VictorJuiz/thesis huggingface dataset:
Cruel and Unusual: Contesting Human Rights Violations in the American Carceral System

Lindsay Newfeld’s capstone project investigated the lack of avenues available to incarcerated individuals in the United States who contest human rights violations occurring during their confinement. Her paper specifically focused on two available avenues for redress - prisoner grievance mechanisms and civil litigation and sought to answer (1) why these processes are so ineffective at producing remedies, and (2) the justification for denying this population effective access to the courts. Through interviews with formerly incarcerated individuals from Illinois, Lindsay gave insight to the depth, texture, and the perspectives of those who have experienced human rights violations while incarcerated. Motivated by her advocacy through various criminal justice and prison reform organizations, Lindsay hopes to understand prisoners’ rights through a human rights framework and create tangible change within the prison system.

Keywords: Mass Incarceration
Prisons > Law and legislation
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Thesis
Cryogenic electron microscopy and tomography on radiation sensitive crystalline materials

Cryogenic electron microscopy (Cryo-EM) has been one of the most exciting technology advancements in biological field in recent years. The idea of freezing samples in liquid nitrogen temperature provides a unique advantage to preserve and observe the object of interest in their native state. The improvements in both electron microscopy related hardware and data processing software enable the retrieval of structural information to atomic details. However, many technical features in electron microscope have not been fully explored for cryo-EM and many other sample systems other than biological specimens could utilize cryo-EM to accelerate their structural characterization research. This dissertation focuses on developing a new cryo-EM based experimental and data processing pipeline to further extend the applications of cryo-EM to broader research fields, where the samples of interest are normally not amenable to conventional electron microscopy methods. There are two common features across the specimens aforementioned: first, they are crystalline materials, meaning they are either single crystals or contain certain degree of periodic structures; second, they are sensitive to electron radiation, thus conventional transmission electron microscopy is not suitable to study such materials due to its high electron radiation damage. To investigate those specimens using cryo-EM, we developed several new methods: customized freezing protocol to preserve the samples from ambient environment; cryogenic focused ion beam milling to prepare electron transparent samples; continuous tilting electron diffraction to solve the atomic structure from single crystals; electron diffraction and high resolution low dose electron imaging to acquire the sub-nanometer structural information in both real-space and Fourier space; electron tomography and automated annotation to recover the morphology of non-biological samples in 3D. Those lead to many unprecedented findings in those representative specimens, which will be described in this dissertation. We anticipate those new technical developments will facilitate the usage of cryo-EM in structural biology, chemical biology, organic chemistry and material science in the near future

Advisor: Chiu Wah
Brünger Axel T.
Wakatsuki Soichi
Cyber risks in networked autonomous systems

Operation of autonomous unmanned vehicles introduces new risks about which decisionmakers have neither exhaustive statistics nor similar systems from which to derive priors. This model-based risk analysis combines algorithms used for autonomous control, Monte Carlo simulations, and learning parameters from data to improve the risk model's performance. The results inform high-level decisionmakers on when and how best to employ autonomous unmanned vehicles in security and military applications where risk tolerance is higher than for civilian applications, while explicitly maintaining high-risk decisions as the responsibility of human decisionmakers, even when software is used in the process of executing those decisions. This risk analysis has applications in use of unmanned vehicles for localization of radio-frequency threats and maritime tracking of non-cooperative targets using linear array sonar

Keywords: Management Science and Engineering.
Advisor: Paté-Cornell M. Elisabeth (Marie Elisabeth)
Shachter Ross D.
Weyant John P. (John Peter)
Cytokine receptor engineering for mechanistic and therapeutic studies

The activity of immune cells is regulated by proteins on the cell surface called cytokine receptors. These proteins allow cells to grow in a controlled manner and to balance immunosuppressive and stimulatory activities. My dissertation has focused on engineering cytokine receptors to understand their function and to target cells of interest therapeutically. I first focused on the interleukin-2 receptor (IL-2R), both to engineer orthogonal activity for selective cell growth, and to determine how unique signals are transmitted when binding to interleukin-15 (IL-15) and IL-15 receptor (IL-15R) complexes. I then focused on the colony-stimulating factor 3 receptor (CSF3R) which controls the growth of neutrophils, the most abundant type of white blood cell. I investigated if a mutated CSF3R epitope could be targeted therapeutically, why this amino acid substitution is the predominant mutation in chronic neutrophilic leukemia, and how it leads to uncontrolled neutrophil growth. Combined, these engineering approaches highlight the function and therapeutic potential of cytokine receptors

Keywords: Bioengineering.
Advisor: Bertozzi Carolyn R.
Cochran Jennifer R.
Gotlib Jason R. 
Huang Possu
Data, digital risks, and financial markets

Big data and pervasive digitization have transformed both the risks corporations face and the methods used to insure against them. What does this mean for the existence and efficiency of markets for risk-sharing and speculation? In my dissertation, Data, Digital Risks, and Financial Markets, I model algorithmic approaches, data dimensionality, and novel hazards to modernize our understanding of insurability, information aggregation, and optimal regulation in financial markets. The dissertation consists of three chapters. The first chapter (joint work with Yilin (David) Yang) studies the equilibrium coexistence, in financial markets, of traders using high-dimensional datasets and algorithmic strategies. The second chapter describes the extent to which insurance in markets for new or nonstationary risks exists and when government data policy can help. The third chapter argues how insurance availability might actually worsen ransomware, a pressing new cyberrisk, and presents sublimit caps as a practical regulatory measure

Keywords: Business.
Advisor: Ostrovsky Michael
Reiss Peter C. (Peter Clemens)
Skrzypacz Andrzej
Data driven generative accelerator model

With the development of accelerator physics and synchrotron radiation technology, relativistic electron beams accelerated by a linear accelerator (Linac) are capable of amplifying shot noise and radiating an electromagnetic field in the X-ray regime. This system is referred to as the X-ray Free Electron Laser (XFEL) under self-amplifying spontaneous emission (SASE) mode. Serving as the first XFEL facility in the world, the Linac Coherent Light Source (LCLS) has been operating at the SLAC National Accelerator Laboratory (SLAC) since 2009 and benefiting various fields of photon science. Accumulating more than a decade of data, we have collected many experimental records and established systematic coding packages of simulation. Driven by this data, researchers can build surrogate models in order to efficiently and reliably reproduce radiation. In addition, well-trained accelerator models can also support other learning algorithms or guide facility pre-tuning. In this dissertation, we first discuss the SASE XFEL system by solving the radiation field under a one-dimensional approximation. The statistical optic theory helps us to evaluate the distribution and correlation properties of the radiation power profiles. In addition, we introduce learning-based generative algorithms and how they inspired us to use generative adversarial networks (GAN) to build XFEL surrogate models. Next, we show five datasets from various simulated or experimental environments. Lastly, data-driven accelerator models can stably converge and efficiently reproduce training datasets. These models are further investigated by statistical diagnosis and network analysis. In summary, this research implements a XFEL generative model driven by simulated and experimental power profiles data. The algorithmic ideas and surrogate models are promising techniques for data-supporting and facility tuning

Keywords: Physics.
Advisor: Raubenheimer Tor O.
Edelen Auralee 
Huang Zhirong
Tompkins Lauren Alexandra
Data representations and data quality in the context of machine learning and healthcare

The focus of this thesis is data representations and data quality in the context of machine learning and healthcare. In the first half of this thesis, we discuss three projects focusing on data representations. First, inspired by the recent progress in implicit neural networks, we show how to find a compact implicit representation to fit three-dimensional dose distribution data accurately. We evaluate the quality of the proposed representation using dose distributions of prostate, spine, and head and neck tumor cases. This study lays the groundwork for future applications of neural representations of dose data in radiation oncology. Second, Monte Carlo (MC) simulation is considered the most accurate of the available radiotherapy dose calculation methods. However, its clinical use is limited due to the extensive required computation times. To overcome this, we develop a model to predict high-resolution low-uncertainty MC dose distributions from low-resolution high-uncertainty MC dose distributions with reduced computation time. We evaluate the model using dose distributions of spine tumor cases. Our results show that the predicted dose distributions are qualitatively and quantitatively comparable to those generated from high-resolution low-uncertainty MC simulations. Third, we fill the gap in the literature by investigating image classification using graphs generated from a multiscale superpixel representation that can be considered as in between the regular-grid and similar-sized superpixel representations. Prior studies using graph neural networks have focused either on regular-grid or similar-sized superpixel representations. To perform the study we propose WaveMesh, a new wavelet-based superpixeling algorithm, and WavePool, a novel spatially heterogeneous pooling scheme tailored to WaveMesh superpixels. We perform extensive experiments on three benchmark datasets to show the effect of choice of superpixel representation and pooling scheme on the performance of the network. In the second half of this thesis, we design a framework to assess the quality of Coronavirus disease 2019 (COVID-19) data reporting. We use this framework to assess the data reporting quality from India, and to calculate a COVID-19 data reporting score for each state in India during three time periods between 2020 and 2021. We discuss the significant disparities in reporting observed during our initial assessment and the improvements observed in the subsequent assessments. We further discuss the lessons learned from our assessments and the societal impact of our work

Advisor: Xing Lei
Ye Yinyu
Bibault Jean-Emmanuel
Data-driven energy efficiency and flexibility in commercial buildings

Commercial buildings offer significant potential to reduce cooling load by raising zone temperature setpoints. This is useful for demand response, avoiding blackouts during heat waves, and during normal operations to reduce costs, water usage, and emissions. We performed experiments in seven commercial buildings on the Stanford University campus. We found that buildings responded differently to temperature setpoint adjustments with load reductions ranging from 4 to 29 percent. This work explores the drivers for response heterogeneity by analyzing equipment-level behavior. Distributed sensor data throughout the buildings is used to diagnose the behavior of Air Handling Units, Variable Air Volume systems and Fan Coil Units. The main driver for load reduction was changes in chilled water valve positions, which differs from previous experiments reported on in literature. One possible explanation is that the HVAC equipment in these buildings implement the most recent ASHRAE standards of supply air temperature (SAT) reset through logic called Trim-and-Respond. This finding suggests that new building control sequences could alter conclusions from previous DR field experiments. It is also found that the interaction of ventilation and cooling requirements can impact operational energy efficiency, sometimes causing over-cooling of certain zones. Over-cooling is quantified in terms of temperature difference and the cooling load that is wasted by keeping zone temperatures below the cooling setpoint. We also find that dominant zones with high cooling requirements play an important role in determining building flexibility. Dominant zones included in experiments can drive large load reductions observed from setpoint changes. If the zone is excluded, the building will not be responsive to setpoint changes. High flexibility is seen from buildings without ventilation constraints and buildings with dominant zones included in experiments. This is true even if the building has high levels of wasted cooling. In this case, the building is inefficient but has high flexibility. Buildings with ventilation constraints or dominant zones excluded from experiments are less dependable flexibility resources.

Keywords:  Energy Resources Engineering
demand response, energy efficiency, buildings
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Thesis
Advisor: Brandt Adam
Data-driven methods for improving social interaction in virtual environments

The COVID-19 pandemic highlighted the value of remote social communication between individuals. The fundamentals of such communication are actively studied for virtual reality interaction, remote video calls, and social networking, but research on these methods infrequently focuses on encoding explicit social use-case. In addition, systems that do incorporate modalities for explicit social information, such as touch, are often hand-tuned or manually generated. In this work, we seek to leverage data-driven methods to improve social interaction between individuals in virtual environments. In order to socialize effectively in shared virtual environments, we seek to learn how a person physically interacts in such environments. For the first contribution of this thesis, we develop the interaction-expectation model to improve hand tracking and interaction. The purpose of this model is to predict hand-object interaction before such interaction occurs. This allows for smooth interaction in virtual environments, which can be used to improve haptic feedback in shared-object settings. We find that we are able to predict human-object interaction before it occurs over short timescales (approximately 100ms). In order to improve social interaction, we must also understand the emotional intent of actions between individuals. In the second contribution of this thesis, we collected a dataset of pairs of individuals interacting to convey affective information through touch. We record the data using a soft pressure sensor on one participants arm. This dataset was collected in a more natural environment than existing ones, and utilized scenario prompts rather than single word prompts. For the third contribution we then develop a system to automatically convey social touch information using our dataset. We develop an algorithm that leverages computer vision algorithms to map from the recorded data to an actuator sleeve with an array of actuators which indent the skin. We find that humans accurately interpret the affective intent of our system with accuracy comparable to human touch interaction. Finally, we consider the visual mode of social experience by improving affective facial expressions for 2D virtual avatars. In recent years socialization via virtual avatars has dramatically increased, with the growing use of 2D drawn, rigged, virtual avatars with face tracking. As a last contribution of this thesis, we create a novel dataset of 2D avatar expressions, of high quality and richer data than previous datasets. We then propose use cases for this dataset to automate the creation of 2D avatars. Through the collection of contributions in this thesis, we seek to push the field of virtual social interaction forward with multi-modal interaction. This will allow people to interact in virtual environments, connect with remote loved ones, and represent a version of themselves online more easily and effectively

Keywords: Mechanical Engineering.
Advisor: Bohg Jeannette
Okamura Allison
Follmer Sean
Data-driven modeling of neurodegeneration in Alzheimer's disease

Alzheimer's disease constitutes one of the biggest public health challenges of our time. About one out of nine Americans over the age of 65 is affected today, with numbers rising steeply as our demographics shift towards an older society. The hallmarks of Alzheimer's disease entail an abnormal aggregation of the proteins amyloid-beta and tau in the brain, widespread brain atrophy, and gradually advancing cognitive impairment. The development of disease-modifying treatments requires early diagnosis and a deep understanding of disease causes and progression. However, to date much of our understanding of the early disease mechanisms and their interactions is vague and based on qualitative observations. In this thesis, we introduce computational models describing the prion-like aggregation of protein, the accumulation of pathological tau protein in the brain, and the coupling between tau pathology and regional tissue atrophy in Alzheimer's disease. Validating these models with longitudinal imaging data of real subjects allows us to identify numerical ranges for disease parameters on a personalized basis, thereby shifting our understanding of pathological events and causal relationships to a quantitative level. We leverage hierarchical modeling and Bayesian inference to take advantage of group level similarities and quantify uncertainty in our simulations. With the calibrated models we are able to generate personalized predictions forecasting how tau pathology and atrophy may evolve in a certain patient over time. Our approach also enables us to discover supporting evidence that the presence of amyloid-beta is a driver for tau pathology and tau-induced atrophy

Keywords: Mechanical Engineering.
Advisor: Kuhl Ellen
Chaudhuri Ovijit
Delp Scott
Data-driven sequential decision making with deep probabilistic modeling

A central ability of intelligent agents is learning by interacting with the surrounding environment. Bayesian optimization (BO) and reinforcement learning (RL) provide techniques to balance exploration and exploitation in face of uncertainty. The first part of this dissertation focuses on improving and generalizing current BO approaches. From the perspective of utility maximization, we develop a general likelihood-free BO method, which directly models the acquisition function without an explicit probabilistic surrogate model and applies to any acquisition function with a non-negative utility, thus leading to a more scalable and sample-efficient optimization procedure. From the perspective of maximal uncertainty reduction, we propose a generalized BO framework based on decision theoretic entropies, which not only provides a unified view of multiple commonly used acquisition functions, but also yields a flexible family of acquisition functions that can be easily customized for novel tasks beyond black-box optimization such as top-k selection and level sets estimation. The second part focuses on learning from demonstrations to bypass limitations of RL (which requires carefully designed reward functions and unsafe online exploration, and struggles to generalize to new tasks and multi-agent systems), including extensions of maximum entropy inverse reinforcement learning to multi-agent systems and the meta-learning setting, as well as an offline imitation learning algorithm to seek proper regularization from suboptimal demonstrations via relaxed distribution matching. As deep probabilistic modeling is important to capture uncertainty and balance exploration and exploitation during decision making, the third part centers around the fundamental problem of learning expressive probabilistic models. We present new methods for training deep energy-based models by minimizing general f-divergences and maximizing homogeneous proper scoring rules to achieve more flexible modeling preferences, better inference performance, and robust parameter estimation under data contamination. These contributions enable machines to make better decisions in face of uncertainty

Advisor: Ermon Stefano
Grover Aditya 
Ma Tengyu
Days Gone Forever: Women’s Role in the 1936-37 Flint Sit-Down Strike and its Influence on the United Auto Workers Union

This senior thesis discusses women’s role in the 1936-37 Flint sit-down strike, long seen as one of the pivotal events in bringing unionization to the nation’s automotive industry. It argues that women’s experiences during the strike and contributions to its success resulted in fundamental structural change in the United Auto Workers (UAW) union in subsequent years.

Keywords: International Union, United Automobile Workers of America (CIO)
General Motors Corporation Sit-Down Strike (1936-1937)
General Motors Company
Labor movement
Dollinger, Genora Johnson
Jeffrey, Mildred, 1911-2004
Kraus, Dorothy
Reuther, Victor G. (Victor George), 1912-2004
Martin, Warren Homer, 1902-1968
Strikes and lockouts
Women > Employment
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Thesis
Advisor: Gienapp Jonathan
Burns Jennifer