosophical factors. However, we hope that this thesis serves as an important step along the never-ending path to improve resource allocation in our society

Advisor: Goel Ashish
Reingold Omer
Rubinstein Aviad